Tumour heterogeneity and personalized treatment screening based on single-cell transcriptomics
According to global cancer statistics for the year 2022, based on updated estimates from the International Agency for Research on Cancer, there were approximately 20 million new cases of cancer in 2022 alongside 9.7 million related deaths. Lung, breast, colorectal, gastric, and liver cancers are the...
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037024004409 |
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| author | Xinying Zhang Jiajie Xie Zixin Yang Carisa Kwok Wai Yu Yaohua Hu Jing Qin |
| author_facet | Xinying Zhang Jiajie Xie Zixin Yang Carisa Kwok Wai Yu Yaohua Hu Jing Qin |
| author_sort | Xinying Zhang |
| collection | DOAJ |
| description | According to global cancer statistics for the year 2022, based on updated estimates from the International Agency for Research on Cancer, there were approximately 20 million new cases of cancer in 2022 alongside 9.7 million related deaths. Lung, breast, colorectal, gastric, and liver cancers are the most common types of cancer. Despite advancements in anticancer drugs and optimised chemotherapy regimens that have improved cure rates for malignant tumours, the presence of tumour heterogeneity has resulted in substantial variations among patients in terms of disease progression, clinical response, sensitivity to therapy, and prognosis, posing significant challenges in attaining optimal therapeutic outcomes for each patient. Here, we collected five single-cell transcriptome datasets from patients with lung, breast, colorectal, gastric, and liver cancers and constructed multiple cancer blueprints of tumour cell heterogeneity. By integrating multiple bioinformatics analyses, we explored the biological differences underlying tumour cell heterogeneity at the single-cell level and identified tumour cell subcluster-specific biomarkers and potential therapeutic drugs for each subcluster. Interestingly, although tumour cell subpopulations exhibit dramatic differences within the same cancer type and between different cancers at both the genomic and transcriptomic levels, some demonstrate similar oncogenic pathway activities and phenotypes. Tumour cell subpopulations from the five cancers listed above were classified into three major groups corresponding to different treatment strategies. The findings of this study not only focus on the differences but also on the similarities among tumour cell subpopulations across different cancers, providing new insights for individualised therapy. |
| format | Article |
| id | doaj-art-a17e38f8afad469c96f14a5785258d9a |
| institution | Kabale University |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-a17e38f8afad469c96f14a5785258d9a2025-01-12T05:24:51ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-0127307320Tumour heterogeneity and personalized treatment screening based on single-cell transcriptomicsXinying Zhang0Jiajie Xie1Zixin Yang2Carisa Kwok Wai Yu3Yaohua Hu4Jing Qin5School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, ChinaSchool of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, ChinaSchool of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, ChinaDepartment of Mathematics, Statistics and Insurance, The Hang Seng University of Hong Kong, Shatin, Hong Kong; Corresponding authors.School of Mathematical Sciences, Shenzhen University, Shenzhen, Guangdong 518060, China; Corresponding authors.School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China; Corresponding authors.According to global cancer statistics for the year 2022, based on updated estimates from the International Agency for Research on Cancer, there were approximately 20 million new cases of cancer in 2022 alongside 9.7 million related deaths. Lung, breast, colorectal, gastric, and liver cancers are the most common types of cancer. Despite advancements in anticancer drugs and optimised chemotherapy regimens that have improved cure rates for malignant tumours, the presence of tumour heterogeneity has resulted in substantial variations among patients in terms of disease progression, clinical response, sensitivity to therapy, and prognosis, posing significant challenges in attaining optimal therapeutic outcomes for each patient. Here, we collected five single-cell transcriptome datasets from patients with lung, breast, colorectal, gastric, and liver cancers and constructed multiple cancer blueprints of tumour cell heterogeneity. By integrating multiple bioinformatics analyses, we explored the biological differences underlying tumour cell heterogeneity at the single-cell level and identified tumour cell subcluster-specific biomarkers and potential therapeutic drugs for each subcluster. Interestingly, although tumour cell subpopulations exhibit dramatic differences within the same cancer type and between different cancers at both the genomic and transcriptomic levels, some demonstrate similar oncogenic pathway activities and phenotypes. Tumour cell subpopulations from the five cancers listed above were classified into three major groups corresponding to different treatment strategies. The findings of this study not only focus on the differences but also on the similarities among tumour cell subpopulations across different cancers, providing new insights for individualised therapy.http://www.sciencedirect.com/science/article/pii/S2001037024004409Tumour heterogeneitySingle-cell transcriptomesIndividualized therapyTumour biomarkerDrug repurposing |
| spellingShingle | Xinying Zhang Jiajie Xie Zixin Yang Carisa Kwok Wai Yu Yaohua Hu Jing Qin Tumour heterogeneity and personalized treatment screening based on single-cell transcriptomics Computational and Structural Biotechnology Journal Tumour heterogeneity Single-cell transcriptomes Individualized therapy Tumour biomarker Drug repurposing |
| title | Tumour heterogeneity and personalized treatment screening based on single-cell transcriptomics |
| title_full | Tumour heterogeneity and personalized treatment screening based on single-cell transcriptomics |
| title_fullStr | Tumour heterogeneity and personalized treatment screening based on single-cell transcriptomics |
| title_full_unstemmed | Tumour heterogeneity and personalized treatment screening based on single-cell transcriptomics |
| title_short | Tumour heterogeneity and personalized treatment screening based on single-cell transcriptomics |
| title_sort | tumour heterogeneity and personalized treatment screening based on single cell transcriptomics |
| topic | Tumour heterogeneity Single-cell transcriptomes Individualized therapy Tumour biomarker Drug repurposing |
| url | http://www.sciencedirect.com/science/article/pii/S2001037024004409 |
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