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Background: Ovarian cancer (OC) is often associated with an unfavorable prognosis. Given the crucial involvement of lysosomes in tumor advancement, lysosome-related genes (LRGs) hold promise as potential therapeutic targets. Methods: To identify differentially expressed lysosome-related genes (DE-LRGs), we performed a matching analysis between differentially expressed genes (DEGs) in OC and the pool of LRGs. Genes with prognostic significance were analyzed using multiple regression analyses to construct a prognostic risk signature. The model's efficacy was validated through survival analysis in various cohorts. We further explored the model's correlation with clinical attributes, tumor microenvironment (TME), mutational patterns, and drug sensitivity. The quantitative real-time polymerase chain reaction (qRT-PCR) validated gene expression in OC cells. Results: A 10-gene prognostic risk signature was established. Survival analysis confirmed its predictive accuracy across cohorts. The signature served as an independent prognostic element for OC. The high-risk and low-risk groups demonstrated notable disparities in terms of immune infiltration patterns, mutational characteristics, and sensitivity to therapeutic agents. The qRT-PCR results corroborated and validated the findings obtained from the bioinformatic analyses. Conclusions: We devised a 10-LRG prognostic model linked to TME, offering insights for tailored OC treatments. [ABSTRACT FROM AUTHOR]

Background: Anoikis is intricately associated with the malignant progression of cancer. Thyroid cancer (THCA) is the most common endocrine tumor, metastasis is closely related to treatment response and prognosis of THCA. Hence, it is imperative to comprehensively identify predictive prognostic genes and novel molecular targets for effective THCA therapy. Methods: Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were utilized to mine differentially expressed anoikis-related (DE-ARGs). Then, the prognostic genes were identified and a risk signature was constructed for THCA using univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) method. Furthermore, the associations between risk signature and immune infiltration, immunotherapy, as well as potential mechanisms of action were determined using multiple R packages and Wilcoxon test. Finally, Mendelian randomized (MR) analysis was conducted to investigate the causal relationship between the prognostic genes and THCA. Results: In total, six prognostic genes (LRRC75A, METTL7B, ADRA1B, TPD52L1, TNFRSF10C, and CXCL8) related to anoikis were identified, and the corresponding risk signature were constructed to assess the survival time of THCA patients. Immunocorrelation analysis demonstrated the anoikis-relevant risk signature could be used to evaluate immunotherapy effects in THCA patients, and the infiltration of immune cells was correlated with the degree of risk in THCA patients. According to two-sample MR analysis, there was the significant causal relationship between CXCL8 and THCA (odds ratio [OR] > 1 & p< 0.05), and the increase of its gene expression would lead to an increased risk of THCA. Furthermore, real-time quantitative polymerase chain reaction (RT-qPCR) confirmed the upregulated expression patterns of these prognostic genes in THCA tissues. Conclusion: In conclusion, we constructed the risk signature related to anoikis for THCA, which might have important clinical significance for improving the quality of life and treatment effect of THCA patients. [ABSTRACT FROM AUTHOR]

Background: Colon cancer is a malignant tumor with high malignancy and a low survival rate whose heterogeneity limits systemic immunotherapy. Transforming growth factor-β (TGF-β) signaling pathway-related genes are associated with multiple tumors, but their role in prognosis prediction and tumor microenvironment (TME) regulation in colon cancer is poorly understood. Using bioinformatics, this study aimed to construct a risk prediction signature for colon cancer, which may provide a means for developing new effective treatment strategies. Methods: Using consensus clustering, patients in The Cancer Genome Atlas (TCGA) with colon adenocarcinoma were classified into several subtypes based on the expression of TGF-β signaling pathway-related genes, and differences in survival, molecular, and immunological TME characteristics and drug sensitivity were examined in each subtype. Ten genes that make up a TGF-β-related predictive signature were found by least absolute shrinkage and selector operation (LASSO) regression using colon cancer data from the TCGA database and confirmed using a Gene Expression Omnibus (GEO) dataset. A nomogram incorporating risk scores and clinicopathologic factors was developed to stratify the prognosis of patients with colon cancer for accurate clinical diagnosis and therapy. Results: Two TGF-β subtypes were identified, with the TGF-β-high subtype being associated with a poorer prognosis and superior sensitivity to immunotherapy. Mutation analyses showed a high incidence of gene mutations in the TGF-β-high subtype. After completing signature construction, patients with colon cancer were categorized into high- and low-risk subgroups based on the median risk score of the TGF-β-related predictive signature. The risk score exhibited superior predictive performance relative to age, gender, and stage, as evidenced by its AUC of 0.686. Patients in the high-risk subgroup had higher levels of immunosuppressive cell infiltration and immune checkpoints in the TME, suggesting that these patients had better responses to immunotherapy. Conclusions: Patients with colon cancer were divided into two subtypes with different survival and immune characteristics using consensus clustering analysis based on TGF-β signaling pathway-related genes. The constructed risk prediction signature may show promise as a biomarker for evaluating the prognosis of colon cancer, with potential utility for screening individuals for immunotherapy.

Abstract Background Hypoxia-related genes are linked to the prognosis of various solid malignant tumors. However, the role of hypoxia-related long non-coding RNAs (HRLs) in uveal melanoma (UVM) remains unclear. This study aimed to identify HRLs associated with UVM prognosis and develop a novel risk signature to predict patient outcomes. Methods Data from 80 UVM samples were obtained from The Cancer Genome Atlas. Prognostic HRLs were screened using Cox univariate and Pearson correlation analyses. HRL signature were constructed using Lasso analysis, and gene enrichment analysis was performed to explore the association between HRLs and immune features. Cell Counting Kit-8 assay was used to measure the propagation of human uveal melanoma (MuM2B) cells, while tumor invasion and migration were evaluated using Transwell and wound-healing experiments. Inflammatory factors and macrophage polarization were evaluated using quantitative PCR. Results In total, 621 prognostic HRLs were screened and constructed in 12 HRLs. The risk score showed a significant correlation with the survival time of patients with UVM. Additionally, HRL correlated with diverse key immune checkpoints, revealing possible targets for immunotherapy. Immune-related pathways were highly enriched in the high-risk group. LINC02367, a protective HRL, was associated with the tumor microenvironment and survival time of patients with UVM. In vitro, LINC02367 significantly influenced MuM2B proliferation and migration. It also modulated macrophage polarization by regulating inflammatory factor levels, thereby affecting the immune microenvironment. Conclusions We developed a novel HRL signature to predict prognosis in patients with UVM. HRLs are potential biomarkers and therapeutic targets for the treatment of UVM.

Bing Yu,1,&ast; Zhimei Xing,2,&ast; Xiaoxuan Tian,2 Rui Feng1 1Department of Breast Surgery, Tianjin Central Hospital of Obstetrics and Gynecology, Tianjin, 300100, People’s Republic of China; 2State Key Laboratory of Component‑Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Rui Feng, Department of Breast Surgery, Tianjin Central Hospital of Obstetrics and Gynecology, No. 156 of Nankai SAN Road, Nankai District, Tianjin, 300100, People’s Republic of China, Tel +86 022-58287167, Email dr.fr@126.comBackground: Triple-negative breast cancer (TNBC) is recognized as the most aggressive molecular subtype of breast cancer. Recent studies have highlighted the complex role of autophagy in the pathogenesis of TNBC.Methods: In this study, we evaluated 18,330 genes, including 1111 autophagy-related genes, (ARGs), across 579 TNBC samples from online databases. Differentially expressed ARGs in TNBC were identified using high-throughput RNA-seq data from the Cancer Genome Atlas (TCGA). Prognostic factors were examined through Cox regression and multivariate Cox analyses, with predictive efficacy assessed using receiver operating characteristic (ROC) curves. A nomogram integrating the risk signature with clinicopathological factors, such as TNM stage, was developed. Immunohistochemical analysis of clinical samples was also conducted.Results: EIF4EBP1 and NPAS3 were significantly correlated with prognostic outcomes in patients with TNBC. Multivariate Cox regression analysis demonstrated that the expression levels of these two genes were accurate predictors of disease progression in TNBC samples from TCGA and the GSE31519 dataset. The efficacy of this predictive model was validated using ROC curve analysis and calibration plots, confirming its ability to accurately estimate the 1-, 2-, and 3-year survival rates for individuals with TNBC. Additionally, EIF4EBP1 and NPAS3 expression influenced drug sensitivity in TNBC cell lines, with notably lower NPAS3 expression in TNBC tissues, particularly in Stage III cases. This study is the first to report NPAS3 expression in patients with TNBC.Conclusion: The autophagy-related genes EIF4EBP1 and NPAS3 may serve as independent prognostic factors for individuals with TNBC.Keywords: autophagy, nomogram, prognosis, risk signature, triple-negative breast cancer

Background: Hypoxia-related genes are linked to the prognosis of various solid malignant tumors. However, the role of hypoxia-related long non-coding RNAs (HRLs) in uveal melanoma (UVM) remains unclear. This study aimed to identify HRLs associated with UVM prognosis and develop a novel risk signature to predict patient outcomes. Methods: Data from 80 UVM samples were obtained from The Cancer Genome Atlas. Prognostic HRLs were screened using Cox univariate and Pearson correlation analyses. HRL signature were constructed using Lasso analysis, and gene enrichment analysis was performed to explore the association between HRLs and immune features. Cell Counting Kit-8 assay was used to measure the propagation of human uveal melanoma (MuM2B) cells, while tumor invasion and migration were evaluated using Transwell and wound-healing experiments. Inflammatory factors and macrophage polarization were evaluated using quantitative PCR. Results: In total, 621 prognostic HRLs were screened and constructed in 12 HRLs. The risk score showed a significant correlation with the survival time of patients with UVM. Additionally, HRL correlated with diverse key immune checkpoints, revealing possible targets for immunotherapy. Immune-related pathways were highly enriched in the high-risk group. LINC02367, a protective HRL, was associated with the tumor microenvironment and survival time of patients with UVM. In vitro, LINC02367 significantly influenced MuM2B proliferation and migration. It also modulated macrophage polarization by regulating inflammatory factor levels, thereby affecting the immune microenvironment. Conclusions: We developed a novel HRL signature to predict prognosis in patients with UVM. HRLs are potential biomarkers and therapeutic targets for the treatment of UVM. [ABSTRACT FROM AUTHOR]

Introduction: Autophagy functions as a prosurvival mechanism in multiple myeloma (MM). The objective of this research was to establish an autophagy-related gene (ARG) signature for predicting the survival outcomes of MM patients with TP53 mutations. Material and methods: Information about MM patients with TP53 mutations was downloaded from the Gene Expression Omnibus (GEO) database. Cox proportional hazard regression was employed to determine the independent prognostic ARG and construct a risk signature. Time-dependent receiver-operating characteristic (tROC) curve analysis was used to explore the predictive accuracy of the prognostic model. A nomogram was constructed to give a more precise prediction of the probability of 5-year, 8-year and 10-year overall survival (OS). In addition, we used the CIBERSORT algorithm to explore the distribution difference of 22 immune-infiltrating cells. Results: Three differentially expressed ARGs (CASP8, MAPK8, RB1CC1) were finally incorporated to construct the risk model. Area under the curve (AUC) values of the corresponding tROC curve for 5-year, 8-year and 10-year OS were 0.735, 0.686 and 0.662, respectively. Multiple myeloma patients were categorized into high and low-risk groups in accordance with the median threshold value (–1.724549). An ARG-based risk score model was an independent prognostic element correlated with OS, giving an hazard ratio (HR) of 3.29 (95% CI 2.35–4.60, p < 0.001). Thirteen immune infiltrating cells were found to have distribution differences between the two groups. Conclusions: We established a three-ARG risk signature which manifested an independent prognostic factor. The nomogram was testified to perform well in forecasting the long-term survival of TP53-mutated MM patients. [ABSTRACT FROM AUTHOR]

Acute myeloid leukaemia (AML) is a common and highly aggressive haematological malignancy in adults. Senescence‐associated secretory phenotype (SASP) plays important roles in tumorigenesis and progression of tumour. However, the prognostic value of SASP in patients with AML has not been clarified. The present study aims to explore the prognostic value of SASP and develop a prognostic risk signature for AML. The RNA‐sequencing data was collected from the TCGA, GTEx and TARGET databases. Subsequently, differentially expressed gene analysis, univariate Cox regression and LASSO regression were applied to identified prognostic SASP‐related genes and construct a prognostic risk‐scoring model. The risk score of each patient were calculated and patients were divided into high‐ or low‐risk groups by the median risk score. This novel prognostic signature included 11 genes: G6PD, CDK4, RPS6KA1, UBC, H2BC12, KIR2DL4, HSF1, IFIT3, PIM1, RUNX3 and TRIM21. The patients with AML in the high‐risk group had shorter OS, demonstrating that the risk score acted as a prognostic predictor, which was validated in the TAGET‐AML dataset. Univariate and multivariate analysis revealed the risk score was an independent prognostic factor in patients with AML. Furthermore, the present study revealed that the risk score was associated with immune landscape, immune checkpoint gene expression and chemotherapeutic efficacy. In the present study, we constructed and validated a unique SASP‐related prognostic model to assess therapeutic effect and prognosis in patients with AML, which might contribute to understanding the role of SASP in AML and guiding the treatment for AML. [ABSTRACT FROM AUTHOR]

BackgroundLower-grade glioma (LGG) exhibits significant heterogeneity in clinical outcomes, and current prognostic markers have limited predictive value. Despite the growing recognition of histone modifications in tumor progression, their role in LGG remains poorly understood. This study aimed to develop a histone modification-based risk signature and investigate its relationship with drug sensitivity to guide personalized treatment strategies.MethodsWe performed single-cell RNA sequencing analysis on LGG samples (n = 4) to characterize histone modification patterns. Through integrative analysis of TCGA-LGG (n = 513) and CGGA datasets (n = 693 and n = 325), we constructed a histone modification-related risk signature (HMRS) using machine learning approaches. The model's performance was validated in multiple independent cohorts. We further conducted comprehensive analyses of molecular mechanisms, immune microenvironment, and drug sensitivity associated with the risk stratification.ResultsWe identified distinct histone modification patterns across five major cell populations in LGG and developed a robust 20-gene HMRS from 129 candidate genes that effectively stratified patients into high- and low-risk groups with significantly different survival outcomes (training set: AUC = 0.77, 0.73, and 0.71 for 1-, 3-, and 5-year survival; P < 0.001). Integration of HMRS with clinical features further improved prognostic accuracy (C-index >0.70). High-risk tumors showed activation of TGF-β and IL6-JAK-STAT3 signaling pathways, and distinct mutation profiles including TP53 (63% vs 28%), IDH1 (68% vs 85%), and ATRX (46% vs 20%) mutations. The high-risk group demonstrated significantly elevated immune and stromal scores (P < 0.001), with distinct patterns of immune cell infiltration, particularly in memory CD4+ T cells (P < 0.001) and CD8+ T cells (P = 0.001). Drug sensitivity analysis revealed significant differential responses to six therapeutic agents including Temozolomide and targeted drugs (P < 0.05).ConclusionOur study establishes a novel histone modification-based prognostic model that not only accurately predicts LGG patient outcomes but also reveals potential therapeutic targets. The identified associations between risk stratification and drug sensitivity provide valuable insights for personalized treatment strategies. This integrated approach offers a promising framework for improving LGG patient care through molecular-based risk assessment and treatment selection.

Background Immunogenic cell death (ICD) has been associated with enhanced anti-tumor immunotherapy by stimulating adaptive immune responses and remodeling the immune microenvironment in tumors. Nevertheless, the role of ICD-related genes in ovarian cancer (OC) and tumor microenvironment remains unexplored. Methods In this study, high-throughput transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases as training and validation sets separately were obtained and proceeded to explore ICD-related clusters, and an ICD-related risk signature was conducted based on the least absolute shrinkage and selection operator (LASSO) Cox regression model by iteration. Multiple tools including CIBERSORT, ESTIMATE, GSEA, TIDE, and immunohistochemistry were further applied to illustrate the biological roles of ICD-related genes as well as the prognostic capacity of ICD risk signature in OC. Results Two ICD-related subtypes were identified, with the ICD-high subtype showing more intense immune cell infiltration and higher activities of immune response signaling, along with a favorable prognosis. Additionally, four candidate ICD genes (IFNG, NLRP3, FOXP3, and IL1B) were determined to potentially impact OC prognosis, with an upregulated expression of NLRP3 in OC and metastatic omental tissues. A prognostic model based on these genes was established, which could predict overall survival (OS) and response to immunotherapy for OC patients, with lower-risk patients benefiting more from immunotherapy. Conclusion Our research conducted a prognostic and prediction of immunotherapy response model based on ICD genes, which could be instrumental in assessing prognosis and assigning immunotherapeutic strategies for OC patients. NLRP3 is a promising target for prognosis in OC.

BackgroundAnoikis is intricately associated with the malignant progression of cancer. Thyroid cancer (THCA) is the most common endocrine tumor, metastasis is closely related to treatment response and prognosis of THCA. Hence, it is imperative to comprehensively identify predictive prognostic genes and novel molecular targets for effective THCA therapy.MethodsDifferential expression analysis and weighted gene co-expression network analysis (WGCNA) were utilized to mine differentially expressed anoikis-related (DE-ARGs). Then, the prognostic genes were identified and a risk signature was constructed for THCA using univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) method. Furthermore, the associations between risk signature and immune infiltration, immunotherapy, as well as potential mechanisms of action were determined using multiple R packages and Wilcoxon test. Finally, Mendelian randomized (MR) analysis was conducted to investigate the causal relationship between the prognostic genes and THCA.ResultsIn total, six prognostic genes (LRRC75A, METTL7B, ADRA1B, TPD52L1, TNFRSF10C, and CXCL8) related to anoikis were identified, and the corresponding risk signature were constructed to assess the survival time of THCA patients. Immunocorrelation analysis demonstrated the anoikis-relevant risk signature could be used to evaluate immunotherapy effects in THCA patients, and the infiltration of immune cells was correlated with the degree of risk in THCA patients. According to two-sample MR analysis, there was the significant causal relationship between CXCL8 and THCA (odds ratio [OR] > 1 & p< 0.05), and the increase of its gene expression would lead to an increased risk of THCA. Furthermore, real-time quantitative polymerase chain reaction (RT-qPCR) confirmed the upregulated expression patterns of these prognostic genes in THCA tissues.ConclusionIn conclusion, we constructed the risk signature related to anoikis for THCA, which might have important clinical significance for improving the quality of life and treatment effect of THCA patients.

