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INHBA is a prognostic predictor for patients with colon adenocarcinoma.

ABSTRACT: BACKGROUND:Colon adenocarcinoma (COAD) is one of the most lethal cancers. It is particularly important to accurately predict prognosis and to provide individualized treatment. Several lines of evidence suggest that genetic factors and clinicopathological characteristics are related to cancer onset and progression. The aim of this study was to identify potential prognostic genes and to develop a nomogram to predict survival and recurrence of COAD. METHODS:To identify potential prognostic genes in COAD, microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained from GEO2R. Venn diagram was drawn to select those genes that were overexpressed in all datasets, and survival analyses were performed to determine the prognostic values of the selected genes. New nomograms were developed based on the genes that were significantly associated with prognosis. Clinicopathological data were obtained from The Cancer Genome Atlas (TCGA). Finally, the new nomograms were compared head-to-head comparison with the TNM nomogram. RESULTS:From GSE21510, GSE110223, GSE113513 and GSE110224, a total of 834, 218, 236 and 613 overexpressed DEGs were screened out, respectively. The Venn diagram revealed that 12 genes appeared in all four profiles. After survival analyses, only INHBA expression was associated with both overall survival (OS) and disease-free survival (DFS). Multivariate analyses revealed that age, pathological N and pathological M were significant independent risk factors for OS. Age, pathological N, pathological M and INHBA were significant independent risk factors for DFS. Two prediction models predicted the probability of 3-year survival and 5-year survival for OS and DFS, respectively. The concordance indexes were 0.785 for 3-year overall survival, 0.759 for 5-year overall survival, 0.789 for 3-year disease-free survival and 0.757 for 5-year disease-free survival. The head-to-head comparison according to time-dependent ROC curves indicated that the new models had higher predictive accuracy. Decision curve analyses (DCA) indicated that the clinical value of the new models were higher than TNM models for predicting disease-free survival. CONCLUSION:The combination of INHBA expression with a clinical nomogram improves prognostic power in colon adenocarcinoma, especially for predicting recurrence.

SUBMITTER: Li X

PROVIDER: S-EPMC7161248 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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INHBA is a prognostic predictor for patients with colon adenocarcinoma.

Li Xueying X Yu Weiming W Liang Chao C Xu Yuan Y Zhang Miaozun M Ding Xiaoyun X Cai Xianlei X

BMC cancer 20200415 1

<h4>Background</h4>Colon adenocarcinoma (COAD) is one of the most lethal cancers. It is particularly important to accurately predict prognosis and to provide individualized treatment. Several lines of evidence suggest that genetic factors and clinicopathological characteristics are related to cancer onset and progression. The aim of this study was to identify potential prognostic genes and to develop a nomogram to predict survival and recurrence of COAD.<h4>Methods</h4>To identify potential prog ...[more]

PMID: 32293338

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Project description:ObjectiveThis study endeavored to explore the optimal treatment strategy and conduct a prognostic analysis for patients diagnosed with pT4M0 (pathologic stage T4) colon adenocarcinoma (COAD).Methods and materialsA total of 8,843 patients diagnosed with pT4M0 COAD between January 2010 and December 2015 were included in this study from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly divided into a training set and an internal validation set using a 7:3 ratio. Variables that demonstrated statistical significance (P<0.05) in univariate COX regression analysis or held clinical significance were incorporated into the multivariate COX regression model. Subsequently, this model was utilized to formulate a nomogram. The predictive accuracy and discriminability of the nomogram were assessed using the C-index, area under the curve (AUC), and calibration curves. Decision curve analysis (DCA) was conducted to confirm the clinical validity of the model.ResultsIn the entire SEER cohort, the 3-year overall survival (OS) rate (74.22% vs. 63.20%, P<0.001) and the 3-year cancer-specific survival (CSS) rate (76.25% vs. 66.98%, P<0.001) in the surgery combined with postoperative adjuvant therapy (S+ADT) group surpassed those in the surgery (S) group. Multivariate COX regression analysis of the training set unveiled correlations between age, race, N stage, serum CEA (carcinoembryonic antigen), differentiation, number of resected lymph nodes, and treatment modalities with OS and CSS. Nomograms for OS and CSS were meticulously crafted based on these variables, achieving C-indexes of 0.692 and 0.690 in the training set, respectively. The robust predictive ability of the nomogram was further affirmed through receiver operating characteristic (ROC) and calibration curves in both the training and validation sets.ConclusionIn individuals diagnosed with pT4M0 COAD, the integration of surgery with adjuvant chemoradiotherapy demonstrated a substantial extension of long-term survival. The nomogram, which incorporated key factors such as age, race, differentiation, N stage, serum CEA level, tumor size, and the number of resected lymph nodes, stood as a dependable tool for predicting OS and CSS rates. This predictive model held promise in aiding clinicians by identifying high-risk patients and facilitating the development of personalized treatment plans.

