Dataset Information

Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors.

ABSTRACT:

Motivation

Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor.

Results

Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line-derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times (P = .048) and in patients with pancreatic cancer treated with gemcitabine (P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

SUBMITTER: Wells JD

PROVIDER: S-EPMC7983245 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: PDZ binding kinase (PBK) is a serine/threonine kinase, which belongs to the mitogen-activated protein kinase kinase (MAPKK) family. It has been shown to be a critical gene in the regulation of mitosis and tumorigenesis, but the role of PBK in various cancers remains unclear. In this study, we systematically explored the prognostic and predictive value of PBK expression in 33 cancer types. Methods: Public databases including the cBioPortal database, GDSC database, GTEx database, CCLE database, and TCGA database were used to detect the PBK expression and its association with the prognosis, clinicopathologic stage, TMB, MSI, immune microenvironment, immune checkpoints, immune cell infiltration, enrichment pathways, and IC50 across pan-cancer. The statistical analyses and visualization were conducted using R software. Results: PBK expression is relatively high in most cancers compared to their normal counterparts, and this gene is barely expressed in normal tissues. High expression of PBK is significantly associated with poor prognosis and clinicopathologic stages I, II, and III in different cancers. Furthermore, PBK expression is strongly associated with TMB in 23 cancer types and associated with MSI in nine cancer types. Moreover, the correlation analysis of the microenvironment and immune cells indicated that PBK is negatively correlated with the immune infiltration levels but positively correlated with the infiltration levels of M0 and M1 macrophages, T cells CD4 memory activated, and T cells follicular helper. GSEA analysis revealed that the biological function or pathways relevant to the cell cycle and mitosis were frequently enriched at the level of high expression of PBK. Conclusion: These results revealed the oncogenic role of PBK, which is significantly upregulated in various cancers and indicated poor prognosis and immune infiltration in multiple cancers. It also suggested that PBK may serve as a biomarker in multiple tumor progress and patient survival.

Project description:Background: Deoxythymidylate kinase (DTYMK) has been reported to correlate with the progression of hepatocellular carcinoma. However, the role of DTYMK in human cancers is not studied. In this study, we studied the prognostic value, functional states, and correlations with immune infiltration of DTYMK in human cancers. Methods: The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), UALCAN, Clinical Proteomic Tumor Analysis Consortium (CPTAC), the search tool for the retrieval of interacting genes (STRING), GeneMANIA, cBioPortal, Cancer Single-cell State Atlas (CancerSEA), and Tumor IMmune Estimation Resource (TIMER) databases were utilized to analyze DTYMK in cancers. Results: In general, DTYMK is abnormally expressed between most human cancer and normal tissues from a pan-cancer perspective. DTYMK can be used as a diagnostic biomarker to differentiate tumor tissues from normal tissues in most tumors. Upregulation of DTYMK predicted poor survival status in most cancer types in TCGA. Moreover, DTYMK expression was correlated with the T stage in ACC, BRCA, KIRC, LIHC, and LUAD, with the N stage in BLCA, HNSC, KICH, KIRC, LUAD, LUSC, and THCA, with the M stage in ACC, KIRC, KIRP, and LUAD, with TNM stage in ACC, KIRC, LIHC, LUAD, and LUSC. In addition, based on single-cell sequencing data, we concluded that the expression of DTYMK was correlated with the functional status of the cell cycle, DNA damage, DNA repair, invasion, EMT, and proliferation. Finally, DTYMK expression was correlated with six infiltrating immune cells, including B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells by investigating TIMER. Conclusion: Our findings suggested that abnormally expressed DTYMK was correlated with poor survival, malignant functional status, and immune infiltrates. DTYMK might be served as a potential biomarker for diagnosis and poor prognosis in various cancer types. DTYMK might act as a potential target for immune therapy.

Dataset Information

Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors.

Motivation

Results

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