Abstract Purpose Bladder cancer (BLCA) is a prevalent malignancy. Dysregulated propionate metabolism, a key cancer factor, suggests a potential target for treating metastatic cancer. However, a complete understanding of the link between propionate metabolism-related genes (PMRGs) and bladder cancer is lacking. Methods From the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we gathered BLCA patient data, which was classified into distinct subgroups using non-negative matrix factorization (NMF). Survival and pathway analyses were conducted between these clusters. The PMRGs model, created through univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses, was assessed for prognostic significance using Kaplan–Meier and receiver operating characteristic (ROC) curves. A comprehensive evaluation included clinical, tumor microenvironment (TME), drug sensitivity, and immunotherapy analyses. Finally, the expression of HSD17B1 essential genes was confirmed via quantitative real-time polymerase chain reaction (qRT-PCR), with further validation through Transwell, wound healing, colony-formation, and EDU assays. Results We discovered two distinct subcategories (CA and CB) within BLCA using NMF analysis, with CA demonstrating significantly better overall survival compared to CB. Additionally, six PMRGs emerged as critical factors associated with propionate metabolism and prognosis. Kaplan–Meier analysis revealed that high-risk PMRGs were correlated with a poorer prognosis in BLCA patients. Moreover, significant differences were observed between the two groups in terms of infiltrated immune cells, immune checkpoint expression, TME scores, and drug sensitivity. Notably, we found that suppressing HSD17B1 gene expression inhibited the invasion of bladder cancer cells. Conclusion Our study proposes molecular subtypes and a PMRG-based score as promising prognostic indicators in BLCA. Additionally, cellular experiments underscore the pivotal role of HSD17B1 in bladder cancer metastasis and invasion, suggesting its potential as a novel therapeutic target.

Abstract Background Colorectal cancer (CRC) is a prevalent malignancy with high mortality and morbidity rates. Although the significant efficacy of immunotherapy is well established, it is only beneficial for a limited number of individuals with CRC. Methods Differentially expressed immune-related genes (DE-IRGs) were retrieved from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and ImmPort databases. A prognostic signature comprising DE-IRGs was developed using univariate, LASSO, and multivariate Cox regression analyses. A nomogram integrating the independent prognostic factors was also developed. CIBERSORT was used to assess immune cell infiltration (ICI). Furthermore, wound-healing, colony formation, migration, and invasion assays were performed to study the involvement of ACTG1 in CRC. Results A signature including six DE-IRGs was developed. The overall survival (OS) rate was accurately estimated for TCGA and GSE38832 cohorts. The risk score (RS) of the signature was an independent factor for OS. Moreover, a nomogram encompassing age, RS, and pathological T stage accurately predicted the long-term OS probability of individuals with CRC. The high-risk group had an elevated proportion of patients treated with ICIs, including native B cells, relative to the low-risk group. Additionally, ACTG1 expression was upregulated, which supported the proliferation, migration, and invasion abilities of CRC cells. Conclusions An immune-related prognostic signature was developed for predicting OS and for determining the immune status of individuals with CRC. The present study provides new insights into accurate immunotherapy for individuals with CRC. Moreover, ACTG1 may serve as a new immune biomarker.

Abstract Purpose Metabolic reprogramming is a hallmark of cancer and plays a key role in precision oncology treatment. Long non-coding RNAs (lncRNAs) regulate cancer cell behavior, including metabolism. Disulfidptosis, a newly identified form of regulated cell death triggered by glucose starvation, has yet to be fully understood in colon adenocarcinoma (COAD). This study aimed to confirm the existence and role of disulfidptosis in COAD and identify disulfidptosis-related lncRNAs that may be targeted to induce disulfidptosis in COAD. Methods PI and F-actin staining were used to observe disulfidptosis in COAD cell lines. Disulfidptosis-related lncRNAs were identified based on the expression of disulfidptosis-associated genes in the TCGA-COAD database. A four-lncRNA signature for disulfidptosis was established. Subsequently, loss-of-function assays explored the roles of AC013652.1 and MCM3AP-AS1 in disulfidptosis. Results Disulfidptosis was observed in COAD cells under glucose starvation and could be reversed by agents that prevent disulfide stress, such as dithiothreitol (DTT) and tris-(2-carboxyethyl)-phosphine (TCEP). The prognostic value of disulfidptosis-associated genes in COAD patients was confirmed, with higher expression indicating longer survival. A disulfidptosis-related lncRNA signature comprising four lncRNAs was established based on the expression of these genes. Among these, AC013652.1 and MCM3AP-AS1 predicted worse prognoses. Furthermore, inhibiting AC013652.1 or MCM3AP-AS1 increased disulfidptosis-associated gene expression and cellular death, which could be reversed by DTT and TCEP. Conclusions This study provides hitherto undocumented evidence of the existence of disulfidptosis and the prognostic value of disulfidptosis-associated genes in COAD. Importantly, we identified lncRNAs AC013652.1 and MCM3AP-AS1, which suppress disulfidptosis and may serve as potential therapeutic targets for COAD.

Abstract Background Emerging investigations have increasingly highlighted the critical role of tumor-associated macrophages (TAMs) and M2 macrophages in cancer development, progression, and metastasis, marking them as potential targets in various cancer types. The main objective of this research is to discover new biomarkers associated with TAM-M2 macrophages in colorectal cancer (CRC) and to dissect the molecular heterogeneity of CRC by combining single-cell RNA sequencing and bulk RNA-seq data. Methods By utilizing weighted gene co-expression network analysis (WGCNA), we acquired TAM-M2-associated genes by intersecting TAM marker genes obtained from scRNA-seq data with module genes of M2 macrophages derived from bulk RNA-seq data. We employed least absolute shrinkage and selection operator (LASSO) Cox analysis to select predictive biomarkers from these TAM-M2-related genes. Quantitative polymerase chain reaction (qPCR) was employed to validate the mRNA expression levels of the genes identified in the screening. This led to the development of the TAM-M2-related signature (TAMM2RS). We also conducted functional and immune landscape analyses of different risk groups. Results The combination of scRNA-seq and bulk RNA-seq analyses yielded 377 TAM-M2-related genes. DAPK1, NAGK, and TRAF1 emerged as key prognostic genes in CRC, which were identified through LASSO Cox analysis. Utilizing these genes, we constructed and validated the TAMM2RS, demonstrating its effectiveness in predicting survival in CRC patients. Conclusion Our research offers a thorough investigation into the molecular mechanisms associated with TAM-M2 macrophages in CRC and unveils potential therapeutic targets, offering new insights for treatment strategies in colorectal cancer.

Purpose: Bladder cancer (BLCA) is a prevalent malignancy. Dysregulated propionate metabolism, a key cancer factor, suggests a potential target for treating metastatic cancer. However, a complete understanding of the link between propionate metabolism-related genes (PMRGs) and bladder cancer is lacking. Methods: From the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we gathered BLCA patient data, which was classified into distinct subgroups using non-negative matrix factorization (NMF). Survival and pathway analyses were conducted between these clusters. The PMRGs model, created through univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses, was assessed for prognostic significance using Kaplan–Meier and receiver operating characteristic (ROC) curves. A comprehensive evaluation included clinical, tumor microenvironment (TME), drug sensitivity, and immunotherapy analyses. Finally, the expression of HSD17B1 essential genes was confirmed via quantitative real-time polymerase chain reaction (qRT-PCR), with further validation through Transwell, wound healing, colony-formation, and EDU assays. Results: We discovered two distinct subcategories (CA and CB) within BLCA using NMF analysis, with CA demonstrating significantly better overall survival compared to CB. Additionally, six PMRGs emerged as critical factors associated with propionate metabolism and prognosis. Kaplan–Meier analysis revealed that high-risk PMRGs were correlated with a poorer prognosis in BLCA patients. Moreover, significant differences were observed between the two groups in terms of infiltrated immune cells, immune checkpoint expression, TME scores, and drug sensitivity. Notably, we found that suppressing HSD17B1 gene expression inhibited the invasion of bladder cancer cells. Conclusion: Our study proposes molecular subtypes and a PMRG-based score as promising prognostic indicators in BLCA. Additionally, cellular experiments underscore the pivotal role of HSD17B1 in bladder cancer metastasis and invasion, suggesting its potential as a novel therapeutic target. [ABSTRACT FROM AUTHOR]

Aberrant expression of gene is driven by its promoter methylation and is the key molecular basis of carcinogenic processes. This study aimed at identifying a risk signature of methylation-driven (MD) genes and evaluating its prognostic value for colon cancer (CC) patients. The expression profiles of methylation and mRNA in CC samples were obtained from the TCGA database, and the MethylMix algorithm was used to identify MD genes. The relationships between their expression levels and overall survival (OS) of CC patients were analyzed, and a prognostic signature of MD genes was established. The risk score of gene signature was calculated, and the median was used to divide all patients into high (H) and low (L) risk groups. The prognostic value of gene signature was tested by the TCGA cohort and an independent validation cohort (GSE17538 dataset). In total, 69 MD genes were identified, and 7 were associated with OS of CC patients. Ultimately, 4 (TWIST1, LDOC1, EPHX3, and STC2) were screened out to establish a risk signature. The H-risk patients (>0.923) had a worse OS than L-risk patients (≤0.923) in both the TCGA (5-year cumulative survival: 52.9% vs 72.0%, P=0.005) and GSE17538 cohort (49.4% vs 69.3%, P=0.004). The AUC values of MD genes signature for the prediction of 3- and 5-year OS were 0.648 and 0.643 in the TCGA dataset and 0.634 and 0.624 in the GSE17538 dataset, respectively. The risk signature of four MD genes was identified as an independent predictor of OS for CC patients (HR for TCGA dataset: 2.071, 95% CI=1.196–3.586, P=0.009; HR for GSE17538 dataset: 2.021, 95% CI=1.290–3.166, P=0.002). The risk signature of four MD genes might be a useful prognostic tool and help doctors improve the clinical management of CC patients. [ABSTRACT FROM AUTHOR]

