Evaluation of Stromal Cell Infiltration in the Tumor Microenvironment Enable Prediction of Treatment Sensitivity and Prognosis in Colon Cancer
Computational and Structural Biotechnology Journal(2022)
Southern Med Univ
Abstract
Current clinical factors for screening candidates that might benefit from adjuvant chemotherapy in colon cancer are inadequate. Tumor microenvironment, especially the stromal components, has the potential to determine treatment response. However, clinical translation of the tumor-associated stromal characterization into a practical biomarker for helping treatment decision has not been established. Using machine learning, we established a novel 31-gene signature, called stromal cell infiltration intensity score (SIIS), to distinguish patients characterized by the enrichment of abundant stromal cells in five colon cancer datasets from GEO (N = 990). Patients with high-SIIS were at higher risk for recurrence and mortality, and could not benefit from adjuvant chemotherapy due to their intrinsic drug resistance; however, the opposite was reported for patients with low-SIIS. The role of SIIS in detection of patients with high stromal cell infiltration and reduced drug efficiency was consistently validated in the TCGA-COAD cohort (N = 382), Sun Yat-sen University Cancer Center cohort (N = 30), and could also be observed in TCGA pan-cancer settings (N = 4898) and four independent immunotherapy cohorts (N = 467). Based on multi-omics data analysis and the CRISPR library screen, we reported that lack of gene mutation, hypomethylation in ADCY4 promoter region, activation of WNT-PCP pathway and SIAH2-GPX3 axis were potential mechanisms responsible for the chemoresistance of patients within high-SIIS group. Our findings demonstrated that SIIS provide an important reference for those making treatment decisions for such special patients.
MoreTranslated text
Key words
Stromal cell infiltration,Machine learnin,Gene signature,Treatment sensitivity,CRISPR library screen,Colon cancer
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined