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Integrated Bioinformatic Analysis of the Expression and Prognosis of Caveolae-Related Genes in Human Breast Cancer.

ABSTRACT: Caveolae-related genes, including CAVs that encodes caveolins and CAVINs that encodes caveolae-associated proteins cavins, have been identified for playing significant roles in a variety of biological processes including cholesterol transport and signal transduction, but evidences related to tumorigenesis and cancer progression are not abundant to correlate with clinical characteristics and prognosis of patients with cancer. In this study, we investigated the expression of these genes at transcriptional and translational levels in patients with breast cancer using Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), cBioPortal databases, and immunohistochemistry of the patients in our hospital. Prognosis of patients with breast cancer based on the expressions of CAVs and CAVINs was summarized using Kaplan-Meier Plotter with their correlation to different subtyping. The relevant molecular pathways of these genes were further analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database and Gene Set Enrichment Analysis (GSEA). Results elucidated that expression levels of CAV1, CAV2, CAVIN1, CAVIN2, and CAVIN3 were significantly lower in breast cancer tissues than in normal samples, while the expression level of CAVIN2 was correlated with advanced tumor stage. Furthermore, investigations on survival of patients with breast cancer indicated outstanding associations between prognosis and CAVIN2 levels, especially for the patients with estrogen receptor positive (ER+) breast cancer. In conclusion, our investigation indicated CAVIN2 is a potential therapeutic target for patients with ER+ breast cancer, which may relate to functions of cancer cell surface receptors and adhesion molecules.

SUBMITTER: Tian Y

PROVIDER: S-EPMC8427033 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Colorectal cancer (CRC) is one gastrointestinal malignancy, accounting for 10% of cancer diagnoses and cancer-related deaths worldwide each year. Therefore, it is urgent to identify genes involved in CRC predicting the prognosis. Methods: CRC's data were acquired from the Gene Expression Omnibus (GEO) database (GSE39582 and GSE41258 datasets) and The Cancer Genome Atlas (TCGA) database. The differentially expressed necroptosis-related genes (DENRGs) were sorted out between tumor and normal tissues. Univariate Cox regression analysis and least absolute shrinkage and selectionator operator (LASSO) analysis were applied to selected DENRGs concerning patients' overall survival and to construct a prognostic biomarker. The effectiveness of this biomarker was assessed by the Kaplan-Meier curve and the receiver operating characteristic (ROC) analysis. The GSE39582 dataset was utilized as external validation for the prognostic signature. Moreover, using univariate and multivariate Cox regression analyses, independent prognostic factors were identified to construct a prognostic nomogram. Next, signaling pathways regulated by the signature were explored through the gene set enrichment analysis (GSEA). The single sample gene set enrichment analysis (ssGSEA) algorithm and tumor immune dysfunction and exclusion (TIDE) were used to explore immune correlation in the two groups, high-risk and low-risk ones. Finally, prognostic genes' expression was examined in the GSE41258 dataset. Results: In total, 27 DENRGs were filtered, and a necroptosis-related prognostic signature based on 6 DENRGs was constructed, which may better understand the overall survival (OS) of CRC. The Kaplan-Meier curve manifested the effectiveness of the prognostic signature, and the ROC curve showed the same result. In addition, univariate and multivariate Cox regression analyses revealed that age, pathology T, and risk score were independent prognostic factors, and a nomogram was established. Furthermore, the prognostic signature was most significantly associated with the apoptosis pathway. Meanwhile, 24 immune cells represented significant differences between two groups, like the activated B cell. Furthermore, 32 immune checkpoints, TIDE scores, PD-L1 scores, and T-cell exclusion scores were significantly different between the two groups. Finally, a 6-gene prognostic signature represented different expression levels between tumor and normal samples significantly in the GSE41258 dataset. Conclusion: Our study established a signature including 6 genes and a prognostic nomogram that could significantly assess the prognosis of patients with CRC.

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Integrated Bioinformatic Analysis of the Expression and Prognosis of Caveolae-Related Genes in Human Breast Cancer.

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