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Tumor-infiltrating immune cells (TICs) play a central role in the tumor microenvironment, which can reflect the host anti-tumor immune response. However, few studies have explored TICs in predicting the prognosis of lung adenocarcinoma (LUAD). In our study, we enrolled 2470 LUAD patients from TCGA and GEO databases, and the normalized enrichment scores for 65 immune cell types were quantified for each patient. An immune-related risk score (IRRS) was built on the basis of 17 selected TICs using LASSO regression analysis, and the results showed that high-risk patients were correlated with shorter survival time for the LUAD cohorts. Correlation analyses between IRRS and clinical characteristics were also evaluated to validate the clinical use of IRRS. In addition, we analyzed the differences in the distribution of immune cell infiltration and immunoregulatory gene expression, which may facilitate individual immunotherapy. Based on the above result, we conclude that IRRS can act as a powerful predictor for risk stratification and prognosis prediction, and may facilitate the decision-making process for LUAD patients.

Breast cancer (BCa) is the most common malignancy in women and claudin-low breast cancer (CL-BCa) is a newly identified BCa subtype characterized by low expression of claudin 3&4&7. However, the hub genes associated with the recruitment of immune cells into CL-BCa were rarely described. This study aimed at exploring the differentially expressed hub genes associated with tumor-infiltrating immune cells in CL-BCa by a multi-approach bioinformatics analysis. The top 200 genes associated with CL-BCa were screened in the METABRIC dataset; the PPI network was constructed using STRING and Cytoscape; tumor-infiltrating immune cells were analyzed by TIMER 2.0; and the correlation of feature cytokines and claudins on survival was examined in METABRIC and TCGA datasets. Consequently, we found that the fraction of tumor-infiltrating immune cells, especially CD8+T cells and macrophages, increased in the CL-BCa. Differentially expressed cytokines (CCL5, CCL19, CXCL9 and CXCL10) were related to the overall survival, and their expression levels were also examined both in tumor tissues of CL-BCa patients by IHC and in typical CL-BCa cell lines by qPCR. Moreover, the BCa patients with low expression of these differentially expressed claudins (CLDN8, CLDN11 and CLDN19) showed a worse overall survival. This study sheds light on molecular features of CL-BCa on immune microenvironments and contributes to identification of prognosis biomarkers for the CL-BCa patients. [ABSTRACT FROM AUTHOR]

Gastric cancer is a kind of tumor with strong heterogeneity. Long noncoding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) play significant roles in the development of tumors. In this study, we divided all TCGA gastric cancer patients into the whole, intestinal and diffuse cohorts for further analysis, and constructed competitive endogenous RNA network and evaluated immune cells using CIBERSORTx. The support vector machines recursive feature elimination (SVM-RFE) was used for screening significant signatures and the support vector machines (SVM) for establishing model predicting the lymph node metastasis. The performance of SVM model was good in the intestinal and diffuse cohort, while the model in the whole cohort was relatively poor. Some important co-expression patterns between immune cells and ceRNAs network indicated significant correlation CD70 with dendritic cells and so on. Our research inferred competing endogenous RNA network of lymph node metastasis and built an excellent predicting model. [ABSTRACT FROM AUTHOR]

Tumor-infiltrating immune cells (TICs) affect tumorigenesis and tumor development in head and neck squamous cell carcinoma (HNSCC). We constructed a novel predictive model for HNSCC based on immune-related genes (IRGs) from The Cancer Genome Atlas and the Immunology Database and Analysis Portal. After identifying the IRGs, a predictive model involving 13 IRGs with high stratification value of overall survival (OS) was constructed by multiple support vector machine recursive feature elimination and least absolute shrinkage and selection operator regression. We explored the relationship between the risk score (RS) and clinical characteristics. The nomogram showed high concordance and good agreement in OS. Four TICs affected the OS and were in agreement with the abundance analysis of the RS levels. Furthermore, the low-risk HNSCC group showed higher expression of PD-1, CTLA4, and TIGIT, while the high-risk group showed higher expression of EGFR. The high-risk HNSCC showed high sensitivity to eight drugs.

Objective：This study aims to explore the prognostic value of butyrylcholinesterase（BCHE）in gastric cancer（GC）and analyze its impact on the biological functions of gastric cancer cells. Methods：Firstly，Lasso regression method was used to screen prognostic genes，and the relationships between BCHE expression levels and clinical pathological parameters were verified using chi ⁃square test. In addition，TIMER 2.0 database and CIBERSORT algorithm were utilized to investigate the correlation between BCHE and immune cell infiltration，and enrichment analysis was performed using WGCNA algorithm and DAVID Bioinformatics Resources database. Finally，the expression levels of BCHE in normal gastric epithelial cell line（GES⁃1）and gastric cancer cell lines （HGC27 and MKN1）were detected by real ⁃time qPCR. Following transient transfection of small interfering RNA was conducted to knockdown the expression of BCHE in HGC27 and MKN1 cell lines. Cell proliferation was detected by CCK⁃8 and colony formation assay. Cell adhesion ability was detected by adhesion assay. Cell migration and invasion were detected by transwell and scratch assay. Results：Fifteen genes related to the prognosis of GC patients were screened out using Lasso regression analysis. Among them，BCHE has the best predictive value. Further analysis indicated that the expression level of BCHE in GC tissues was higher than those in the adjacent tissues or healthy controls，while the expression of BCHE in tumor cells was closely related to immune cell infiltration related cules. Enrichment analysis showed that the high expression of BCHE was related to cell proliferation，migration and adhesion. The results of in vitro experiments further confirmed that reducing the expression of BCHE significantly inhibited the malignant biological function of gastric cancer cell line. Conclusion：BCHE serve as a prognostic biomarker for GC and can promote immune cell infiltration in tumors. In addition，reducing the expression of BCHE can inhibit its mediated malignant biological function. [ABSTRACT FROM AUTHOR]

Abstract Background STIL centriolar assembly protein (STIL) is a cytoplasmic protein implicated in cellular growth and proliferation as well as chromosomal stability, which abnormal condition affected tumor immunity and tumor progression. However, the role of STIL in the biological mechanism of hepatocellular carcinoma (HCC) remains unclear. Methods Comprehensive bioinformatic approaches, in vitro functional assays, and validation were conducted to elucidate the oncogenic value of STIL in HCC. Results In the present study, we found that STIL may serve as an independent prognostic indicator and a potential oncogene in HCC. Gene set enrichment analysis (GSEA), and Gene set variation analysis (GSVA) showed that upregulated expression of STIL was positively associated with pathways enriched in the cell cycle and DNA damage response. Subsequently, we identified several non-coding RNAs (ncRNAs) accounting for the upregulation of STIL expression using a combination of in silico bioinformatics approaches (including expression analysis, correlation analysis, and survival analysis). Finally, CCNT2-AS1/SNHG1-has-miR-204-5p-STIL axis was screened out as the most potential upstream ncRNA-related pathway of STIL in HCC. Moreover, STIL expression is highly associated with the infiltration of immune cells, the expression of immune checkpoints, as well as the survival benefit of immunotherapy/chemotherapy. Conclusions Our study discloses that ncRNAs-mediated overexpression of STIL independently predicted poor prognosis and correlated with the efficacy of PD-1-targeted immunotherapy in HCC.

Prostate cancer (PCa) is one of the most prevalent cancers in men, yet its pathogenic pathways remain poorly understood. Transcriptomics and high-throughput sequencing can help uncover cancer diagnostic targets and understand biological circuits. Using prostate adenocarcinoma (PRAD) datasets of various web-based applications (GEPIA, UALCAN, cBioPortal, SR Plot, hTFtarget, Genome Browser, and MetaCore), we found that upregulated dysbindin domain-containing 1 (DBNDD1) expression in primary prostate tumors was strongly correlated with pathways involving the cell cycle, mitotic in KEGG, WIKI, and REACTOME database, and transcription factor-binding sites with the DBNDD1 gene in prostate samples. DBNDD1 gene expression was influenced by sample type, cancer stage, and promoter methylation levels of different cancers, such as PRAD, liver hepatocellular carcinoma (LIHC), and lung adenocarcinoma (LUAD). Regulation of glycogen synthase kinase (GSK)-3β in bipolar disorder and ATP/ITP/GTP/XTP/TTP/CTP/UTP metabolic pathways was closely correlated with the DBNDD1 gene and its co-expressed genes in PCa. DBNDD1 gene expression was positively associated with immune infiltration of B cells, Myeloid-derived suppressor cell (MDSC), M2 macrophages, andneutrophil, whereas negatively correlated with CD8+ T cells, T follicular helper cells, M1 macrophages, and NK cells in PCa. These findings suggest that DBNDD1 may serve as a viable prognostic marker not only for early-stage PCa but also for immunotherapies. [ABSTRACT FROM AUTHOR]

Introduction: Bladder cancer (BLCA) is one of the most lethal diseases. COL10A1 is secreted small-chain collagen in the extracellular matrix associated with various tumors, including gastric, colon, breast, and lung cancer. However, the role of COL10A1 in BLCA remains unclear. This is the first research focusing on the prognostic value of COL10A1 in BLCA. In this research, we aimed to uncover the association between COL10A1 and the prognosis, as well as other clinicopathological parameters in BLCA. Methods: We obtained gene expression profiles of BLCA and normal tissues from the TCGA, GEO, and ArrayExpress databases. Immunohistochemistry staining was performed to investigate the protein expression and prognostic value of COL10A1 in BLCA patients. GO and KEGG enrichment along with GSEA analyses were performed to reveal the biological functions and potential regulatory mechanisms of COL10A1 based on the gene co-expression network. We used the "maftools" R package to display the mutation profiles between the high and low COL10A1 groups. GIPIA2, TIMER, and CIBERSORT algorithms were utilized to explore the effect of COL10A1 on the tumor immune microenvironment. Results: We found that COL10A1 was upregulated in the BLCA samples, and increased COL10A1 expression was related to poor overall survival. Functional annotation of 200 co-expressed genes positively correlated with COL10A1 expression, including GO, KEGG, and GSEA enrichment analyses, indicated that COL10A1 was basically involved in the extracellular matrix, protein modification, molecular binding, ECM-receptor interaction, protein digestion and absorption, focal adhesion, and PI3K-Akt signaling pathway. The most commonly mutated genes of BLCA were different between high and low COL10A1 groups. Tumor immune infiltrating analyses showed that COL10A1 might have an essential role in recruiting infiltrating immune cells and regulating immunity in BLCA, thus affecting prognosis. Finally, external datasets and biospecimens were used, and the results further validated the aberrant expression of COL10A1 in BLCA samples. Conclusions: In conclusion, our study demonstrates that COL10A1 is an underlying prognostic and predictive biomarker in BLCA. [ABSTRACT FROM AUTHOR]

Background: STIL centriolar assembly protein (STIL) is a cytoplasmic protein implicated in cellular growth and proliferation as well as chromosomal stability, which abnormal condition affected tumor immunity and tumor progression. However, the role of STIL in the biological mechanism of hepatocellular carcinoma (HCC) remains unclear. Methods: Comprehensive bioinformatic approaches, in vitro functional assays, and validation were conducted to elucidate the oncogenic value of STIL in HCC. Results: In the present study, we found that STIL may serve as an independent prognostic indicator and a potential oncogene in HCC. Gene set enrichment analysis (GSEA), and Gene set variation analysis (GSVA) showed that upregulated expression of STIL was positively associated with pathways enriched in the cell cycle and DNA damage response. Subsequently, we identified several non-coding RNAs (ncRNAs) accounting for the upregulation of STIL expression using a combination of in silico bioinformatics approaches (including expression analysis, correlation analysis, and survival analysis). Finally, CCNT2-AS1/SNHG1-has-miR-204-5p-STIL axis was screened out as the most potential upstream ncRNA-related pathway of STIL in HCC. Moreover, STIL expression is highly associated with the infiltration of immune cells, the expression of immune checkpoints, as well as the survival benefit of immunotherapy/chemotherapy. Conclusions: Our study discloses that ncRNAs-mediated overexpression of STIL independently predicted poor prognosis and correlated with the efficacy of PD-1-targeted immunotherapy in HCC. [ABSTRACT FROM AUTHOR]

IntroductionBladder cancer (BLCA) is one of the most lethal diseases. COL10A1 is secreted small-chain collagen in the extracellular matrix associated with various tumors, including gastric, colon, breast, and lung cancer. However, the role of COL10A1 in BLCA remains unclear. This is the first research focusing on the prognostic value of COL10A1 in BLCA. In this research, we aimed to uncover the association between COL10A1 and the prognosis, as well as other clinicopathological parameters in BLCA.MethodsWe obtained gene expression profiles of BLCA and normal tissues from the TCGA, GEO, and ArrayExpress databases. Immunohistochemistry staining was performed to investigate the protein expression and prognostic value of COL10A1 in BLCA patients. GO and KEGG enrichment along with GSEA analyses were performed to reveal the biological functions and potential regulatory mechanisms of COL10A1 based on the gene co-expression network. We used the “maftools” R package to display the mutation profiles between the high and low COL10A1 groups. GIPIA2, TIMER, and CIBERSORT algorithms were utilized to explore the effect of COL10A1 on the tumor immune microenvironment.ResultsWe found that COL10A1 was upregulated in the BLCA samples, and increased COL10A1 expression was related to poor overall survival. Functional annotation of 200 co-expressed genes positively correlated with COL10A1 expression, including GO, KEGG, and GSEA enrichment analyses, indicated that COL10A1 was basically involved in the extracellular matrix, protein modification, molecular binding, ECM-receptor interaction, protein digestion and absorption, focal adhesion, and PI3K-Akt signaling pathway. The most commonly mutated genes of BLCA were different between high and low COL10A1 groups. Tumor immune infiltrating analyses showed that COL10A1 might have an essential role in recruiting infiltrating immune cells and regulating immunity in BLCA, thus affecting prognosis. Finally, external datasets and biospecimens were used, and the results further validated the aberrant expression of COL10A1 in BLCA samples.ConclusionsIn conclusion, our study demonstrates that COL10A1 is an underlying prognostic and predictive biomarker in BLCA.

Purpose: The purpose of our study is to identify potential biomarkers of hepatoblastoma (HB) and further explore the pathogenesis of it. Methods: Differentially expressed genes (DEGs) were incorporated into the combined random forest and artificial neural network diagnosis model to screen candidate genes for HB. Gene set enrichment analysis (GSEA) was used to analyze the ARHGEF2. Student's t test was performed to evaluate the difference of tumor-infiltrating immune cells (TIICs) between normal and HB samples. Spearson correlation analysis was used to calculate the correlation between ARHGEF2 and TIICs. Results: ARHGEF2, TCF3, TMED3, STMN1 and RAVER2 were screened by the new model. The GSEA of ARHGEF2 included cell cycle pathway and antigen processing presenting pathway. There were significant differences in the composition of partial TIICs between HB and normal samples (p < 0.05). ARHGEF2 was significantly correlated with memory B cells (Cor = 0.509, p < 0.05). Conclusion: These 5 candidate genes contribute to the molecular diagnosis and targeted therapy of HB. And we found "ARHGEF2–RhoA–Cyclin D1/CDK4/CDK6–EF2" is a key mechanism regulating cell cycle pathway in HB. This will be helpful in the treatment of HB. The occurrence of HB is related to abnormal TIICs. We speculated that memory B cells play an important role in HB. [ABSTRACT FROM AUTHOR]

Abstract Background Laryngeal squamous cell carcinoma (LSCC) is the most common squamous cell carcinoma. Though significant effort has been focused on molecular pathogenesis, development, and recurrence of LSCC, little is known about its relationship with the immune-related long non-coding RNA (lncRNA) pairs. Methods After obtaining the transcriptome profiling data sets and the corresponding clinical characteristics of LSCC patients and normal samples from The Cancer Genome Atlas (TCGA) database, a series of bioinformatic analysis was conducted to select the differently expressed immune-related lncRNAs and build a signature of immune-related lncRNA pairs. Then, the effectiveness of the signature was validated. Results A total of 111 LSCC patients and 12 normal samples’ transcriptome profiling data sets were retrieved from TCGA. 301 differently expressed immune-related lncRNAs were identified and 35,225 lncRNA pairs were established. After univariate Cox analysis, LASSO regression and multivariate Cox analysis, 7 lncRNA pairs were eventually selected to construct a signature. The riskscore was computed using the following formula: Riskscore = 0.95 × (AL133330.1|AC132872.3) + (-1.23) × (LINC01094|LINC02154) + 0.65 × (LINC02575|AC122685.1) + (-1.15) × (MIR9-3HG|LINC01748) + 1.45 × (AC092687.3|SNHG12) + (-0.87) × (AC090204.1|AL158166.1) + 0.64 × (LINC01063|Z82243.1). Patients were classified into the high-risk group (> 1.366) and the low-risk group (

Abstract Background A significant subset of mismatch repair-deficient (dMMR)/microsatellite instability-high (MSI-H) gastric adenocarcinomas (GAC) are resistant to immune checkpoint inhibitors (ICIs), yet the underlying mechanism remains largely unknown. We sought to investigate the genomic correlates of the density of tumor-infiltrating immune cells (DTICs) and primary resistance to ICI treatment. Methods Four independent cohorts of MSI-H GAC were included: (i) the surgery cohort (n = 175) with genomic and DTIC data, (ii) the 3DMed cohort (n = 32) with genomic and PD-L1 data, (iii) the Cancer Genome Atlas (TCGA) cohort (n = 73) with genomic, transcriptomic, and survival data, and (iv) the ICI treatment cohort (n = 36) with pre-treatment genomic profile and ICI efficacy data. Results In the dMMR/MSI-H GAC, the number of mutated genes in the PI3K-AKT-mTOR pathway (NMP) was positively correlated with tumor mutational burden (P

Abstract Background An increasing number of studies have shown that immune-related long noncoding RNAs (lncRNAs) do not require a unique expression level. This finding may help predict the survival and drug sensitivity of patients with colon cancer. Methods We retrieved original transcriptome and clinical data from The Cancer Genome Atlas (TCGA), sorted the data, differentiated mRNAs and lncRNAs, and then downloaded immune-related genes. Coexpression analysis predicted immune-related lncRNAs (irlncRNAs) and univariate analysis identified differentially expressed irlncRNAs (DEirlncRNAs). We have also amended the lasso pending region. Next, we compared the areas under the curve (AUCs), counted the Akaike information standard (AIC) value of the 3-year receiver operating characteristic (ROC) curve, and determined the cutoff point to establish the best model to differentiate the high or low disease risk group of colon cancer patients. Results We reevaluated the patients regarding the survival rate, clinicopathological features, tumor-infiltrating immune cells, immunosuppressive biomarkers, and chemosensitivity. A total of 155 irlncRNA pairs were confirmed, 31 of which were involved in the Cox regression model. After the colon cancer patients were regrouped according to the cutoff point, we could better distinguish the patients based on adverse survival outcomes, invasive clinicopathological features, the specific tumor immune cell infiltration status, high expression of immunosuppressive biomarkers, and low chemosensitivity. Conclusions In this study, we established a characteristic model by pairing irlncRNAs to better predict the survival rate, chemotherapy efficacy, and prognostic value of patients with colon cancer.

IntroductionWe aimed to develop a copper-related gene (CRG) signature that can be used to evaluate prognosis and guide therapeutic management in bladder cancer patients.MethodsThe raw transcriptome profiles and clinical data of 405 bladder samples were downloaded from The Cancer Genome Atlas (TCGA) database, and differentially expressed copper-related genes were identifified using the Molecular Signatures Database (MSigDB) database and univariate and multivariate Cox regression analysis. A multigene prognostic signature based on 14 CRGs was developed by least absolute shrinkage and selection operation (LASSO) analysis in the TCGA cohort and validated in the Gene Expression Omnibus (GEO) cohort. Multiple analyses were then conducted in which the nomograms, clinicopathological features, immune-related cell infifiltration characteristics, and therapy responses of the high- and low-risk score groups were compared.ResultsA 14 CRGs signature was constructed and used to classify patients into high-risk and low-risk groups. Compared to patients classifified as high-risk, low-risk patients in both the TCGA cohort and the GEO cohort had better overall survival. Patients in high-risk groups had more aggressive clinical features, immunologically “cold” infifiltrating characteristics, and experienced lower therapeutic effificacy. We identifified a CRG signature of bladder cancer and validated it using unsupervised clustering analysis. Monooxygenase DBH-like 1 (MOXD1) was further identifified, and its potential for evaluating the tumor immune microenvironment and predicting the immunotherapy response was explored.DiscussionThese results suggest a novel research direction for precision therapy of bladder cancer and demonstrate that copper-related genes can play a promising role in predicting prognosis and may serve as therapeutic targets for bladder cancer.

OBJECTIVE: Traditional diagnostic strategies are unable to accurately discriminate between patients with poor and satisfied prognosis in colon cancer. Therefore, it is urgently recommended to identify new biomarkers in favor of better selection of patients at higher risk of recurrence or poor outcomes, with the aim of early intervention or avoiding overtreatment. MATERIALS AND METHODS: The weighted gene correlation network analysis (WGCNA), together with the proportion of tumor infiltrating immune cells, were employed to screen the key module related to immune infiltration. Using these genes among the key module, a predictive signature was generated via LASSO and multi-Cox regression method. Moreover, a novel nomogram was further developed by combining important clinical parameters and the predictive signature. RESULTS: Genes among the green module, indicating the highest correlation with regulatory T cells (Tregs), were incorporated into the establishment of predictive model. Then, a Tregs-related risk signature (TRRS) consisting of four genes (NRG1, TEX11, OVOL3 and FCRL2) was established, which performed well in predicting the mortality risk of colon cancer in both internal and external validation groups (p=0.004 for TCGA training set, p=0.016 for TCGA testing set and p=0.03 for GSE39582 dataset). Combining TNM stage and age, we developed a nomogram for 1-, 3-, 5-year OS, presenting a more reliable predictive performance in survival based on the receiver operating characteristic (ROC) curves and calibration curves (3-year AUC: 0.83 and 0.74 in the TCGA and GEO database, respectively). CONCLUSIONS: We constructed a four-gene signature for predicting the prognosis of patients with colon cancer, and further developed the nomogram together with TNM stage and age to improve the predictive efficacy. [ABSTRACT FROM AUTHOR]

The contour of the tumor immune microenvironment (TIME) is very important for tumor prognostic prediction but hard to be characterized in clinical practice. It is unclear practice whether the peripheral immune signature (pIS) reflects the TIME as a feasible prognostic indicator for head and neck squamous cell carcinoma (HNSCC) patients. Here, we enrolled 599 HNSCC patients from three domestic institutes to explore the relationship between the pIS and survival. The peripheral neutrophil-to-lymphocyte ratio (pNLR) was screened out as a significant prognostic variable through multivariable COX regression analyses. An inverse correlation between pNLR and survival was found in the data of these 599 patients. Meanwhile, the bulk tumor RNA-seq data of 913 cases were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify the prognosisassociated TIME features. The TIME feature was consistent to the finding of clinical data, in which high tissue NLR predicted a poor prognosis. Differentially expressed immune-related gene (DEIRG) enrichment analysis also showed a trend that the gene sets in patients with a good prognosis were enriched in lymphocyte-related functions, while those with a poor prognosis were enriched in neutrophil-related functions. At the same time, the well prediction performance of our model based on DEIRGs was verified in both TCGA and GEO cohorts. Finally, the correlation between pIS and the TIME was confirmed in a small independent cohort of 30 HNSCC patients. A positive correlation was confirmed prospectively between the pNLR and the TIME pattern in our independent cohort. Our findings provide evidence that the pNLR is a feasible prognostic signature that reflects the TIME patterns to some extent in HNSCC. [ABSTRACT FROM AUTHOR]

Objective: Thymidylate synthase (TYMS) constitutes a pivotal and potent target in the context of chemoresistance. However, the oncogenic role of TYMS has received insufficient attention., Methods: Leveraging data from the Cancer Genome Atlas (TCGA) and various public databases, we conducted an extensive investigation into the oncogenic role of TYMS across 33 cancer types. Subsequently, TYMS was inhibited using small interfering RNA (siRNA) in four different cell lines, and cell proliferation and migration were assessed using CellTiter-Glo and Transwell assays., Results: TYMS exhibited pronounced expression across a spectrum of cancers and demonstrated associations with clinical outcome in diverse cancer patient cohorts. Furthermore, genetic alterations were identified as potential influencers of overall survival in specific tumor types. Notably, the expression of thymidylate synthase correlated with tumor-infiltrating CD4+ cells in select cancers. Additionally, the functional mechanism of TYMS encompassed nucleotidase activity, chromosome segregation, and DNA replication progress. In vitro experiments further substantiated these findings, demonstrating that the suppression of TYMS impeded the cell growth and invasive capabilities of HeLa, A549, 786-O, and U87&#95;MG cells., Conclusions: This study furnishes a comprehensive understanding of the oncogenic role played by TYMS in human tumors., Competing Interests: None., (AJTR Copyright © 2024.)