Project description:The aim of the present study was to examine the molecular factors associated with the prognosis of colon cancer. Gene expression datasets were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases to screen differentially expressed genes (DEGs) between colon cancer samples and normal samples. Survival?related genes were selected from the DEGs using the Cox regression method. A co?expression network of survival?related genes was then constructed, and functional clusters were extracted from this network. The significantly enriched functions and pathways of the genes in the network were identified. Using Bayesian discriminant analysis, a prognostic prediction system was established to distinguish the positive from negative prognostic samples. The discrimination efficacy of the system was validated in the GSE17538 dataset using Kaplan?Meier survival analysis. A total of 636 and 1,892 DEGs between the colon cancer samples and normal samples were screened from the TCGA and GSE44861 dataset, respectively. There were 155 survival?related genes selected. The co?expression network of survival?related genes included 138 genes, 534 lines (connections) and five functional clusters, including the signaling pathway, cellular response to cAMP, and immune system process functional clusters. The molecular function, cellular components and biological processes were the significantly enriched functions. The peroxisome proliferator?activated receptor signaling pathway, Wnt signaling pathway, B cell receptor signaling pathway, and cytokine?cytokine receptor interactions were the significant pathways. A prognostic prediction system based on a 65?gene signature was established using this co?expression network. Its discriminatory effect was validated in the TCGA dataset (P=3.56e?12) and the GSE17538 dataset (P=1.67e?6). The 65?gene signature included kallikrein?related peptidase 6 (KLK6), collagen type XI ?1 (COL11A1), cartilage oligomeric matrix protein, wingless?type MMTV integration site family member 2 (WNT2) and keratin 6B. In conclusion, a 65?gene signature was screened in the present study, which showed a prognostic prediction effect in colon adenocarcinoma. KLK6, COL11A1, and WNT2 may be suitable prognostic predictors for colon adenocarcinoma.

Project description:BACKGROUND Colon adenocarcinoma (COAD) is one of the most common and fatal malignant tumors, which increases the difficulty of prognostic predictions. Thus, new biomarkers for the diagnosis and prognosis of COAD should be explored. Ferroptosis is a recently identified programmed cell death process that has the characteristics of iron-dependent lipid peroxide accumulation. However, the predictive value of ferroptosis-related genes (FRGs) for COAD still needs to be further clarified. AIM To identify some critical FRGs and construct a COAD patient prognostic signature for clinical utilization. METHODS The Cancer Genome Atlas database (TCGA) and Gene Expression Omnibus databases were the data sources for mRNA expression and corresponding COAD patient clinical information. Differentially expressed FRGs were recognized using R and Perl software. We constructed a multi-FRG signature of the TCGA-COAD cohort by performing a univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis. COAD patients from the Gene Expression Omnibus cohort were utilized for verification. RESULTS Our research showed that most of the FRGs (85%) were differentially expressed between the corresponding adjacent normal tissues and cancer tissues in the TCGA-COAD cohort. Seven FRGs were related to overall survival (OS) in the univariate Cox analysis (all P < 0.05). A model with five FRGs (AKR1C1, AKR1C3, ALOX12, CRYAB, and FDFT1) was constructed to divide patients into high- and low-risk groups. The OS of patients in the high-risk group was significantly lower than that of the low-risk group (all P < 0.01 in the TCGA and Gene Expression Omnibus cohorts). The risk score was an independent prognosticator of OS in the multivariate Cox analysis (hazard ratio > 1, P < 0.01). The predictive capacity of the model was verified by a receiver operating characteristic curve analysis. In addition, a nomogram based on the expression of five hub FRGs and risk score can precisely predict the OS of individual COAD cancer patients. Immune correlation analysis and functional enrichment analysis results revealed that immunology-related pathways were abundant, and the immune states of the high-risk group and the low-risk group were different. CONCLUSION In conclusion, a novel five FRG model can be utilized for predicting prognosis in COAD. Targeting ferroptosis may be a treatment option for COAD.

Project description:BackgroundColon adenocarcinoma (COAD) is one of the leading causes of death worldwide. Cancer stem cells (CSCs) are vital for COAD chemoresistance and recurrence, however little is known about stem cell-related biomarkers in drug resistance and COAD prognosis prediction.MethodsTo uncover the roles of CSC in COAD tumorigenesis, chemoresistance, and prognosis, we retrieved COAD patients' RNAseq data from TCGA (The Cancer Genome Atlas). We further performed analysis of differentially expressed genes (DEGs) and mRNA expression-based stemness index (mRNAsi) to identify stemness-related COAD biomarkers. We then evaluated the roles of mRNAsi in tumorigenesis, clinical-stage, overall survival (OS), and chemoresistance. Afterward, we used identified prognostic stemness-related genes (PSRGs) to construct a prediction model. After constructing the prediction model, we used elastic Net regression and area under the curve (AUC) to explore the prediction value of PSRGs based on risk scores and the receiver operator characteristic (ROC) curve. To elucidate the underlying interconnected systems, we examined relationships between the levels of TFs, PSRGs, and 50 cancer hallmarks by a Pearson correlation analysis.ResultsTwelve thousand one hundred eight DEGs were identified by comparing 456 primary COADs and 41 normal solid tissue samples. Furthermore, we identified 4351 clinical stage-related DEGs, 16,516 stemness-associated DEGs, and 54 chemoresistance-related DEGs from cancer stages: mRNAsi, and COAD chemoresistance. Compared to normal tissue samples, mRNAsi in COAD patients were marked on an elevation and involved in prognosis (p = 0.027), stemness-related DEGs based on chemoresistance (OR = 3.28, p ≤ 0.001) and AJCC clinical stage relating (OR = 4.02, p ≤ 0.001) to COAD patients. The prediction model of prognosis were constructed using the 6 PSRGs with high accuracy (AUC: 0.659). The model identified universal correlation between NRIP2 and FDFT1 (key PRSGs), and some cancer related transcription factors (TFs) and trademarks of cancer gene were in the regulatory network.ConclusionWe found that mRNAsi is a reliable predictive biomarker of tumorigenesis and COAD prognosis. Our established prediction model of COAD chemoresistance, which includes the six PSRGs, is effective, as the model provides promising therapeutic targets in the COAD.

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INHBA is a prognostic predictor for patients with colon adenocarcinoma.

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INHBA is a prognostic predictor for patients with colon adenocarcinoma.

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