RESUMO
PURPOSE: The main function of cartilage oligomeric matrix protein (COMP) is to maintain the synthesis and stability of the extracellular matrix by interacting with collagen. At present, there are relatively few studies on the role of this protein in tumors. This study aimed to explore the relationship between COMP and pan-cancer, and analyzed its diagnostic and prognostic value. METHODS: The Cancer Genome Atlas database, the Genotype-Tissue Expression database and the Cancer Cell Line Encyclopedia database was used for gene expression analysis. The receiver operating characteristic curve was used to assess the diagnostic value of COMP in pan-cancer. Kaplan-Meier plots were used to assess the relationship between COMP expression and prognosis of cancers. R software v4.1.1 was used for statistical analysis, and the ggplot2 package was used for visualization. RESULTS: COMP was significantly overexpressed in 15 human cancers and showed significantly difference in 12 molecular subtypes and 16 immune subtypes. In addition, the expression of COMP is associated with tumor immune evasion. The ROC curve showed that the expression of COMP could predict the occurrence of 16 kinds of tumors with relative accuracy, including adrenocortical carcinoma (ACC) (AUC = 0.737), breast invasive carcinoma (BRCA) (AUC = 0.896), colon adenocarcinoma (COAD) (AUC = 0.760), colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma (COADREAD) (AUC = 0.775), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) (AUC = 0.875), kidney renal papillary cell carcinoma (KIRP) (AUC = 0.773), kidney chromophobe (KICH) (AUC = 0.809), ovarian serous cystadenocarcinoma (OV) (AUC = 0.906), prostate adenocarcinoma (PRAD) (AUC = 0.721), pancreatic adenocarcinoma (PAAD) (AUC = 0.944), rectum adenocarcinoma (READ) (AUC = 0.792), skin cutaneous melanoma (SKCM) (AUC = 0.746), stomach adenocarcinoma (STAD) (AUC = 0.711), testicular germ cell tumors (TGCT) (AUC = 0.823), thymoma (THYM) (AUC = 0.777) and uterine carcinosarcoma (UCS) (AUC = 0.769). Furthermore, COMP expression was correlated with overall survival (OS), disease-specific survival (DSS) and progression-free interval (PFI) in ACC (OS, HR = 4.95, DSS, HR = 5.55, PFI, HR = 2.79), BLCA (OS, HR = 1.59, DSS, HR = 1.72, PFI, HR = 1.36), KIRC (OS, HR = 1.36, DSS, HR = 1.94, PFI, HR = 1.57) and COADREAD (OS, HR = 1.46, DSS, HR = 1.98, PFI, HR = 1.43). We selected previously unreported bladder urothelial carcinoma (BLCA) for further study and found that COMP could be an independent risk factor for OS, DSS and PFI. Moreover, we found differentially expressed genes of COMP in BLCA and obtained top 10 hub genes, including LGR4, LGR5, RSPO2, RSPO1, RSPO3, RNF43, ZNRF3, FYN, LYN and SYK. Finally, we verified the function of COMP at the cellular level by using J82 and T24 cells and found that knockdown of COMP could significantly inhibit migration and invasion. This finding supports that COMP could be a potential biomarker for pan-cancer diagnosis and prognosis encompassing tumor microenvironment, disease stage and prognosis. CONCLUSION: This finding supports that COMP could be a potential biomarker for pan-cancer diagnosis and prognosis encompassing tumor microenvironment, disease stage and prognosis.
Assuntos
Adenocarcinoma , Carcinoma de Células Renais , Carcinoma de Células de Transição , Neoplasias do Colo , Neoplasias Renais , Melanoma , Neoplasias Pancreáticas , Neoplasias Retais , Neoplasias Cutâneas , Neoplasias da Bexiga Urinária , Masculino , Humanos , Proteína de Matriz Oligomérica de Cartilagem/genética , Biomarcadores , Prognóstico , Melanoma Maligno CutâneoRESUMO
The availability of large-scale repositories and integrated cancer genome efforts have created unprecedented opportunities to study and describe cancer biology. In this sense, the aim of translational researchers is the integration of multiple omics data to achieve a better identification of homogeneous subgroups of patients in order to develop adequate diagnostic and treatment strategies from the personalized medicine perspective. So far, existing integrative methods have grouped together omics data information, leaving out individual omics data phenotypic interpretation. Here, we present the Massive and Integrative Gene Set Analysis (MIGSA) R package. This tool can analyze several high throughput experiments in a comprehensive way through a functional analysis strategy, relating a phenotype to its biological function counterpart defined by means of gene sets. By simultaneously querying different multiple omics data from the same or different groups of patients, common and specific functional patterns for each studied phenotype can be obtained. The usefulness of MIGSA was demonstrated by applying the package to functionally characterize the intrinsic breast cancer PAM50 subtypes. For each subtype, specific functional transcriptomic profiles and gene sets enriched by transcriptomic and proteomic data were identified. To achieve this, transcriptomic and proteomic data from 28 datasets were analyzed using MIGSA. As a result, enriched gene sets and important genes were consistently found as related to a specific subtype across experiments or data types and thus can be used as molecular signature biomarkers.