A transcription factor signature can identify the CMS4 subtype and stratify the prognostic risk of colorectal cancer

Jun 30, 2022·
Min-Er ZHONG
Min-Er ZHONG
1st Author
Ze-Ping HUANG
Ze-Ping HUANG
Co-1st Author
Xun WANG
Xun WANG
Co-1st Author
Du CAI
Du CAI
Cheng-Hang LI
Cheng-Hang LI
Feng GAO
Feng GAO
Co-corresponding Author
,
Xiao-Jian Wu
Co-corresponding Author
,
Wei Wang
Corresponding Author
· 0 min read
Abstract

Background: Colorectal cancer (CRC) is a heterogeneous disease, and current classification systems are insufficient for stratifying patients with different risks. This study aims to develop a generalized, individualized prognostic consensus molecular subtype (CMS)-transcription factors (TFs)-based signature that can predict the prognosis of CRC.

Methods: We obtained differentially expressed TF signature and target genes between the CMS4 and other CMS subtypes of CRC from The Cancer Genome Atlas (TCGA) database. A multi-dimensional network inference integrative analysis was conducted to identify the master genes and establish a CMS4-TFs-based signature. For validation, an in-house clinical cohort (n = 351) and another independent public CRC cohort (n = 565) were applied. Gene set enrichment analysis (GSEA) and prediction of immune cell infiltration were performed to interpret the biological significance of the model.

Results: A CMS4-TFs-based signature termed TF-9 that includes nine TF master genes was developed. Patients in the TF-9 high-risk group have significantly worse survival, regardless of clinical characteristics. The TF-9 achieved the highest mean C-index (0.65) compared to all other signatures reported (0.51 to 0.57). Immune infiltration revealed that the microenvironment in the high-risk group was highly immune suppressed, as evidenced by the overexpression of TIM3, CD39, and CD40, suggesting that high-risk patients may not directly benefit from the immune checkpoint inhibitors.

Conclusions: The TF-9 signature allows a more precise categorization of patients with relevant clinical and biological implications, which may be a valuable tool for improving the tailoring of therapeutic interventions in CRC patients.

Type
Publication
Frontiers in Oncology
publication
Min-Er ZHONG
Authors
Postdoc
I am a surgeon and clinical researcher focused on deep learning and translational studies in colorectal cancer.
Ze-Ping HUANG
Authors
Medical Student
I am a medical trainee in colorectal surgery, focusing on bioinformatics and translational research in colorectal cancer.
Xun WANG
Authors
Research Student
I work on deep learning and medical big data, with a focus on medical image processing and intelligent healthcare.
Du CAI
Authors
Postdoc
I focus on leveraging explainable AI and large foundation models to advance medical imaging and digital pathology in colorectal cancer research.
Cheng-Hang LI
Authors
Research Assistant
I am a research assistant focusing on deep learning, multimodal feature fusion, and medical AI system development.
Feng GAO
Authors
Professor
My research leverages AI and big data to improve diagnostics, prognostics, and ultimately, outcomes in cancer and other biomedical fields.