Dataset Information

Prognosis Prediction of Colorectal Cancer Using Gene Expression Profiles.

ABSTRACT: Background: Investigation on prognostic markers for colorectal cancer (CRC) deserves efforts, but data from China are scarce. This study aimed to build a prognostic algorithm using differentially expressed gene (DEG) profiles and to compare it with the TNM staging system in their predictive accuracy for CRC prognosis in Chinese patients. Methods: DEGs in six paired tumor and corresponding normal tissues were determined using RNA-Sequencing. Subsequently, matched tumor and normal tissues from 127 Chinese patients were assayed for further validation. Univariate and multivariate Cox regressions were used to identify informative DEGs. A predictive index (PI) was derived as a linear combination of the products of the DEGs and their Cox regression coefficients. The combined predictive accuracy of the DEGs-based PI and tumors' TNM stages was also examined by a logistic regression model including the two predictors. The predictive performance was evaluated with the area under the receiver operating characteristics (AUCs). Results: Out of 75 candidate DEGs, we identified 10 DEGs showing statistically significant associations with CRC survival. A PI based on these 10 DEGs (PI-10) predicted CRC survival probability more accurately than the TNM staging system [AUCs for 3-year survival probability 0.73 (95% confidence interval: 0.64, 0.81) vs. 0.68 (0.59, 0.76)] but comparable to a simplified PI (PI-5) using five DEGs (LOC646627, BEST4, KLF9, ATP6V1A, and DNMT3B). The predictive accuracy was improved further by combining PI-5 and the TNM staging system [AUC for 3-year survival probability: 0.72 (0.63, 0.80)]. Conclusion: Prognosis prediction based on informative DEGs might yield a higher predictive accuracy in CRC prognosis than the TNM staging system does.

SUBMITTER: Pan F

PROVIDER: S-EPMC6465763 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Prognosis Prediction of Colorectal Cancer Using Gene Expression Profiles.

Pan Feixia F Chen Tianhui T Sun Xiaohui X Li Kuanrong K Jiang Xiyi X Försti Asta A Zhu Yimin Y Lai Maode M

Frontiers in oncology 20190409

<b>Background:</b> Investigation on prognostic markers for colorectal cancer (CRC) deserves efforts, but data from China are scarce. This study aimed to build a prognostic algorithm using differentially expressed gene (DEG) profiles and to compare it with the TNM staging system in their predictive accuracy for CRC prognosis in Chinese patients. <b>Methods:</b> DEGs in six paired tumor and corresponding normal tissues were determined using RNA-Sequencing. Subsequently, matched tumor and normal ti ...[more]

PMID: 31024853

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Project description:BackgroundColorectal cancer (CRC) is the third-most prevalent cancer globally. The biological significance of telomeres in CRC carcinogenesis and progression is underscored by accumulating data. Nevertheless, not much is known about how telomere-related genes (TRGs) affect CRC prognosis. Therefore, the aim of this study was to investigate the role of TRGs in CRC prognosis.MethodsWe retrospectively obtained the expression profiles and clinical data of CRC patients from public databases. Utilizing least absolute shrinkage and selection operator (LASSO) regression analysis, we created a telomere-related risk model to predict survival outcomes, identifying ten telomere-related differentially expressed genes (TRDEGs). Based on TRDEGs, we stratified patients from The Cancer Genome Atlas (TCGA) into low- and high-risk subsets. Subsequently, we conducted comprehensive analyses, including survival assessment, immune cell infiltration, drug sensitivity, and prediction of molecular interactions using Kaplan-Meier curves, ESTIMATE, CIBERSORT, OncoPredict, and other approaches.ResultsThe model showed exceptional predictive accuracy for survival. Significant differences in survival were observed between the two groups of participants grouped according to the model (P<0.001), and this difference was further confirmed in the external validation set (GSE39582) (P=0.004). Additionally, compared to the low-risk group, the high-risk group exhibited significantly advanced tumor node metastasis (TNM) stages, lower proportions of activated CD4+ T cells, effector memory CD4+ T cells, and memory B cells, but increased ratios of M2 macrophages and regulatory T cells (Tregs), elevated tumor immune dysfunction and exclusion (TIDE) scores, and diminished sensitivity to dabrafenib, lapatinib, camptothecin, docetaxel, and telomerase inhibitor IX, reflecting the signature's capacity to distinguish clinical pathological characteristics, immune environment, and drug efficacy. Finally, we validated the expression of the ten TRDEGs (ACACB, TPX2, SRPX, PPARGC1A, CD36, MMP3, NAT2, MMP10, HIGD1A, and MMP1) through quantitative real-time polymerase chain reaction (qRT-PCR) and found that compared to normal cells, the expression levels of ACACB, HIGD1A, NAT2, PPARGC1A, and TPX2 in CRC cells were elevated, whereas those of CD36, SRPX, MMP1, MMP3, and MMP10 were reduced.ConclusionsOverall, we constructed a telomere-related biomarker capable of predicting prognosis and treatment response in CRC individuals, offering potential guidance for drug therapy selection and prognosis prediction.

