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Establishment and validation of an autophagy-related prognostic signature for survival predicting in cutaneous melanoma.

ABSTRACT: Existing staging system for prognosis evaluating for Skin Cutaneous Melanoma (SKCM) patients had defects of subjective, inaccuracy and inconsistently, therefore, to identify specific and applicable prognostic markers and promote personalized therapeutic interventions is urgently required. This study aims to build a robust autophagy-related genes (ARGs) signature for prognosis monitoring of SKCM patients. We determined 26 ARGs as differentially expressed autophagy-related genes (DEARGs) from 103 SKCM and 23 normal skin samples in GSE15605 and GSE3189 datasets. Optimal prognostic DEARGs composed the risk model were screened and verified in 458 SKCM patients in TCGA cohort as the training cohort and 209 patients in GSE65904 as the test cohort. Finally, 4 optimal independent prognostic DEARGs (CAPNS1, DAPK2, PARP1 and PTK6) were filtered out in the training cohort to establish the risk model. A prognostic nomogram was established for quantitative survival prediction. The risk model grouped high-risk SKCM cancer patients exhibited significantly shorter survival times in both training and test cohorts. The area under the ROC curve for risk score model was 0.788 and 0.627 in the training and test cohorts indicated the risk model was relatively accurate for prognosis monitoring. Clinical correlation analysis exhibited that risk score was an independent predictor for prognosis significantly associated with T/N classification. The prognostic value of the 4 risk genes formed the risk model was also validated respectively. We identified a novel autophagy-related signature for prognosis monitoring. It has the potential to be an independent prognostic indicator and can benefit targeted therapy.

SUBMITTER: Fei H

PROVIDER: S-EPMC8727816 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe tumor microenvironment (TME) plays a critical role in tumorigenesis, development, and therapeutic efficacy. Major advances have been achieved in the treatment of various cancers through immunotherapy. Nevertheless, only a minority of patients have positive responses to immunotherapy, which is partly due to conditions of the immunosuppressive microenvironment. Therefore, it is essential to identify prognostic biomarkers that reflect heterogeneous landscapes of the TME.Methods and materialsBased upon the ESTIMATE algorithm, we evaluated the infiltrating levels of immune and stromal components derived from patients afflicted by various types of cancer from The Cancer Genome Atlas database (TCGA). According to respective patient immune and stromal scores, we categorized cases into high- and low-scoring subgroups for each cancer type to explore associations between TME and patient prognosis. Gene Set Enrichment Analyses (GSEA) were conducted and genes enriched in IFNγ response signaling pathway were selected to facilitate establishment of a risk model for predicting overall survival (OS). Furthermore, we investigated the associations between the prognostic signature and tumor immune infiltration landscape by using CIBERSORT algorithm and TIMER database.ResultsAmong the cancers assessed, the immune scores for skin cutaneous melanoma (SKCM) were the most significantly correlated with patients' survival time (P < .0001). We identified and validated a five-IFNγ response-related gene signature (UBE2L6, PARP14, IFIH1, IRF2, and GBP4), which was closely correlated with the prognosis for SKCM afflicted patients. Multivariate Cox regression analysis indicated that this risk model was an independent prognostic factor for SKCM. Tumor-infiltrating lymphocytes and specific immune checkpoint molecules had notably differential levels of expression in high- compared to low-risk samples.ConclusionIn this study, we established a novel five-IFNγ response-related gene signature that provided a better and increasingly comprehensive understanding of tumor immune landscape, and which demonstrated good performance in predicting outcomes for patients afflicted by SKCM.

Project description:Background: Clear cell renal cell carcinoma (ccRCC) is a common type of malignant tumors in urinary system. Evaluating the prognostic outcome at the time of initial diagnosis is essential for patients. Autophagy is known to play a significant role in tumors. Here, we attempted to construct an autophagy-related prognostic risk signature based on the expression profile of autophagy-related genes (ARGs) for predicting the long-term outcome and effect of precise treatments for ccRCC patients. Methods: We obtained the expression profile of ccRCC from the cancer genome atlas (TCGA) database and extract the portion of ARGs. We conducted differentially expressed analysis on ARGs and then performed enrichment analyses to confirm the anomalous autophagy-related biological functions. Then, we performed univariate Cox regression to screen out overall survival (OS)-related ARGs. With these genes, we established an autophagy-related risk signature by least absolute shrinkage and selection operator (LASSO) Cox regression. We validated the reliability of the risk signature with receiver operating characteristic (ROC) analysis, survival analysis, clinic correlation analysis, and Cox regression. Then we analyzed the function of each gene in the signature by single-gene gene set enrichment analysis (GSEA). Finally, we analyzed the correlation between our risk score and expression level of several targets of immunotherapy and targeted therapy. Results: We established a seven-gene prognostic risk signature, according to which we could divide patients into high or low risk groups and predict their outcomes. ROC analysis and survival analysis validated the reliability of the signature. Clinic correlation analysis found that the risk group is significantly correlated with severity of ccRCC. Multivariate Cox regression revealed that the risk score could act as an independent predictor for the prognosis of ccRCC patients. Correlation analysis between risk score and targets of precise treatments showed that our risk signature could predict the effects of precise treatment powerfully. Conclusion: Our study provided a brand new autophagy-related seven-gene prognostic risk signature, which could perform as a prognostic indicator for ccRCC. Meanwhile, our study provides a novel sight to understand the role of autophagy and suggest therapeutic strategies in the category of precise treatment in ccRCC.

Project description:BackgroundAutophagy, a highly conserved self-digesting process, has been deeply involved in the development and progression of oral squamous cell carcinoma (OSCC). However, the prognostic value of autophagy-related genes (ARGs) for OSCC still remains unclear. Our study set out to develop a multigene expression signature based on ARGs for individualized prognosis assessment in OSCC patients.MethodsBased on The Cancer Genome Atlas (TCGA) database, we identified prognosis-related ARGs through univariate COX regression analysis. Then we performed the least absolute shrinkage and selection operator (LASSO) regression analysis to identify an optimal autophagy-related multigene signature with the subsequent validation in testing set, GSE41613 and GSE42743 datasets.ResultsWe identified 36 prognosis-related ARGs for OSCC. Subsequently, the multigene signature based on 13 prognostic ARGs was constructed and successfully divided OSCC patients into low and high-risk groups with significantly different overall survival in TCGA training set (p < 0.0001). The autophagy signature remained as an independent prognostic factor for OSCC in univariate and multivariate Cox regression analyses. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curves for 1, 3, and 5-year survival were 0.758, 0.810, 0.798, respectively. Then the gene signature was validated in TCGA testing set, GSE41613 and GSE42743 datasets. Moreover, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and single-sample gene set enrichment analysis (ssGSEA) revealed the underlying biological characteristics and signaling pathways associated with this signature in OSCC. Finally, we constructed a nomogram by combining the gene signature with multiple clinical parameters (age, gender, TNM-stage, tobacco, and alcohol history). The concordance index (C-index) and calibration plots demonstrated favorable predictive performance of our nomogram.ConclusionIn summary, we identified and verified a 13-ARGs prognostic signature and nomogram, which provide individualized prognosis evaluation and show insight for potential therapeutic targets for OSCC.

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Establishment and validation of an autophagy-related prognostic signature for survival predicting in cutaneous melanoma.

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