Background: This study endeavored to develop a nicotinamide adenine dinucleotide (NAD+) metabolism-related biomarkers in gastric cancer (GC), which could provide a theoretical foundation for prognosis and therapy of GC patients. Methods: In this study, differentially expressed genes (DEGs1) between GC and paraneoplastic tissues were overlapped with NAD+ metabolism-related genes (NMRGs) to identify differentially expressed NMRGs (DE-NMRGs). Then, GC patients were divided into high and low score groups by gene set variation analysis (GSVA) algorithm for differential expression analysis to obtain DEGs2, which was overlapped with DEGs1 for identification of intersection genes. These genes were further analyzed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses to obtain prognostic genes for constructing a risk model. Enrichment and immune infiltration analyses further investigated investigate the different risk groups, and qRT-PCR validated the prognostic genes. Results: Initially, we identified DE-NMRGs involved in NAD biosynthesis, with seven (DNAJB13, CST2, THPO, CIDEA, ONECUT1, UPK1B and SNCG) showing prognostic significance in GC. Subsequent, a prognostic model was constructed in which the risk score, derived from the expression profiles of these genes, along with gender, emerged as robust independent predictors of patient outcomes in GC. Enrichment analysis linked high-risk patients to synaptic membrane pathways and low-risk to the CMG complex pathway. Tumor immune infiltration analysis revealed correlations between risk scores and immune cell abundance, suggesting a relationship between NAD+ metabolism and immune response in GC. The prognostic significance of our identified genes was validated by qRT-PCR, which confirmed their upregulated expression in GC tissue samples. Conclusion: In this study, seven NAD+ metabolism-related markers were established, which is of great significance for the development of prognostic molecular biomarkers and clinical prognosis prediction for gastric cancer patients. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer (GC) remains one of the most prevalent malignant tumors worldwide. At present, tumor-associated macrophages (TAMs) are essential in the progression, metastasis, and drug resistance of tumors. Therefore, TAMs can be a crucial target for tumor treatment. Aims: We intended to investigate the TAM characteristics in GC and develop a risk signature based on TAM to predict the prognosis of GC patients. Methods: The single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data were acquired from a publicly available database. We utilized the Seurat pipeline to process the scRNA-seq data and determine TAM cell types using marker genes. Univariate Cox regression analysis was utilized to examine TAM-related prognostic genes, and then we employed Lasso-Cox regression analysis, and Multivariate Cox regression analysis established a novel risk profile to forecast the clinical value of the model with a new nomogram combining risk profiles and clinicopathological characteristics. Results: The current study employed scRNA-seq data to identify five TAM clusters in GC, among which four were significantly associated with GC prognosis. Accordingly, we further developed a TAM-related risk signature utilizing nine genes. After evaluation, our model accurately predicted the prognosis of gastric cancer. Generally, GC patients with low TAMS scores exhibited a more favorable prognosis, greater benefits from immunotherapy, and higher levels of immune cell infiltration. Conclusions: The prognosis of GC can be effectively predicted by TAM-based risk signatures, and the signature may provide a new perspective for comprehensively guiding clinical diagnosis, prediction, and immunotherapy for gastric cancer. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) is a prevalent malignancy with high mortality and morbidity rates. Although the significant efficacy of immunotherapy is well established, it is only beneficial for a limited number of individuals with CRC. Methods: Differentially expressed immune-related genes (DE-IRGs) were retrieved from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and ImmPort databases. A prognostic signature comprising DE-IRGs was developed using univariate, LASSO, and multivariate Cox regression analyses. A nomogram integrating the independent prognostic factors was also developed. CIBERSORT was used to assess immune cell infiltration (ICI). Furthermore, wound-healing, colony formation, migration, and invasion assays were performed to study the involvement of ACTG1 in CRC. Results: A signature including six DE-IRGs was developed. The overall survival (OS) rate was accurately estimated for TCGA and GSE38832 cohorts. The risk score (RS) of the signature was an independent factor for OS. Moreover, a nomogram encompassing age, RS, and pathological T stage accurately predicted the long-term OS probability of individuals with CRC. The high-risk group had an elevated proportion of patients treated with ICIs, including native B cells, relative to the low-risk group. Additionally, ACTG1 expression was upregulated, which supported the proliferation, migration, and invasion abilities of CRC cells. Conclusions: An immune-related prognostic signature was developed for predicting OS and for determining the immune status of individuals with CRC. The present study provides new insights into accurate immunotherapy for individuals with CRC. Moreover, ACTG1 may serve as a new immune biomarker. [ABSTRACT FROM AUTHOR]

Background: Emerging investigations have increasingly highlighted the critical role of tumor-associated macrophages (TAMs) and M2 macrophages in cancer development, progression, and metastasis, marking them as potential targets in various cancer types. The main objective of this research is to discover new biomarkers associated with TAM-M2 macrophages in colorectal cancer (CRC) and to dissect the molecular heterogeneity of CRC by combining single-cell RNA sequencing and bulk RNA-seq data. Methods: By utilizing weighted gene co-expression network analysis (WGCNA), we acquired TAM-M2-associated genes by intersecting TAM marker genes obtained from scRNA-seq data with module genes of M2 macrophages derived from bulk RNA-seq data. We employed least absolute shrinkage and selection operator (LASSO) Cox analysis to select predictive biomarkers from these TAM-M2-related genes. Quantitative polymerase chain reaction (qPCR) was employed to validate the mRNA expression levels of the genes identified in the screening. This led to the development of the TAM-M2-related signature (TAMM2RS). We also conducted functional and immune landscape analyses of different risk groups. Results: The combination of scRNA-seq and bulk RNA-seq analyses yielded 377 TAM-M2-related genes. DAPK1, NAGK, and TRAF1 emerged as key prognostic genes in CRC, which were identified through LASSO Cox analysis. Utilizing these genes, we constructed and validated the TAMM2RS, demonstrating its effectiveness in predicting survival in CRC patients. Conclusion: Our research offers a thorough investigation into the molecular mechanisms associated with TAM-M2 macrophages in CRC and unveils potential therapeutic targets, offering new insights for treatment strategies in colorectal cancer. [ABSTRACT FROM AUTHOR]

Purpose: Metabolic reprogramming is a hallmark of cancer and plays a key role in precision oncology treatment. Long non-coding RNAs (lncRNAs) regulate cancer cell behavior, including metabolism. Disulfidptosis, a newly identified form of regulated cell death triggered by glucose starvation, has yet to be fully understood in colon adenocarcinoma (COAD). This study aimed to confirm the existence and role of disulfidptosis in COAD and identify disulfidptosis-related lncRNAs that may be targeted to induce disulfidptosis in COAD. Methods: PI and F-actin staining were used to observe disulfidptosis in COAD cell lines. Disulfidptosis-related lncRNAs were identified based on the expression of disulfidptosis-associated genes in the TCGA-COAD database. A four-lncRNA signature for disulfidptosis was established. Subsequently, loss-of-function assays explored the roles of AC013652.1 and MCM3AP-AS1 in disulfidptosis. Results: Disulfidptosis was observed in COAD cells under glucose starvation and could be reversed by agents that prevent disulfide stress, such as dithiothreitol (DTT) and tris-(2-carboxyethyl)-phosphine (TCEP). The prognostic value of disulfidptosis-associated genes in COAD patients was confirmed, with higher expression indicating longer survival. A disulfidptosis-related lncRNA signature comprising four lncRNAs was established based on the expression of these genes. Among these, AC013652.1 and MCM3AP-AS1 predicted worse prognoses. Furthermore, inhibiting AC013652.1 or MCM3AP-AS1 increased disulfidptosis-associated gene expression and cellular death, which could be reversed by DTT and TCEP. Conclusions: This study provides hitherto undocumented evidence of the existence of disulfidptosis and the prognostic value of disulfidptosis-associated genes in COAD. Importantly, we identified lncRNAs AC013652.1 and MCM3AP-AS1, which suppress disulfidptosis and may serve as potential therapeutic targets for COAD. [ABSTRACT FROM AUTHOR]

Osteosarcoma, one of the most common primary malignancies in children and adolescents, has the primary characteristics of a poor prognosis and high rate of metastasis. This study used super-enhancer-related genes derived from two different cell lines to construct five novel super-enhancer-related gene prognostic models for patients with osteosarcoma. The training and testing datasets were used to confirm the prognostic models of the five super-enhancer-related genes, which resulted in an impartial predictive element for osteosarcoma. The immunotherapy and prediction of the response to anticancer drugs have shown that the risk signature of the five super-enhancer-related genes positively correlate with chemosensitivity. Furthermore, functional analysis of the risk signature genes revealed a significant relationship between gene groups and the malignant characteristics of tumours. TNF Receptor Superfamily Member 11b (TNFRSF11B) was selected for functional verification. Silencing of TNFRSF11B suppressed the proliferation, migration, and invasion of osteosarcoma cells in vitro and suppressed osteosarcoma growth in vivo. Moreover, transcriptome sequencing was performed on MG-63 cells to study the regulatory mechanism of TNFRSF11B in osteosarcoma cells, and it was discovered that TNFRSF11B is involved in the development of osteosarcoma via the phosphoinositide 3-kinase signalling pathway. Following the identification of TNFRSF11B as a key gene, we selected an inhibitor that specifically targeted this gene and performed molecular docking simulations. In addition, risedronic acid inhibited osteosarcoma growth at both cellular and molecular levels. In conclusion, the super-enhancer-related gene signature is a viable therapeutic tool for osteosarcoma prognosis and treatment.

Background: Kidney renal clear cell carcinoma (KIRC) is a common and clinically significant subtype of kidney cancer. A potential therapeutic target in KIRC is disulfidptosis, a novel mode of cell death induced by disulfide stress. The aim of this study was to develop a prognostic model to explore the clinical significance of different disulfidptosis gene typings from KIRC. Methods: A comprehensive analysis of the chromosomal localization, expression patterns, mutational landscape, copy number variations, and prognostic significance of 10 disulfide death genes was conducted. Patients were categorized into distinct subtypes using the Non-negative Matrix Factorization (NMF) typing method based on disulfidptosis gene expression patterns. Weighted Gene Co-expression Network Analysis (WGCNA) was used on the KIRC dataset to identify differentially expressed genes between subtype clusters. A risk signature was created using LASSO-Cox regression and validated by survival analysis. An interaction between risk score and immune cell infiltration, tumor microenvironment characteristics and pathway enrichment analysis were investigated. Results: Initial findings highlight the differential expression of specific DRGs in KIRC, with genomic instability and somatic mutation analysis revealing key insights into their role in cancer progression. NMF clustering differentiates KIRC patients into subgroups with distinct survival outcomes and immune profiles, and hierarchical clustering identifies gene modules associated with key biological and clinical parameters, leading to the development of a risk stratification model (LRP8, RNASE2, CLIP4, HAS2, SLC22A11, and KCTD12) validated by survival analysis and predictive of immune infiltration and drug sensitivity. Pathway enrichment analysis further delineates the differential molecular pathways between high-risk and low-risk patients, offering potential targets for personalized treatment. Lastly, differential expression analysis of model genes between normal and KIRC cells provides insights into the molecular mechanisms underlying KIRC, highlighting potential biomarkers and therapeutic targets. Conclusion: This study contributes to the understanding of KIRC and provides a potential prognostic model using disulfidptosis gene for personalized management in KIRC patients. The risk signature shows clinical applicability and sheds light on the biological mechanisms associated with disulfide-induced cell death. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Pancreatic adenocarcinoma (PAAD) is a highly malignant cancer that urgently needs more effective therapeutic strategies. The discovery of cuproptosis brings great inspiration for the treatment and clinical assessment of cancers. MATERIALS AND METHODS: A novel cuproptosis- related (CR) risk signature was constructed using the Lasso regression analysis. Its prognostic value was assessed via a series of survival analyses and validated in four GEO cohorts. The effects of CR risk signature on tumor immune microenvironment (TIM) were explored through CIBERSORT, ESTIMATE, and ssGSEA algorithms. Using GESA, we investigated its associations with various patterns of programmed cell death (PCD) and the metabolism process. The somatic mutation features of each CR-risk group were also probed using 'maftools' R package and cBioPortal database. The potential linkages between CR risk score and the efficacy of multiple therapeutic approaches were elucidated using tumor mutation burden, the expressions of immune checkpoints, the TIDE score, and the GDSC database. Finally, we ascertained the biofunctions of LIPT1 (Lipoyltransferase 1) in pancreatic cancer (PC) cells through immunohistochemistry, qPCR (quantitative polymerase chain reaction), colony formation, and Transwell assays. RESULTS: LIPT1, LIAS (lipoyl synthase), PDP1 (Pyruvate dehydrogenase phosphatase1), and GCSH (Glycine cleavage system H protein) constituted the CR risk signature. The CR risk signature possessed a high prognostic value and could improve the traditional prognostic model. Moreover, the CR risk score was indicative of the changes in infiltration levels of CD8+T cells and macrophages, whereas it was not associated with the enrichment of various PCD patterns and multiple metabolic processes. As for therapeutic correlation, CR risk score was a potential biomarker for predicting the efficacy of ICBs but failed in targeted drugs and chemotherapeutic agents. Through qPCR and immunohistochemistry detection in clinical samples, we confirmed that LIPT1 was significantly downregulated in pancreatic adenocarcinoma (PAAD) samples. Experiments in vitro revealed that silencing LIPT1 promoted the proliferation, migration, and invasion of PANC-1 and SW1990 cells. CONCLUSIONS: The novel CR risk signature contributed to the risk stratification of PAAD patients. Cuproptosis regulatory genes, well represented by LIPT1, provided new insights into PAAD treatment and assessment. [ABSTRACT FROM AUTHOR]

Background: Esophageal squamous cell carcinoma (ESCC) is an aggressive and fatal malignancy that leads to epithelial cancer. The association between epithelial cell heterogeneity, prognosis, and immune response in this cancer remains uncertain. This study aimed to investigate epithelial cell heterogeneity in ESCC and develop a predictive risk model using the identified cell types. Methods: Single-cell RNA sequencing (scRNA-seq) and differential ESCC gene data were accessed from the Gene Expression Omnibus. Functional enrichment analysis, inferCNV, cell development trajectories, and intercellular communication were analyzed following epithelial cell characterization. Differentially expressed ESCC (n = 773) and epithelial cell marker genes (n = 3407) were intersected to obtain core genes, and epithelial cell-related prognostic genes were identified. LASSO regression analysis was used to construct a prognostic model. The external dataset GSE53624 was used to further validate the stability of the model. Drug sensitivity predictions, and immune cell infiltration were analyzed. Molecular docking clarified the possible therapeutic role of β-sitosterol in ESCC. Finally, wound healing assay, cell colony, and transwell assay were constructed to detect the effects of the core gene PDLIM2 on ESCC cell proliferation, invasion, and migration. Results: Eight cell clusters were identified, and epithelial cells were categorized into tumor and paratumor groups. The tumor group possessed more chromosomal variants than the paratumor group. Epithelial cells were associated with multiple cell types and significantly correlated with the Wnt, transforming growth factor, and epidermal growth factor signaling pathways. From 231 intersected genes, five core genes were screened for use in the risk model: CTSL, LAPTM4B, MYO10, NCF2, and PDLIM2. These genes may contribute to the cancerous transformation of normal esophageal epithelial cells and thereby act as biomarkers and potential therapeutic targets in patients with ESCC. β-Sitosterol furthermore displayed excellent docking potential with these genes. Meanwhile, further experiments demonstrated that the gene PDLIM2 plays a major role in the progression of oesophageal squamous carcinoma. Conclusion: We successfully developed a risk model for the prognosis of ESCC based on epithelial cells that addresses the response of ESCC to immunotherapy and offers novel cancer treatment options. [ABSTRACT FROM AUTHOR]

Background This study endeavored to develop a nicotinamide adenine dinucleotide (NAD+) metabolism-related biomarkers in gastric cancer (GC), which could provide a theoretical foundation for prognosis and therapy of GC patients. Methods In this study, differentially expressed genes (DEGs1) between GC and paraneoplastic tissues were overlapped with NAD+ metabolism-related genes (NMRGs) to identify differentially expressed NMRGs (DE-NMRGs). Then, GC patients were divided into high and low score groups by gene set variation analysis (GSVA) algorithm for differential expression analysis to obtain DEGs2, which was overlapped with DEGs1 for identification of intersection genes. These genes were further analyzed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses to obtain prognostic genes for constructing a risk model. Enrichment and immune infiltration analyses further investigated investigate the different risk groups, and qRT-PCR validated the prognostic genes. Results Initially, we identified DE-NMRGs involved in NAD biosynthesis, with seven (DNAJB13, CST2, THPO, CIDEA, ONECUT1, UPK1B and SNCG) showing prognostic significance in GC. Subsequent, a prognostic model was constructed in which the risk score, derived from the expression profiles of these genes, along with gender, emerged as robust independent predictors of patient outcomes in GC. Enrichment analysis linked high-risk patients to synaptic membrane pathways and low-risk to the CMG complex pathway. Tumor immune infiltration analysis revealed correlations between risk scores and immune cell abundance, suggesting a relationship between NAD+ metabolism and immune response in GC. The prognostic significance of our identified genes was validated by qRT-PCR, which confirmed their upregulated expression in GC tissue samples. Conclusion In this study, seven NAD+ metabolism-related markers were established, which is of great significance for the development of prognostic molecular biomarkers and clinical prognosis prediction for gastric cancer patients.

Testicular germ cell tumors (TGCT) are the most common type of testicular cancer, comprising 90–95% of cases and representing the most prevalent solid malignancy in young adult men. Immune infiltrates play important regulatory roles in tumors, but their role in TGCT remains unclear. Molecular subtyping is a promising way to provide precisely personalized treatment and avoid unnecessary toxicities. This study investigated immune infiltrates, key biomarkers, and immune subtyping of TGCT. In GSE3218, 24 differentially expressed immune genes (immDEGs) were identified. A new risk signature consisting of six immDEGs was developed using these genes. Individuals in the high‐risk group had poor overall survival (OS; hazard ratio of 4.61 and P‐value

Abstract Background Endometrial cancer (EC) is one of the worldwide gynecological malignancies. Endoplasmic reticulum (ER) stress is the cellular homeostasis disturbance that participates in cancer progression. However, the mechanisms of ER Stress on EC have not been fully elucidated. Method The ER Stress-related genes were obtained from Gene Set Enrichment Analysis (GSEA) and GeneCards, and the RNA-seq and clinical data were downloaded from The Cancer Genome Atlas (TCGA). The risk signature was constructed by the Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis. The significance of the risk signature and clinical factors were tested by time-dependent receiver operating characteristic (ROC) curves, and the selected were to build a nomogram. The immunity correlation was particularly analyzed, including the related immune cells, pathways, and immune checkpoints. Functional enrichment, potential chemotherapies, and in vitro validation were also conducted. Result An ER Stress-based risk signature, consisting of TRIB3, CREB3L3, XBP1, and PPP1R15A was established. Patients were randomly divided into training and testing groups with 1:1 ratio for subsequent calculation and validation. Based on risk scores, high- and low-risk subgroups were classified, and low-risk subgroup demonstrated better prognosis. The Area Under Curve (AUC) demonstrated a reliable predictive capability of the risk signature. The majority of significantly different immune cells and pathways were enriched more in low-risk subgroup. Similarly, several typical immune checkpoints, expressed higher in low-risk subgroup. Patients of the two subgroups responded differently to chemotherapies. Conclusion We established an ER Stress-based risk signature that could effectively predict EC patients’ prognosis and their immune correlation.

