Development and validation of a novel DNA damage repair-related long non-coding RNA signature in predicting prognosis, immunity, and drug sensitivity in uterine corpus endometrial carcinoma

Wang, Tao; Ji, Mei; Liu, Wenwen; Sun, Jing

doi:10.1016/j.csbj.2023.10.025

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…DDR encompasses a complex series of mechanisms and pathways within cells that facilitate the repair of DNA damage, thereby maintaining genome integrity and stability 24 . DRGs serve as common targets for cancer treatment, leading to the development of numerous antitumor drugs 25 .…”

Section: Discussionmentioning

confidence: 99%

Clinical prognostication and immunotherapy response prediction in esophageal squamous cell carcinoma using the DNA damage repair‐associated signature

Zhang,

Wen,

Gong

et al. 2024

Environmental Toxicology

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BackgroundThe relationship between DNA damage repair (DDR) and cancer is intricately intertwined; however, its specific role in esophageal squamous cell carcinoma (ESCC) remains enigmatic.MethodsEmploying single‐cell analysis, we delineated the functionality of DDR‐related genes within the tumor microenvironment (TME). A diverse array of scoring mechanisms, including AUCell, UCell, singscore, ssgsea, and AddModuleScore, were harnessed to scrutinize the activity of DDR‐related genes across different cell types. Differential pathway alterations between high‐and low‐DDR activity cell clusters were compared. Furthermore, leveraging multiple RNA‐seq datasets, we constructed a robust DDR‐associated signature (DAS), and through integrative multiomics analysis, we explored differences in prognosis, pathways, mutational landscapes, and immunotherapy predictions among distinct DAS groups.ResultsNotably, high‐DDR activity cell subpopulations exhibited markedly enhanced cellular communication. The DAS demonstrated uniformity across multiple datasets. The low‐DAS group exhibited improved prognoses, accompanied by heightened immune infiltration and elevated immune checkpoint expression. SubMap analysis of multiple immunotherapy datasets suggested that low‐DAS group may experience enhanced immunotherapy responses. The “oncopredict” R package analyzed and screened sensitive drugs for different DAS groups.ConclusionThrough the integration of single‐cell and bulk RNA‐seq data, we have developed a DAS associated with prognosis and immunotherapy response. This signature holds promise for the future stratification and personalized treatment of ESCC patients in clinical settings.

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Section: Discussionmentioning

confidence: 99%

Clinical prognostication and immunotherapy response prediction in esophageal squamous cell carcinoma using the DNA damage repair‐associated signature

Zhang,

Wen,

Gong

et al. 2024

Environmental Toxicology

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“…LncRNAs have been shown to play complex roles in biological processes in various tissues, with possible implications in DNA repair, cellular proliferation, and human diseases, which made them a common target for recent literature to investigate in cancer 6 . lncRNAs have further been used as biomarkers for overcoming chemoresistance, as well as for the diagnosis of several cancers, including PC [7][8][9][10] .…”

Section: Introductionmentioning

confidence: 99%

“…Emerging research has been able to provide evidence regarding the use of lncRNAs for improved diagnostic accuracy, prognosis prediction, and treatment adjustment using various methods, including machine learning (ML) techniques [8][9][10] . Literature regarding the utilization of ML algorithms has been rapidly rising, with literature urging more rapid use of such algorithms in oncology to increase diagnostic accuracy or to further improve on the available algorithms [11][12][13] .…”

Section: Introductionmentioning

confidence: 99%

Machine learning predicts metastatic progression using novel differentially expressed lncRNAs as potential markers in pancreatic cancer

Alsharoh

2023

Preprint

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Pancreatic cancer (PC) is associated with high mortality overall. Recent literature has focused on investigating long noncoding RNAs (lncRNAs) in several cancers, but studies on their functions in PC are lacking. The purpose of this study was to identify novel lncRNAs and utilize machine learning to techniques to predict metastatic cases of PC using the identified lncRNAs. To identify significantly altered expression of lncRNA in PC, data was collected from The Cancer Genome Atlas (TCGA) and RNA-sequencing (RNA-seq) transcriptomic profiles of pancreatic carcinomas were extracted for differential gene expression analysis. To assess the contribution of these lncRNAs to metastatic progression, different ML algorithms were used, including logistic regression (LR), support vector machine (SVM), random forest classifier (RFC) and eXtreme Gradient Boosting Classifier (XGBC). To improve the predictive accuracy of these models, hyperparameter tuning was performed, in addition to reducing bias through the synthetic minority oversampling technique. Out of 60,660 gene transcripts shared between 151 PC patients, 38 lncRNAs that were significantly differentially expressed were identified. To further investigate the functions of the novel lncRNAs, gene set enrichment analysis (GSEA) was performed on the population lncRNA panel. GSEA results revealed enrichment of several terms implicated in proliferation. Moreover, using the 4 ML algorithms to predict metastatic progression returned 76% accuracy for both SVM and RFC, explicitly based on the novel lncRNA panel. To the best of my knowledge, this is the first study of its kind to identify this lncRNA panel to differentiate between non-metastatic PC and metastatic PC, with many novel lncRNAs previously unmapped to PC. The ML accuracy score reveals important involvement of the detected RNAs. Based on these findings, I suggest further investigations of this lncRNA panelin vitroandin vivo, as they could be targeted for improved outcomes in PC patients, as well as assist in the diagnosis of metastatic progression based on RNA-seq data of primary pancreatic tumors.

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lncRNA-microRNA axis in cancer drug resistance: particular focus on signaling pathways

Saleh,

Al-Ouqaili,

Ali

et al. 2024

Med Oncol

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Development and validation of a novel DNA damage repair-related long non-coding RNA signature in predicting prognosis, immunity, and drug sensitivity in uterine corpus endometrial carcinoma

Cited by 5 publications

References 42 publications

Clinical prognostication and immunotherapy response prediction in esophageal squamous cell carcinoma using the DNA damage repair‐associated signature

Clinical prognostication and immunotherapy response prediction in esophageal squamous cell carcinoma using the DNA damage repair‐associated signature

Machine learning predicts metastatic progression using novel differentially expressed lncRNAs as potential markers in pancreatic cancer

lncRNA-microRNA axis in cancer drug resistance: particular focus on signaling pathways

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