Background: Laryngeal squamous cell carcinoma (LSCC) is the most common squamous cell carcinoma. Though significant effort has been focused on molecular pathogenesis, development, and recurrence of LSCC, little is known about its relationship with the immune-related long non-coding RNA (lncRNA) pairs.Methods: After obtaining the transcriptome profiling data sets and the corresponding clinical characteristics of LSCC patients and normal samples from The Cancer Genome Atlas (TCGA) database, a series of bioinformatic analysis was conducted to select the differently expressed immune-related lncRNAs and build a signature of immune-related lncRNA pairs. Then, the effectiveness of the signature was validated.Results: A total of 111 LSCC patients and 12 normal samples' transcriptome profiling data sets were retrieved from TCGA. 301 differently expressed immune-related lncRNAs were identified and 35,225 lncRNA pairs were established. After univariate Cox analysis, LASSO regression and multivariate Cox analysis, 7 lncRNA pairs were eventually selected to construct a signature. The riskscore was computed using the following formula: Riskscore = 0.95 × (AL133330.1|AC132872.3) + (-1.23)  × (LINC01094|LINC02154) + 0.65 × (LINC02575|AC122685.1) + (-1.15)  × (MIR9-3HG|LINC01748) + 1.45 × (AC092687.3|SNHG12) + (-0.87)  × (AC090204.1|AL158166.1) + 0.64 × (LINC01063|Z82243.1). Patients were classified into the high-risk group (> 1.366) and the low-risk group (< 1.366) according to the cutoff value (1.366), which is based on the 5-year riskscore ROC curve. The survival analysis showed that the low-risk group had a better prognosis (P < 0.001). The riskscore was better than other clinical characteristics in prognostic prediction and the area under the curves (AUCs) for the 1-, 3-, and 5-year survivals were 0.796, 0.946, and 0.895, respectively. Combining age, gender, grade, stage, and riskscore, a nomograph was developed to predict survival probability in LSCC patients. Then, the riskscore was confirmed to be related with the content of tumor-infiltration immune cells and the model could serve as a potential predictor for chemosensitivity.Conclusion: We successfully established a more stable signature of 7 immune-related lncRNA pairs, which has demonstrated a better prognostic ability for LSCC patients and may assist clinicians to precisely prescribe chemo drugs. [ABSTRACT FROM AUTHOR]

Background: A significant subset of mismatch repair-deficient (dMMR)/microsatellite instability-high (MSI-H) gastric adenocarcinomas (GAC) are resistant to immune checkpoint inhibitors (ICIs), yet the underlying mechanism remains largely unknown. We sought to investigate the genomic correlates of the density of tumor-infiltrating immune cells (DTICs) and primary resistance to ICI treatment.Methods: Four independent cohorts of MSI-H GAC were included: (i) the surgery cohort (n = 175) with genomic and DTIC data, (ii) the 3DMed cohort (n = 32) with genomic and PD-L1 data, (iii) the Cancer Genome Atlas (TCGA) cohort (n = 73) with genomic, transcriptomic, and survival data, and (iv) the ICI treatment cohort (n = 36) with pre-treatment genomic profile and ICI efficacy data.Results: In the dMMR/MSI-H GAC, the number of mutated genes in the PI3K-AKT-mTOR pathway (NMP) was positively correlated with tumor mutational burden (P < 0.001) and sensitivity to PI3K-AKT-mTOR inhibitors and negatively correlated with CD3+ (P < 0.001), CD4+ (P = 0.065), CD8+ (P = 0.004), and FOXP3+ cells (P = 0.033) in the central-tumor rather than invasive-margin area, and the transcription of immune-related genes. Compared to the NMP-low (NMP = 0/1) patients, the NMP-high (NMP ≥ 2) patients exhibited a poorer objective response rate (29.4% vs. 85.7%, P < 0.001), progression-free survival (HR = 3.40, P = 0.019), and overall survival (HR = 3.59, P = 0.048) upon ICI treatment.Conclusions: Higher NMP was identified as a potential predictor of lower DTICs and primary resistance to ICIs in the dMMR/MSI-H GAC. Our results highlight the possibility of using mutational data to estimate DTICs and administering the PI3K-AKT-mTOR inhibitor as an immunotherapeutic adjuvant in NMP-high subpopulation to overcome the resistance to ICIs. [ABSTRACT FROM AUTHOR]

Breast cancer (BCa) is the most common malignancy in women and claudin-low breast cancer (CL-BCa) is a newly identified BCa subtype characterized by low expression of claudin 3&4&7. However, the hub genes associated with the recruitment of immune cells into CL-BCa were rarely described. This study aimed at exploring the differentially expressed hub genes associated with tumor-infiltrating immune cells in CL-BCa by a multi-approach bioinformatics analysis. The top 200 genes associated with CL-BCa were screened in the METABRIC dataset; the PPI network was constructed using STRING and Cytoscape; tumor-infiltrating immune cells were analyzed by TIMER 2.0; and the correlation of feature cytokines and claudins on survival was examined in METABRIC and TCGA datasets. Consequently, we found that the fraction of tumor-infiltrating immune cells, especially CD8+T cells and macrophages, increased in the CL-BCa. Differentially expressed cytokines (CCL5, CCL19, CXCL9 and CXCL10) and claudins (CLDN8, CLDN11 and CLDN19) were related to the overall survival, and their expression levels were also examined both in tumor tissues of CL-BCa patients by IHC and in typical CL-BCa cell lines by qPCR. Finally, the BCa patients with high expression of these DEGs (CCL5, CCL19, CXCL9, CLDN8 and CLDN11) showed a better overall survival. This study sheds light on molecular features of CL-BCa on immune microenvironments and contributes to identification of prognosis biomarkers for the CL-BCa patients. [ABSTRACT FROM AUTHOR]

Background: An increasing number of studies have shown that immune-related long noncoding RNAs (lncRNAs) do not require a unique expression level. This finding may help predict the survival and drug sensitivity of patients with colon cancer. Methods: We retrieved original transcriptome and clinical data from The Cancer Genome Atlas (TCGA), sorted the data, differentiated mRNAs and lncRNAs, and then downloaded immune-related genes. Coexpression analysis predicted immune-related lncRNAs (irlncRNAs) and univariate analysis identified differentially expressed irlncRNAs (DEirlncRNAs). We have also amended the lasso pending region. Next, we compared the areas under the curve (AUCs), counted the Akaike information standard (AIC) value of the 3-year receiver operating characteristic (ROC) curve, and determined the cutoff point to establish the best model to differentiate the high or low disease risk group of colon cancer patients. Results: We reevaluated the patients regarding the survival rate, clinicopathological features, tumor-infiltrating immune cells, immunosuppressive biomarkers, and chemosensitivity. A total of 155 irlncRNA pairs were confirmed, 31 of which were involved in the Cox regression model. After the colon cancer patients were regrouped according to the cutoff point, we could better distinguish the patients based on adverse survival outcomes, invasive clinicopathological features, the specific tumor immune cell infiltration status, high expression of immunosuppressive biomarkers, and low chemosensitivity. Conclusions: In this study, we established a characteristic model by pairing irlncRNAs to better predict the survival rate, chemotherapy efficacy, and prognostic value of patients with colon cancer. [ABSTRACT FROM AUTHOR]

The signature composed of immune-related long noncoding ribonucleic acids (irlncRNAs) with no requirement of specific expression level seems to be valuable in predicting the survival of patients with hepatocellular carcinoma (HCC). Here, we retrieved raw transcriptome data from The Cancer Genome Atlas (TCGA), identified irlncRNAs by co-expression analysis, and recognized differently expressed irlncRNA (DEirlncRNA) pairs using univariate analysis. In addition, we modified Lasso penalized regression. Then, we compared the areas under curve, counted the Akaike information criterion (AIC) values of 5-year receiver operating characteristic curve, and identified the cut-off point to set up an optimal model for distinguishing the high- or low-disease-risk groups among patients with HCC. We then reevaluated them from the viewpoints of survival, clinic-pathological characteristics, tumor-infiltrating immune cells, chemotherapeutics efficacy, and immunosuppressed biomarkers. 36 DEirlncRNA pairs were identified, 12 of which were included in a Cox regression model. After regrouping the patients by the cut-off point, we could more effectively differentiate between them based on unfavorable survival outcome, aggressive clinic-pathological characteristics, specific tumor immune infiltration status, low chemotherapeutics sensitivity, and highly expressed immunosuppressed biomarkers. The signature established by paring irlncRNA regardless of expression levels showed a promising clinical prediction value.

Breast cancer (BCa) is the most common malignancy in women and claudin-low breast cancer (CL-BCa) is a newly identified BCa subtype characterized by low expression of claudin 3&4&7. However, the hub genes associated with the recruitment of immune cells into CL-BCa were rarely described. This study aimed at exploring the differentially expressed hub genes associated with tumor-infiltrating immune cells in CL-BCa by a multi-approach bioinformatics analysis. The top 200 genes associated with CL-BCa were screened in the METABRIC dataset; the PPI network was constructed using STRING and Cytoscape; tumor-infiltrating immune cells were analyzed by TIMER 2.0; and the correlation of feature cytokines and claudins on survival was examined in METABRIC and TCGA datasets. Consequently, we found that the fraction of tumor-infiltrating immune cells, especially CD8+T cells and macrophages, increased in the CL-BCa. Differentially expressed cytokines (CCL5, CCL19, CXCL9 and CXCL10) were related to the overall survival, and their expression levels were also examined both in tumor tissues of CL-BCa patients by IHC and in typical CL-BCa cell lines by qPCR. Moreover, the BCa patients with low expression of these differentially expressed claudins (CLDN8, CLDN11 and CLDN19) showed a worse overall survival. This study sheds light on molecular features of CL-BCa on immune microenvironments and contributes to identification of prognosis biomarkers for the CL-BCa patients.

BackgroundImmune-related long non-coding RNAs (irlncRNAs) appear valuable in predicting prognosis in patients with cancer. In this study, we used a fresh modeling algorithm to construct irlncRNAs signature and then assessed its predictive value for prognosis, tumor immune infiltration, and chemotherapy efficacy in gastric cancer (GC) patients.Materials and MethodsThe raw transcriptome data were extracted from the Cancer Genome Atlas (TCGA). Patients were randomly divided into the training and testing cohort. irlncRNAs were identified through co-expression analysis, after which differentially expressed irlncRNA (DEirlncRNA) pairs were identified. Next, we developed a model to distinguish between high- or low-risk groups in GC patients through univariate and LASSO regression analyses. A ROC curve was used to verify this model. After subgrouping patients according to the median risk score, we investigated the connection between the risk score of GC and clinicopathological characteristics. Functional enrichment analysis was also performed.ResultsWe find that the results indicate that immune-related lncRNA signaling has essential value in predicting prognosis, and it may be potential to measure the Efficacy for immunotherapy. This feature may be a guide to the selection of GC immunotherapy.ConclusionOur data revealed that immune-related lncRNA signaling had essential value in predicting prognosis, and it may be potentially used to measure the efficacy for immunotherapy. This feature may also be used to guide the selection of GC immunotherapy.

Background: Accumulating evidence has suggested that the extracellular matrix (ECM) plays a vital role in the development and progression of cancer, and could be recognized as a biomarker of the response to immunotherapy. However, the effect of the ECM signature in hepatocellular carcinoma (HCC) is not well understood.Methods: HCC patients derived from the TCGA-LIHC dataset were clustered according to the ECM signature. The differences in prognosis, functional enrichment, immune infiltration, and mutation characteristics between distinct molecular clusters were examined, and its predictive value on the sensitivities to chemotherapy and immunotherapy was further analyzed. Then, a prognostic model was built based on the ECM-related gene expression pattern.Results: HCC patients were assigned into two molecular subtypes. Approximately 80% of HCC patients were classified into cluster A with poor prognosis, more frequent TP53 mutation, and lower response rate to immunotherapy. In contrast, patients in cluster B had better survival outcomes and higher infiltration levels of dendritic cells, macrophages, and regulatory T cells. The prognostic risk score model based on the expression profiles of six ECM-related genes (SPP1, ADAMTS5, MMP1, BSG, LAMA2, and CDH1) demonstrated a significant association with higher histologic grade and advanced TNM stage. Moreover, the prognostic risk score showed good performance in both the training dataset and validation dataset, as well as improved prognostic capacity compared with TNM stage.Conclusions: We characterized two HCC subtypes with distinct clinical outcomes, immune infiltration, and mutation characteristics. A novel prognostic model based on the ECM signature was further developed, which may contribute to individualized prognostic prediction and aid in clinical decision-making.

Background: Targeting the mRNA splicing process has been identified as a therapeutic strategy for human cancer. PRPF19 is an RNA binding protein that is involved in pre-mRNA processing and repairing DNA damage; the aberrant expression of PRPF19 is potentially associated with carcinogenesis. However, the biological role of PRPF19 in hepatocellular carcinoma (HCC) is still elusive.Methods: Data obtained from TCGA, Oncomine, and GEO were used to investigate the PRPF19 expression level and its role in tumor immune infiltration, prognosis, and the tumor progression of cohorts from HCC. Using various databases and tools (UALCAN, TIMER, TISMO, and PathCards), we presented the potential mechanisms of PFPF19 upregulation, PRPF19-related pathways, and its biological functions in liver cancer.Results: For HCC, PRPF19 expression was found upregulated both in single tumor cells and tissues. Furthermore, the increased expression of PRPF19 was significantly correlated to clinical characteristics: advanced stage, vascular invasion, high AFP, and poor prognosis of HCC. According to the tumor-immunological analysis, we found that PRPF19 is positively correlated with infiltrating myeloid-derived suppressor cells (MDSCs). Moreover, the microenvironment of HCC tissues with high expression of PRPF19 is highly immunosuppressive (lower T-lymphocytes, multiple immune checkpoints upregulated). Patients with high expression of PRPF19 and high MDSCs had a worse survival prognosis as well. TP53 mutation may have a positive effect on PRPF19 expression via decreased promoter methylation of PRPF19. By TF-mRNA network analysis, key transcription factors (TFs) in TC-NER and PCS pathways (PRPF19 involved) were identified.Conclusion: This work implied that PRPF19 is associated with tumor immune evasion and progression, and serves as a prognostic marker for worse clinical outcomes with HCC. Thus, this critical regulator could serve as a potential therapeutic target of HCC.

Tumor-infiltrating immune cells (TICs) play a central role in the tumor microenvironment, which can reflect the host anti-tumor immune response. However, few studies have explored TICs in predicting the prognosis of lung adenocarcinoma (LUAD). In our study, we enrolled 2470 LUAD patients from TCGA and GEO databases, and the normalized enrichment scores for 65 immune cell types were quantified for each patient. An immune-related risk score (IRRS) was built on the basis of 17 selected TICs using LASSO regression analysis, and the results showed that high-risk patients were correlated with shorter survival time for the LUAD cohorts. Correlation analyses between IRRS and clinical characteristics were also evaluated to validate the clinical use of IRRS. In addition, we analyzed the differences in the distribution of immune cell infiltration and immunoregulatory gene expression, which may facilitate individual immunotherapy. Based on the above result, we conclude that IRRS can act as a powerful predictor for risk stratification and prognosis prediction, and may facilitate the decision-making process for LUAD patients., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Nasopharyngeal carcinoma (NPC) is a highly invasive and metastatic carcinoma with different molecular characteristics and clinical outcomes. In this work, we aimed to establish a novel gene signature that could predict the prognosis of NPC patients. A total of 13 significant genes between the recurrence/metastasis (RM) group and the no recurrence/metastasis (no-RM) group were identified by machine learning from RNA-Seq data including 60 NPC tumor biopsies. Based on these genes, a 4-mRNA signature (considering U2AF1L5, TMEM265, GLB1L and MLF1) was identified. Receiver operating characteristic (ROC) and Kaplan-Meier (K-M) analyses indicated that this signature had good prognostic value for NPC. The overall survival (OS) and progression-free survival (PFS) of the patients in the high-risk group were significantly shorter than those of the patients in the low-risk group (p = 0.00126 and p = 0.000059, respectively). The area under the ROC curve (AUC) values of the 4-mRNA signature were higher than those of T stage and N stage for OS (0.893 vs 0.619 and 0.582, respectively) and PFS (0.86 vs 0.538 and 0.622, respectively). Furthermore, the 4-mRNA signature was closely associated with cell proliferation and the immune response. The expression of GLB1L and TMEM265 was associated with the level of tumor-infiltrating immune cells (r > 0.4, p < 0.05). We have validated the model through measuring the expression levels of the 4-mRNA signature by qRT-PCR, in an independent cohort of NPC patients. Here, we report a novel gene signature that can serve as a new tool for predicting the prognosis of NPC patients. [ABSTRACT FROM AUTHOR]

Uterine carcinosarcoma (UCS) is a malignant tumor with a high tendency to invasion and metastasis. However, the underlying invasion and metastasis mechanisms of UCS remain poorly understood. Genetic alteration and tumor-infiltrating immune cells play important roles in tumorigenesis, progression, and metastasis. To better understand the underlying mechanisms of UCS, we screened tumor-infiltrating immune cells by applying CIBERSORT algorithm and constructed nomograms to predict the prognosis of UCS patients based on metastasis-specific tumor-infiltrating immune cells and genes, and demonstrated their utility by the high AUC values. Combining gene co-expression and experimental validation results, we propose a potential mechanism of AK8, MPZ, and mast cells activated might play important parts in UCS metastasis. [ABSTRACT FROM AUTHOR]

