A novel gene signature for predicting outcome in colorectal cancer patients based on tumor cell-endothelial cell interaction via single-cell sequencing and machine learning
Background: The intricate interactions between malignant cells and endothelial cells (ECs) are crucial in the progress of colorectal cancer (CRC). Identifying molecular signatures associated with this interaction could yield critical prognostic insights and inform personalized therapeutic approaches...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025006176 |
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| author | Lina Pang Qingxia Sun Wenyue Wang Mingjie Song Ying Wu Xin Shi Xiaonan Shi |
| author_facet | Lina Pang Qingxia Sun Wenyue Wang Mingjie Song Ying Wu Xin Shi Xiaonan Shi |
| author_sort | Lina Pang |
| collection | DOAJ |
| description | Background: The intricate interactions between malignant cells and endothelial cells (ECs) are crucial in the progress of colorectal cancer (CRC). Identifying molecular signatures associated with this interaction could yield critical prognostic insights and inform personalized therapeutic approaches. Methods: We conducted an in silico study integrating single-cell RNA sequencing and bulk transcriptome data to characterize the cellular heterogeneity of CRC. Through computational cell interaction analysis facilitated the elucidation of signaling dynamics among cell subpopulations linked to CRC prognosis. Prognostic signatures were developed using various machine learning algorithms based on marker genes linked to the identified cell subpopulations. Immune cell infiltration assessment and gene enrichment analysis were performed to characterize CRC patients stratified by the signature. Results: Our analysis revealed two distinct cell subgroups, Malignant Cluster01 tumor cells, and Tip-like endothelial cells, showing significant interaction and closely associated with colorectal cancer prognosis. Specifically, Malignant Cluster01 subpopulations primarily served as signal senders, while Tip-like endothelial cells acted as receivers in PARs signaling. The Malignant Cluster01 and Tip-like endothelial cells related machine learning-derived prognostic signature (MTMLDPS), demonstrated potent prognostic capability, effectively predicting colorectal cancer patient outcomes across diverse databases. The colorectal cancer group with a high Malignant Cluster01 and Tip-like endothelial cells related machine learning-derived prognostic signature score exhibited significant associations with invasion, epithelial-mesenchymal transition, and angiogenesis pathways, along with immune cell infiltration. Conclusion: The Malignant Cluster01 and Tip-like endothelial cells related machine learning-derived prognostic signature holds promise for improving prognostic precision and guiding individual therapeutic strategies in colorectal cancer patients. Moreover, our findings emphasize the importance of considering tumor-endothelial cell interactions in cancer prognosis, providing insights for future therapeutic interventions targeting these interactions. |
| format | Article |
| id | doaj-art-d64ab064cf664e2da46fb3bf464f09d1 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-d64ab064cf664e2da46fb3bf464f09d12025-01-30T05:14:40ZengElsevierHeliyon2405-84402025-02-01113e42237A novel gene signature for predicting outcome in colorectal cancer patients based on tumor cell-endothelial cell interaction via single-cell sequencing and machine learningLina Pang0Qingxia Sun1Wenyue Wang2Mingjie Song3Ying Wu4Xin Shi5Xiaonan Shi6Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, ChinaChina Medical University, Shenyang, 110001, ChinaDepartment of General Practice, the First Hospital of China Medical University, Shenyang, 110001, ChinaDepartment of General Practice, the First Hospital of China Medical University, Shenyang, 110001, ChinaDepartment of General Practice, the First Hospital of China Medical University, Shenyang, 110001, China; Department of Phase I Clinical Trial, the First Hospital of China Medical University, Shenyang, 110001, Liaoning, China; Corresponding author. The First Hospital of China Medical University, 155 Nanjing Street, Shenyang, Liaoning Province, 110001, China.School of Health Management, Institute of Health Sciences of China Medical University, Shenyang, 110122, China; School of Mathematics and Information Science, Shandong Technology and Business University, Yantai, 264003, China; Corresponding author. School of Health Management, Institute of Health Sciences of China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning Province, 110122, China.Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China; Corresponding author. The First Affiliated Hospital of Zhengzhou University, No.1 East Jian She Road, Zhengzhou City, Henan Province, China.Background: The intricate interactions between malignant cells and endothelial cells (ECs) are crucial in the progress of colorectal cancer (CRC). Identifying molecular signatures associated with this interaction could yield critical prognostic insights and inform personalized therapeutic approaches. Methods: We conducted an in silico study integrating single-cell RNA sequencing and bulk transcriptome data to characterize the cellular heterogeneity of CRC. Through computational cell interaction analysis facilitated the elucidation of signaling dynamics among cell subpopulations linked to CRC prognosis. Prognostic signatures were developed using various machine learning algorithms based on marker genes linked to the identified cell subpopulations. Immune cell infiltration assessment and gene enrichment analysis were performed to characterize CRC patients stratified by the signature. Results: Our analysis revealed two distinct cell subgroups, Malignant Cluster01 tumor cells, and Tip-like endothelial cells, showing significant interaction and closely associated with colorectal cancer prognosis. Specifically, Malignant Cluster01 subpopulations primarily served as signal senders, while Tip-like endothelial cells acted as receivers in PARs signaling. The Malignant Cluster01 and Tip-like endothelial cells related machine learning-derived prognostic signature (MTMLDPS), demonstrated potent prognostic capability, effectively predicting colorectal cancer patient outcomes across diverse databases. The colorectal cancer group with a high Malignant Cluster01 and Tip-like endothelial cells related machine learning-derived prognostic signature score exhibited significant associations with invasion, epithelial-mesenchymal transition, and angiogenesis pathways, along with immune cell infiltration. Conclusion: The Malignant Cluster01 and Tip-like endothelial cells related machine learning-derived prognostic signature holds promise for improving prognostic precision and guiding individual therapeutic strategies in colorectal cancer patients. Moreover, our findings emphasize the importance of considering tumor-endothelial cell interactions in cancer prognosis, providing insights for future therapeutic interventions targeting these interactions.http://www.sciencedirect.com/science/article/pii/S2405844025006176Colorectal cancerEndothelial cellsPrognosisSingle-cell RNA sequencingMachine learning |
| spellingShingle | Lina Pang Qingxia Sun Wenyue Wang Mingjie Song Ying Wu Xin Shi Xiaonan Shi A novel gene signature for predicting outcome in colorectal cancer patients based on tumor cell-endothelial cell interaction via single-cell sequencing and machine learning Heliyon Colorectal cancer Endothelial cells Prognosis Single-cell RNA sequencing Machine learning |
| title | A novel gene signature for predicting outcome in colorectal cancer patients based on tumor cell-endothelial cell interaction via single-cell sequencing and machine learning |
| title_full | A novel gene signature for predicting outcome in colorectal cancer patients based on tumor cell-endothelial cell interaction via single-cell sequencing and machine learning |
| title_fullStr | A novel gene signature for predicting outcome in colorectal cancer patients based on tumor cell-endothelial cell interaction via single-cell sequencing and machine learning |
| title_full_unstemmed | A novel gene signature for predicting outcome in colorectal cancer patients based on tumor cell-endothelial cell interaction via single-cell sequencing and machine learning |
| title_short | A novel gene signature for predicting outcome in colorectal cancer patients based on tumor cell-endothelial cell interaction via single-cell sequencing and machine learning |
| title_sort | novel gene signature for predicting outcome in colorectal cancer patients based on tumor cell endothelial cell interaction via single cell sequencing and machine learning |
| topic | Colorectal cancer Endothelial cells Prognosis Single-cell RNA sequencing Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025006176 |
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