Dataset Information

Prognostic and predictive value of immune/stromal-related gene biomarkers in renal cell carcinoma.

ABSTRACT: Immune/stromal-associated genes may be promising biomarkers for cancer diagnosis and the determination of clinical cancer treatment options. The aim of the present study was to identify prognostic stromal/immune-associated genes in renal cell carcinoma (RCC). RCC gene expression data (885 cases) were obtained from The Cancer Genome Atlas database. Immune/stromal scores were calculated by using the ESTIMATE package in R. Immune/stromal scores were significantly associated with Tumor-Node-Metastasis stage, clinical stage and overall survival rate (P<0.05). There were 419 differentially expressed genes (DEGs) based on immune scores and 738 DEGs based on stromal scores. Among these DEGs, 406 DEGs based on stromal scores and 252 DEGs based on immune scores were significantly associated with overall survival rate (P<0.05). The biological functions of these DEGs were primarily enriched in the 'immune response' and 'regulation of cell migration and proliferation'. These DEGs were observed in a protein-protein interaction network. A LASSO Cox regression model was used to build a prognostic 6 gene-based classifier, including the IL21R, ATP6V1C2, GBP1, P2RY10, GBP4 and TNNC2 genes [area under the curve (AUC) =0.776]. The predictive model which combined this classifier with clinical prognostic factors had a high accuracy in predicting patient survival in RCC (combined AUC =0.899). Taken together, these results demonstrated that there are significant associations between immune/stromal scores and clinicopathological staging. A set of tumor microenvironment-associated genes that have powerful prognostic value in patients with RCC were identified in the present study.

SUBMITTER: Wang S

PROVIDER: S-EPMC7285855 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prognostic and predictive value of immune/stromal-related gene biomarkers in renal cell carcinoma.

Wang Sen S Zheng Xiangguang X Chen Xinglu X Shi Xiaojun X Chen Sansan S

Oncology letters 20200424 1

Immune/stromal-associated genes may be promising biomarkers for cancer diagnosis and the determination of clinical cancer treatment options. The aim of the present study was to identify prognostic stromal/immune-associated genes in renal cell carcinoma (RCC). RCC gene expression data (885 cases) were obtained from The Cancer Genome Atlas database. Immune/stromal scores were calculated by using the ESTIMATE package in R. Immune/stromal scores were significantly associated with Tumor-Node-Metastas ...[more]

PMID: 32565957

Similar Datasets

Project description:Background: Kidney renal clear cell carcinoma (KIRC) is a representative histologic subtype of renal cell carcinoma (RCC). RCC exhibits a strong immunogenicity with a prominent dysfunctional immune infiltration. Complement C1q C chain (C1QC) is a polypeptide in serum complement system and is involved in tumorigenesis and the modulation of tumor microenvironment (TME). However, researches have not explored the effect of C1QC expression on prognosis and tumor immunity of KIRC. Methods: The difference in a wide variety of tumor tissues and normal tissues in terms of the C1QC expression was detected using TIMER and TCGA portal databases, and further validation of protein expression of C1QC was conducted via Human Protein Atlas. Then, the associations of C1QC expression with clinicopathological data and other genes were studied with the use of UALCAN database. Subsequently, the association of C1QC expression with prognosis was predicted by searching the Kaplan-Meier plotter database. A protein-protein interaction (PPI) network with the Metascape database was built using STRING software, such that the mechanism underlying the C1QC function can be studied in depth. The TISCH database assisted in the evaluation of C1QC expression in different cell types in KIRC at the single-cell level. Moreover, the association of C1QC and the infiltration level of tumor immune cell was assessed using TIMER platform. The TISIDB website was selected to deeply investigate the Spearman correlation between C1QC and immune-modulator expression. Lastly, how C1QC affected the cell proliferation, migration, and invasion in vitro was assessed using knockdown strategies. Results: KIRC tissues had notably upregulated C1QC level in comparison with adjacent normal tissues, with showed a positive relevance to clinicopathological features including tumor stage, grade, and nodal metastasis, and a negative relevance to clinical prognosis in KIRC. C1QC knockdown inhibited KIRC cell proliferation, migration, and invasion, as indicated by the results of the in vitro experiment. Furthermore, functional and pathway enrichment analysis demonstrated that C1QC was involved in immune system-related biological processes. According to single-cell RNA analysis, C1QC exhibited a specific upregulation in macrophages cluster. Additionally, there was an obvious association of C1QC and a wide variety of tumor-infiltrating immune cells in KIRC. Also, high C1QC expression presented inconsistent prognosis in different enriched immune cells subgroups in KIRC. Immune factors might contribute to C1QC function in KIRC. Conclusion: C1QC is qualified to predict KIRC prognosis and immune infiltration biologically. Targeting C1QC may bring new hope for the treatment of KIRC.

