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Aims: Growth differentiation factor 15 (GDF15) is involved in lots of crucial inflammatory and immune response. The clinical and immune features for GDF15 in glioma have not been specifically investigated so far., Methods: Gene expression profiles obtained from public glioma datasets were used to explore the biological function of GDF15 and its impact on immune microenvironment. Interference with GDF15 in several glioma cell lines to verify its functions in vitro. Survival data were used for the survival analysis and establishment of a nomogram predictive model., Results: GDF15 was up-regulated in various malignant phenotypes of glioma. Function analysis and in vitro experiments revealed that GDF15 was associated with malignant progression and NF-κB pathway. GDF15 was closely correlated to inflammatory response, infiltrating immune cells, and immune checkpoint molecules, especially in lower grade glioma (LGG). High expression level of GDF15 predicted poor survival in LGG, while the effect on glioblastoma (GBM) was not significant. A nomogram predictive model combining GDF15 and other prognostic factors was constructed and showed ideal predictive performance., Conclusions: GDF15 could serve as an interesting prognostic biomarker for LGG. Regulating the expression of GDF15 may help solve the dilemma of immunotherapy in glioma., (© 2021 The Authors. CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

Gremlin 1 (GREM1) can regulate the development of many cancers. However, a few studies have revealed the role of GREM1 in bladder cancer (BC). To evaluate the expression and potential function of GREM1 in bladder cancer, we used R version 3.6.3 and related packages to analyze the data from common databases. Samples from our institution were assessed by immunohistochemical staining (IHC), which was approved by the Institutional Ethics Committee (K20220830). GREM1 was highly expressed in BC tissues according to the TCGA and IHC data. Data from TCGA, GSE31684, GSE32894, and IHC showed that GREM1 has significant prognostic value for BC patients. GREM1 is involved in immune and metabolism-related pathways. According to the TIDE algorithm, 61.0% of patients with low GREM1 expression responded well to immunotherapy, compared to only 13.3% in the high GREM1 expression group. High GREM1 expression was associated with sensitivity to cisplatin, docetaxel, gemcitabine, and vinblastine. Thus, GREM1 can predict prognosis and responses to immunotherapy and chemotherapy in BC patients, making it a potential biomarker and therapeutic target. [ABSTRACT FROM AUTHOR]

Background: The metabolism of abnormal bile acids (BAs) is implicated in the initiation and development of gastrointestinal (GI) cancer. However, there was a lack of research on the molecular mechanisms of BAs metabolism in GI. Methods: Genes involved in BAs metabolism were excavated from public databases of The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, and Molecular Signatures Database (MSigDB). ConsensusClusterPlus was used to classify molecular subtypes for GI. To develop a RiskScore model for predicting GI prognosis, univariate Cox analysis was performed on the genes in protein–protein interaction (PPI) network, followed by using Lasso regression and stepwise regression to refine the model and to determine the key prognostic genes. Tumor immune microenvironment in GI patients from different risk groups was assessed using the ESTIMATE algorithm and enrichment analysis. Reverse transcription–quantitative real‐time PCR (RT‐qPCR), Transwell assay, and wound healing assay were carried out to validate the expression and functions of the model genes. Results: This study defined three molecular subtypes (C1, C2, and C3). Specifically, C1 had the best prognosis, while C3 had the worst prognosis with high immune checkpoint gene expression levels and TIDE scores. We selected nine key genes (AXIN2, ATOH1, CHST13, PNMA2, GYG2, MAGEA3, SNCG, HEYL, and RASSF10) that significantly affected the prognosis of GI and used them to develop a RiskScore model accordingly. Combining the verification results from a nomogram, the prediction of the model was proven to be accurate. The high RiskScore group was significantly enriched in tumor and immune‐related pathways. Compared with normal gastric mucosal epithelial cells, the mRNA levels of the nine genes were differential in the gastric cancer cells. Inhibition of PNMA2 suppressed migration and invasion of the cancer cells. Conclusion: We distinguished three GI molecular subtypes with different prognosis based on the genes related to BAs metabolism and developed a RiskScore model, contributing to the diagnosis and treatment of patients with GI. [ABSTRACT FROM AUTHOR]

Background: Ovarian cancer (OC) is a global malignancy characterized by metastatic invasiveness and recurrence. Long non-coding RNAs (lncRNAs) and Telomeres are closely connected with several cancers, but their potential as practical prognostic markers in OC is less well-defined. Methods: Relevant mRNA and clinical data for OC were sourced from The Cancer Genome Atlas (TCGA) database. The telomere-related lncRNAs (TRLs) prognostic model was established by univariate/LASSO/multivariate regression analyses. The effectiveness of the TRLs model was evaluated and measured via the nomogram. Additionally, immune infiltration, tumor mutational load (TMB), and drug sensitivity were evaluated. We validated the expression levels of prognostic genes. Subsequently, PTPRD-AS1 knockdown was utilized to perform the CCK8 assay, colony formation assay, transwell assay, and wound healing assay of CAOV3 cells. Results: A six-TRLs prognostic model (PTPRD-AS1, SPAG5-AS1, CHRM3-AS2, AC074286.1, FAM27E3, and AC018647.3) was established, which can effectively predict patient survival rates and was successfully validated using external datasets. According to the nomogram, the model could effectively predict prognosis. Furthermore, we detected the levels of regulatory T cells and M2 macrophages were comparatively higher in the high-risk TRLs group, but the levels of activated CD8 T cells and monocytes were the opposite. Finally, the low-risk group was more sensitive to anti-cancer drugs. The mRNA levels of PTPRD-AS1, SPAG5-AS1, FAM27E3, and AC018647.3 were significantly over-expressed in OC cell lines (SKOV3, A2780, CAOV3) in comparison to normal IOSE-80 cells. AC074286.1 were over-expressed in A2780 and CAOV3 cells and CHRM3-AS2 only in A2780 cells. PTPRD-AS1 knockdown decreased the proliferation, cloning, and migration of CAOV3 cells. Conclusion: Our study identified potential biomarkers for the six-TRLs model related to the prognosis of OC. [ABSTRACT FROM AUTHOR]

Background. Emerging research has identified ferroptosis as a novel form of programmed cell death, and Arachidonic acid 15-lipoxygenase-1 (ALOX15) stands out as a pivotal gene in mediating this process. Nonetheless, the role of ALOX15 in human tumors remains elusive. Methods. We utilized TIMER 2.0 to investigate the differential expression profiles of ALOX15 between pan-cancer and normal tissues. Further data from the TCGA, GEPIA, UALCAN, HPA, and CPTAC databases were analyzed to verify the levels of mRNA, protein expression, and promoter methylation across various cancer types. The survival prognosis, clinical features, and genetic alterations of ALOX15 were also evaluated. GO/KEGG enrichment analyses and single-cell transcriptome sequencing were employed for functional enrichment analysis. The gene mutation of ALOX15 and its prognostic value were analyzed using the cBioPortal platform. Finally, the relationship between ALOX15 and immune cell infiltration, Immune Checkpoints (ICKs), genomic instability, and drug sensitivity was further explored using GSCA. Results. Our findings revealed that the transcription and protein expression of ALOX15 were significantly reduced in HNSC, LUAD, LUSC, SKCM, KICH, and THCA, while they were up-regulated in ESCA, LIHC, PRAD, and UCEC. Notably, the expression of ALOX15 had prognostic value for certain cancers, including LUAD, LUSC, LIHC, KIRC, HNSC, THCA, and LGG. Additionally, ALOX15 expression was markedly correlated with clinical characteristics, immune cell infiltration, ICKs, genomic instability, and antitumor drug sensitivity in various tumors. Gene mutations of ALOX15 and their prognostic value were discovered in pan-cancers. Furthermore, GO/KEGG analysis and single-cell transcriptome sequencing indicated that ALOX15 was significantly associated with cancer-related pathways. Conclusion. Our comprehensive pan-cancer analysis shed light on the role and significance of ALOX15, suggesting its potential as a prognostic and immunotherapeutic marker for pan-cancer. These findings may provide new directions and evidence for cancer therapeutics. [ABSTRACT FROM AUTHOR]

Background: Aberrant TGF‐β signaling pathway can lead to invasive phenotype of colorectal cancer (CRC), resulting in poor prognosis. It is pivotal to develop an effective prognostic factor on the basis of TGF‐β‐related genes to accurately identify risk of CRC patients. Methods: We performed differential analysis of TGF‐β‐related genes in CRC patients from databases and previous literature to obtain TGF‐β‐related differentially expressed genes (TRDEGs). LASSO‐Cox regression was utilized to build a CRC prognostic feature model based on TRDEGs. The model was validated using two GEO validation sets. Wilcoxon rank‐sum test was utilized to test correlation of model with clinical factors. ESTIMATE algorithm and ssGSEA and tumor mutation burden (TMB) analysis were used to analyze immune landscape and mutation burden of high‐risk (HR) and low‐risk (LR) groups. CellMiner database was utilized to identify therapeutic drugs with high sensitivity to the feature genes. Results: We established a six‐gene risk prognostic model with good predictive accuracy, which independently predicted CRC patients' prognoses. The HR group was more likely to experience immunotherapy benefits due to higher immune infiltration and TMB. The feature gene TGFB2 could inhibit the efficacy of drugs such as XAV‐939, Staurosporine, and Dasatinib, but promote the efficacy of drugs such as CUDC‐305 and by‐product of CUDC‐305. Similarly, RBL1 could inhibit the drug action of Fluphenazine and Imiquimod but promote that of Irofulven. Conclusion: A CRC risk prognostic signature was developed on basis of TGF‐β‐related genes, which provides a reference for risk and further therapeutic selection of CRC patients. [ABSTRACT FROM AUTHOR]

Abstract Background Aberrant TGF‐β signaling pathway can lead to invasive phenotype of colorectal cancer (CRC), resulting in poor prognosis. It is pivotal to develop an effective prognostic factor on the basis of TGF‐β‐related genes to accurately identify risk of CRC patients. Methods We performed differential analysis of TGF‐β‐related genes in CRC patients from databases and previous literature to obtain TGF‐β‐related differentially expressed genes (TRDEGs). LASSO‐Cox regression was utilized to build a CRC prognostic feature model based on TRDEGs. The model was validated using two GEO validation sets. Wilcoxon rank‐sum test was utilized to test correlation of model with clinical factors. ESTIMATE algorithm and ssGSEA and tumor mutation burden (TMB) analysis were used to analyze immune landscape and mutation burden of high‐risk (HR) and low‐risk (LR) groups. CellMiner database was utilized to identify therapeutic drugs with high sensitivity to the feature genes. Results We established a six‐gene risk prognostic model with good predictive accuracy, which independently predicted CRC patients' prognoses. The HR group was more likely to experience immunotherapy benefits due to higher immune infiltration and TMB. The feature gene TGFB2 could inhibit the efficacy of drugs such as XAV‐939, Staurosporine, and Dasatinib, but promote the efficacy of drugs such as CUDC‐305 and by‐product of CUDC‐305. Similarly, RBL1 could inhibit the drug action of Fluphenazine and Imiquimod but promote that of Irofulven. Conclusion A CRC risk prognostic signature was developed on basis of TGF‐β‐related genes, which provides a reference for risk and further therapeutic selection of CRC patients.

Abstract SLAMF9, a member of the conserved lymphocyte activation molecules family (SLAMF), has been less investigated compared to other SLAMs, especially concerning its implications across various cancer types. In our systematic pan-cancer investigation, we observed elevated SLAMF9 expression in various tumor tissues, which was correlated with reduced patient survival across most malignancies. Correlation analyses further revealed significant associations between SLAMF9 expression and immune cell infiltrates, immune checkpoint inhibitors, tumor mutation load, microsatellite instability, and epithelial–mesenchymal transition (EMT) scores. Cell-based assays demonstrated that SLAMF9 knockdown attenuated the proliferative, motile, and invasive capacities of colorectal cancer (CRC) cells. In a nude mouse xenograft model, suppression of SLAMF9 expression substantially inhibited tumor growth. These findings highlight the potential of SLAMF9 as a prognostic and therapeutic biomarker across tumors, with notable implications for CRC cell proliferation and migration.

Abstract Background Acute myeloid leukemia, constituting a majority of leukemias, grapples with a 24% 5-year survival rate. Recent strides in research have unveiled fresh targets for drug therapies. LIM-only, a pivotal transcription factor within LIM proteins, oversees cell development and is implicated in tumor formation. Among these critical LIM proteins, CSRP1, a Cysteine-rich protein, emerges as a significant player in various diseases. Despite its recognition as a potential prognostic factor and therapeutic target in various cancers, the specific link between CSRP1 and acute myeloid leukemia remains unexplored. Our previous work, identifying CSRP1 in a prognostic model for AML patients, instigates a dedicated exploration into the nuanced role of CSRP1 in acute myeloid leukemia. Methods R tool was conducted to analyze the public data. qPCR was applied to evaluate the expression of CSRP1 mRNA for clinical samples and cell line. Unpaired t test, Wilcoxon Rank Sum test, KM curves, spearman correlation test and Pearson correlation test were included in this study. Results CSRP1 displays notable expression variations between normal and tumor samples in acute myeloid leukemia (AML). It stands out as an independent prognostic factor for AML patients, showing correlations with clinical factors like age and cytogenetics risk. Additionally, CSRP1 correlates with immune-related pathways, immune cells, and immune checkpoints in AML. Furthermore, the alteration of CSRP1 mRNA levels is observed upon treatment with a DNMT1 inhibitor for THP1 cells. Conclusion The CSRP1 has potential as a novel prognostic factor and appears to influence the immune response in acute myeloid leukemia. Additionally, there is an observed association between CSRP1 and DNA methylation in acute myeloid leukemia.

Abstract In order to construct a prognostic evaluation model of TLS features in COAD and better realize personalized precision medicine in COAD. Colon adenocarcinoma (COAD) is a common malignant tumor of the digestive system. At present, there is no effective prognostic marker to predict the prognosis of patients. Tertiary lymphoid structure (TLS) affects cancer progression by regulating immune microenvironment. Mining COAD biomarkers based on TLS-related genes helps to improve the prognosis of patients. In order to construct a prognostic evaluation model of TLS features in COAD and better realize personalized precision medicine in COAD. The mRNA expression data and clinical information of COAD and adjacent tissues were downloaded from the Cancer Genome Atlas database. The differentially expressed TLS-related genes of COAD relative to adjacent tissues were obtained by differential analysis. TLS gene co-expression analysis was used to mine genes highly related to TLS, and the intersection of the two was used to obtain candidate genes. Univariate, LASSO, and multivariate Cox regression analysis were performed on candidate genes to screen prognostic markers to construct a risk assessment model. The differences of immune characteristics were evaluated by ESTIMATE, ssGSEA and CIBERSORT in high and low risk groups of prognostic model. The difference of genomic mutation between groups was evaluated by tumor mutation burden score. Screening small molecule drugs through the GDSC library. Finally, a nomogram was drawn to evaluate the clinical value of the prognostic model. Seven TLS-related genes ADAM8, SLC6A1, PAXX, RIMKLB, PTH1R, CD1B, and MMP10 were screened to construct a prognostic model. Survival analysis showed that patients in the high-risk group had significantly lower overall survival rates. Immune microenvironment analysis showed that patients in the high-risk group had higher immune indicators, indicating higher immunity. The genomic mutation patterns of the high-risk and low-risk groups were significantly different, especially the KRAS mutation frequency was significantly higher in the high-risk group. Drug sensitivity analysis showed that the low-risk group was more sensitive to Erlotinib, Savolitinib and VE _ 822, which may be used as a potential drug for COAD treatment. Finally, the nomogram constructed by pathological features combined with RiskScore can accurately evaluate the prognosis of COAD patients. This study constructed and verified a TLS model that can predict COAD. More importantly, it provides a reference standard for guiding the prognosis and immunotherapy of COAD patients.

Abstract The prognosis for Cutaneous Melanoma (CM), a skin malignant tumor that is extremely aggressive, is not good. A recently identified type of controlled cell death that is intimately related to immunotherapy and the development of cancer is called cuproptosis. Using The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, we developed and validated a DNA-methylation located in cuproptosis death-related gene prognostic signature (CRG-located DNA-methylation prognostic signature) to predict CM’s prognosis. Kaplan–Meier analysis of our TCGA and GEO cohorts showed that high-risk patients had a shorter overall survival. The area under the curve (AUC) for the TCGA cohort was 0.742, while for the GEO cohort it was 0.733, according to the receiver operating characteristic (ROC) analysis. Furthermore, this signature was discovered as an independent prognostic indicator over CM patients based on Cox-regression analysis. Immunogenomic profiling indicated that majority immune-checkpoints got an opposite relationship with the signature, and patients in the group at low risk got higher immunophenoscore. Several immune pathways were enriched, according to functional enrichment analysis. In conclusion, a prognostic methylation signature for CM patients was established and confirmed. Because of its close relationship to the immune landscape, this signature may help clinicians make more accurate and individualized choices regarding therapy.

Colorectal cancer (COCA) has a poor prognosis, with growing evidence implicating basement membranes (BMs) in cancer progression. Our goal was to investigate the role and predictive significance of BMs in COCA patients. We obtained BMs‐related genes from cutting‐edge research and used TCGA and GTEx databases for mRNA expression and patient information. Cox regression and LASSO regression were used for prognostic gene selection and risk model construction. We compared prognosis using Kaplan–Meier analysis and examined drug sensitivity differences. The CMAP dataset identified potential small molecule drugs. In vitro tests involved suppressing a crucial gene to observe its impact on tumour metastasis. We developed a 12 BMs‐based approach, finding it to be an independent prognostic factor. Functional analysis showed BMs concentrated in cancer‐associated pathways, correlating with immune cell infiltration and immune checkpoint activation. High‐risk individuals exhibited increased drug sensitivity. AGRN levels were linked to decreased progression‐free survival (p < 0.001). AGRN knockdown suppressed tumour growth and metastasis. Our study offers new perspectives on BMs in COCA, concluding that AGRN is a dependable biomarker for patient survival and prognosis. [ABSTRACT FROM AUTHOR]

SLAMF9, a member of the conserved lymphocyte activation molecules family (SLAMF), has been less investigated compared to other SLAMs, especially concerning its implications across various cancer types. In our systematic pan-cancer investigation, we observed elevated SLAMF9 expression in various tumor tissues, which was correlated with reduced patient survival across most malignancies. Correlation analyses further revealed significant associations between SLAMF9 expression and immune cell infiltrates, immune checkpoint inhibitors, tumor mutation load, microsatellite instability, and epithelial–mesenchymal transition (EMT) scores. Cell-based assays demonstrated that SLAMF9 knockdown attenuated the proliferative, motile, and invasive capacities of colorectal cancer (CRC) cells. In a nude mouse xenograft model, suppression of SLAMF9 expression substantially inhibited tumor growth. These findings highlight the potential of SLAMF9 as a prognostic and therapeutic biomarker across tumors, with notable implications for CRC cell proliferation and migration. [ABSTRACT FROM AUTHOR]

AbstractHepatocellular carcinoma (HCC), as a malignancy derived from liver tissue, is typically associated with poor prognosis. Increasing evidence suggests a connection between pyrimidine metabolism and HCC progression. The purpose of this study was to establish a model applied to the prediction of HCC patients’ overall survival. Transcriptomic data of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) website. Pyrimidine metabolism-related genes (PMRGs) were collected from the Gene Set Enrichment Analysis (GSEA) website. Differential gene expression analysis was carried out on the HCC data, followed by an intersection of the differentially expressed genes (DEGs) and PMRGs. Subsequently, a prognostic model incorporating nine genes was established using univariate/multivariate Cox regression and Least absolute shrinkage and selection operator (LASSO) regression. Survival analysis demonstrated that the high-risk group defined by this model had considerably shorter overall survival than the low-risk group in both TCGA and Gene Expression Omnibus (GEO) datasets. Receiver operating characteristic (ROC) analysis indicated the good predictive capability of the model. CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms revealed significantly higher levels of Macrophages M0 and lower levels of natural killer (NK)_cells in the high-risk group compared to the low-risk group. The immunophenoscore (IPS) and the tumor immune dysfunction and exclusion (TIDE) score demonstrated that the model could significantly differentiate patients who would be more suitable for immunotherapy. Moreover, the CellMiner database was utilized to predict anti-tumor drugs significantly associated with the model genes. Collectively, the potential prognostic significance of pyrimidine metabolism in HCC was revealed in this study. The prognostic model aids in evaluating the survival time and immune status of HCC patients. [ABSTRACT FROM AUTHOR]

Objective: Disulfidptosis is a newly recognized form of regulated cell death that has been linked to cancer progression and prognosis. Despite this association, the prognostic significance, immunological characteristics and treatment response of disulfidptosis-related lncRNAs (DRLs) in ovarian cancer have not yet been elucidated. Methods: The lncRNA data and clinical information for ovarian cancer and normal samples were obtained from the UCSC XENA. Differential expression analysis and Pearson analysis were utilized to identify core DRLs, followed by LASSO algorithm. Random Survival Forest was used to construct a prognostic model. The relationships between risk scores, RNA methylation, immune cell infiltration, mutation, responses to immunotherapy and drug sensitivity analysis were further examined. Additionally, qRT-PCR experiments were conducted to validate the expression of the core DRLs in human ovarian cancer cells and normal ovarian cells and the scRNA-seq data of the core DRLs were obtained from the GEO dataset, available in the TISCH database. Results: A total of 8 core DRLs were obtained to construct a prognostic model for ovarian cancer, categorizing all patients into low-risk and high-risk groups using an optimal cutoff value. The AUC values for 1-year, 3-year and 5-year OS in the TCGA cohort were 0.785, 0.810 and 0.863 respectively, proving a strong predictive capability of the model. The model revealed the high-risk group patients exhibited lower overall survival rates, higher TIDE scores and lower TMB levels compared to the low-risk group. Variations in immune cell infiltration and responses to therapeutic drugs were observed between the high-risk and low-risk groups. Besides, our study verified the correlations between the DRLs and RNA methylation. Additionally, qRT-PCR experiments and single-cell RNA sequencing data analysis were conducted to confirm the significance of the core DRLs at both cellular and scRNA-seq levels. Conclusion: We constructed a reliable and novel prognostic model with a DRLs cluster for ovarian cancer, providing a foundation for further researches in the management of this disease. [ABSTRACT FROM AUTHOR]

Background: The anti-tumor immune reaction has a critical part in the progression together with survival of colorectal cancer (CRC). The expression of T cell-inflamed gene as a potential indicator that predicts the reaction of immunogenic cancer types to checkpoint inhibitor immunotherapy, but its prognostic value in CRC is unclear. Materials and Methods: Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses identified enrichment analysis of T cell-inflamed associated genes. The prognostic model is built and the prognostic genes screened using the LASSO Cox regression model. Relationship among risk score, disease subtypes and immune infiltration was constructed. Besides, risk score was combined with clinicopathological features to further improve prognostic models and survival predictions. Results: We identified 14 T cell-inflamed associated genes were differential expressed in CRC tissues. GO along with KEGG analyses revealed that T cell -inflamed associated genes were implicated in the regulation of immune cells. Through LASSO Cox regression, a 5-gene prognostic model was constructed as well as classified all CRC patients into a low- or high-risk group or different disease subtypes (C1, C2 and C3). In the C3 group the OS rate of CRC patients was longer compared to the C1 group. Additionally, the prognosis Decision Curve Analysis (DCA) map displayed that the risk score was increased compared to the extreme curve and higher than other indicators, showing the strongest survival prediction ability. Conclusion: These results indicated that T cell-inflamed associated gene-based risk score reflected immune microenvironment features as well as predicted prognosis in CRC. [ABSTRACT FROM AUTHOR]

Background. Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, and while advancements in diagnosis, surgery, radiotherapy, and molecular therapy have improved clinical prognosis, the long-term survival rate and quality of life of patients remain unsatisfactory. Therefore, identifying new prognostic biomarkers and potential therapeutic targets is crucial. Objectives. This study aimed to analyze the role of anoikis-related gene characteristics in LUAD. Materials and methods. The anoikis-related genes were obtained from the GeneCards database. Genetic expression data and clinical characteristic information were collected from The Cancer Genome Atlas (TCGA)-LUAD, and the Gene Expression Omnibus (GEO) GSE31210, GSE37745, and GSE68465 datasets. Random survival forest and least absolute shrinkage and selection operator (LASSO) models were applied to construct the risk model. An analysis of immune cell infiltration and function was performed with the scores. Results. Four prognosis-related genes (TLE1, GLI2, PLK1, and BAK1) were obtained and used to construct the anoikis score. We found that the patient survival rate was higher in the low-anoikis score (LAS) group. Moreover, both the stromal and immune scores were negatively correlated with the anoikis score. With the increase of the anoikis score, the levels of natural killer cells, regulatory T cells, CD4+ T cells, CD8+ T cells, B cells, and macrophages decreased. The anoikis score had a negative regulatory relationship with the immune response, natural killer cell activation and T cell activation. The TP53 mutation was significant in LUAD patients and was present in 56% of the high-anoikis score (HAS) group and in 40% of the LAS group. Conclusions. The anoikis score was associated with poor prognosis in LUAD patients. Anoikis-related genes were associated with tumor immune dysregulation and TP53 mutation in LUAD. This study opens a new perspective for LUAD therapy. [ABSTRACT FROM AUTHOR]

Background: Acute myeloid leukemia, constituting a majority of leukemias, grapples with a 24% 5-year survival rate. Recent strides in research have unveiled fresh targets for drug therapies. LIM-only, a pivotal transcription factor within LIM proteins, oversees cell development and is implicated in tumor formation. Among these critical LIM proteins, CSRP1, a Cysteine-rich protein, emerges as a significant player in various diseases. Despite its recognition as a potential prognostic factor and therapeutic target in various cancers, the specific link between CSRP1 and acute myeloid leukemia remains unexplored. Our previous work, identifying CSRP1 in a prognostic model for AML patients, instigates a dedicated exploration into the nuanced role of CSRP1 in acute myeloid leukemia. Methods: R tool was conducted to analyze the public data. qPCR was applied to evaluate the expression of CSRP1 mRNA for clinical samples and cell line. Unpaired t test, Wilcoxon Rank Sum test, KM curves, spearman correlation test and Pearson correlation test were included in this study. Results: CSRP1 displays notable expression variations between normal and tumor samples in acute myeloid leukemia (AML). It stands out as an independent prognostic factor for AML patients, showing correlations with clinical factors like age and cytogenetics risk. Additionally, CSRP1 correlates with immune-related pathways, immune cells, and immune checkpoints in AML. Furthermore, the alteration of CSRP1 mRNA levels is observed upon treatment with a DNMT1 inhibitor for THP1 cells. Conclusion: The CSRP1 has potential as a novel prognostic factor and appears to influence the immune response in acute myeloid leukemia. Additionally, there is an observed association between CSRP1 and DNA methylation in acute myeloid leukemia. [ABSTRACT FROM AUTHOR]

IL33 plays an important role in cancer. However, the role of liver cancer remains unclear. Open‐accessed data was obtained from the Cancer Genome Atlas, Xena, and TISCH databases. Different algorithms and R packages are used to perform various analyses. Here, in our comprehensive study on IL33 in HCC, we observed its differential expression across cancers, implicating its role in cancer development. The single‐cell analysis highlighted its primary expression in endothelial cells, unveiling correlations within the HCC microenvironment. Also, the expression level of IL33 was correlated with patients survival, emphasizing its potential prognostic value. Biological enrichment analyses revealed associations with stem cell division, angiogenesis, and inflammatory response. IL33's impact on the immune microenvironment showcased correlations with diverse immune cells. Genomic features and drug sensitivity analyses provided insights into IL33's broader implications. In a pan‐cancer context, IL33 emerged as a potential tumour‐inhibitor, influencing immune‐related molecules. This study significantly advances our understanding of IL33 in cancer biology. IL33 exhibited differential expression across cancers, particularly in endothelial cells within the HCC microenvironment. IL33 is correlated with the survival of HCC patients, indicating potential prognostic value and highlighting its broader implications in cancer biology. [ABSTRACT FROM AUTHOR]