Nasopharyngeal carcinoma (NPC) is a highly invasive and metastatic carcinoma with different molecular characteristics and clinical outcomes. In this work, we aimed to establish a novel gene signature that could predict the prognosis of NPC patients. A total of 13 significant genes between the recurrence/metastasis (RM) group and the no recurrence/metastasis (no-RM) group were identified by machine learning from RNA-Seq data including 60 NPC tumor biopsies. Based on these genes, a 4-mRNA signature (considering U2AF1L5, TMEM265, GLB1L and MLF1) was identified. Receiver operating characteristic (ROC) and Kaplan-Meier (K-M) analyses indicated that this signature had good prognostic value for NPC. The overall survival (OS) and progression-free survival (PFS) of the patients in the high-risk group were significantly shorter than those of the patients in the low-risk group (p = 0.00126 and p = 0.000059, respectively). The area under the ROC curve (AUC) values of the 4-mRNA signature were higher than those of T stage and N stage for OS (0.893 vs 0.619 and 0.582, respectively) and PFS (0.86 vs 0.538 and 0.622, respectively). Furthermore, the 4-mRNA signature was closely associated with cell proliferation and the immune response. The expression of GLB1L and TMEM265 was associated with the level of tumor-infiltrating immune cells (r > 0.4, p < 0.05). We have validated the model through measuring the expression levels of the 4-mRNA signature by qRT-PCR, in an independent cohort of NPC patients. Here, we report a novel gene signature that can serve as a new tool for predicting the prognosis of NPC patients.

Pancreatic adenocarcinoma (PAAD) is characterized by high malignancy, frequent metastasis, and recurrence with an unfavorable prognosis. This study is aimed at constructing a prognostic model for tumor-infiltrating immune cells and a competing endogenous RNA (ceRNA) network in PAAD and analyzing susceptibilities of chemotherapy and immunotherapy of PAAD. Gene expression profiles and clinical information of PAAD were downloaded from The Cancer Genome Atlas (TCGA) database and divided into the tumor group and the normal group. A total of five PAAD survival-related key genes in the ceRNA network and three survival-related immune infiltrating cells were uncovered, and two survival risk models and nomograms were constructed. The efficiency and performance of the two models were verified using multi-index area under the curve analysis at different time points, decision curve analysis, and calibration curves. Co-expression analysis showed that LRRC1, MIR600HG, and RNF166 in the ceRNA network and tumor-infiltrating immune cells including CD8 T cells and M1 macrophages were likely related to the PAAD prognosis, and the expression of key ceRNA-related genes was experimently validated in tissues and cell lines by RT-qPCR. Patients with low risk scores for key genes in the ceRNA network displayed a positive response to anti-programmed death-1 (PD-1) treatment and greater sensitivity to chemotherapeutic drugs such as docetaxel, lapatinib, and paclitaxel. More importantly, our results suggested that the IC50 values of gemcitabine in PAAD were not significantly different between the high and low risk groups. The expression levels of immune checkpoints were significantly different in the high-risk and low-risk groups. The prognostic model, nomogram, and drug analysis may provide an essential reference for PAAD patient management in the clinic.

Purpose: This study aimed to construct a novel signature to predict the survival of patients with colon cancer and the associated immune landscape, based on immune-related long noncoding ribonucleic acids (irlncRNAs).Methods: Expression profiles of irlncRNAs in 457 patients with colon cancer were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed (DE) irlncRNAs were identified and irlncRNA pairs were recognized using Lasso regression and Cox regression analyses. Akaike information criterion (AIC) values of receiver operating characteristic (ROC) curve were calculated to identify the ideal cut-off point for dividing patients into two groups and constructing the prognosis signature. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to validate the expression of LINC02195 and SCARNA9 in colon cancer.Results: We identified 22 irlncRNA pairs and patients were divided into high-risk and low-risk groups based on the calculated risk score using these 22 irlncRNA pairs. The irlncRNA pairs were significantly related to patient survival. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). The area under the curve of the signature to predict 5-year survival was 0.951. The risk score correlated with tumor stage, infiltration depth, lymph node metastasis, and distant metastasis. The risk score remained significant after univariate and multivariate Cox regression analyses. A nomogram model to predict patient survival was developed based on the results of Cox regression analysis. Immune cell infiltration status, expression of some immune checkpoint genes, and sensitivity to chemotherapeutics were also related to the risk score. The results of qRT-PCR revealed that LINC02195 and SCARNA9 were significantly upregulated in colon cancer tissues.Conclusion: The constructed prognosis signature showed remarkable efficiency in predicting patient survival, immune cell infiltration status, expression of immune checkpoint genes, and sensitivity to chemotherapeutics.

Mammalian target of rapamycin (mTOR), a serine/threonine kinase involved in cell proliferation, survival, metabolism and immunity, was reportedly activated in various cancers. However, the clinical role of mTOR in renal cell carcinoma (RCC) is controversial. Here we detected the expression and prognosis of total mTOR and phosphorylated mTOR (p-mTOR) in clear cell RCC (ccRCC) patients, and explored the interactions between mTOR and immune infiltrates in ccRCC. The protein level of mTOR and p-mTOR was determined by western blotting (WB), and their expression was evaluated in 145 ccRCC and 13 non-tumor specimens by immunohistochemistry (IHC). The relationship to immune infiltration of mTOR was further investigated using TIMER and TISIDB databases, respectively. WB demonstrated the ratio of p-mTOR to mTOR was higher in ccRCC than adjacent specimens (n = 3), and IHC analysis elucidated that p-mTOR expression was positively correlated with tumor size, stage and metastasis status, and negatively correlated with cancer-specific survival (CSS). In univariate analysis, high grade, large tumor, advanced stage, metastasis, and high p-mTOR expression were recognized as prognostic factors of poorer CSS, and multivariate survival analysis elucidated that tumor stage, p-mTOR and metastasis were of prognostic value for CSS in ccRCC patients. Further TIMER and TISIDB analyses uncovered that mTOR gene expression was significantly associated with numerous immune cells and immunoinhibitors in patients with ccRCC. Collectively, these findings revealed p-mTOR was identified as an independent predictor of poor survival, and mTOR was associated with tumor immune infiltrates in ccRCC patients, which validated mTOR could be implicated in the initiation and progression of ccRCC.

PurposeThe aim of this study is to investigate the prognostic role of programmed death ligand-1 (PD-L1) on tumor-infiltrating immune cells (TIICs) in patients after radical cystectomy (RC) for bladder cancer (BCa).Materials and MethodsWe retrospectively reviewed 92 “high-risk” (≥pT3a and/or pN+) patients who underwent RC for BCa, without adjuvant chemotherapy (AC), between April 2014 and December 2019. PD-L1 on TIICs was measured only using the VENTANA (SP-142) immunohistochemistry assay. Patients were categorized into three groups based to the percentage of the tumor area covered by PD-L1 on TIICs: IC0 (

PurposeWe aimed to construct an immune-related long noncoding ribonucleic acids (irlncRNA) signature to evaluate the prognosis of patients without specific expression level of these irlncRNA.MethodsThe raw transcriptome data were downloaded from The Cancer Genome Atlas (TCGA), irlncRNAs were filtered out using an online immune related gene database and coexpression analysis, differently expressed irlncRNA (DEirlncRNA) pairs were identified by univariate analysis. The areas under curve (AUC) were compared and the Akaike information criterion (AIC) values of receiver operating curve (ROC) was counted, the most optimal model was constructed to divide bladder cancer patients into high- and low-risk groups usingõ the cut-off point of ROC. Then, we evaluated them from multiple perspectives, such as survival time, clinic-pathological characteristics, immune-related cells infiltrating, chemotherapeutics efficacy and immune checkpoint inhibitors.Results14 DEirlncRNA pairs were included in this signature. Patients in high-risk groups demonstrated apparent shorter survival time, more aggressive clinic-pathological characteristics, different immune-related cells infiltrating status, lower chemotherapeutics efficacy.ConclusionThe irlncRNA signature demonstrated a promising prediction value for bladder cancer patients and was important in guiding clinical treatment.

Increasing studies have proved that malignant tumors are associated with energy metabolism. This study was aimed to explore biological variables that impact the prognosis of patients in the glycolysis-related subgroups of lung adenocarcinoma (LUAD). The mRNA expression profiling and mutation data in large LUAD samples were collected from the Cancer Genome Atlas (TCGA) database. Then, we identified the expression level and prognostic value of glycolysis-related genes, as well as the fractions of 22 immune cells in the tumor microenvironment. The differences between glycolysis activity, mutation, and immune infiltrates were discussed in these groups, respectively. Two hundred fifty-five glycolysis-related genes were identified from gene set enrichment analysis (GSEA), of which 43 genes had prognostic values (p < 0.05). Next, we constructed a glycolysis-related competing endogenous RNA (ceRNA) network which related to the survival of LUAD. Then, two subgroups of LUAD (clusters 1 and 2) were identified by applying unsupervised consensus clustering to 43 glycolysis-related genes. The survival analysis showed that the cluster 1 patients had a worse prognosis (p < 0.001), and upregulated differentially expressed genes (DEGs) are interestingly enriched in malignancy-related biological processes. The differences between the two subgroups are SPTA1, KEAP1, USH2A, and KRAS among top 10 mutated signatures, which may be the underlying mechanism of grouping. Combined high tumor mutational burden (TMB) with tumor subgroups preferably predicts the prognosis of LUAD patients. The CIBERSORT algorithm results revealed that low TMB samples were concerned with increased infiltration level of memory resting CD4+ T cell (p = 0.03), resting mast cells (p = 0.044), and neutrophils (p = 0.002) in cluster 1 and high TMB samples were concerned with increased infiltration level of memory B cells, plasma cells, CD4 memory-activated T cells, macrophages M1, and activated mast cells in cluster 2, while reduced infiltration of monocytes, resting dendritic cells, and resting mast cells was captured in cluster 2. In conclusion, significant different gene expression characteristics were pooled according to the two subgroups of LUAD. The combination of subgroups, TMB and tumor-infiltrating immune cell signature, might be a novel prognostic biomarker in LUAD.

Purpose: The aim of this study is to investigate the prognostic role of programmed death ligand-1 (PD-L1) on tumor-infiltrating immune cells (TIICs) in patients after radical cystectomy (RC) for bladder cancer (BCa). Materials and Methods: We retrospectively reviewed 92 "high-risk" (≥pT3a and/or pN+) patients who underwent RC for BCa, without adjuvant chemotherapy (AC), between April 2014 and December 2019. PD-L1 on TIICs was measured only using the VENTANA (SP-142) immunohistochemistry assay. Patients were categorized into three groups based to the percentage of the tumor area covered by PD-L1 on TIICs: IC0 (<1%), IC1 (≥1% and <5%), and IC2/3 (≥5%). Positive PD-L1 was defined as IC2/3 (≥5%). Kaplan–Meier survival analysis was used to illustrate recurrence-free survival (RFS), and Cox proportional hazard models were used to identify predictive factors of tumor recurrence. Results: Within the cohort, the proportions of PD-L1 IC0, IC1, and IC2/3 were 21.7%, 23.9%, and 54.4%, respectively. At follow-up (mean 31.3 months), tumor recurrence was identified in 49 patients (53.3%). Using multivariable analysis, tumor stage (pT4; P =0.005), positive lymph nodes (P =0.021), and positive PD-L1 on TIICs (P =0.010) were independent predictors of tumor recurrence. The 2- and 3-year RFS rates were 67.7% and 64.2% in negative PD-L1 on TIICs, while 27.8% and 22.3% in positive PD-L1 on TIICs, respectively. Conclusions: Positive PD-L1 on TIICs was significantly associated with poorer RFS in "high-risk" patients after RC without AC. Our results support the use of adjuvant immunotherapy in "high-risk" patients with positive PD-L1 on TIICs after RC. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to construct an immune-related long noncoding ribonucleic acids (irlncRNA) signature to evaluate the prognosis of patients without specific expression level of these irlncRNA. Methods: The raw transcriptome data were downloaded from The Cancer Genome Atlas (TCGA), irlncRNAs were filtered out using an online immune related gene database and coexpression analysis, differently expressed irlncRNA (DEirlncRNA) pairs were identified by univariate analysis. The areas under curve (AUC) were compared and the Akaike information criterion (AIC) values of receiver operating curve (ROC) was counted, the most optimal model was constructed to divide bladder cancer patients into high- and low-risk groups usingõ the cut-off point of ROC. Then, we evaluated them from multiple perspectives, such as survival time, clinic-pathological characteristics, immune-related cells infiltrating, chemotherapeutics efficacy and immune checkpoint inhibitors. Results: 14 DEirlncRNA pairs were included in this signature. Patients in high-risk groups demonstrated apparent shorter survival time, more aggressive clinic-pathological characteristics, different immune-related cells infiltrating status, lower chemotherapeutics efficacy. Conclusion: The irlncRNA signature demonstrated a promising prediction value for bladder cancer patients and was important in guiding clinical treatment. [ABSTRACT FROM AUTHOR]

PurposeTo explore the profiles of immune and stromal components of the tumor microenvironment (TME) and their related key genes in gliomas.MethodsWe applied bioinformatic techniques to identify the core gene that participated in the regulation of the TME of the gliomas. And immunohistochemistry staining was used to calculate the gene expressions in clinical cases.ResultsThe CIBERSORT and ESTIMATE were used to figure out the composition of TME in 698 glioma cases from The Cancer Genome Atlas (TCGA) database. Differential expression analysis identified 2103 genes between the high and the low-score group. Then the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, univariate Cox regression analysis, and protein–protein interaction (PPI) network construction were conducted based on these genes. MYD88 was identified as the key gene by the combination univariate Cox and PPI analysis. Furthermore, MYD88 expression was significantly associated with the overall survival and WHO grade of glioma patients. The genes in the high-expression MYD88 group were mainly in immune-related pathways in the Gene Set Enrichment Analysis (GSEA). We found that macrophage M2 accounted for the largest portion with an average of 27.6% in the glioma TIICs and was associated with high expression of MYD88. The results were verified in CGGA database and clinical cases in our hospital. Furthermore, we also found the MYD88 expression was higher in IDH1 wild types. The methylation rate was lower in high grade gliomas.ConclusionMYD88 had predictive prognostic value in glioma patients by influencing TIICs dysregulation especially the M2-type macrophages.

Purpose: To explore the profiles of immune and stromal components of the tumor microenvironment (TME) and their related key genes in gliomas. Methods: We applied bioinformatic techniques to identify the core gene that participated in the regulation of the TME of the gliomas. And immunohistochemistry staining was used to calculate the gene expressions in clinical cases. Results: The CIBERSORT and ESTIMATE were used to figure out the composition of TME in 698 glioma cases from The Cancer Genome Atlas (TCGA) database. Differential expression analysis identified 2103 genes between the high and the low-score group. Then the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, univariate Cox regression analysis, and protein–protein interaction (PPI) network construction were conducted based on these genes. MYD88 was identified as the key gene by the combination univariate Cox and PPI analysis. Furthermore, MYD88 expression was significantly associated with the overall survival and WHO grade of glioma patients. The genes in the high-expression MYD88 group were mainly in immune-related pathways in the Gene Set Enrichment Analysis (GSEA). We found that macrophage M2 accounted for the largest portion with an average of 27.6% in the glioma TIICs and was associated with high expression of MYD88. The results were verified in CGGA database and clinical cases in our hospital. Furthermore, we also found the MYD88 expression was higher in IDH1 wild types. The methylation rate was lower in high grade gliomas. Conclusion: MYD88 had predictive prognostic value in glioma patients by influencing TIICs dysregulation especially the M2-type macrophages. [ABSTRACT FROM AUTHOR]

Background: This study aimed to deconvolve the levels of infiltrating immune cells in non-small cell lung cancer (NSCLC) and to identify specific gene co-expression modules associated with prognosis of NSCLC. Materials and Methods: CIBERSORT algorithm was employed to infer the relative abundance of 22 immune cell subtypes in 1751 NSCLC subjects. The patterns of immune infiltration were identified for NSCLC with different clinical and genomic features and were used to construct an immunoscore by LASSO regression associated with NSCLC survival. Weighted gene co-expression network analysis (WGCNA) was employed to identify specific modules related to immunoscore and NSCLC survival. An integrated prognostic model was constructed with immunoscore combined with the available clinical variables and the selected gene modules to predict the prognosis of NSCLC. Results: We found distinct immune infiltration patterns for NSCLC with different genotype. EGFR-mutant NSCLC was characterized by enriched resting memory CD4+ T cell. An immunoscore was established based on the infiltration abundance of 17 selected immune cell subtypes. Patients with a low immunoscore had a prolonged survival and higher abundance of CD4+ T cell, resting dendritic cells and resting mast cells. The WGCNA analysis identified the gene modules significantly associated with immunoscore and the prognosis of NSCLC. The immunoscore was further incorporated with clinical parameters and selected gene modules to fit a predictive model which stratified patients into subgroups with significantly different survival. Conclusion: The distinct immune profiles are associated with differential overall survival of NSCLC and the integrated model can robustly predict the prognosis of NSCLC.

Purpose: To investigate the prognostic value of programmed death ligand-1 (PD-L1) expression in tumor-infiltrating immune cells (ICs) in men treated with adjuvant chemotherapy (AC) following radical cystectomy (RC) for muscle-invasive bladder cancer (MIBC). Materials and Methods: We retrospectively reviewed 219 "high-risk" (≥pT3a and/or pN+) patients who underwent RC and received cisplatin-based AC for MIBC between March 2015 and September 2019. PD-L1 expression was measured using the VENTANA (SP-142) immunohistochemistry assay and categorized into the three groups according to the percentage of the tumor area covered by PD-L1 expression on ICs: IC0 (< 1%), IC1 (≥ 1% and < 5%), and IC2/3 (≥ 5%). Positive PD-L1 expression was defined as IC2/3 (≥ 5%). Kaplan–Meier survival analysis was used to assess recurrence-free survival (RFS), and Cox proportional hazard models were applied to identify factors predicting tumor recurrence. Results: In the entire cohort, the overall prevalence of PD-L1 IC0, IC1, and IC2/3 was 13.2%, 27.4%, and 59.4%, respectively. During the mean follow-up of 32.5 months, tumor recurrence was detected in 115 (52.5%) patients. On multivariable analysis, tumor stage (≥pT3; P=0.032), positive lymph nodes (P=0.001), and positive PD-L1 on ICs (P=0.005) were independent predictors of tumor recurrence. The 3 year RFS was 54.7% in patients with negative PD-L1 and 31.7% in patients with positive PD-L1. Conclusion: PD-L1 is widely expressed in ICs. Positive PD-L1 on ICs was significantly associated with shorter RFS in patients treated with cisplatin-based AC following RC. The present results support the use of adjuvant immunotherapy in "high-risk" patients with PD-L1-expressing ICs. [ABSTRACT FROM AUTHOR]

Lung adenocarcinoma (LUAD) is one of the major types of lung cancer. Tumor-infiltrating immune cells (TIICs) are positively associated with overall survival (OS) in LUAD. The SPARC/osteonectin, cwcv and kazal-like domains proteoglycan 2 (SPOCK2) is a complex type of secreted proteoglycan involved in forming a protective barrier against viral infection. The purpose of this study was to investigate the relationship between SPOCK2 and TIICs and the prognostic role of SPOCK2 in LUAD. The GEPIA2, GEO, CPTAC, and HPA databases were analyzed to examine both the mRNA and protein expression of SPOCK2 in LUAD. GEPIA2 and the Kaplan-Meier Plotter (KM Plotter) were used to evaluate the prognostic value of SPOCK2 in LUAD patients. TCGA data were examined for a correlation between SPOCK2 expression and clinical characteristics. Gene enrichment analyses were performed to explore the underlying mechanism of SPOCK2 based on LinkedOmics. RegNetwork was used to predict the regulators of SPOCK2. The correlation between SPOCK2 and TIICs, including immune infiltration level and relative proportion was investigated via TIMER. KM Plotter was also used to evaluate the prognostic role of SPOCK2 expression in LUAD with enriched and decreased TIIC subgroups. We found SPOCK2 was significantly downregulated in LUAD compared with that in non-tumor controls and was correlated with clinical parameters. Moreover, a high SPOCK2 expression level predicted better survival. The SPOCK2-associated regulatory network was constructed. SPOCK2 influenced the TIIC infiltration level and relative proportion in LUAD. Furthermore, a high SPOCK2 expression level was associated with a favorable prognosis in enriched CD4 + T cells and macrophage subgroups in LUAD. In conclusion, a high level of SPOCK2 expression predicted favorable prognosis and was significantly correlated with TIICs in LUAD. Therefore, the expression of SPOCK2 may affect the prognosis of LUAD partly due to TIICs.

BackgroundColon adenocarcinoma (COAD) is a malignant and lethal tumor in digestive system and distance metastasis lead to poor prognosis. The metastasis-specific ceRNAs (competitive endogenous RNAs) and tumor-infiltrating immune cells might associate with tumor prognosis and distance metastasis. Nonetheless, few studies have concentrated on ceRNAs and Immune cells in COAD.MethodsThe gene expression profile and clinical information of COAD were downloaded from TCGA and divided into two groups: primary tumors with or without distance metastasis. We applied comprehensive bioinformatics methods to analyze differential expression genes (DEGs) related to metastasis and establish the ceRNA networks. The Cox analysis and Lasso regression were utilized to screen the pivotal genes and prevent overfitting. Based on them, the prognosis prediction nomograms were established. The cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was then applied to screen significant tumor immune-infiltrating cells associated with COAD metastasis and established another prognosis prediction model. Ultimately, co-expression analysis was applied to explore the relationship between key genes in ceRNA networks and significant immune cells. Multiple databases and preliminary clinical specimen validation were used to test the expressions of key biomarkers at the cellular and tissue levels.ResultsWe explored 1 significantly differentially expressed lncRNA, 1 significantly differentially expressed miRNA, 8 survival-related immune-infiltrating cells, 5 immune cells associated with distance metastasis. Besides, 3 pairs of important biomarkers associated with COAD metastasis were also identified: T cells follicular helper and hsa-miR-125b-5p (R = −0.200, P < 0.001), Macrophages M0 and hsa-miR-125b-5p (R = 0.170, P < 0.001) and Macrophages M0 and FAS (R = −0.370, P < 0.001). Multidimensional validation and preliminary clinical specimen validation also supported the results.ConclusionIn this research, we found some significant ceRNAs (FAS and hsa-miR-125b-5p) and tumor-infiltrating immune cells (T cells follicular helper and Macrophages M0) might related to distance metastasis and prognosis of COAD. The nomograms could assist scientific and medical researchers in clinical management.

Lung adenocarcinoma (LUAD) is one of the major types of lung cancer. Tumor-infiltrating immune cells (TIICs) are positively associated with overall survival (OS) in LUAD. The SPARC/osteonectin, cwcv and kazal-like domains proteoglycan 2 (SPOCK2) is a complex type of secreted proteoglycan involved in forming a protective barrier against viral infection. The purpose of this study was to investigate the relationship between SPOCK2 and TIICs and the prognostic role of SPOCK2 in LUAD. The GEPIA2, GEO, CPTAC, and HPA databases were analyzed to examine both the mRNA and protein expression of SPOCK2 in LUAD. GEPIA2 and the Kaplan-Meier Plotter (KM Plotter) were used to evaluate the prognostic value of SPOCK2 in LUAD patients. TCGA data were examined for a correlation between SPOCK2 expression and clinical characteristics. Gene enrichment analyses were performed to explore the underlying mechanism of SPOCK2 based on LinkedOmics. RegNetwork was used to predict the regulators of SPOCK2. The correlation between SPOCK2 and TIICs, including immune infiltration level and relative proportion was investigated via TIMER. KM Plotter was also used to evaluate the prognostic role of SPOCK2 expression in LUAD with enriched and decreased TIIC subgroups. We found SPOCK2 was significantly downregulated in LUAD compared with that in non-tumor controls and was correlated with clinical parameters. Moreover, a high SPOCK2 expression level predicted better survival. The SPOCK2-associated regulatory network was constructed. SPOCK2 influenced the TIIC infiltration level and relative proportion in LUAD. Furthermore, a high SPOCK2 expression level was associated with a favorable prognosis in enriched CD4 + T cells and macrophage subgroups in LUAD. In conclusion, a high level of SPOCK2 expression predicted favorable prognosis and was significantly correlated with TIICs in LUAD. Therefore, the expression of SPOCK2 may affect the prognosis of LUAD partly due to TIICs. [ABSTRACT FROM AUTHOR]

Tumor-infiltrating immune cells (TICs) affect tumorigenesis and tumor development in head and neck squamous cell carcinoma (HNSCC). We constructed a novel predictive model for HNSCC based on immune-related genes (IRGs) from The Cancer Genome Atlas and the Immunology Database and Analysis Portal. After identifying the IRGs, a predictive model involving 13 IRGs with high stratification value of overall survival (OS) was constructed by multiple support vector machine recursive feature elimination and least absolute shrinkage and selection operator regression. We explored the relationship between the risk score (RS) and clinical characteristics. The nomogram showed high concordance and good agreement in OS. Four TICs affected the OS and were in agreement with the abundance analysis of the RS levels. Furthermore, the low-risk HNSCC group showed higher expression of PD-1, CTLA4, and TIGIT, while the high-risk group showed higher expression of EGFR. The high-risk HNSCC showed high sensitivity to eight drugs., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)