Abstract Background Multiple myeloma (MM) is a fatal malignant tumor in hematology. Mitophagy plays vital roles in the pathogenesis and drug sensitivity of MM. Methods We acquired transcriptomic expression data and clinical index of MM patients from NCI public database, and 36 genes involved in mitophagy from the gene set enrichment analysis (GSEA) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted to construct a risk score prognostic model. Kaplan–Meier survival analysis and receiver operation characteristic curves (ROC) were conducted to identify the efficiency of prognosis and diagnosis. ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) was performed to uncover the level of immune infiltration. QRT-PCR was performed to verify gene expression in clinical samples of MM patients. The sensitivity to chemotherapy drugs was evaluated upon the database of the genomics of drug sensitivity in cancer (GDSC). Results Fifty mitophagy-related genes were differently expressed in two independent cohorts. Ten out of these genes were identified to be related to MM overall survival (OS) rate. A prognostic risk signature model was built upon on these genes: VDAC1, PINK1, VPS13C, ATG13, and HUWE1, which predicted the survival of MM accurately and stably both in training and validation cohorts. MM patients suffered more adverse prognosis showed more higher risk core. In addition, the risk score was considered as an independent prognostic element for OS of MM patients by multivariate cox regression analysis. Functional pathway enrichment analysis of differentially expressed genes (DEGs) based on risk score showed terms of cell cycle, immune response, mTOR pathway, and MYC targets were obviously enriched. Furthermore, MM patients with higher risk score were observed lower immune scores and lower immune infiltration levels. The results of qRT-PCR verified VDAC1, PINK1, and HUWE1 were dysregulated in new diagnosed MM patients. Finally, further analysis indicated MM patients showed more susceptive to bortezomib, lenalidomide and rapamycin in high-risk group. Conclusion Our research provided a neoteric prognostic model of MM based on mitophagy genes. The immune infiltration level based on risk score paved a better understanding of the participation of mitophagy in MM.

Abstract Background Lung adenocarcinoma (LUAD) is an extraordinarily malignant tumor, with rapidly increasing morbidity and poor prognosis. Immunotherapy has emerged as a hopeful therapeutic modality for lung adenocarcinoma. Furthermore, a prognostic model (based on immune genes) can fulfill the purpose of early diagnosis and accurate prognostic prediction. Methods Immune-related mRNAs (IRmRNAs) were utilized to construct a prognostic model that sorted patients into high- and low-risk groups. Then, the prediction efficacy of our model was evaluated using a nomogram. The differences in overall survival (OS), the tumor mutation landscape, and the tumor microenvironment were further explored between different risk groups. In addition, the immune genes comprising the prognostic model were subjected to single-cell RNA sequencing to investigate the expression of these immune genes in different cells. Finally, the functions of BIRC5 were validated through in vitro experiments. Results Patients in different risk groups exhibited sharply significant variations in OS, pathway activity, immune cell infiltration, mutation patterns, and immune response. Single-cell RNA sequencing revealed that the expression level of BIRC5 was significantly high in T cells. Cell experiments further revealed that BIRC5 knockdown markedly reduced LUAD cell proliferation. Conclusion This model can function as an instrumental variable in the prognostic, molecular, and therapeutic prediction of LUAD, shedding new light on the optimal clinical practice guidelines for LUAD patients.

Simple Summary: The accurate stratification of head and neck squamous cell carcinoma (HNSCC) patients based on prognostic differences, using robust biomarkers or signatures, is crucial for guiding clinical interventions. Our study aimed to develop a predictive signature for head and neck squamous cell carcinoma outcomes based on T-cell receptor-related genes (TCRRGs). Using The Cancer Genome Atlas HNSCC dataset, GSE41613, and GSE65858, we identified two HNSCC clusters based on TCRRG expression. These clusters showed differences in overall survival (OS) and immune infiltration. A robust TCRRG-based prognostic signature comprising MAP2K7, MAPK3, MAPK9, ORAI1, PSMA1, UBB, and ZAP70 was subsequently constructed and validated across multiple HNSCC cohorts. A nomogram model was then constructed for personalized HNSCC treatment guidance. Functional analyses indicated notable changes in biological functions and pathways between high- and low-risk groups, with the high-risk group exhibiting a suppressive immune environment. Utilizing this TCRRG-based signature, we may precisely forecast HNSCC outcomes, offering enhanced therapeutic strategies. Backgroud: The stratification of head and neck squamous cell carcinoma (HNSCC) patients based on prognostic differences is critical for therapeutic guidance. This study was designed to construct a predictive signature derived from T-cell receptor-related genes (TCRRGs) to forecast the clinical outcomes in HNSCC. Methods: We sourced gene expression profiles from The Cancer Genome Atlas (TCGA) HNSCC dataset, GSE41613, and GSE65858 datasets. Utilizing consensus clustering analysis, we identified two distinct HNSCC clusters according to TCRRG expression. A TCRRG-based signature was subsequently developed and validated across diverse independent HNSCC cohorts. Moreover, we established a nomogram model based on TCRRGs. We further explored differences in immune landscapes between high- and low-risk groups. Results: The TCGA HNSCC dataset was stratified into two clusters, displaying marked variations in both overall survival (OS) and immune cell infiltration. Furthermore, we developed a robust prognostic signature based on TCRRG utilizing the TCGA HNSCC train cohort, and its prognostic efficacy was validated in the TCGA HNSCC test cohort, GSE41613, and GSE65858. Importantly, the high-risk group was characterized by a suppressive immune microenvironment, in contrast to the low-risk group. Our study successfully developed a robust TCRRG-based signature that accurately predicts clinical outcomes in HNSCC, offering valuable strategies for improved treatments. [ABSTRACT FROM AUTHOR]

Simple Summary: Small cell lung cancer (SCLC) is a high-grade neuroendocrine carcinoma with a poor prognosis, accounting for approximately 15% of lung cancer cases. Acquired resistance to standard chemotherapy is quite common in SCLC patients, and the survival benefit of surgery and the selection of surgical candidates are still controversial. This highlights the necessity of identifying predictive biomarkers that can serve to select patients who will benefit from various treatments and discovering novel molecular targets for SCLC treatment. In this study, for the first time, the association between tumoral transcriptional changes and prognosis was examined, and a novel multigene prognostic risk signature with strong discriminatory power was constructed and validated to predict the overall survival of SCLC patients who have undergone curative-intent surgical resection. The risk signature worked better than existing clinical and demographic parameters in predicting overall survival in patients with resected SCLC. Prognostic genes were predicted to have roles in pathways including regulation of transcription, cell cycle, cell metabolism, and angiogenesis. Small cell lung cancer (SCLC) is a malignancy with a poor prognosis whose treatment has not progressed for decades. The survival benefit of surgery and the selection of surgical candidates are still controversial in SCLC. This study is the first report to identify transcriptomic alterations associated with prognosis and propose a gene expression-based risk signature that can be used to predict overall survival (OS) in SCLC patients who have undergone potentially curative surgery. An integrative transcriptome analysis of three gene expression datasets (GSE30219, GSE43346, and GSE149507) revealed 1734 up-regulated and 2907 down-regulated genes. Cox-Mantel test, Cox regression, and Lasso regression analyses were used to identify genes to be included in the risk signature. EGAD00001001244 and GSE60052-cohorts were used for internal and external validation, respectively. Overall survival was significantly poorer in patients with high-risk scores compared to the low-risk group. The discriminatory performance of the risk signature was superior to other parameters. Multivariate analysis showed that the risk signature has the potential to be an independent predictor of prognosis. The prognostic genes were enriched in pathways including regulation of transcription, cell cycle, cell metabolism, and angiogenesis. Determining the roles of the identified prognostic genes in the pathogenesis of SCLC may contribute to the development of new treatment strategies. The risk signature needs to be validated in a larger cohort of patients to test its usefulness in clinical decision-making. [ABSTRACT FROM AUTHOR]

Abstract Background As a component of membrane lipids and the precursor of oxysterols and steroid hormones, reprogrammed cholesterol metabolism contributes to the initiation and progression of multiple cancers. Thus, we aim to further investigate the significances of cholesterol metabolism in lower-grade gliomas (LGGs). Methods The present study included 413 LGG samples from TCGA RNA-seq dataset (training cohort) and 172 LGG samples from CGGA RNA-seq dataset (validation cohort). The cholesterol metabolism-related signature was identified by the LASSO regression model. Bioinformatics analyses were performed to explore the functional roles of this signature in LGGs. Kaplan-Meier and Cox regression analyses were enrolled to estimate prognostic value of the risk signature. Results Our findings suggested that cholesterol metabolism was tightly associated clinicopathologic features and genomic alterations of LGGs. Bioinformatics analyses revealed that cholesterol metabolism played a key role in immunosuppression of LGGs, mainly by promoting macrophages polarization and T cell exhaustion. Kaplan-Meier curve and Cox regression analysis showed that cholesterol metabolism was an independent prognostic indicator for LGG patients. To improve the clinical application value of the risk signature, we also constructed a nomogram model to predict the 1-, 3- and 5-year survival of LGG patients. Conclusion The cholesterol metabolism was powerful prognostic indicator and could serve as a promising target to enhance personalized treatment of LGGs.

The high risk of postoperative mortality in lung adenocarcinoma (LUAD) patients is principally driven by cancer recurrence and low response rates to adjuvant treatment. Here, A combined cohort containing 1,026 stage I–III patients was divided into the learning (n = 678) and validation datasets (n = 348). The former was used to establish a 16-mRNA risk signature for recurrence prediction with multiple statistical algorithms, which was verified in the validation set. Univariate and multivariate analyses confirmed it as an independent indicator for both recurrence-free survival (RFS) and overall survival (OS). Distinct molecular characteristics between the two groups including genomic alterations, and hallmark pathways were comprehensively analyzed. Remarkably, the classifier was tightly linked to immune infiltrations, highlighting the critical role of immune surveillance in prolonging survival for LUAD. Moreover, the classifier was a valuable predictor for therapeutic responses in patients, and the low-risk group was more likely to yield clinical benefits from immunotherapy. A transcription factor regulatory protein–protein interaction network (TF-PPI-network) was constructed via weighted gene co-expression network analysis (WGCNA) concerning the hub genes of the signature. The constructed multidimensional nomogram dramatically increased the predictive accuracy. Therefore, our signature provides a forceful basis for individualized LUAD management with promising potential implications.

Abstract Tumor mutation burden (TMB) level is identified as a useful predictor in multiple tumors including colon adenocarcinoma (COAD). However, the function of TMB related genes has not been explored previously. In this study, we obtained patients’ expression and clinical data from The Cancer Genome Atlas (TCGA) and the National Center for Biotechnology Information (NCBI). TMB genes were screened and subjected to differential expression analysis. Univariate Cox and LASSO analyses were utilized to construct the prognostic signature. The efficiency of the signature was tested by using a receiver operating characteristic (ROC) curve. A nomogram was further plotted to assess the overall survival (OS) time of patients with COAD. In addition, we compared the predictive performance of our signature with other four published signatures. Functional analyses indicated that patients in the low-risk group have obviously different enrichment of tumor related pathways and tumor infiltrating immune cells from that of high-risk patients. Our findings suggested that the ten genes’ prognostic signature could exert undeniable prognostic functions in patients with COAD, which might provide significant clues for the development of personalized management of these patients.

Background: Endometrial cancer (EC) is one of the worldwide gynecological malignancies. Endoplasmic reticulum (ER) stress is the cellular homeostasis disturbance that participates in cancer progression. However, the mechanisms of ER Stress on EC have not been fully elucidated. Method: The ER Stress-related genes were obtained from Gene Set Enrichment Analysis (GSEA) and GeneCards, and the RNA-seq and clinical data were downloaded from The Cancer Genome Atlas (TCGA). The risk signature was constructed by the Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis. The significance of the risk signature and clinical factors were tested by time-dependent receiver operating characteristic (ROC) curves, and the selected were to build a nomogram. The immunity correlation was particularly analyzed, including the related immune cells, pathways, and immune checkpoints. Functional enrichment, potential chemotherapies, and in vitro validation were also conducted. Result: An ER Stress-based risk signature, consisting of TRIB3, CREB3L3, XBP1, and PPP1R15A was established. Patients were randomly divided into training and testing groups with 1:1 ratio for subsequent calculation and validation. Based on risk scores, high- and low-risk subgroups were classified, and low-risk subgroup demonstrated better prognosis. The Area Under Curve (AUC) demonstrated a reliable predictive capability of the risk signature. The majority of significantly different immune cells and pathways were enriched more in low-risk subgroup. Similarly, several typical immune checkpoints, expressed higher in low-risk subgroup. Patients of the two subgroups responded differently to chemotherapies. Conclusion: We established an ER Stress-based risk signature that could effectively predict EC patients' prognosis and their immune correlation. [ABSTRACT FROM AUTHOR]

Background: Efferocytosis is a biological process in which phagocytes remove apoptotic cells and vesicles from tissues. This process is initiated by the release of inflammatory mediators from apoptotic cells and plays a crucial role in resolving inflammation. The signals associated with efferocytosis have been found to regulate the inflammatory response and the tumor microenvironment (TME), which promotes the immune escape of tumor cells. However, the role of efferocytosis in glioblastoma multiforme (GBM) is not well understood and requires further investigation. Methods: In this study, we conducted a comprehensive analysis of 22 efferocytosis-related genes (ERGs) by searching for studies related to efferocytosis. Using bulk RNA-Seq and single-cell sequencing data, we analyzed the expression and mutational characteristics of these ERGs. By using an unsupervised clustering algorithm, we obtained ERG clusters from 549 GBM patients and evaluated the immune infiltration characteristics of each cluster. We then identified differential genes (DEGs) in the two ERG clusters and classified GBM patients into different gene clusters using univariate cox analysis and unsupervised clustering algorithms. Finally, we utilized the Boruta algorithm to screen for prognostic genes and reduce dimensionality, and the PCA algorithm was applied to create a novel efferocytosis-related scoring system. Results: Differential expression of ERGs in glioma cell lines and normal cells was analyzed by rt-PCR. Cell function experiments, on the other hand, validated TIMD4 as a tumor risk factor in GBM. We found that different ERG clusters and gene clusters have distinct prognostic and immune infiltration profiles. The ERG signature we developed provides insight into the tumor microenvironment of GBM. Patients with lower ERG scores have a better survival rate and a higher likelihood of benefiting from immunotherapy. Conclusions: Our novel efferocytosis-related signature has the potential to be used in clinical practice for risk stratification of GBM patients and for selecting individuals who are likely to respond to immunotherapy. This can help clinicians design appropriate targeted therapies before initiating clinical treatment. [ABSTRACT FROM AUTHOR]

Background: Depression increases the risk of breast cancer recurrence and metastasis. However, there lacks potential biomarkers for predicting prognosis in breast cancer. 5-hydroxytryptamine (5-HT) plays a key role in the pathogenesis and treatment of depression. In this study, we developed a prognostic signature based on 5-HT receptors (5-HTRs) and elucidated its potential immune regulatory mechanisms for breast cancer prognosis. Methods: Oncomine, GEPIA, UALCAN, cBioPortal, Kaplan-Meier plotter, and TIMER were used to analyze differential expression, prognostic value, genetic alteration, and immune cell infiltration of HTRs in breast cancer patients. The model training and validation assays were based on the analyses of GSE1456 and GSE86166. A risk signature was established by univariate and multivariate Cox regression analyses. The transwell assay was utilized to verify the effect of the 5- HTRs expression on breast cancer invasion. Effects of HTR2A/2B inhibitor on CD8+ T cell proliferation and infiltration as well as apoptosis of 4T1 cells in the tumor microenvironment were detected by flow cytometry and TUNEL assay. Zebrafish and mouse breast cancer xenografts were used to determine the effect of HTR2A/2B inhibitor on breast cancer metastasis. Results: The expression levels of HTR1A, HTR1B, HTR2A, HTR2B, HTR2C, HTR4, and HTR7 were significantly downregulated in highly malignant breast cancer types. 5-HTRs were significantly associated with recurrence-free survival (RFS) in breast cancer patients. The genetic alteration of HTR1D, HTR3A, HTR3B, and HTR6 in breast cancer patients was significantly associated with shorter overall survival (OS). Finally, HTR2A and HTR2B were determined to construct the risk signature. The expression of HTR2A/2B was positively correlated with the infiltration of immune cells such as CD8+ T cells and macrophages. Furthermore, inhibition of HTR2A expression could suppress CD8+ T cell proliferation and enhance invasion and metastasis of breast cancer cells in both zebrafish and mice model. Conclusions: The HTR2A/2B risk signature not only highlights the significance of HTRs in breast cancer prognosis by modulating cancer immune microenvironment, but also provides a novel gene-testing tool for early prevention of depression in breast cancer patients and lead to an improved prognosis and quality of life. [ABSTRACT FROM AUTHOR]

Neuroblastoma is the most common extracranial solid tumor in children. Patients with neuroblastoma have a poor prognosis. The development of therapy targets and the ability to predict prognosis will be enhanced through further exploration of the genetically related genes of neuroblastoma. The present investigation utilized CRISPR-Cas9 genome-wide screening based on the DepMap database to determine essential genes for neuroblastoma cells' continued survival. WGCNA analysis was used to determine the progression-related genes, and a prognostic signature was constructed. The signature gene, NCAPG, was downregulated in neuroblastoma cells to explore its impact on various cellular processes. This research used DepMap and WGCNA to pinpoint 45 progression-related essential genes for neuroblastoma. A risk signature comprising NCAPG and MAD2L1 was established. The suppression of NCAPG prevented neuroblastoma cells from proliferating, migrating, and invading. The results of flow cytometric analysis demonstrated that NCAPG inhibition caused cell cycle arrest during the G2 and S phases and the activation of apoptosis. Additionally, NCAPG downregulation activated the p53-mediated apoptotic pathway, inducing cell apoptosis. The present work showed that NCAPG knockdown reduced neuroblastoma cell progression and may serve as a basis for further investigation into diagnostic indicators and therapy targets for neuroblastoma. [ABSTRACT FROM AUTHOR]

Background: An increasing number of studies indicated that metabolic reprogramming of amino acid metabolism may either promote or inhibit tumor progression. The purpose of this study was to investigate the ability of a gene risk signature associated with amino acid metabolism to predict the prognosis and immune characteristics of invasive breast carcinoma. Methods: LASSO Cox regression analysis was performed to construct and validate the prognostic risk signature based on the expression of 9 amino acid metabolism-related genes. The predictive value of the signature, immune characteristics, and chemotherapeutic drugs was also predicted. Finally, 9 significant genes were examined in MDA-MB-231 and MCF-7 cells, and the predicted chemotherapeutic drugs were also verified. Results: The prognosis of the low-risk group was better than that of the high-risk group. The areas under the curve (AUCs) at 1, 2, and 3 years were 0.852, 0.790, and 0.736, respectively. In addition, the GSEA results for KEGG and GO revealed that samples with a high-risk score exhibited a variety of highly malignant manifestations. The high-risk group was characterized by an increased number of M2 macrophages, a high level of tumor purity, low levels of APC co-stimulation, cytolytic activity, HLA, para-inflammation, and type I IFN response. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) confirmed that MDA-MB-231 and MCF-7 cells express 9 amino acid metabolism-related genes differently. In addition, cell experiments were conducted to examine the effect of cephaeline-induced on cell viability, migration ability, and protein expression of the PI3K/AKT signaling pathway and HIF-1α. Conclusion: We established a risk signature based on 9 amino acid metabolism-related genes for invasive breast carcinoma. Further analyses revealed that this risk signature is superior to other clinical indexes in survival prediction and that the subgroups identified by the risk signature exhibit distinct immune characteristics. Cephaeline was determined to be a superior option for patients in high-risk groups. [ABSTRACT FROM AUTHOR]