FAT1, a substantial transmembrane protein, plays a pivotal role in cellular adhesion and cell signaling. Numerous studies have documented frequent alterations in FAT1 across various cancer types, with its aberrant expression being linked to unfavorable survival rates and tumor progression. In the present investigation, we employed bioinformatic analyses, as well as in vitro and in vivo experiments to elucidate the functional significance of FAT1 in pan-cancer, with a primary focus on lung cancer. Our findings unveiled FAT1 overexpression in diverse cancer types, including lung cancer, concomitant with its association with an unfavorable prognosis. Furthermore, FAT1 is intricately involved in immune-related pathways and demonstrates a strong correlation with the expression of immune checkpoint genes. The suppression of FAT1 in lung cancer cells results in reduced cell proliferation, migration, and invasion. These collective findings suggest that FAT1 has potential utility both as a biomarker and as a therapeutic target for lung cancer. [ABSTRACT FROM AUTHOR]

Recent investigations have underscored the epigenetic modulation of the immune response; however, the interplay between RNA N6-methyladenosine (m6A) modification and immunomodulation in head and neck squamous cell carcinoma (HNSC) remains relatively unexplored. To bridge this knowledge gap, we undertook an extensive examination of the potential contributions of m6A modification and immunomodulation in HNSC. We amalgamated and deduplicated 27 m6A -related genes (m6AGs) and 1342 immune regulation-related genes (IMRGs), resulting in a comprehensive dataset encompassing 1358 genes. This dataset was scrutinized for m6A modification and immunomodulatory patterns within HNSC specimens. Employing Cox regression analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) technique, we developed a prognostic risk model for m6A regulator-mediated methylation modification and immunomodulation-related differentially expressed genes (m6A&IMRDEGs). Our differential expression analysis delineated 29 m6A&IMRDEGs, and Weighted Gene Co-expression Network Analysis (WGCNA) elucidated two module genes (IL11 and MMP13) subjected to correlation analysis. The prognostic prediction models revealed that the clinical predictive efficacy peaked for 1-year forecasts, followed sequentially by 3-year and 5-year predictions. The risk scores derived from the model adeptly categorized HNSC patients into high- and low-risk cohorts, with the high-risk group exhibiting a more unfavorable prognosis. Protein-Protein Interaction (PPI) analysis identified 7 hub genes implicated in m6A and immune regulation, namely BPIFB1, BPIFB2, GP2, MUC5B, MUC7, PIP, and SCGB3A1. Furthermore, we noted marked disparities in the expression profiles of 18 immune cell types between the high- and low-risk groups. Our results substantiate that the clustering subpopulations and risk models associated with m6A and immune regulatory genes portend a poor prognosis in HNSC. The risk score emerges as a potent prognostic biomarker and predictive metric for HNSC patients. A thorough assessment of m6A and immune regulatory genes in HNSC will augment our comprehension of the tumor immune microenvironment and facilitate the advancement of HNSC therapeutics.

The C6orf120 gene is a novel gene whose function has not been fully defined. Previous studies have associated it with various liver pathologies, but its specific role in hepatocellular carcinoma (LIHC) remains unclear. This study aimed to investigate the diagnostic and prognostic value of C6orf120 in LIHC, as well as its potential biological functions. In this preliminary research, we utilized data from various databases and bioinformatics tools, including TCGA, GEO, TIMER2, HPA, GEPIA, Linkeomics, Metascape, CIBERSORT, TargetScan, DIANA-microT, RNAinter, and ENCORI, to analyze the expression patterns and mechanisms of C6orf120 in LIHC. Our bioinformatics analysis revealed that C6orf120 is upregulated in LIHC and may serve as a diagnostic and prognostic biomarker. The aberrant expression of C6orf120 in LIHC was further supported by clinical samples and cell lines. In vitro experiments demonstrated that the knockdown of C6orf120 in HepG2 cells significantly reduced migration capacity without affecting proliferation. Additionally, the downregulation of C6orf120 in LIHC cells appeared to inhibit endothelial cell migration and angiogenesis, which are critical in tumorigenesis and development. In conclusion, our findings suggest that C6orf120 could serve as a novel diagnostic and prognostic biomarker for LIHC and is expected to be a prognostic marker and a potential therapeutic target in the clinical management of LIHC.

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest tumors worldwide, with extremely aggressive and complicated biology. Krüppel-like factors (KLFs) encode a series of transcriptional regulatory proteins and play crucial roles in a variety of processes, including tumor cell differentiation and proliferation. However, the potential biological functions and possible pathways of KLFs in the progression of PDAC remain elusive. Methods We systematically evaluated the transcriptional variations and expression patterns of KLFs in pancreatic cancer from the UCSC Xena. Based on difference analysis, the non-negative matrix factorization (NMF) algorithm was utilized to identify the immune characteristics and clinical significance of two different subtypes. The multivariate Cox regression was used to construct the risk model and then explore the differences in tumor immune microenvironment (TIME) and drug sensitivity between high and low groups. Through single-cell RNA sequencing (scRNA-seq) analysis, we screened KLF6 and further investigated its biological functions in pancreatic cancer and pan-cancer. Results The KLFs exhibited differential expression and mutations in the transcriptomic profile of PDAC. According to the expression of KLFs, patients were classified into two distinct subtypes, each exhibiting significant differences in prognosis and TIME. Moreover, the KLF signature was developed using univariate Cox and Lasso regression, which proved to be a reliable and effective prognostic model. Furthermore, the KLF_Score was closely associated with immune infiltration, response to immunotherapy, and drug sensitivity and we screened small molecule compounds targeting prognostic genes separately. Through scRNA-seq analysis, KLF6 was selected to further demonstrate its role in the malignance of PC in vitro. Finally, pan-cancer analysis emphasized the biological significance of KLF6 in multiple types of tumors and its clinical utility in assessing cancer prognosis. Conclusion This study elucidated the pivotal role of KLF family genes in the malignant development of PC through comprehensive analysis and revealed that KLF6 would be a novel diagnostic biomolecule marker and potential therapeutic target for PDAC.

Abstract Background Previous studies have largely neglected the role of sulfur metabolism in LUAD, and no study has combine iron, copper, and sulfur-metabolism associated genes together to create prognostic signatures. Methods This study encompasses 1564 LUAD patients, 1249 NSCLC patients, and over 10,000 patients with various cancer types from diverse cohorts. We employed the R package ConsensusClusterPlus to separate patients into different ICSM (Iron, Copper, and Sulfur-Metabolism) subtypes. Various machine-learning methods were utilized to develop the ICSMI. Enrichment analyses were conducted using ClusterProfiler and GSVA, while IOBR quantified immune cell infiltration. GISTIC2.0 and maftools were utilized for CNV and SNV data analysis. The Oncopredict package predicted drug information based on GDSC1. TIDE algorithm and cohorts GSE91061 and IMvigor210 evaluated patient response to immunotherapy. Single-cell data was processed using the Seurat package, AUCell package calculated cells geneset activity scores, and the Scissor algorithm identified ICSMI-associated cells. In vitro experiments was conducted to explore the role of ICSMRGs in LUAD. Results Unsupervised clustering identified two distinct ICSM subtypes of LUAD, each with unique clinical characteristics. The ICSMI, comprising 10 genes, was constructed using integrated machine-learning methods. Its prognostic power was validated in 10 independent datasets, revealing that LUAD patients with higher ICSMI levels had poorer prognoses. Furthermore, ICSMI demonstrated superior predictive abilities compared to 102 previously published signatures. A nomogram incorporating ICSMI and clinical features exhibited high predictive performance. ICSMI positively correlated with patients gene mutations, and integrated analysis of bulk and single-cell transcriptome data revealed its association with TME modulators. Cells representing the high-ICSMI phenotype exhibited more malignant features. LUAD patients with high ICSMI levels exhibited sensitivity to chemotherapy and targeted therapy but displayed resistance to immunotherapy. In a comprehensive analysis across various cancers, ICSMI retained significant prognostic value and emerged as a risk factor for the majority of cancer patients. Conclusions ICSMI provides critical prognostic insights for LUAD patients, offering valuable insights into the tumor microenvironment and predicting treatment responsiveness.

Abstract SPTBN2 is a protein-coding gene that is closely related to the development of malignant tumors. However, its prognostic value and biological function in pan-cancer, especially pancreatic cancer (PAAD), have not been reported. In the present study, a novel exploration of the value and potential mechanism of SPTBN2 in PAAD was conducted using multi-omics in the background of pan-cancer. Via various database analysis, up-regulated expression of SPTBN2 was detected in most of the tumor tissues examined. Overexpression of SPTBN2 in PAAD and kidney renal clear cell cancer patients potentially affected overall survival, disease-specific survival, and progression-free interval. In PAAD, SPTBN2 can be used as an independent factor affecting prognosis. Mutations and amplification of SPTBN2 were detected, with abnormal methylation of SPTBN2 affecting its expression and the survival outcome of PAAD patients. Immunoassay results demonstrate that SPTBN2 was a potential biomarker for predicting therapeutic response in PAAD, and may influence the immunotherapy efficacy of PAAD by regulating levels of CD8 + T cells and neutrophil infiltration. Results from an enrichment analysis indicated that SPTBN2 may regulate the development of PAAD via immune pathways. Thus, SPTBN2 is a potential prognostic biomarker and immunotherapy target based on its crucial role in the development of PAAD.

Abstract Background Tumor cells exhibit a heightened susceptibility to lysosomal-dependent cell death (LCD) compared to normal cells. However, the role of LCD-related genes (LCD-RGs) in Osteosarcoma (OS) remains unelucidated. This study aimed to elucidate the role of LCD-RGs and their mechanisms in OS using several existing OS related datasets, including TCGA-OS, GSE16088, GSE14359, GSE21257 and GSE162454. Results Analysis identified a total of 8,629 DEGs1, 2,777 DEGs2 and 21 intersection genes. Importantly, two biomarkers (ATP6V0D1 and HDAC6) linked to OS prognosis were identified to establish the prognostic model. Significant differences in risk scores for OS survival were observed between high and low-risk cohorts. Additionally, scores of dendritic cells (DC), immature DCs and γδT cells differed significantly between the two risk cohorts. Cell annotations from GSE162454 encompassed eight types (myeloid cells, osteoblastic OS cells and plasma cells). ATP6V0D1 was found to be significantly over-expressed in myeloid cells and osteoclasts, while HDAC6 was under-expressed across all cell types. Moreover, single-cell trajectory mapping revealed that myeloid cells and osteoclasts differentiated first, underscoring their pivotal role in patients with OS. Furthermore, ATP6V0D1 expression progressively decreased with time. Conclusions A new prognostic model for OS, associated with LCD-RGs, was developed and validated, offering a fresh perspective for exploring the association between LCD and OS.

Abstract Background Liver cancer ranks sixth in incidence and third in mortality globally and hepatocellular carcinoma (HCC) accounts for 90% of it. Hypoxia, glycolysis, and lactate metabolism have been found to regulate the progression of HCC separately. However, there is a lack of studies linking the above three to predict the prognosis of HCC. The present study aimed to identify a hypoxia–glycolysis–lactate-related gene signature for assessing the prognosis of HCC. Methods This study collected 510 hypoxia-glycolysis-lactate genes from Molecular Signatures Database (MSigDB) and then classified HCC patients from TCGA-LIHC by analyzing their hypoxia-glycolysis-lactate genes expression. Differentially expressed genes (DEGs) were screened out to construct a gene signature by LASSO-Cox analysis. Univariate and multivariate regression analyses were used to evaluate the independent prognostic value of the gene signature. Analyses of immune infiltration, somatic cell mutations, and correlation heatmap were conducted by “GSVA” R package. Single-cell analysis conducted by “SingleR”, “celldex”, “Seurat”, and “CellCha” R packages revealed how signature genes participated in hypoxia/glycolysis/lactate metabolism and PPI network identified hub genes. Results We classified HCC patients from TCGA-LIHC into two clusters and screened out DEGs. An 18-genes prognostic signature including CDCA8, CBX2, PDE6A, MED8, DYNC1LI1, PSMD1, EIF5B, GNL2, SEPHS1, CCNJL, SOCS2, LDHA, G6PD, YBX1, RTN3, ADAMTS5, CLEC3B, and UCK2 was built to stratify the risk of HCC. The risk score of the hypoxia-glycolysis-lactate gene signature was further identified as a valuable independent factor for estimating the prognosis of HCC. Then we found that the features of clinical characteristics, immune infiltration, somatic cell mutations, and correlation analysis differed between the high-risk and low-risk groups. Furthermore, single-cell analysis indicated that the signature genes could interact with the ligand-receptors of hepatocytes/fibroblasts/plasma cells to participate in hypoxia/glycolysis/lactate metabolism and PPI network identified potential hub genes in this process: CDCA8, LDHA, YBX1. Conclusion The hypoxia–glycolysis–lactate-related gene signature we built could provide prognostic value for HCC and suggest several hub genes for future HCC studies.

Abstract Background Long non-coding RNAs (LncRNAs) regulating the immune microenvironment of cancer is a hot spot. But little is known about the influence of the immune-related lncRNA (IRlncRs) on the chemotherapeutic responses and prognosis of cervical cancer (CC) patients. The purpose of the study was to identify an immune-related lncRNAs (IRlncRs)-based model for the prospective prediction of clinical outcomes in CC patients. Methods CC patients’ relevant data was acquired from The Cancer Genome Atlas (TCGA). Correlation analysis and Cox regression analyses were applied. A risk score formula was formulated. Prognostic factors were combined into a nomogram, while sensitivity for chemotherapy drugs was analyzed using the OncoPredict algorithm. Results Eight optimal IRlncRs(ATP2A1-AS1, LINC01943, AL158166.1, LINC00963, AC009065.8, LIPE-AS1, AC105277.1, AC098613.1.) were incorporated in the IRlncRs model. The overall survival (OS) of the high-risk group of the model was inferior to those in the low-risk group. Further analysis demonstrated this eight-IRlncRs model as a useful prognostic marker. The Nomogram had a concordance index of survival prediction of 0.763(95% CI 0.746–0.780) and more robust predictive accuracy. Furthermore, patients in the low-risk group were found to be more sensitive to chemotherapy, including Paclitaxel, Rapamycin, Epirubicin, Vincristine, Docetaxel and Vinorelbine. Conclusions An eight-IRlncRs-based prediction model was identified that has the potential to be an important tool to predict chemotherapeutic responses and prognosis for CC patients.

Objective To assess the role of the ferroptosis-associated gene GLS2 in the prognosis of pan-cancer and immunity using bioinformatics methods. Methods GLS2 expression levels in pan-cancer were profiled using publicly available databases: The Cancer Genome Atlas, GTEx, Cancer Cell Line Encyclopedia and the International Cancer Genome Consortium. The aim was to explore the role of GLS2 gene expression, gene variation survival analysis, immune infiltration, immune checkpoint related genes, TMB, and MSI in different tumors. Results Difference in GLS2 expression levels between cancer and paraneoplastic tissues was statistically significant in most cancer types, and the expression level was correlated with survival in these cancer types. A positive correlation was found between GLS2 expression and immune cell infiltration in multiple cancer types, and GLS2 expression level was positively correlated with TMB, MSI, and methylation, and its expression is an indicator for potential therapeutic response. Conclusion Pan-cancer analysis shows that the ferroptosis-related gene GLS2 can be used as a diagnostic and prognostic marker of clear cell carcinoma of kidney, adrenocortical carcinoma, lung adenocarcinoma, and pancreatic cancer.

Abstract Uterine corpus endometrial carcinoma (UCEC) is becoming a main malignant cancer that threaten to women's health. Thymidine kinase 1 (TK1) is considering to be associated with tumorigenesis and development. Nevertheless, the function of TK1 in UCEC is still unclear. Herein, we analyzed the TK1 expression level in pan-cancer and found that TK1 was upregulated in a variety of cancers including UCEC. Patients of UCEC with high expression of TK1 were related to poor outcome. TK1 was also related to clinical stage, histologic grade and lymph node metastasis. Abnormal expression of TK1 in UCEC was related to promoter methylation while gene mutation was not frequent. TK1 and its associated genes appeared to be prominent in cell cycle and DNA replication, according to GO and KEGG analysis. Analysis of immune infiltration revealed a negative correlation between TK1 and CD8 + T cells, macrophages, and dendritic cells. In vitro experiments, TK1 knockdown resulted in the inhibition of proliferation, migration, invasion and EMT in UCEC cell lines.

ABSTRACTObjectives Acute myeloid leukemia (AML) is one of the common hematological diseases with low survival rates. Studies have highlighted the dysregulated expression of immune-related and exosome-related genes (ERGs) in cancers. Nevertheless, it remains to be determined whether combining these genes have a prognostic significance in AML.Methods Immune-ERG profiles for 151 AML patients from TCGA were analyzed. A risk model was constructed and optimized through the combination of univariate Cox regression and LASSO regression analysis. GEO datasets were utilized as the external validation for the robustness of the risk model. In addition, we performed KEGG and GO enrichment analyses to investigate the role played by these genes in AML. The variations in immune cell infiltrations among risk groups were assessed through four algorithms. Expression of hub gene in specific cell was analyzed by single-cell RNA seq.Results A total of 85 immune-ERGs associated with prognosis were identified, enabling the construction of a risk model for AML. The risk model based on five immune-ERGs (CD37, NUCB2, LSP1, MGST1, and PLXNB1) demonstrated a correlation with the clinical outcomes. Additionally, age, FAB classification, cytogenetics risk, and risk score were identified as independent prognostic factors. The five immune-ERGs exhibited correlations with cytokine-cytokine receptor interaction, and antigen processing and presentation. Notably, the risk model demonstrated significant associations with immune responses and the expression of immune checkpoints.Conclusions An immune-ERG-based risk model was developed to effectively predict prognostic outcomes for AML patients. There is potential for immune therapy in AML targeting the five hub genes.

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest tumors worldwide, with extremely aggressive and complicated biology. Krüppel-like factors (KLFs) encode a series of transcriptional regulatory proteins and play crucial roles in a variety of processes, including tumor cell differentiation and proliferation. However, the potential biological functions and possible pathways of KLFs in the progression of PDAC remain elusive. Methods: We systematically evaluated the transcriptional variations and expression patterns of KLFs in pancreatic cancer from the UCSC Xena. Based on difference analysis, the non-negative matrix factorization (NMF) algorithm was utilized to identify the immune characteristics and clinical significance of two different subtypes. The multivariate Cox regression was used to construct the risk model and then explore the differences in tumor immune microenvironment (TIME) and drug sensitivity between high and low groups. Through single-cell RNA sequencing (scRNA-seq) analysis, we screened KLF6 and further investigated its biological functions in pancreatic cancer and pan-cancer. Results: The KLFs exhibited differential expression and mutations in the transcriptomic profile of PDAC. According to the expression of KLFs, patients were classified into two distinct subtypes, each exhibiting significant differences in prognosis and TIME. Moreover, the KLF signature was developed using univariate Cox and Lasso regression, which proved to be a reliable and effective prognostic model. Furthermore, the KLF_Score was closely associated with immune infiltration, response to immunotherapy, and drug sensitivity and we screened small molecule compounds targeting prognostic genes separately. Through scRNA-seq analysis, KLF6 was selected to further demonstrate its role in the malignance of PC in vitro. Finally, pan-cancer analysis emphasized the biological significance of KLF6 in multiple types of tumors and its clinical utility in assessing cancer prognosis. Conclusion: This study elucidated the pivotal role of KLF family genes in the malignant development of PC through comprehensive analysis and revealed that KLF6 would be a novel diagnostic biomolecule marker and potential therapeutic target for PDAC. [ABSTRACT FROM AUTHOR]

Background: Previous studies have largely neglected the role of sulfur metabolism in LUAD, and no study has combine iron, copper, and sulfur-metabolism associated genes together to create prognostic signatures. Methods: This study encompasses 1564 LUAD patients, 1249 NSCLC patients, and over 10,000 patients with various cancer types from diverse cohorts. We employed the R package ConsensusClusterPlus to separate patients into different ICSM (Iron, Copper, and Sulfur-Metabolism) subtypes. Various machine-learning methods were utilized to develop the ICSMI. Enrichment analyses were conducted using ClusterProfiler and GSVA, while IOBR quantified immune cell infiltration. GISTIC2.0 and maftools were utilized for CNV and SNV data analysis. The Oncopredict package predicted drug information based on GDSC1. TIDE algorithm and cohorts GSE91061 and IMvigor210 evaluated patient response to immunotherapy. Single-cell data was processed using the Seurat package, AUCell package calculated cells geneset activity scores, and the Scissor algorithm identified ICSMI-associated cells. In vitro experiments was conducted to explore the role of ICSMRGs in LUAD. Results: Unsupervised clustering identified two distinct ICSM subtypes of LUAD, each with unique clinical characteristics. The ICSMI, comprising 10 genes, was constructed using integrated machine-learning methods. Its prognostic power was validated in 10 independent datasets, revealing that LUAD patients with higher ICSMI levels had poorer prognoses. Furthermore, ICSMI demonstrated superior predictive abilities compared to 102 previously published signatures. A nomogram incorporating ICSMI and clinical features exhibited high predictive performance. ICSMI positively correlated with patients gene mutations, and integrated analysis of bulk and single-cell transcriptome data revealed its association with TME modulators. Cells representing the high-ICSMI phenotype exhibited more malignant features. LUAD patients with high ICSMI levels exhibited sensitivity to chemotherapy and targeted therapy but displayed resistance to immunotherapy. In a comprehensive analysis across various cancers, ICSMI retained significant prognostic value and emerged as a risk factor for the majority of cancer patients. Conclusions: ICSMI provides critical prognostic insights for LUAD patients, offering valuable insights into the tumor microenvironment and predicting treatment responsiveness. [ABSTRACT FROM AUTHOR]

Background: Although it has been established that the human Solute Carrier Family 22 (SLC22) functions as a cationic transporter, influencing cellular biological metabolism by modulating the uptake of various cations, its impact on cancer prognosis remains unclear. Methods: We conducted a comprehensive analysis utilizing data from The Cancer Genome Atlas (TCGA) and other databases to assess the prognostic value and functional implications across various tumors. Silence of SLC22A1 RNA in glioma U251 cells was performed to access the impact of SLC22A1 on lowergrade glioma (LGG) progression. Results: Our findings demonstrated a significant correlation between SLC22A1 expression and the survival time of patients with various cancers (p < 0.05). Importantly, we found the potential involvement of SLC22A1 in occurrence and progress of certain cancers, with a pronounced impact on LGG. Further examination of the SLC22A1-LGG relationship revealed its status as an independent risk factor for LGG, suggesting its potential involvement in regulating diverse immune pathways and metabolic activities. Chinese Glioma Genome Atlas (CGGA) data supported the reliability of the risk score as a prognostic and recurrence indicator, emphasizing the accuracy of the nomograph (1, 3, and 5-year-AUC >0.8). Cell proliferation and clone formation experiment proved that decreased expression of SLC22A1 in glioma U251 cells inhibited glioma cell growth. Conclusion: Our findings suggest SLC22A1 has huge prospects as a promising biomarker and therapeutic target for LGG prognosis. SLC22A1 has only been proved to support cellular function previously. Our findings demonstrated a robust connection between the tumor microenvironment and functional proteins that maintain basal cell metabolism, which gifts unique tumor immune characteristics of gliomas. Additionally, we provide a highly practical prediction model for estimating the survival rate of LGG patients. [ABSTRACT FROM AUTHOR]

Fibroblast activation protein (FAP) has been indicated to express in cancer‐associated fibroblasts (CAFs) in most cancers. This work was dedicated to exploring FAP's effects on hepatocellular carcinoma (HCC). The data were extracted from The Cancer Genome Atlas, Gene Expression Omnibus, ImmPort, and Reactome databases. The correlation between FAP and HCC patients' prognosis was explored via survival analysis. The qRT‐PCR and western blot analysis were used to analyze the FAP mRNA and protein expression levels, respectively. The cell proliferation and apoptosis were determined using the cell counting kit‐8 assay kit and Annexin V‐FITC/PI apoptosis kit, respectively. The HCC patients with FAP overexpression displayed a worse prognosis. The FAP expression was positively associated with the infiltration levels of tumor purity, B cell, CD8 + T cell, CD4 + T cell, macrophage, neutrophil, and dendritic cell. The optimal nine immune related genes were screened between two groups (FAP high vs. low). Moreover, we identified 24 energy metabolism related genes (FAP high vs. low) and these 24 genes were highly expressed in the high FAP expression group. The FAP expression had a significant positive correlation with the expression of PD‐1, CTLA4, PDL‐1, and PDL‐2. The FAP overexpression promoted proliferation and migration while inhibiting the apoptosis of HCC cells. The FAP overexpression promoted the progression of HCC by regulating the immunity to affect the prognosis of HCC patients, thereby serving as a poor prognostic marker for HCC patients. [ABSTRACT FROM AUTHOR]

Background: Previous studies have largely neglected the role of ADCC in LUAD, and no study has systematically compiled ADCC-associated genes to create prognostic signatures. Methods: In this study, 1564 LUAD patients, 2057 NSCLC patients, and more than 5000 patients with various cancer types from diverse cohorts were included. R package ConsensusClusterPlus was utilized to classify patients into different subtypes. A number of machine-learning algorithms were used to construct the ADCCRS. GSVA and ClusterProfiler were used for enrichment analyses, and IOBR was used to quantify immune cell infiltration level. GISTIC2.0 and maftools were used to analyze the CNV and SNV data. The Oncopredict package was used to predict drug information based on the GDSC1. Three immunotherapy cohorts were used to evaluate patient response to immunotherapy. The Seurat package was used to process single-cell data, the AUCell package was used to calculate cells' geneset activity scores, and the Scissor algorithm was used to identify ADCCRS-associated cells. Results: Through unsupervised clustering, two distinct subtypes of LUAD were identified, each exhibiting distinct clinical characteristics. The ADCCRS, consisted of 16 genes, was constructed by integrated machine-learning methods. The prognostic power of ADCCRS was validated in 28 independent datasets. Further, ADCCRS shows better predictive abilities than 102 previously published signatures in predicting LUAD patients' survival. A nomogram incorporating ADCCRS and clinical features was constructed, demonstrating high predictive performance. ADCCRS positively correlates with patients' gene mutation, and integrated analysis of bulk and single-cell transcriptome data revealed the association of ADCCRS with TME modulators. Cells representing high-ADCCRS phenotype exhibited more malignant features. LUAD patients with high ADCCRS levels exhibited sensitivity to chemotherapy and targeted therapy, while displaying resistance to immunotherapy. In pan-cancer analysis, ADCCRS still exhibited significant prognostic value and was found to be a risk factor for most cancer patients. Conclusions: ADCCRS offers a critical prognostic insight for patients with LUAD, shedding light on the tumor microenvironment and forecasting treatment responsiveness. [ABSTRACT FROM AUTHOR]

Purpose: Hepatocellular carcinoma is the most common primary liver cancer, with poor prognosis. Complex immune microenvironment of the liver is linked to the development of HCC. PVALB is a calcium-binding protein which has been described as a cancer suppressor gene in thyroid cancer and glioma. Nevertheless, the role of PVALB in HCC is unknown. Materials and Methods: We obtained data from TCGA and GSE54236 datasets. MCP-counter, WGCNA and LASSO model were applied to identify PVALB. With UALCAN, MethSurv, and other websites, we probed the expression, methylation and survival of PVALB. LinkedOmics and GSEA were adopted for functional analysis, while TIMER, TISIDB, Kaplan-Meier plotter, TIDE databases were utilized to evaluate the relevance of PVALB to the tumor immune microenvironment and predict immunotherapy efficacy. TargetScan, DIANA, LncRNASNP2 databases and relevant experiments were employed to construct ceRNA network. Finally, molecular docking and drug sensitivity of PVALB were characterized by GeneMANIA, CTD, and so on. Results: PVALB was recognized as a gene associated with HCC and NK cell. Its expression was down-regulated in HCC tissue, which lead to adverse prognosis. Besides, the hypomethylation of PVALB was related to its reduced expression. Notably, PVALB was tightly linked to immune, and its reduced expression attenuated the anticancer effect of NK cells via the Fas/FasL pathway, leading to a adverse outcome. The lnc-YY1AP1-3/hsa-miR-6735-5p/PVALB axis may regulate the PVALB expression. Finally, we found immunotherapy might be a viable treatment option. Conclusion: In a word, PVALB is a prognostic indicator, whose low expression facilitates HCC progression by impacting NK cell infiltration. [ABSTRACT FROM AUTHOR]

