Dataset Information

Gene expression profile analysis of colon cancer grade II into grade III transition by using system biology.

ABSTRACT:

Aim

Gene expression profile analysis of colon cancer grade II into grade III transition by using system biology.

Background

Colon cancer is one of lethal cancer in men and women. Treatment in advanced colon cancer is difficult and survival rate is low.

Methods

Gene expression profiles of children patients with non-preforated appendicitis in comparison with the samples with non- appendicitis abdominal pain are analysis via protein - protein interaction PPI and the critical compounds are introduced by STITCH.

Results

Six critical genes including MAPK3, AKT1, SRC, TP53, GAPDH, and ALB were identified as a possible biomarker panel related to colon cancer grade II to III transition. Among these critical genes roles of MAPK3, AKT1, SRC, TP53 are highlighted.

Conclusion

It was concluded that target therapy to regulate SRC and TP53 may be the effective therapeutic way to treatment of colon cancer and more researches in necessary to design drugs for these purposes.

SUBMITTER: Rostami-Nejad M

PROVIDER: S-EPMC6441480 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Gene expression profile analysis of colon cancer grade II into grade III transition by using system biology.

Rostami-Nejad Mohammad M Rezaei Tavirani Sina S Mansouri Vahid V Jahani-Sherafat Somayeh S Moravvej Farshi Hamideh H

Gastroenterology and hepatology from bed to bench 20190101 1

<h4>Aim</h4>Gene expression profile analysis of colon cancer grade II into grade III transition by using system biology.<h4>Background</h4>Colon cancer is one of lethal cancer in men and women. Treatment in advanced colon cancer is difficult and survival rate is low.<h4>Methods</h4>Gene expression profiles of children patients with non-preforated appendicitis in comparison with the samples with non- appendicitis abdominal pain are analysis via protein - protein interaction PPI and the critical c ...[more]

PMID: 30949321

Similar Datasets

Project description:Due to the extremely intrinsic heterogeneity among glioma patients, the outcomes of these patients are tremendously different. Therefore, the exploitation of novel biomarker classification of glioma is vitally important for deep insight into the essence and predicting the prognosis of glioma. We aim to analyze the correlation between TP73 mRNA expression, DNA methylated alteration and the prognosis of WHO grade II/III glioma, utilizing bioinformatics to evaluate its significance as a risk-factor in predicting the prognosis of these glioma patients. The analysis found that TP73 expression was positively correlated with the grade of glioma, and showed a strong correlation with glioma molecular classification, which revealed significantly higher TP73 expression in IDH-wildtype than in IDH-mutant subtype of WHO grade II/III glioma. Cox regression analysis indicated that high expression of TP73 shared an independent high-risk factor impacting the prognosis of WHO grade II/III glioma. We discovered 8 DNA promoter methylation sites with prognostic significance, which were negatively associated with TP73 expression, and positively associated with beneficial overall survival (OS) and progression-free survival (PFS). Integrating with four independent glioma datasets, subsequent Meta-analysis verified that low expression of TP73 was closely related to favorable OS, especially in IDH-mutant subtype. Moreover, we found that 1p/19qCodel /TP73low subgroup shared the most favorable OS, 1p/19qNon-codel /TP73high subgroup suffered the worst OS. Meanwhile, the enrichment analysis of TP73-related differential mRNAs demonstrated that TP73 aberration in WHO grade II/III glioma might be closely related to cell cycle and P53 signaling pathways. Finally, TP73 expression of 53 glioma specimens was measured by qRT-PCR, which was consistent with the previous analytical result, and TP73 high-expression subgroup suffered worse PFS than TP73 low-expression subgroup. In summary, our funding supports that TP73 gene can perform as a reliable biomarker to evaluate the survival outcome of patients diagnosed with WHO grade II/III glioma.

Project description:Background:TNM staging alone does not accurately predict outcome in colon cancer (CC) patients who may be eligible for adjuvant chemotherapy. It is unknown to what extent the molecular markers microsatellite instability (MSI) and mutations in BRAF or KRAS improve prognostic estimation in multivariable models that include detailed clinicopathological annotation. Patients and methods:After imputation of missing at random data, a subset of patients accrued in phase 3 trials with adjuvant chemotherapy (n?=?3016)-N0147 (NCT00079274) and PETACC3 (NCT00026273)-was aggregated to construct multivariable Cox models for 5-year overall survival that were subsequently validated internally in the remaining clinical trial samples (n?=?1499), and also externally in different population cohorts of chemotherapy-treated (n?=?949) or -untreated (n?=?1080) CC patients, and an additional series without treatment annotation (n?=?782). Results:TNM staging, MSI and BRAFV600E mutation status remained independent prognostic factors in multivariable models across clinical trials cohorts and observational studies. Concordance indices increased from 0.61-0.68 in the TNM alone model to 0.63-0.71 in models with added molecular markers, 0.65-0.73 with clinicopathological features and 0.66-0.74 with all covariates. In validation cohorts with complete annotation, the integrated time-dependent AUC rose from 0.64 for the TNM alone model to 0.67 for models that included clinicopathological features, with or without molecular markers. In patient cohorts that received adjuvant chemotherapy, the relative proportion of variance explained (R2) by TNM, clinicopathological features and molecular markers was on an average 65%, 25% and 10%, respectively. Conclusions:Incorporation of MSI, BRAFV600E and KRAS mutation status to overall survival models with TNM staging improves the ability to precisely prognosticate in stage II and III CC patients, but only modestly increases prediction accuracy in multivariable models that include clinicopathological features, particularly in chemotherapy-treated patients.

Dataset Information

Gene expression profile analysis of colon cancer grade II into grade III transition by using system biology.

Aim

Background

Methods

Results

Conclusion

Publications

Gene expression profile analysis of colon cancer grade II into grade III transition by using system biology.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure