A nine-gene signature to improve prognosis prediction of colon carcinoma

Zhao, Jinlai; Wang, Yigang; Gao, Jianchao; Yang, Wang; Zhong, Xuan; Wu, Xiaotang; Li, Hua

doi:10.1080/15384101.2021.1919827

Cited by 6 publications

(5 citation statements)

References 34 publications

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“…There are no known tumor associated immune functions of the former genes, whereas CXCL10 has been previously identified as a prognostic marker of colon cancer with a well-known immunoregulatory role in multiple studies. Although no previous study identified CXCL10 as a CMS1-associated prognostic gene, our risk association of CXCL10 gene in colon cancer is supported with current literature [14][15][16][17] , further supporting the validity of our approach. Furthermore, this also provides the basis for the study of the unknown anti-tumor immune functions of our CMS1 associated prognostic genes in colon cancer models.…”

supporting

confidence: 87%

Development of molecular subtype specific prognostic marker signature in immune response associated colon cancer through fuzzy based transcriptomic approach

Koçhan

Dayanç

2023

Preprint

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Objective The molecular heterogeneity of colon cancer makes the prediction of disease prognosis challenging. In order to resolve this heterogeneity, molecular tumor subtyping present solutions. These approaches are expected to contribute to clinical decision-making. In this study, we aimed to identify Consensus Molecular Subtype (CMS) specific prognostic genes of colon cancer, focusing on anti-tumor immune- response associated CMS1, through a fuzzy-based machine learning approach. Materials and Methods We applied Fuzzy C-Means (FCM) clustering to stratify patients into two groups and identified genes that predict significant disease-specific survival difference between groups. We then performed Cox regression analyses to identify the most significant genes associated with disease-specific survival. A subtype-specific risk score and a final risk score formulae were constructed and used to calculate risk scores to stratify patients into low and high-risk groups within each CMS (1 to 4) or independent of CMS respectively. Results We identified CMS-specific genes and an overall 11-gene signature for prognostic risk prediction based on the disease-specific survival of colon cancer patients. The patients in both discovery and test cohorts were stratified into high and low-risk groups using subtype risk scores. The disease-specific survival of these risk groups within each CMS, except CMS3, was significantly different for both discovery and test cohorts. Discussion and Conclusions We have identified novel prognostic genes with potential immune regulatory roles within the immune-response associated CMS1. The low number of patients in the CMS3 cohort prevented subtype-specific prognostic gene validation. Tumor stage grouping of the validation cohort suggested the best prediction of prognosis in tumor stage III patients. In conclusion, newly identified eleven genes can efficiently predict the prognostic risk of colon cancer patients and classify patients into corresponding risk groups.

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supporting

confidence: 87%

Development of molecular subtype specific prognostic marker signature in immune response associated colon cancer through fuzzy based transcriptomic approach

Koçhan

Dayanç

2023

Preprint

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“…Its overexpression is correlated to breast cancer and prostate cancer with high metastatic potential and to gliomas [53][54][55]. It further correlates with metastasis and poor prognosis in colon cancer and can act as a prognostic factor [56][57][58]. Together with the stem cell marker KLF5, TNFRSF11A induces cancer cell proliferation, migration and invasiveness in cervical cancer [55].…”

Section: Simultaneous Bet and Syk Inhibition Identified A Combination...mentioning

confidence: 99%

Evaluation of the Synergistic Potential of Simultaneous Pan- or Isoform-Specific BET and SYK Inhibition in B-Cell Lymphoma: An In Vitro Approach