SPTBN2 is a protein-coding gene that is closely related to the development of malignant tumors. However, its prognostic value and biological function in pan-cancer, especially pancreatic cancer (PAAD), have not been reported. In the present study, a novel exploration of the value and potential mechanism of SPTBN2 in PAAD was conducted using multi-omics in the background of pan-cancer. Via various database analysis, up-regulated expression of SPTBN2 was detected in most of the tumor tissues examined. Overexpression of SPTBN2 in PAAD and kidney renal clear cell cancer patients potentially affected overall survival, disease-specific survival, and progression-free interval. In PAAD, SPTBN2 can be used as an independent factor affecting prognosis. Mutations and amplification of SPTBN2 were detected, with abnormal methylation of SPTBN2 affecting its expression and the survival outcome of PAAD patients. Immunoassay results demonstrate that SPTBN2 was a potential biomarker for predicting therapeutic response in PAAD, and may influence the immunotherapy efficacy of PAAD by regulating levels of CD8 + T cells and neutrophil infiltration. Results from an enrichment analysis indicated that SPTBN2 may regulate the development of PAAD via immune pathways. Thus, SPTBN2 is a potential prognostic biomarker and immunotherapy target based on its crucial role in the development of PAAD. [ABSTRACT FROM AUTHOR]

Background: Skin Cutaneous Melanoma (SKCM) incidence is continually increasing, with chemotherapy and immunotherapy being among the most common cancer treatment modalities. This study aims to identify novel biomarkers for chemotherapy and immunotherapy response in SKCM and explore their association with oxidative stress. Methods: Utilizing TCGA-SKCM RNA-seq data, we employed Weighted Gene Coexpression Network Analysis (WGCNA) and Protein-Protein Interaction (PPI) networks to identify six core genes. Gene co-expression analysis and immunerelated analysis were conducted, and specific markers associated with oxidative stress were identified using Gene Set Variation Analysis (GSVA). Single-cell analysis revealed the expression patterns of Oxidative Stress-Associated Genes (OSAG) in the tumor microenvironment. TIDE analysis was employed to explore the association between immune therapy response and OSAG, while CIBERSORT was used to analyze the tumor immune microenvironment. The BEST database demonstrated the impact of the Oxidative Stress signaling pathway on chemotherapy drug resistance. Immunohistochemical staining and ROC curve evaluation were performed to assess the protein expression levels of core genes in SKCM and normal samples, with survival analysis utilized to determine their diagnostic value. Results: We identified six central genes associated with SKCM metastasis, among which the expression of DSC2 and DSC3 involved in the oxidative stress pathway was closely related to immune cell infiltration. DSC2 influenced drug resistance in SKMC patients. Furthermore, downregulation of DSC2 and DSC3 expression enhanced the response of SKCM patients to immunotherapy. Conclusion: This study identified two Oxidative Stress-Associated genes as novel biomarkers for SKCM. Additionally, targeting the oxidative stress pathway may serve as a new strategy in clinical practice to enhance SKCM chemotherapy and sensitivity. [ABSTRACT FROM AUTHOR]

Background: Polyamine modification patterns in lung adenocarcinoma (LUAD) and their impact on prognosis, immune infiltration, and anti-tumor efficacy have not been systematically explored. Methods: Patients from The Cancer Genome Atlas (TCGA) were classified into subtypes according to polyamine metabolism-related genes using the consensus clustering method, and the survival outcomes and immune profile were compared. Meanwhile, the geneCluster was constructed according to the differentially expressed genes (DEGs) of the subtypes. Subsequently, the polyamine metabolism-related score (PMRS) system was established using the least absolute shrinkage and selection operator (LASSO) multivariate regression analysis in the TCGA training cohort (n = 245), which can be applied to characterize the prognosis. To verify the predictive performance of the PMRS, the internal cohort (n = 245) and the external cohort (n = 244) were recruited. The relationship between the PMRS and immune infiltration and antitumor responses was investigated. Results: Two distinct patterns (C1 and C2) were identified, in which the C1 subtype presented an adverse prognosis, high CD8+ T cell infiltration, tumor mutational burden (TMB), immune checkpoint, and low tumor immune dysfunction and exclusion (TIDE). Furthermore, two geneClusters were established, and similar findings were observed. The PMRS, including three genes (SMS, SMOX, and PSMC6), was then constructed to characterize the polyamine metabolic patterns, and the patients were divided into high- and low-PMRS groups. As confirmed by the validation cohort, the high-PMRS group possessed a poor prognosis. Moreover, external samples and immunohistochemistry confirmed that the three genes were highly expressed in tumor samples. Finally, immunotherapy and chemotherapy may be beneficial to the high-PMRS group based on the immunotherapy cohorts and low half-maximal inhibitory concentration (IC50) values. Conclusion: We identified distinct polyamine modification patterns and established a PMRS to provide new insights into the mechanism of polyamine action and improve the current anti-tumor strategy of LUAD. [ABSTRACT FROM AUTHOR]

Background: Tumor cells exhibit a heightened susceptibility to lysosomal-dependent cell death (LCD) compared to normal cells. However, the role of LCD-related genes (LCD-RGs) in Osteosarcoma (OS) remains unelucidated. This study aimed to elucidate the role of LCD-RGs and their mechanisms in OS using several existing OS related datasets, including TCGA-OS, GSE16088, GSE14359, GSE21257 and GSE162454. Results: Analysis identified a total of 8,629 DEGs1, 2,777 DEGs2 and 21 intersection genes. Importantly, two biomarkers (ATP6V0D1 and HDAC6) linked to OS prognosis were identified to establish the prognostic model. Significant differences in risk scores for OS survival were observed between high and low-risk cohorts. Additionally, scores of dendritic cells (DC), immature DCs and γδT cells differed significantly between the two risk cohorts. Cell annotations from GSE162454 encompassed eight types (myeloid cells, osteoblastic OS cells and plasma cells). ATP6V0D1 was found to be significantly over-expressed in myeloid cells and osteoclasts, while HDAC6 was under-expressed across all cell types. Moreover, single-cell trajectory mapping revealed that myeloid cells and osteoclasts differentiated first, underscoring their pivotal role in patients with OS. Furthermore, ATP6V0D1 expression progressively decreased with time. Conclusions: A new prognostic model for OS, associated with LCD-RGs, was developed and validated, offering a fresh perspective for exploring the association between LCD and OS. [ABSTRACT FROM AUTHOR]

Background: Liver cancer ranks sixth in incidence and third in mortality globally and hepatocellular carcinoma (HCC) accounts for 90% of it. Hypoxia, glycolysis, and lactate metabolism have been found to regulate the progression of HCC separately. However, there is a lack of studies linking the above three to predict the prognosis of HCC. The present study aimed to identify a hypoxia–glycolysis–lactate-related gene signature for assessing the prognosis of HCC. Methods: This study collected 510 hypoxia-glycolysis-lactate genes from Molecular Signatures Database (MSigDB) and then classified HCC patients from TCGA-LIHC by analyzing their hypoxia-glycolysis-lactate genes expression. Differentially expressed genes (DEGs) were screened out to construct a gene signature by LASSO-Cox analysis. Univariate and multivariate regression analyses were used to evaluate the independent prognostic value of the gene signature. Analyses of immune infiltration, somatic cell mutations, and correlation heatmap were conducted by “GSVA” R package. Single-cell analysis conducted by “SingleR”, “celldex”, “Seurat”, and “CellCha” R packages revealed how signature genes participated in hypoxia/glycolysis/lactate metabolism and PPI network identified hub genes. Results: We classified HCC patients from TCGA-LIHC into two clusters and screened out DEGs. An 18-genes prognostic signature including CDCA8, CBX2, PDE6A, MED8, DYNC1LI1, PSMD1, EIF5B, GNL2, SEPHS1, CCNJL, SOCS2, LDHA, G6PD, YBX1, RTN3, ADAMTS5, CLEC3B, and UCK2 was built to stratify the risk of HCC. The risk score of the hypoxia-glycolysis-lactate gene signature was further identified as a valuable independent factor for estimating the prognosis of HCC. Then we found that the features of clinical characteristics, immune infiltration, somatic cell mutations, and correlation analysis differed between the high-risk and low-risk groups. Furthermore, single-cell analysis indicated that the signature genes could interact with the ligand-receptors of hepatocytes/fibroblasts/plasma cells to participate in hypoxia/glycolysis/lactate metabolism and PPI network identified potential hub genes in this process: CDCA8, LDHA, YBX1. Conclusion: The hypoxia–glycolysis–lactate-related gene signature we built could provide prognostic value for HCC and suggest several hub genes for future HCC studies. [ABSTRACT FROM AUTHOR]

Background: Long non-coding RNAs (LncRNAs) regulating the immune microenvironment of cancer is a hot spot. But little is known about the influence of the immune-related lncRNA (IRlncRs) on the chemotherapeutic responses and prognosis of cervical cancer (CC) patients. The purpose of the study was to identify an immune-related lncRNAs (IRlncRs)-based model for the prospective prediction of clinical outcomes in CC patients. Methods: CC patients' relevant data was acquired from The Cancer Genome Atlas (TCGA). Correlation analysis and Cox regression analyses were applied. A risk score formula was formulated. Prognostic factors were combined into a nomogram, while sensitivity for chemotherapy drugs was analyzed using the OncoPredict algorithm. Results: Eight optimal IRlncRs(ATP2A1-AS1, LINC01943, AL158166.1, LINC00963, AC009065.8, LIPE-AS1, AC105277.1, AC098613.1.) were incorporated in the IRlncRs model. The overall survival (OS) of the high-risk group of the model was inferior to those in the low-risk group. Further analysis demonstrated this eight-IRlncRs model as a useful prognostic marker. The Nomogram had a concordance index of survival prediction of 0.763(95% CI 0.746–0.780) and more robust predictive accuracy. Furthermore, patients in the low-risk group were found to be more sensitive to chemotherapy, including Paclitaxel, Rapamycin, Epirubicin, Vincristine, Docetaxel and Vinorelbine. Conclusions: An eight-IRlncRs-based prediction model was identified that has the potential to be an important tool to predict chemotherapeutic responses and prognosis for CC patients. [ABSTRACT FROM AUTHOR]

Osteosarcoma (OS) is a frequent primary malignant bone tumor, with a poor prognosis. Necroptosis is strongly correlated with OS and may be an influential target for treating OS. This study's objective was to establish a necroptosis-related gene (NRG) prognostic signature that could predict OS prognosis and guide OS treatment. First, we identified 20 NRGs associated with OS survival based on the TARGET database. We then derived a 7 NRG prognostic signature. Our findings revealed that the 7 NRG prognostic signature performed well in predicting the survival of OS patients. We next analyzed differences in immunological status and immune cell infiltration. In addition, we examined the relationship between chemo/immunotherapeutic response and the 7-NRG prognostic signature. In addition, to probe the mechanisms underlying the NRG prognostic signature, we performed functional enrichment assays including GO and KEGG. Finally, CHMP4C was selected for functional experiments. Silencing CHMP4C prevented OS cells from proliferating, migrating, and invading. This 7-NRG prognostic signature seems to be an excellent predictor that can provide a fresh direction for OS treatment. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) is a malignant tumor of the gastrointestinal tract with high morbidity and mortality. There is growing evidence that GRK2 plays a key role in the development and progression of several human cancers. However, the role and potential mechanisms of GRK2 in colon cancer (COAD) are unclear. Methods: The expression data of GRK2 was downloaded from The Cancer Genome Atlas database (TCGA). Variation in GRK2 was explored based on the cBioPortal database. The TIMER and TISCH2 databases were used to analyse the relationship between GRK2 expression and tumor immune microenvironment (TME). A log-rank test was used to compare the prognosis of high and low expression of GRK2 groups. Detecting the effect of GRK2 on cell cycle and apoptosis induced by 5-Fluorouracil (5-FU) through the flow cytometry and detection of apoptosis-related molecules by Western blot. Results: We demonstrated that GRK2 has a potential oncogenic role. GRK2 expression was upregulated in COAD, which predicted poorer overall survival in COAD patients. The cellular assays showed that GRK2 plays a role in the growth and proliferation of colon cancer cells, also the expression of GRK2 have relationship with the sensitivity of 5-FU and cell cycle progression. Conclusions: Our results suggest that high GRK2 expression is closely associated with the development of tumor and affects the 5-FU sensitivity. [ABSTRACT FROM AUTHOR]

Background: Tryptophan (Trp) is an essential amino acid. Increasing evidence suggests that tryptophan metabolism plays a complex role in immune escape from Lung adenocarcinoma (LUAD). However, the role of long non-coding RNAs (lncRNAs) in tryptophan metabolism remains to be investigated. Methods: This study uses The Cancer Genome Atlas (TCGA)-LUAD dataset as the training cohort, and several datasets from the Gene Expression Omnibus (GEO) database are merged into the validation cohort. Genes related to tryptophan metabolism were identified from the Molecular Signatures Database (MSigDB) database and further screened for lncRNAs with Trp-related expression. Subsequently, a prognostic signature of lncRNAs related to tryptophan metabolism was constructed using Cox regression analysis, (Least absolute shrinkage and selection operator regression) and LASSO analysis. The predictive performance of this risk score was validated by Kaplan–Meier (KM) survival analysis, (receiver operating characteristic) ROC curves, and nomograms. We also explored the differences in immune cell infiltration, immune cell function, tumor mutational load (TMB), tumor immune dysfunction and exclusion (TIDE), and anticancer drug sensitivity between high- and low-risk groups. Finally, we used real-time fluorescence quantitative PCR, CCK-8, colony formation, wound healing, transwell, flow cytometry, and nude mouse xenotransplantation models to elucidate the role of ZNF8-ERVK3-1 in LUAD. Results: We constructed 16 tryptophan metabolism-associated lncRNA prognostic models in LUAD patients. The risk score could be used as an independent prognostic indicator for the prognosis of LUAD patients. Kaplan–Meier survival analysis, ROC curves, and risk maps validated the prognostic value of the risk score. The high-risk and low-risk groups showed significant differences in phenotypes, such as the percentage of immune cell infiltration, immune cell function, gene mutation frequency, and anticancer drug sensitivity. In addition, patients with high-risk scores had higher TMB and TIDE scores compared to patients with low-risk scores. Finally, we found that ZNF8-ERVK3-1 was highly expressed in LUAD tissues and cell lines. A series of in vitro experiments showed that knockdown of ZNF8-ERVK3-1 inhibited cell proliferation, migration, and invasion, leading to cell cycle arrest in the G0/G1 phase and increased apoptosis. In vivo experiments with xenografts have shown that knocking down ZNF8-ERVK3-1 can significantly inhibit tumor size and tumor proliferation. Conclusion: We constructed a new prognostic model for tryptophan metabolism-related lncRNA. The risk score was closely associated with common clinical features such as immune cell infiltration, immune-related function, TMB, and anticancer drug sensitivity. Knockdown of ZNF8-ERVK3-1 inhibited LUAD cell proliferation, migration, invasion, and G0/G1 phase blockade and promoted apoptosis. [ABSTRACT FROM AUTHOR]

Background: We aimed to comprehensively analyze the clinical value of immune-related eRNAs-driven genes in lung adenocarcinoma (LUAD) and find the potential biomarkers for prognosis and therapeutic response to improve the survival of this malignant disease. Materials and methods: Pearson’s correlation analysis was performed to identify the immune-related eRNAs-driven genes. Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were used to construct this prognostic risk signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to investigate the underlying molecular mechanism. The single sample gene set enrichment analysis (ssGSEA) algorithm was conducted to evaluate the immune status based on the signature. The quantitative real-time PCR (qRT-PCR) analysis was performed to evaluate the expression value of the signature genes between LUAD tissues and adjacent lung tissues. Results: Five immune-related eRNAs-driven genes (SHC1, GDF10, CCL14, FYN, and NOD1) were identified to construct a prognostic risk signature with favorable predictive capacity. The patients with high-risk scores based on the signature were significantly associated with the malignant clinical features compared with those with low-risk scores. Kaplan–Meier analysis demonstrated that the sample in the low-risk group had a prolonged survival compared with those in the high-risk group. This risk signature was validated to have a promising predictive capacity and reliability in diverse clinical situations and independent cohorts. The functional enrichment analysis demonstrated that humoral immune response and intestinal immune network for IgA production pathway might be the underlying molecular mechanism related to the signature. The proportion of the vast majority of immune infiltrating cells in the high-risk group was significantly lower than that in the low-risk group, and the immunotherapy response rate in the low-risk group was significantly higher than that in the high-risk group. Moreover, BI-2536, sepantronium bromide, and ULK1 were the potential drugs for the treatment of patients with higher risk scores. Finally, the experiment in vivo and database analysis indicated that CCL14, FYN, NOD1, and GDF10 are the potential LUAD suppressor and SHC1 is a potential treatment target for LUAD. Conclusion: Above all, we constructed a prognostic risk signature with favorable predictive capacity in LUAD, which was significantly associated with malignant features, immunosuppressive tumor microenvironment, and immunotherapy response and may provide clinical benefit in clinical decisions. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is a heterogeneous disease, often diagnosed at advanced stages, with a 5-year survival rate of approximately 20%. Despite notable technological advancements in cancer research over the past decades, their impact on GC management and outcomes has been limited. Numerous molecular alterations have been identified in GC, leading to various molecular classifications, such as those developed by The Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ACRG). Other authors have proposed alternative perspectives, including immune, proteomic, or epigenetic-based classifications. However, molecular stratification has not yet transitioned into clinical practice for GC, and little attention has been paid to alternative molecular classifications. In this review, we explore diverse molecular classifications in GC from a practical point of view, emphasizing their relationships with clinicopathological factors, prognosis, and therapeutic approaches. We have focused on classifications beyond those of TCGA and the ACRG, which have been less extensively reviewed previously. Additionally, we discuss the challenges that must be overcome to ensure their impact on patient treatment and prognosis. This review aims to serve as a practical framework to understand the molecular landscape of GC, facilitate the development of consensus molecular categories, and guide the design of innovative molecular studies in the field. [ABSTRACT FROM AUTHOR]

Cervical cancer is a serious threat to women's health. The aim of this study was to provide new insights into the mechanism of cervical cancer by constructing immune-related prognostic model and ceRNA network. The mRNA and circRNA datasets of cervical cancer were downloaded from NCBI GEO database. Wilcox.test was used to screen the differential immune cells between cervical cancer patients and normal participants. WGCNA was performed for identification immune related genes. A circRNA-lncRNA-mRNA network was constructed and the genes in the network were further screened for genes related to prognosis using survival package in R software. The prognostic risk model was further validated in the TCGA database. Finally, GSEA was performed to investigate the different enrichment pathways between high_risk and low_risk groups. Nine genes (BEX4, CCL14, CCL3, CMPK2, FMOD, GHR, HLF, IGFBP5, PAG1) were selected to construct the prognostic model. Patients in the low_risk group had a significantly better prognosis than those in the high_risk group. hsa_circ_0021727-hsa-miR-133b-PAG1 regulatory axis may participate in the regulatory of cervical cancer. The enrichment pathways to patients in the high-risk group and the low-risk group were different. The results were not validated by in vitro and in vivo experiments. We developed an immune-related prognostic model and lncRNA-miRNA-mRNA ceRNA network, which can predict prognosis and understand the mechanism of cervical cancer. [ABSTRACT FROM AUTHOR]