Project description:BACKGROUND:Broad copy number aberrations (BCNAs) represent a common form of genome instability in colorectal cancer (CRC). CRCs show large variations in their level of aneuploidy: microsatellite-instable (MSI) tumors are known to have a near-diploid karyotype while microsatellite-stable (MSS) tumors show high level of chromosomal instability. However, MSS tumors have great heterogeneity in the number of BCNAs, with a minor percentage of samples showing an almost normal karyotype. In the present work we subdivided MSS CRCs according to a "BCNA score" and characterized their transcriptome profiles, considered as a proxy to their phenotypic features. METHODS:Microsatellite testing, genome-wide DNA copy number and whole-transcript expression analysis (HTA) were performed on 33 tumor samples and 25 normal colonic tissue samples from 32 CRC patients. 15.1% of the samples were MSI tumors (n?=?5), whereas 84.9% were MSS tumors (n?=?28). Gene expression data of 34 additional MSI tumors was retrieved from a public functional genomics data repository. RESULTS:Using as a threshold the first quartile of the BCNA score distribution, MSS samples were classified as low-BCNA (LB, n?=?7) or high-BCNA (HB, n?=?21). LB tumors were enriched for mucinous CRCs and their gene-expression profile resembled that of MSI samples for what concerns a subset of genes involved in secretory processes, mucosal protection, and extracellular matrix remodeling. HB tumors were predominantly non-mucinous adenocarcinomas and showed overexpression of a subset of genes typical of surface colonocytes and EGF signaling. A large percentage of unclassified samples according to the consensus molecular subtypes (CMS) classifier was found in the LB group (43%), whereas 76% HB tumors belonged to CMS2. CONCLUSIONS:A classification of colorectal tumors based on the number of BCNAs identifies two groups of MSS tumors which differ for histopathology and gene expression profile. Such information can be exploited for its translational relevance in different aspects of CRC clinical management.

Project description:MiRNAs regulate gene expression by post-transcriptionally suppressing mRNA translation or by causing mRNA degradation. It has been proposed that unique miRNAs influence specific tumor molecular phenotype. In this paper, we test the hypotheses that miRNA expression differs by tumor molecular phenotype and that those differences may influence prognosis. Data come from population-based studies of colorectal cancer conducted in Utah and the Northern California Kaiser Permanente Medical Care Program. A total of 1893 carcinoma samples were run on the Agilent Human miRNA Microarray V19.0 containing 2006 miRNAs. We assessed differences in miRNA expression between TP53-mutated and non-mutated, KRAS-mutated and non-mutated, BRAF-mutated and non-mutated, CpG island methylator phenotype (CIMP) high and CIMP low, and microsatellite instability (MSI) and microsatellite stable (MSS) colon and rectal tumors. Using a Cox proportional hazard model we evaluated if those miRNAs differentially expressed by tumor phenotype influenced survival after adjusting for age, sex, and AJCC stage. There were 22 differentially expressed miRNAs for TP53-mutated colon tumors and 5 for TP53-mutated rectal tumors with a fold change of >1.49 (or <0.67). Additionally, 13 miRNAS were differentially expressed for KRAS-mutated rectal tumors, 8 differentially expressed miRNAs for colon CIMP high tumors, and 2 differentially expressed miRNAs for BRAF-mutated colon tumors. The majority of differentially expressed miRNAS were observed between MSI and MSS tumors (94 differentially expressed miRNAs for colon; 41 differentially expressed miRNAs for rectal tumors). Of these miRNAs differentially expressed between MSI and MSS tumors, the majority were downregulated. Ten of the differentially expressed miRNAs were associated with survival; after adjustment for MSI status, five miRNAS, miR-196b-5p, miR-31-5p, miR-99b-5p, miR-636, and miR-192-3p, were significantly associated with survival. In summary, it appears that the majority of miRNAs that are differentially expressed by tumor molecular phenotype are MSI tumors. However, these miRNAs appear to have minimal effect on prognosis.

Dataset Information

Prognosis Prediction of Colorectal Cancer Using Gene Expression Profiles.

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Prognosis Prediction of Colorectal Cancer Using Gene Expression Profiles.

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