Sender

Sultan

Palmer

et al. 2022

Cancers

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Background: Both bromodomain and extra-terminal domain (BET) proteins and spleen tyrosine kinase (SYK) represent promising targets in diffuse large B-cell (DLBCL) and Burkitt’s lymphoma (BL). We evaluated the anti-lymphoma activity of the isoform-specific bivalent BET inhibitor AZD5153 (AZD) and the pan-BET inhibitor I-BET151 (I-BET) as single agents and in combination with SYK inhibitor Entospletinib (Ento) in vitro. Methods: The effect of the single agents on cell proliferation and metabolic activity was evaluated in two DLBCL and two BL cell lines. Proliferation, metabolic activity, apoptosis, cell cycle and morphology were further investigated after a combined treatment of AZD or I-BET and Ento. RNAseq profiling of combined AZD+Ento treatment was performed in SU-DHL-4 cells. Results: Both BET inhibitors reduced cell proliferation and metabolic activity in a dose- and time-dependent manner. Combined BET and SYK inhibition enhanced the anti-proliferative effect and induced a G0/G1 cell cycle arrest. SU-DHL-4 demonstrated a pronounced modulation of gene expression by AZD, which was markedly increased by additional SYK inhibition. Functional enrichment analyses identified combination-specific GO terms related to DNA replication and cell division. Genes such as ADGRA2, MYB, TNFRSF11A, S100A10, PLEKHH3, DHRS2 and FOXP1-AS1 were identified as possible key regulators. Conclusion: Simultaneous inhibition of BET and SYK enhanced the anti-proliferative effects, and induced a combination-specific gene expression signature.

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“…Machine learning techniques have significantly contributed to the evaluation of metastasis and prognosis in contemporary studies on colorectal cancer, exemplified by the utilization of the nomogram model (9), the 9-gene COX regression model (10), the random forest model (11), and the social ecological model (SEM) (12). These models employ a comprehensive approach to assess the pre-onset or post-onset condition of colorectal cancer in a population by simultaneously considering multiple risk factors.…”

Section: Introductionmentioning

confidence: 99%

Predicting colorectal cancer risk: a novel approach using anemia and blood test markers

Zhang,

Zhang

et al. 2024

Front. Oncol.

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Background and objectivesColorectal cancer remains an important public health problem in the context of the COVID-19 (Corona virus disease 2019) pandemic. The decline in detection rates and delayed diagnosis of the disease necessitate the exploration of novel approaches to identify individuals with a heightened risk of developing colorectal cancer. The study aids clinicians in the rational allocation and utilization of healthcare resources, thereby benefiting patients, physicians, and the healthcare system.MethodsThe present study retrospectively analyzed the clinical data of colorectal cancer cases diagnosed at the Affiliated Hospital of Guilin Medical University from September 2022 to September 2023, along with a control group. The study employed univariate and multivariate logistic regression as well as LASSO (Least absolute shrinkage and selection operator) regression to screen for predictors of colorectal cancer risk. The optimal predictors were selected based on the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. These predictors were then utilized in constructing a Nomogram Model for predicting colorectal cancer risk. The accuracy of the risk prediction Nomogram Model was assessed through calibration curves, ROC curves, and decision curve analysis (DCA) curves.ResultsClinical data of 719 patients (302 in the case group and 417 in the control group) were included in this study. Based on univariate logistic regression analysis, there is a correlation between Body Mass Index (BMI), red blood cell count (RBC), anemia, Mean Corpuscular Volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), platelet count (PLT), Red Cell Distribution Width-Standard Deviation (RDW-SD), and the incidence of colorectal cancer. Based on the findings of multivariate logistic regression analysis, the variables of BMI and RBC exhibit a decrease, while anemia and PLT demonstrate an increase, all of which are identified as risk factors for the occurrence of colorectal cancer. LASSO regression selected BMI, RBC, anemia, and PLT as prediction factors. LASSO regression and multivariate logistic regression analysis yielded the same results. A nomogram was constructed based on the 4 prediction factors identified by LASSO regression analysis to predict the risk of colorectal cancer. The AUC of the nomogram was 0.751 (95% CI, OR: 0.708-0.793). The calibration curves in the validation and training sets showed good performance, indicating that the constructed nomogram model has good predictive ability. Additionally, the DCA demonstrated that the nomogram model has diagnostic accuracy.ConclusionThe Nomogram Model offers precise prognostications regarding the likelihood of Colorectal Cancer in patients, thereby helping healthcare professionals in their decision-making processes and promoting the rational categorization of patients as well as the allocation of medical resources.

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A nine-gene signature to improve prognosis prediction of colon carcinoma

Cited by 6 publications

References 34 publications

Development of molecular subtype specific prognostic marker signature in immune response associated colon cancer through fuzzy based transcriptomic approach

Development of molecular subtype specific prognostic marker signature in immune response associated colon cancer through fuzzy based transcriptomic approach

Evaluation of the Synergistic Potential of Simultaneous Pan- or Isoform-Specific BET and SYK Inhibition in B-Cell Lymphoma: An In Vitro Approach

Predicting colorectal cancer risk: a novel approach using anemia and blood test markers

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