BackgroundColorectal cancer (CRC) is still one of the most frequently diagnosed malignancy around the world. The complex etiology and high heterogeneity of CRC necessitates the identification of new reliable signature to identify different tumor prognosis, which may help more precise understanding of the molecular properties of CRC and identify the appropriate treatment for CRC patients. In this study, we aimed to identify a combined immune and metabolism gene signature for prognosis prediction of CRC from large volume of CRC transcriptional data.MethodsGene expression profiling and clinical data of HCC samples was retrieved from the from public datasets. IRGs and MRGs were identified from differential expression analysis. Univariate and multivariate Cox regression analysis were applied to establish the prognostic metabolism-immune status-related signature. Kaplan-Meier survival and receiver operating characteristic (ROC) curves were generated for diagnostic efficacy estimation. Real-time polymerase chain reaction (RT-PCR), Western blot and immunohistochemistry (IHC) was conducted to verified the expression of key genes in CRC cells and tissues.ResultsA gene signature comprising four genes (including two IRGs and two MRGs) were identified and verified, with superior predictive performance in discriminating the overall survival (OS) of high-risk and low-risk compared to existing signatures. A prognostic nomogram based on the four-gene signature exhibited a best predictive performance, which enabled the prognosis prediction of CRC patients. The hub gene ESM1 related to CRC were selected via the machine learning and prognostic analysis. RT-PCR, Western blot and IHC indicated that ESM1 was high expressed in tumor than normal with superior predictive performance of CRC survival.ConclusionsA novel combined MRGs and IRGs-related prognostic signature that could stratify CRC patients into low-and high- risk groups of unfavorable outcomes for survival, was identified and verified. This might help, to some extent, to individualized treatment and prognosis assessment of CRC patients. Similarly, the mining of key genes provides a new perspective to explore the molecular mechanisms and targeted therapies of CRC.
BackgroundHypoxia and angiogenesis, as prominent characteristics of malignant tumors, are implicated in the progression of hepatocellular carcinoma (HCC). However, the role of hypoxia in the angiogenesis of liver cancer is unclear. Therefore, we explored the regulatory mechanisms of hypoxia-related angiogenic genes (HRAGs) and the relationship between these genes and the prognosis of HCC.MethodsThe transcriptomic and clinical data of HCC samples were downloaded from public datasets, followed by identification of hypoxia- and angiogenesis-related genes in the database. A gene signature model was constructed based on univariate and multivariate Cox regression analyses, and validated in independent cohorts. Kaplan-Meier survival and time-dependent receiver operating characteristic (ROC) curves were generated to evaluate the model’s predictive capability. Gene set enrichment analysis (GSEA) was performed to explore signaling pathways regulated by the gene signature. Furthermore, the relationships among gene signature, immune status, and response to anti-angiogenesis agents and immune checkpoint blockade (ICB) were analyzed.ResultsThe prognostic model was based on three HRAGs (ANGPT2, SERPINE1 and SPP1). The model accurately predicted that low-risk patients would have longer overall survival than high-risk patients, consistent with findings in other cohorts. GSEA indicated that high-risk group membership was significantly associated with hypoxia, angiogenesis, the epithelial-mesenchymal transition, and activity in immune-related pathways. The high-risk group also had more immunosuppressive cells and higher expression of immune checkpoints such as PD-1 and PD-L1. Conversely, the low-risk group had a better response to anti-angiogenesis and ICB therapy.ConclusionsThe gene signature based on HRAGs was predictive of prognosis and provided an immunological perspective that will facilitate the development of personalized therapies.
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