Stromal Score-Based Gene Signature: A Prognostic Prediction Model for Colon Cancer

Jing Jia; Yuhan Dai; Qing Zhang; Peiyu Tang; Qiang Fu; Guanying Xiong

doi:10.3389/fgene.2021.655855

Stromal Score-Based Gene Signature: A Prognostic Prediction Model for Colon Cancer

Front Genet. 2021 May 12:12:655855. doi: 10.3389/fgene.2021.655855. eCollection 2021.

Authors

Jing Jia¹, Yuhan Dai², Qing Zhang¹, Peiyu Tang³, Qiang Fu¹, Guanying Xiong¹

Affiliations

¹ Medical Center for Digestive Diseases, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China.
² The First School of Clinical Medicine, Nanjing Medical University, Nanjing, China.
³ The School of Stomatology, Nanjing Medical University, Nanjing, China.

Abstract

Background: Growing evidence has revealed the crucial roles of stromal cells in the microenvironment of various malignant tumors. However, efficient prognostic signatures based on stromal characteristics in colon cancer have not been well-established yet. The present study aimed to construct a stromal score-based multigene prognostic prediction model for colon cancer.

Methods: Stromal scores were calculated based on the expression profiles of a colon cancer cohort from TCGA database applying the ESTIMATE algorithm. Linear models were used to identify differentially expressed genes between low-score and high-score groups by limma R package. Univariate, LASSO, and multivariate Cox regression models were used successively to select the prognostic gene signature. Two independent datasets from GEO were used as external validation cohorts.

Results: Low stromal score was demonstrated to be a favorable factor to the overall survival of colon cancer patients in TCGA cohort (p = 0.0046). Three hundred and seven stromal score-related differentially expressed genes were identified. Through univariate, LASSO, and multivariate Cox regression analyses, a gene signature consisting of LEP, NOG, and SYT3 was recognized to build a prognostic prediction model. Based on the predictive values estimated by the established integrated model, patients were divided into two groups with significantly different overall survival outcomes (p < 0.0001). Time-dependent Receiver operating characteristic curve analyses suggested the satisfactory predictive efficacy for the 5-year overall survival of the model (AUC value = 0.733). A nomogram with great predictive performance combining the multigene prediction model and clinicopathological factors was developed. The established model was validated in an external cohort (AUC value = 0.728). In another independent cohort, the model was verified to be of significant prognostic value for different subgroups, which was demonstrated to be especially accurate for young patients (AUC value = 0.763).

Conclusion: The well-established model based on stromal score-related gene signature might serve as a promising tool for the prognostic prediction of colon cancer.

Keywords: colon cancer; gene signature; prediction model; prognosis; stromal score; tumor biomarkers; tumor microenvironment.