Developing hypoxia and lactate metabolism-related molecular subtypes and prognostic signature for clear cell renal cell carcinoma through integrating machine learning DOI Creative Commons
Jinhui Liu, Tao Yang, Jiayuan Liu

et al.

Discover Oncology, Journal Year: 2024, Volume and Issue: 15(1)

Published: Nov. 13, 2024

The microenvironment of clear cell renal carcinoma (ccRCC) is characterized by hypoxia and increased lactate production. However, the impact metabolism on ccRCC remains incompletely understood. In this study, a new molecular subtype developed based hypoxia-related genes (HRGs) metabolism-related (LMRGs), aiming to create tool that can predict survival rate, immune status, responsiveness treatment patients. We obtained RNA-seq data clinical information patients with from TCGA GEO. HRGs LMRGs are sourced Molecular Signatures Database. Integrating 10 machine learning algorithms 101 frameworks, we constructed prognostic model related metabolism. Its accuracy reliability evaluated through constructing nomograms, drawing ROC curves, validating datasets. Additionally, risk subgroups functional enrichment, tumor mutational burden (TMB), infiltration degree, checkpoint expression level. Finally, evaluate immunotherapy determine personalized drugs for specific subgroups. 85 valuable were screened out. Functional enrichment analysis shows group high-risk scores (HLMRGS) mainly involved in activation immune-related activities, while low HLMRGS more active metabolic tumor-related pathways. At same time, differences cellular states between high observed. potential determined. have novel signature integrates It expected become an effective prognosis prediction, medicine ccRCC.

Language: Английский

Early recurrence as a pivotal event in nasopharyngeal carcinoma: identifying predictors and key molecular signals for survivors DOI Creative Commons
Ying Li, Zongwei Huang,

Ximing Zeng

et al.

Head & Face Medicine, Journal Year: 2024, Volume and Issue: 20(1)

Published: Sept. 28, 2024

Language: Английский

Citations

2
Intratumor heterogeneity in breast cancer: Tracing its origins and translating findings into clinical practice DOI Creative Commons

Tian-Qi Gu,

Yu-Ling Xiao,

Zhi-Ming Shao

et al.

Precision medicine and engineering., Journal Year: 2024, Volume and Issue: 1(1), P. 100006 - 100006

Published: Aug. 28, 2024

Language: Английский

Citations

1
Decoding the impact of fibroblast heterogeneity on prognosis and drug resistance in high-grade serous ovarian cancer through tumor evolution analysis DOI Open Access

tingjie wang

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: June 12, 2024

Background: Tumor heterogeneity is associated with poor prognosis and drug resistance, leading to therapeutic failure. Here, we aim utilize tumor evolution analysis decode the intra- inter-tumoral of high-grade serous ovarian cancer (HGSOC), unraveling correlation between as well chemotherapy response through single-cell spatial transcriptomic analysis. Methods: We collected curated 28 HGSOC patients data from five datasets. Then, developed a novel text mining-based machine learning approach deconstruct evolutionary patterns cell functions. This allowed us identify key tumor-related genes within different branches, elucidate microenvironmental compositions that various functional cells depend on, analyze inter-heterogeneity tumors their microenvironments in relation patients. further validated our findings two seven bulk datasets, totally 1,030 Results: By employing clusters proxies for clonality, identified significant increase state heterogeneity, which was strongly correlated patient treatment response. Furthermore, increased clonality characteristics cancer-associated fibroblast (CAF). also found proximity CXCL12-positive CAF cells, mediated CXCL12/CXCR4 interaction, highly positively resistance HGSOC. Finally, constructed panel 24 statistical modeling, are fibroblasts can predict both Conclusions: Our study offers insights into collective behavior communities HGSOC, potential drivers therapy. Functional analyses experiments revealed strong association progression outcomes. provide an important theoretical basis clinical treatment.

Language: Английский

Citations

0
Decoding the effect of fibroblast heterogeneity on prognosis and drug resistance in high-grade serous ovarian cancer through tumor evolution analysis DOI Creative Commons

tingjie wang

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 5, 2024

Abstract Tumor heterogeneity is associated with poor prognosis and drug resistance, leading to therapeutic failure. Here, we used tumor evolution analysis determine the intra- intertumoral of high-grade serous ovarian cancer (HGSOC) analyze correlation between prognosis, as well chemotherapy response, through single-cell spatial transcriptomic analysis. We collected curated 28 HGSOC patients’ data from five datasets. Then, developed a novel text-mining-based machine-learning approach deconstruct evolutionary patterns cell functions. then identified key tumor-related genes within different branches, characterized microenvironmental compositions that various functional cells depend on, analyzed microenvironments. These analyses were conducted in relation response patients. validated our findings two seven bulk datasets (total: 1,030 patients). Using clusters proxies for clonality, significant increase state was strongly correlated patient treatment response. Furthermore, increased clonality characteristics cancer-associated fibroblasts (CAFs). The proximity CXCL12-positive CAFs cells, mediated CXCL12/CXCR4 interaction, highly positively resistance HGSOC. In this study, constructed panel 24 statistical modeling correlate can predict both

Language: Английский

Citations

0
Effect of fibroblast heterogeneity on prognosis and drug resistance in high-grade serous ovarian cancer DOI Creative Commons
Tingjie Wang, Linhua Tian, Bing Wei

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 4, 2024

Tumor heterogeneity is associated with poor prognosis and drug resistance, leading to therapeutic failure. Here, we used tumor evolution analysis determine the intra- intertumoral of high-grade serous ovarian cancer (HGSOC) analyze correlation between prognosis, as well chemotherapy response, through single-cell spatial transcriptomic analysis. We collected curated 28 HGSOC patients' data from five datasets. Then, developed a novel text-mining-based machine-learning approach deconstruct evolutionary patterns cell functions. then identified key tumor-related genes within different branches, characterized microenvironmental compositions that various functional cells depend on, analyzed microenvironments. These analyses were conducted in relation response patients. validated our findings two seven bulk datasets (total: 1,030 patients). Using clusters proxies for clonality, significant increase state was strongly correlated patient treatment response. Furthermore, increased clonality characteristics cancer-associated fibroblasts (CAFs). The proximity CXCL12-positive CAFs cells, mediated CXCL12/CXCR4 interaction, highly positively resistance HGSOC. Finally, constructed panel 24 statistical modeling correlate can predict both Our study offers insights into collective behavior communities HGSOC, potential drivers therapy. There strong association progression, outcomes.

Language: Английский

Citations

0
Developing hypoxia and lactate metabolism-related molecular subtypes and prognostic signature for clear cell renal cell carcinoma through integrating machine learning DOI Creative Commons
Jinhui Liu, Tao Yang, Jiayuan Liu

et al.

Discover Oncology, Journal Year: 2024, Volume and Issue: 15(1)

Published: Nov. 13, 2024

The microenvironment of clear cell renal carcinoma (ccRCC) is characterized by hypoxia and increased lactate production. However, the impact metabolism on ccRCC remains incompletely understood. In this study, a new molecular subtype developed based hypoxia-related genes (HRGs) metabolism-related (LMRGs), aiming to create tool that can predict survival rate, immune status, responsiveness treatment patients. We obtained RNA-seq data clinical information patients with from TCGA GEO. HRGs LMRGs are sourced Molecular Signatures Database. Integrating 10 machine learning algorithms 101 frameworks, we constructed prognostic model related metabolism. Its accuracy reliability evaluated through constructing nomograms, drawing ROC curves, validating datasets. Additionally, risk subgroups functional enrichment, tumor mutational burden (TMB), infiltration degree, checkpoint expression level. Finally, evaluate immunotherapy determine personalized drugs for specific subgroups. 85 valuable were screened out. Functional enrichment analysis shows group high-risk scores (HLMRGS) mainly involved in activation immune-related activities, while low HLMRGS more active metabolic tumor-related pathways. At same time, differences cellular states between high observed. potential determined. have novel signature integrates It expected become an effective prognosis prediction, medicine ccRCC.

Language: Английский

Citations

0