Integrative Genome-Wide Analysis of the Determinants of Rna Splicing in Kidney Renal Clear Cell Carcinoma

Lehmann, Kjong-Van; Kahles, André; Kandoth, Cyriac; Lee, William; Schultz, Nikolaus; Stegle, Oliver; Rätsch, Gunnar

doi:10.1142/9789814644730_0006

Cited by 6 publications

(5 citation statements)

References 34 publications

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“…Furthermore, the full TuPro cohort represents a valuable resource to explore the use of Level 3 data in order to identify putative novel biomarker predictors in cancer (e.g., marker expression, activated pathways, sample composition). Additionally, association studies of data across modalities and detailed analyses of DNA, RNA, protein, and pathway aberrations in cancer will likely lead to a better and more comprehensive understanding of the TuPro tumors’ biology (Lehmann et al 2015; PCAWG Transcriptome Core Group et al 2018; Kahles et al 2018). These analyses can then support clinical trials on these Level 3 data biomarkers for their establishment as Level 1 or 2 data, closing the loop from exploratory science to clinical practice ( Figure 1A ).…”

Section: Exploratory and Integrative Analysesmentioning

confidence: 99%

The Tumor Profiler Study: Integrated, multi-omic, functional tumor profiling for clinical decision support

Irmisch

Bonilla

Chevrier

et al. 2020

Preprint

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Recent technological advances allow profiling of tumor samples to an unparalleled level with respect to molecular and spatial composition as well as treatment response. We describe a prospective, observational clinical study performed within the Tumor Profiler (TuPro) Consortium that aims to show the extent to which such comprehensive information leads to advanced mechanistic insights of a patient's tumor, enables prognostic and predictive biomarker discovery, and has the potential to support clinical decision making. For this study of melanoma, ovarian carcinoma, and acute myeloid leukemia tumors, in addition to the emerging standard diagnostic approaches of targeted NGS panel sequencing and digital pathology, we perform extensive characterization using the following exploratory technologies: single-cell genomics and transcriptomics, proteotyping, CyTOF, imaging CyTOF, pharmacoscopy, and 4i drug response profiling (4i DRP). In this work, we outline the aims of the TuPro study and present preliminary results on the feasibility of using these technologies in clinical practice showcasing the power of an integrative multi-modal and functional approach for understanding a tumor's underlying biology and for clinical decision support.

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Section: Exploratory and Integrative Analysesmentioning

confidence: 99%

The Tumor Profiler Study: Integrated, multi-omic, functional tumor profiling for clinical decision support

Irmisch

Bonilla

Chevrier

et al. 2020

Preprint

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“…Recent studies have demonstrated the role of abnormal AS in KIRC (Lehmann et al, 2015;Li et al, 2017a (Li et al, 2017b). Differential expression of LENG8 in breast cancer has been confirmed (Ye et al, 2015).…”

Section: Discussionmentioning

confidence: 94%

“…Recent studies have demonstrated the role of abnormal AS in KIRC (Lehmann et al, ; Li et al, ). However, there have been no reports of a comprehensive assessment of the prognostic power of AS events in KIRC.…”

Section: Discussionmentioning

confidence: 99%

Systematic analysis of alternative splicing signature unveils prognostic predictor for kidney renal clear cell carcinoma

Song

Liu

et al. 2019

Journal Cellular Physiology

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There is growing evidence that alternative splicing (AS) plays an important role in cancer development. However, a comprehensive analysis of AS signatures in kidney renal clear cell carcinoma (KIRC) is lacking and urgently needed. It remains unclear whether AS acts as diagnostic biomarkers in predicting the prognosis of KIRC patients. In the work, gene expression and clinical data of KIRC were obtained from The Cancer Genome Atlas (TCGA), and profiles of AS events were downloaded from the SpliceSeq database. The RNA sequence/AS data and clinical information were integrated, and we conducted the Cox regression analysis to screen survival‐related AS events and messenger RNAs (mRNAs). Correlation between prognostic AS events and gene expression were analyzed using the Pearson correlation coefficient. Protein‐protein interaction analysis was conducted for the prognostic AS‐related genes, and a potential regulatory network was built using Cytoscape (version 3.6.1). Meanwhile, functional enrichment analysis was conducted. A prognostic risk score model is then established based on seven hub genes (KRT222, LENG8, APOB, SLC3A1, SCD5, AQP1, and ADRA1A) that have high performance in the risk classification of KIRC patients. A total 46,415 AS events including 10,601 genes in 537 patients with KIRC were identified. In univariate Cox regression analysis, 13,362 survival associated AS events and 8,694 survival‐specific mRNAs were detected. Common 3,105 genes were screen by overlapping 13,362 survival associated AS events and 8,694 survival‐specific mRNAs. The Pearson correlation analysis suggested that 13 genes were significantly correlated with AS events (Pearson correlation coefficient >0.8 or <−0.8). Then, We conducted multivariate Cox regression analyses to select the potential prognostic AS genes. Seven genes were identified to be significantly related to OS. A prognostic model based on seven genes was constructed. The area under the ROC curve was 0.767. In the current study, a robust prognostic prediction model was constructed for KIRC patients, and the findings revealed that the AS events could act as potential prognostic biomarkers for KIRC.

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“…Deregulated splicing occurs in carcinomas, resulting in various products with function and nonfunction. The events from cancer-specific splicing easily promote the development of disease [ 32 ]. Abnormal splicing exerts an effect on many carcinomas and brought about multiple features.…”

Section: Discussionmentioning

confidence: 99%

Upregulation Mitochondrial Carrier 1 (MTCH1) Is Associated with Cell Proliferation, Invasion, and Migration of Liver Hepatocellular Carcinoma

Chen

Mo²,

2021

BioMed Research International

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Among the primary causes of cancer-associated death in the world, liver hepatocellular carcinoma (LIHC) ranks the third. LIHC is defined as the sixth most frequently diagnosed carcinoma. The gene mitochondrial carrier 1 (MTCH1) is a protein-coding gene. Recent research suggests that MTCH1 may be associated with some diseases. Here, our study attempts to explore the role and implication of MTCH1 in LIHC. Kaplan Meier Plotter and GEPIA (Gene Expression Profiling Interactive Analysis) databases were employed to determine the expression of MTCH1 and its correlation with prognostic status in LIHC patients. For the first time, our results suggested that MTCH1 was aberrantly expressed in human pan-cancer and highly expressed in LIHC. Its high expression was closely associated with metastasis of tumor, stage of cancer, and poor survival of patients. Then, through enrichment analysis, MTCH1 was found to be closely related to RNA splicing in LIHC. Subsequently, we conducted a series of functional experiments. PCR data showed that LIHC cell lines and samples are highly expressed MTCH1. CCK-8 (Cell Counting Kit-8) assays and Transwell assays indicated that silencing MTCH1 certainly suppressed cell proliferation, migration, and invasion. These findings shed the clue that MTCH1 could be regarded as the potential prognosis biomarker of LIHC and a promising therapeutic target for LIHC.

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Integrative Genome-Wide Analysis of the Determinants of Rna Splicing in Kidney Renal Clear Cell Carcinoma

Cited by 6 publications

References 34 publications

The Tumor Profiler Study: Integrated, multi-omic, functional tumor profiling for clinical decision support

The Tumor Profiler Study: Integrated, multi-omic, functional tumor profiling for clinical decision support

Systematic analysis of alternative splicing signature unveils prognostic predictor for kidney renal clear cell carcinoma

Upregulation Mitochondrial Carrier 1 (MTCH1) Is Associated with Cell Proliferation, Invasion, and Migration of Liver Hepatocellular Carcinoma

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