Project description:The immune response within the tumor microenvironment plays a key role in tumorigenesis and determines the clinical outcomes of head and neck squamous cell carcinoma (HNSCC). However, to date, a paucity of robust, reliable immune-related biomarkers has been identified that are capable of estimating prognosis in HNSCC patients. High-throughput RNA sequencing was performed in tumors and matched adjacent tissues from five HNSCC patients, and the immune signatures expression of 730 immune-related transcripts selected from the nCounter PanCancer Immune Profiling Panel were assessed. Survival analyzes were performed in a training cohort, consisting of 416 HNSCC cases, retrieved from The Cancer Genome Atlas (TCGA) database. A prognostic signature was built, using elastic net-penalized Cox regression and backward, stepwise Cox regression analyzes. The outcomes were validated by an independent cohort of 115 HNSCC patients, using tissue microarrays and immunohistochemistry staining. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was also used to estimate the relative fractions of 22 immune-cell types and their correlations coefficients with prognostic biomarkers. Collectively, 248 immune-related genes were differentially expressed in paired tumors and normal tissues using RNA sequencing. After process screening in the training TCGA cohort, four immune-related genes (PVR, TNFRSF12A, IL21R, and SOCS1) were significantly associated with overall survival (OS). Integrating these genes with Path_N stage, a multiplex model was built and suggested better performance in determining 5 years OS (receiver operating characteristic (ROC) analysis, area under the curve (AUC)=0.709) than others. Further protein-based validation was conducted in 115 HNSCC patients. Similarly, high expression of PVR and TNFRSF12A were associated with poor OS (Kaplan-Meier p=0.017 and 0.0032), while high expression of IL21R and SOCS1 indicated favorable OS (Kaplan-Meier p<0.0001 and =0.0018). The integrated model with Path_N stage still demonstrated efficacy in OS evaluation (Kaplan-Meier p<0.0001, ROC AUC=0.893). Besides, the four prognostic genes were significantly correlated with activated CD8+ T cells, CD4+ T cells, follicular helper T cells and regulatory T cells, implying the possible involvement of these genes in the immunoregulation and development of HNSCC. The well-established model encompassing both immune-related biomarkers and clinicopathological factor might serve as a promising tool for the prognostic prediction of HNSCC.

Project description:BackgroundThe immune microenvironment is a critical regulator of clear cell renal cell carcinoma (ccRCC) progression. However, the underlying mechanisms the regulatory role of immune-related long non-coding RNAs (irlncRNAs) in the ccRCC tumor microenvironment (TME) are still obscure. Herein, we investigated prognostics role of irlncRNAs for ccRCC.MethodsThe raw data of patients with ccRCC were downloaded from The Cancer Genome Atlas (TCGA) database, and immune-related genes were obtained from the ImmPort database. First, we investigated the correlation between the immune-related genes and irlncRNAs. Then, we identified the differentially expressed irlncRNA pairs (ILRPs) between normal and cancer tissue samples, and prognostic model was constructed with the differentially expressed ILRPs. We further explored whether the signature risk scores of ILRPs had a considerable impact on immune cell infiltration. Finally, we performed a drug sensitivity analysis based on risk score.ResultsThere were 13 upregulated and 40 downregulated irlncRNAs between the ccRCC and normal tissue samples. We further selected the irlncRNAs that significantly affect the prognosis of patients with ccRCC via univariate Cox, lasso regression, and multivariate regression analyses. Twelve ILRPs were used to construct a prognostic signature. The model showed the ILRPs model could be used to assess the prognosis of ccRCC patients. Study of the influence of risk score and clinical characteristics on the prognosis of patients with ccRCC showed risk score to be an independent factor affecting the outcome of ccRCC. We further performed the difference analysis of immune cell abundance between ccRCC and normal tissue samples. The results showed that patients with higher abundance of M0 macrophages, plasma cells, follicular helper T cells, and regulatory T cells (Tregs) had a poor outcome. Finally, we performed a drug sensitivity analysis based on risk score. The results showed that high-risk score patients are sensitive to orafenib, sunitinib, temsirolimus, cisplatin, and gemcitabine.ConclusionsOur study has developed a novel and reasonable ILPRs model for prognostic prediction, which does not require transcriptional levels to be detected.

Dataset Information

Prognostic and predictive value of immune/stromal-related gene biomarkers in renal cell carcinoma.

Publications

Prognostic and predictive value of immune/stromal-related gene biomarkers in renal cell carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets