Dataset Information

Expression of GIV/Girdin, a metastasis-related protein, predicts patient survival in colon cancer.

ABSTRACT: Metastasis accounts for the majority of cancer-related deaths. Accurate prediction of metastatic potential of tumors has been elusive, and the search for clinically useful markers continues. We previously reported that GIV/Girdin triggers tumor cell migration by virtue of a C-terminal guanine-nucleotide exchange factor motif that activates G?i. Here we identify GIV as a metastasis-related protein whose full-length transcript (GIV-fl) is expressed exclusively in highly invasive colon, breast, and pancreatic carcinoma cells and not in their poorly invasive counterparts. A prospective, exploratory biomarker study conducted on a cohort of 56 patients with stage II colorectal cancer revealed a significant correlation between GIV-fl expression in tumor epithelium and shortened metastasis-free survival. Survival rate for patients with GIV-fl-positive tumors is significantly reduced compared with the patients with GIV-fl-negative tumors [P<0.0001; hazard ratio=0.076; CI=0.052-0.30 (95%)]. At the 5-yr mark, survival is 100% in the GIV-fl-negative group and 62 ± 9% (mean±SE; P=6×10(-5)) in the GIV-fl-positive group. Furthermore, GIV-fl expression predicts a risk of mortality independent of the microsatellite stability status, a well-established prognosticator of colorectal cancers. We conclude that GIV-fl is a novel metastasis-related protein and an independent adverse prognosticator that may serve as a useful adjunct to traditional staging strategies in colorectal carcinoma.

SUBMITTER: Garcia-Marcos M

PROVIDER: S-EPMC3023389 | biostudies-other | 2011 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Expression of GIV/Girdin, a metastasis-related protein, predicts patient survival in colon cancer.

Garcia-Marcos Mikel M Jung Barbara H BH Ear Jason J Cabrera Betty B Carethers John M JM Ghosh Pradipta P

FASEB journal : official publication of the Federation of American Societies for Experimental Biology 20101025 2

Metastasis accounts for the majority of cancer-related deaths. Accurate prediction of metastatic potential of tumors has been elusive, and the search for clinically useful markers continues. We previously reported that GIV/Girdin triggers tumor cell migration by virtue of a C-terminal guanine-nucleotide exchange factor motif that activates Gαi. Here we identify GIV as a metastasis-related protein whose full-length transcript (GIV-fl) is expressed exclusively in highly invasive colon, breast, and ...[more]

PMID: 20974669

Similar Datasets

Project description:The degree of histologic cellular differentiation of a cancer has been associated with prognosis but is subjectively assessed. We hypothesized that information about tumor differentiation of individual cancers could be derived objectively from cancer gene expression data, and would allow creation of a cancer phylogenetic framework that would correlate with clinical, histologic and molecular characteristics of the cancers, as well as predict prognosis. Here we utilized mRNA expression data from 4,413 patient samples with 7 diverse cancer histologies to explore the utility of ordering samples by their distance in gene expression from that of stem cells. A differentiation baseline was obtained by including expression data of human embryonic stem cells (hESC) and human mesenchymal stem cells (hMSC) for solid tumors, and of hESC and CD34+ cells for liquid tumors. We found that the correlation distance (the degree of similarity) between the gene expression profile of a tumor sample and that of stem cells orients cancers in a clinically coherent fashion. For all histologies analyzed (including carcinomas, sarcomas, and hematologic malignancies), patients with cancers with gene expression patterns most similar to that of stem cells had poorer overall survival. We also found that the genes in all undifferentiated cancers of diverse histologies that were most differentially expressed were associated with up-regulation of specific oncogenes and down-regulation of specific tumor suppressor genes. Thus, a stem cell-oriented phylogeny of cancers allows for the derivation of a novel cancer gene expression signature found in all undifferentiated forms of diverse cancer histologies, that is competitive in predicting overall survival in cancer patients compared to previously published prediction models, and is coherent in that gene expression was associated with up-regulation of specific oncogenes and down-regulation of specific tumor suppressor genes associated with regulation of the multicellular state.

Project description:Colon adenocarcinoma (COAD) is a serious public health problem, the third most common cancer and the second most deadly cancer in the world. About 9.4% of cancer-related deaths in 2020 were due to COAD. Anoikis is a specialized form of programmed cell death that plays an important role in tumor invasion and metastasis. The presence of anti-anoikis factors is associated with tumor aggressiveness and drug resistance. Various bioinformatic methods, such as differential expression analysis, and functional annotation analysis, machine learning, were used in this study. RNA-sequencing and clinical data from COAD patients were obtained from the Gene expression omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Construction of a prognostic nomogram for predicting overall survival (OS) using multivariate analysis and Lasso-Cox regression. Immunohistochemistry (IHC) was our method of validating the expression of seven genes that are linked to anoikis in COAD. We identified seven anoikis-related genes as predictors of COAD survival and prognosis, and confirmed their accuracy in predicting colon adenocarcinoma prognosis by KM survival curves and ROC curves. A seven-gene risk score consisting of NAT1, CDC25C, ATP2A3, MMP3, EEF1A2, PBK, and TIMP1 showed strong prognostic value. Meanwhile, we made a nomogram to predict the survival rate of COAD patients. The immune infiltration assay showed T cells. CD4 memory. Rest and macrophages. M0 has a higher proportion in COAD, and 11 genes related to tumor immunity are important. GDSC2-based drug susceptibility analysis showed that 6 out of 198 drugs were significant in COAD. Anoikis-related genes have potential value in predicting the prognosis of COAD and provide clues for developing new therapeutic strategies for COAD. Immune infiltration and drug susceptibility results provide important clues for finding new personalized treatment options for COAD. These findings also suggest possible mechanisms that may affect prognosis. These results are the starting point for planning individualized treatment and managing patient outcomes.

Dataset Information

Expression of GIV/Girdin, a metastasis-related protein, predicts patient survival in colon cancer.

Publications

Expression of GIV/Girdin, a metastasis-related protein, predicts patient survival in colon cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets