AUTHOR=Liu Cong , Liu Dingwei , Wang Fangfei , Xie Jun , Liu Yang , Wang Huan , Rong Jianfang , Xie Jinliang , Wang Jinyun , Zeng Rong , Zhou Feng , Peng Jianxiang , Xie Yong 

TITLE=Identification of a glycolysis- and lactate-related gene signature for predicting prognosis, immune microenvironment, and drug candidates in colon adenocarcinoma

JOURNAL=Frontiers in Cell and Developmental Biology

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2022.971992

DOI=10.3389/fcell.2022.971992

ISSN=2296-634X

ABSTRACT=Background: Colon adenocarcinoma (COAD) is one of the most common malignant digestive tumors, carrying a high mortality risk and poor prognosis. The Warburg effect is characterized by enhanced glycolysis and lactate fermentation, which supports biosynthesis and provides energy to sustain tumor cell growth and proliferation. However, an analysis of the effect of glycolysis and lactate-related gene on the prognosis of COAD patients and the potential association with immune cell infiltration and chemotherapeutic drug sensitivity is lacking. 

Methods: Glycolysis- and lactate-related genes and COAD patient data were obtained from the GSEA and TCGA database, respectively. After univariate Cox regression analysis, a nonnegative matrix factorization algorithm was used to identify glycolysis- and lactate-related molecular subtypes. Twelve glycolysis- and lactate-related genes were found to be associated with prognosis and used to construct a prognostic model using LASSO Cox regression. Patients were divided into high-risk and low-risk groups. KM survival and ROC curve analyses were used to evaluate the prognostic value of the glycolysis- and lactate-related gene signature. The nomogram and calibration curves were used to enhance the clinical applicability of the model. CIBERSORT, ESTIMATE and ssGSEA algorithms were used to calculate the proportions of immune cells. The expression levels of glycolysis- and lactate-related genes were validated by qRT-PCR assays.

Results: We identified four molecular subtypes of COAD. COAD patients in the high-risk group had lower OS than those in the low-risk group. The nomogram could predict the 1-, 3- and 5-year OS of COAD patients. Patients in the low-risk group had greater infiltration of memory CD4+ T cells and dendritic cells than those in the high-risk group. Patients classified by risk scores had distinguishable differences in their sensitivity to chemotherapeutic drugs. Immunohistochemistry confirmed that CLCA1, CTXN1, FLNA, PTPRU, NAT2, and SNCG were highly expressed in normal tissues compared with COAD tissues, and ADTRP, ALDOB, CEACAM7, and OLFM4 were highly expressed in COAD tissues compared with normal tissues.
Conclusion: In summary, we identified molecular subtypes of COAD and developed a glycolysis- and lactate-related gene signature with significant prognostic value, which could aid in providing more precise and effective individualized treatment to COAD patients.