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A Hypoxia Signature for Predicting Prognosis and Tumor Immune Microenvironment in Adrenocortical Carcinoma.

ABSTRACT: Adrenocortical carcinoma (ACC) is a rare malignancy with dismal prognosis. Hypoxia is one of characteristics of cancer leading to tumor progression. For ACC, however, no reliable prognostic signature on the basis of hypoxia genes has been built. Our study aimed to develop a hypoxia-associated gene signature in ACC. Data of ACC patients were obtained from TCGA and GEO databases. The genes included in hypoxia risk signature were identified using the Cox regression analysis as well as LASSO regression analysis. GSEA was applied to discover the enriched gene sets. To detect a possible connection between the gene signature and immune cells, the CIBERSORT technique was applied. In ACC, the hypoxia signature including three genes (CCNA2, COL5A1, and EFNA3) was built to predict prognosis and reflect the immune microenvironment. Patients with high-risk scores tended to have a poor prognosis. According to the multivariate regression analysis, the hypoxia signature could be served as an independent indicator in ACC patients. GSEA demonstrated that gene sets linked to cancer proliferation and cell cycle were differentially enriched in high-risk classes. Additionally, we found that PDL1 and CTLA4 expression were significantly lower in the high-risk group than in the low-risk group, and resting NK cells displayed a significant increase in the high-risk group. In summary, the hypoxia risk signature created in our study might predict prognosis and evaluate the tumor immune microenvironment for ACC.

SUBMITTER: Chen X

PROVIDER: S-EPMC8481041 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Hypoxia plays an indispensable role in the development of hepatocellular carcinoma (HCC). However, there are few studies on the application of hypoxia molecules in the prognosis predicting of HCC. We aim to identify the hypoxia-related genes in HCC and construct reliable models for diagnosis, prognosis and recurrence of HCC patients as well as exploring the potential mechanism. METHODS:Differentially expressed genes (DEGs) analysis was performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and four clusters were determined by a consistent clustering analysis. Three DEGs closely related to overall survival (OS) were identified using Cox regression and LASSO analysis. Then the hypoxia-related signature was developed and validated in TCGA and International Cancer Genome Consortium (ICGC) database. The Gene Set Enrichment Analysis (GSEA) was performed to explore signaling pathways regulated by the signature. CIBERSORT was used for estimating the fractions of immune cell types. RESULTS:A total of 397 hypoxia-related DEGs in HCC were detected and three genes (PDSS1, CDCA8 and SLC7A11) among them were selected to construct a prognosis, recurrence and diagnosis model. Then patients were divided into high- and low-risk groups. Our hypoxia-related signature was significantly associated with worse prognosis and higher recurrence rate. The diagnostic model also accurately distinguished HCC from normal samples and nodules. Furthermore, the hypoxia-related signature could positively regulate immune response. Meanwhile, the high-risk group had higher fractions of macrophages, B memory cells and follicle-helper T cells, and exhibited higher expression of immunocheckpoints such as PD1and PDL1. CONCLUSIONS:Altogether, our study showed that hypoxia-related signature is a potential biomarker for diagnosis, prognosis and recurrence of HCC, and it provided an immunological perspective for developing personalized therapies.

Project description:Hypoxia, a typical hallmark of numerous tumors, indicates poor infiltration of antitumor lymphocytes, as well as facilitates the development, progression, and drug resistance of malignant cells. Here, the present research was performed to identify novel hypoxia-related molecular markers and their correlation to the tumor immune microenvironment (TIME) in colon cancer. The expression of hypoxia-related gene signature was extracted from The Cancer Genome Atlas (TCGA) COAD cohort. Based on this signature, a risk score model was constructed using the Lasso regression model. Its discrimination ability and stability were validated in another independent cohort (GSE17536) from Gene Expression Omnibus (GEO) database. Moreover, molecular biology experiments (quantitative real-time PCR and multiple immunohistochemistry) were performed to validate the results of bioinformatics analyses. Three hub genes, including PPFIA4, SERPINE1, and STC2, were chosen to build the risk score model. All of these genes were increasingly expressed in the hypoxia subgroup (HS). Compared with the normoxia subgroup (NS), HS had worse pathological features (T, N, M, and stage) and overall survival (OS), more expression of immune checkpoint molecules, poorer infiltration of some pro-inflammation immune cells (CD4+ T cells and CD8+ T cells), and enriched infiltration of M0/M2 macrophages. After the risk model was proven to be valuable and stable, a nomogram was built based on this model and some clinicopathological factors. Moreover, it had been identified that three hub genes were all increasingly expressed in hypoxic conditions by quantitative real-time PCR (qPCR). The results of multiple immunohistochemistry (mIHC) also showed that higher expression of hub genes was associated with poorer infiltration of pro-inflammation immune cells (CD8+ T cells and M1 macrophages) and richer infiltration of anti-inflammation immune cells (Treg cells and M2 macrophages). In conclusion, the present study uncovered the relations among hypoxia, TIME, and clinicopathological features of colon cancer. It might provide new insight and a potential therapeutic target for immunotherapy.

Project description:Background: Renal cell carcinoma (RCC) is a common malignant tumor worldwide, and immune checkpoint inhibitors are a new therapeutic option for metastatic RCC. Infiltrating immune cells in the tumor microenvironment (TME) play a critical part in RCC biology, which is important for tumor therapy and prediction. Hypoxia is a common condition that occurs in the TME and may lead to RCC immunosuppression and immune escape. This study was conducted to analyze the extent of the hypoxia immune microenvironment in the TME of RCC and develop a hypoxia-related risk model for predicting the prognosis of patients with RCC. Methods: The gene expression profiles of 526 patients with RCC were downloaded from The Cancer Genome Atlas database. Combined with the hallmark-hypoxia gene dataset downloaded from Gene Set Enrichment Analysis, prognosis-related hypoxia genes were selected by survival analysis. A protein-protein interaction network and functional enrichment analysis were performed. A hypoxia-related risk model predicting the prognosis of patients with RCC was established using the least absolute shrinkage and selection operator. Data of 91 cases downloaded from the International Cancer Genome Consortium (ICGC) database were used for validation. CIBERSORT was applied to analyze the fractions of 22 immune cell types in the TME of RCC between low- and high-risk groups. The expression profiles of immunomodulators and immunosuppressive cytokines were also analyzed. Results: Ninety-three genes were significantly associated with poor overall survival of patients with RCC and were mainly involved in 10 pathways. Using the established hypoxia-related risk model, the receiver operating characteristic curves showed an accuracy of 76.1% (95% CI: 0.719-0.804), and Cox proportional hazards regression analysis revealed that the model was an independent predictor of the prognosis of patients with RCC [hazard ratio (HR) = 2.884; 95% CI: 2.090-3.979] (p < 0.001). Using the ICGC database, we verified that the low-risk score group had a better overall survival outcome than the high-risk group. Additionally, dividing the hypoxia risk score into high-risk and low-risk groups could predict the immune microenvironment of RCC. Conclusions: We demonstrated that a hypoxia-related risk model can be used to predict the outcomes of patients with RCC and reflect the immune microenvironment of RCC, which may help improve the overall clinical response to immune checkpoint inhibitors.

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A Hypoxia Signature for Predicting Prognosis and Tumor Immune Microenvironment in Adrenocortical Carcinoma.

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