An immune, stroma, and epithelial--mesenchymal transition-related signature for predicting recurrence and chemotherapy benefit in stage II--III colorectal cancer
Abstract
Background: Debates exist on the treatment decision of the stage II/III colorectal cancer (CRC) due to the insufficiency of the current TNM stage‐based risk stratification system. Epithelial–mesenchymal transition (EMT) and tumor microenvironment (TME) have both been linked to CRC progression in recent studies. We propose to improve the prognosis prediction of CRC by integrating TME and EMT.
Methods: In total, 2382 CRC patients from seven datasets and one in‐house cohort were collected, and 1640 stage II/III CRC patients with complete survival information and gene expression profiles were retained and divided into a training cohort and three independent validation cohorts. Integrated analysis of 398 immune, stroma, and epithelial‐mesenchymal transition (ISE)‐related genes identified an ISE signature independently associated with the recurrence of CRC. The underlying biological mechanism of the ISE signature and its influence on adjuvant chemotherapy was further explored.
Results: We constructed a 26‐gene signature which was significantly associated with poor outcome in Training cohort (p < 0.001, HR [95%CI] = 4.42 [3.25–6.01]) and three independent validation cohorts (Validation cohort‐1: p < 0.01, HR [95%CI] = 1.70 [1.15–2.51]; Validation cohort‐2: p < 0.001, HR [95% CI] = 2.30 [1.67–3.16]; Validation cohort‐3: p < 0.01, HR [95% CI] = 2.42 [1.25–4.70]). After adjusting for known clinicopathological factors, multivariate cox analysis confirmed the ISE signature’s independent prognostic value. Subgroup analysis found that stage III patients with low ISE score might benefit from adjuvant chemotherapy (p < 0.001, HR [95%CI] = 0.15 [0.04–0.55]). Hypergeometric test and enrichment analysis revealed that low‐risk group was enriched in the immune pathway while high‐risk group was associated with the EMT pathway and CMS4 subtype.
Conclusion: We proposed an ISE signature for robustly predicting the recurrence of stage II/III CRC and help treatment decision by identifying patients who will not benefit from current standard treatment.
Du CAI
Postdoc
I focus on leveraging explainable AI and large foundation models to advance medical imaging and digital pathology in colorectal cancer research.
Min-Er ZHONG
Surgeon
Cheng-Hang LI
Research Assistant
Ze-Ping HUANG
Medical Student
Qi-Qi ZHU
Surgeon
Min-Yi LV
PhD Student
Chu-Ling HU
PhD Student
Xin DUAN
Postdoc
Feng GAO
Professor
My research leverages AI and big data to improve diagnostics, prognostics, and ultimately, outcomes in cancer and other biomedical fields.