Testicular germ cell tumors (TGCT) are the most common type of testicular cancer, comprising 90–95% of cases and representing the most prevalent solid malignancy in young adult men. Immune infiltrates play important regulatory roles in tumors, but their role in TGCT remains unclear. Molecular subtyping is a promising way to provide precisely personalized treatment and avoid unnecessary toxicities. This study investigated immune infiltrates, key biomarkers, and immune subtyping of TGCT. In GSE3218, 24 differentially expressed immune genes (immDEGs) were identified. A new risk signature consisting of six immDEGs was developed using these genes. Individuals in the high‐risk group had poor overall survival (OS; hazard ratio of 4.61 and P‐value < 0.001). We validated the six‐immDEGs risk signature in pure seminoma and mixed TGCT types. Two distinct immune patterns (Cluster 1 and Cluster 2) were identified using the consensusclusterplus, and Cluster 1 possessed an unfavorable OS compared with Cluster 2 (hazard ratio, 2.56; P < 0.001). Cluster 1 patients had significantly lower naive B cells, memory B cells, plasma cells, naive CD4 T cells, gamma delta T cells, and activated dendritic cells than Cluster 2 patients. Genes relating to the WNT signaling pathway, TGF‐β signaling pathway, antigen processing and presentation, and NK cell‐mediated cytotoxicity were associated with TGCT. STC1 was elevated in TGCT tissues, and its high expression showed advanced clinicopathological characteristics and poor prognosis of TGCT. Our findings may contribute to an increased understanding of the onset and progression of TGCT. [ABSTRACT FROM AUTHOR]

Background: Lung adenocarcinoma (LUAD) is an extraordinarily malignant tumor, with rapidly increasing morbidity and poor prognosis. Immunotherapy has emerged as a hopeful therapeutic modality for lung adenocarcinoma. Furthermore, a prognostic model (based on immune genes) can fulfill the purpose of early diagnosis and accurate prognostic prediction. Methods: Immune-related mRNAs (IRmRNAs) were utilized to construct a prognostic model that sorted patients into high- and low-risk groups. Then, the prediction efficacy of our model was evaluated using a nomogram. The differences in overall survival (OS), the tumor mutation landscape, and the tumor microenvironment were further explored between different risk groups. In addition, the immune genes comprising the prognostic model were subjected to single-cell RNA sequencing to investigate the expression of these immune genes in different cells. Finally, the functions of BIRC5 were validated through in vitro experiments. Results: Patients in different risk groups exhibited sharply significant variations in OS, pathway activity, immune cell infiltration, mutation patterns, and immune response. Single-cell RNA sequencing revealed that the expression level of BIRC5 was significantly high in T cells. Cell experiments further revealed that BIRC5 knockdown markedly reduced LUAD cell proliferation. Conclusion: This model can function as an instrumental variable in the prognostic, molecular, and therapeutic prediction of LUAD, shedding new light on the optimal clinical practice guidelines for LUAD patients. [ABSTRACT FROM AUTHOR]

Background: Multiple myeloma (MM) is a fatal malignant tumor in hematology. Mitophagy plays vital roles in the pathogenesis and drug sensitivity of MM. Methods: We acquired transcriptomic expression data and clinical index of MM patients from NCI public database, and 36 genes involved in mitophagy from the gene set enrichment analysis (GSEA) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted to construct a risk score prognostic model. Kaplan–Meier survival analysis and receiver operation characteristic curves (ROC) were conducted to identify the efficiency of prognosis and diagnosis. ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) was performed to uncover the level of immune infiltration. QRT-PCR was performed to verify gene expression in clinical samples of MM patients. The sensitivity to chemotherapy drugs was evaluated upon the database of the genomics of drug sensitivity in cancer (GDSC). Results: Fifty mitophagy-related genes were differently expressed in two independent cohorts. Ten out of these genes were identified to be related to MM overall survival (OS) rate. A prognostic risk signature model was built upon on these genes: VDAC1, PINK1, VPS13C, ATG13, and HUWE1, which predicted the survival of MM accurately and stably both in training and validation cohorts. MM patients suffered more adverse prognosis showed more higher risk core. In addition, the risk score was considered as an independent prognostic element for OS of MM patients by multivariate cox regression analysis. Functional pathway enrichment analysis of differentially expressed genes (DEGs) based on risk score showed terms of cell cycle, immune response, mTOR pathway, and MYC targets were obviously enriched. Furthermore, MM patients with higher risk score were observed lower immune scores and lower immune infiltration levels. The results of qRT-PCR verified VDAC1, PINK1, and HUWE1 were dysregulated in new diagnosed MM patients. Finally, further analysis indicated MM patients showed more susceptive to bortezomib, lenalidomide and rapamycin in high-risk group. Conclusion: Our research provided a neoteric prognostic model of MM based on mitophagy genes. The immune infiltration level based on risk score paved a better understanding of the participation of mitophagy in MM. Highlights: The study reported a mitophagy-related genes signature in multiple myeloma. The mitophagy-related genes signature had ideal prognostic independence in multiple myeloma. A nomogram to predict the overall survival of multiple myeloma was built by combining the five-gene signature, LDH and ISS stage. Immune infiltration was related to the mitophagy-related risk signature. [ABSTRACT FROM AUTHOR]

Pancreatic cancer (PC) is a highly malignant digestive tract tumor, with a dismal 5-year survival rate. Recently, cuproptosis was found to be copper-dependent cell death. This work aims to establish a cuproptosis-related lncRNA signature which could predict the prognosis of PC patients and help clinical decision-making. Firstly, cuproptosis-related lncRNAs were identified in the TCGA-PAAD database. Next, a cuproptosis-related lncRNA signature based on five lncRNAs was established. Besides, the ICGC cohort and our samples from 30 PC patients served as external validation groups to verify the predictive power of the risk signature. Then, the expression of CASC8 was verified in PC samples, scRNA-seq dataset CRA001160, and PC cell lines. The correlation between CASC8 and cuproptosis-related genes was validated by Real-Time PCR. Additionally, the roles of CASC8 in PC progression and immune microenvironment characterization were explored by loss-of-function assay. As showed in the results, the prognosis of patients with higher risk scores was prominently worse than that with lower risk scores. Real-Time PCR and single cell analysis suggested that CASC8 was highly expressed in pancreatic cancer and related to cuproptosis. Additionally, gene inhibition of CASC8 impacted the proliferation, apoptosis and migration of PC cells. Furthermore, CASC8 was demonstrated to impact the expression of CD274 and several chemokines, and serve as a key indicator in tumor immune microenvironment characterization. In conclusion, the cuproptosis-related lncRNA signature could provide valuable indications for the prognosis of PC patients, and CASC8 was a candidate biomarker for not only predicting the progression of PC patients but also their antitumor immune responses. [ABSTRACT FROM AUTHOR]

Abstract Background Clear cell renal cell carcinoma (ccRCC) is common in urinary system tumors. Cuproptosis is a non-apoptotic cell death pathway. Copper binds to fatty acylated mitochondrial proteins and activates various forms of cell death. LncRNA LINC02154 is significantly highly expressed in cells and tissues of many types of tumors, and the risk signature of LINC02154 in some tumors has been validated for effectiveness. Methods We constructed a risk prognostic signature by obtaining differentially expressed long noncoding RNAs (lncRNAs) associated with ccRCC outcomes and cuproptosis from The Cancer Genome Atlas (TCGA). We used TCGA to construct training and testing sets to analyze the risk signature and the impact of LINC02154, and we performed relevant survival analyses. Tumor mutational burdens were analyzed in different LINC02154 expression groups and risk score groups. We next analyzed the immune microenvironment of LINC20154. We performed LINC20154-related drug sensitivity analyses. We also investigated the cellular function of LINC02154 in the ACHN cell line and performed CCK-8 assay, EdU, wound-healing assay, and Transwell assay. The essential genes FDX1 and DLST of cuproptosis were detected by western blot. Results We demonstrated that LINC02154’s impact on outcomes was statistically significant. We also demonstrated the association of different ages, genders, stages, and grades with LINC02154 and risk models. The results showed a significant difference in tumor mutation burden between the groups, which was closely related to clinical prognosis. We found differences in immune cells among groups with different levels of LINC02154 expression and significant differences in immune function, immunotherapeutic positive markers, and critical steps of the immune cycle. The sensitivity analysis showed that differential expression of LINC02154 discriminated between sensitivity to axitinib, doxorubicin, gemcitabine, pazopanib, sorafenib, sunitinib, and temsirolimus. This difference was also present in the high-risk group and low-risk group. We demonstrated that the proliferation and migration of t ACHN cells in the LINC02154 knockdown group were inhibited. The western blot results showed that the knockdown of LINC02154 significantly affected the expression of FDX1 and DLST, critical genes of cuproptosis. Conclusion Finally, we demonstrated that LINC02154 and our constructed risk signature could predict outcomes and have potential clinical value.