Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling

Yli-Hietanen, Jari; Ylipää, Antti; Yli‐Harja, Olli

doi:10.1186/s40880-015-0008-8

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…We conducted here a computational analysis of gene expression data of cancer tissues versus normal control tissues of 11 types of human cancer based on The Cancer Genome Atlas (TCGA) gene expression data [ 20 , 21 ], focusing on glutamine and glutamate metabolisms. We addressed the following four questions through our analyses.…”

Section: Introductionmentioning

confidence: 99%

Systematic analyses of glutamine and glutamate metabolisms across different cancer types

Tian

Cao

et al. 2017

Chin J Cancer

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BackgroundGlutamine and glutamate are known to play important roles in cancer biology. However, no detailed information is available in terms of their levels of involvement in various biological processes across different cancer types, whereas such knowledge could be critical for understanding the distinct characteristics of different cancer types. Our computational study aimed to examine the functional roles of glutamine and glutamate across different cancer types.MethodsWe conducted a comparative analysis of gene expression data of cancer tissues versus normal control tissues of 11 cancer types to understand glutamine and glutamate metabolisms in cancer. Specifically, we developed a linear regression model to assess differential contributions by glutamine and/or glutamate to each of seven biological processes in cancer versus control tissues.ResultsWhile our computational predictions were consistent with some of the previous observations, multiple novel predictions were made: (1) glutamine is generally not involved in purine synthesis in cancer except for breast cancer, and is similarly not involved in pyridine synthesis except for kidney cancer; (2) glutamine is generally not involved in ATP production in cancer; (3) glutamine’s contribution to nucleotide synthesis is minimal if any in cancer; (4) glutamine is not involved in asparagine synthesis in cancer except for bladder and lung cancers; and (5) glutamate does not contribute to serine synthesis except for bladder cancer.ConclusionsWe comprehensively predicted the roles of glutamine and glutamate metabolisms in selected metabolic pathways in cancer tissues versus control tissues, which may lead to novel approaches to therapeutic development targeted at glutamine and/or glutamate metabolism. However, our predictions need further functional validation.

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Section: Introductionmentioning

confidence: 99%

Systematic analyses of glutamine and glutamate metabolisms across different cancer types

Tian

Cao

et al. 2017

Chin J Cancer

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“…Development of next generation sequencing technologies that allow for high sensitivity, resolution, throughput and speed have advanced research on biomarker discovery for cancer diagnosis, assessing prognosis, and directing treatment monitoring (19-22). RNA-seq has emerged as a powerful tool for unbiased interrogation of gene expression as well as identification of splice variants and non-coding RNAs (14).…”

Section: Discussionmentioning

confidence: 99%

Deep Sequencing of Urinary RNAs for Bladder Cancer Molecular Diagnostics

Sin

Mach

Sinha

et al. 2017

Clinical Cancer Research

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Purpose The majority of bladder cancer patients present with localized disease and are managed by transurethral resection. However, the high rate of recurrence necessitates lifetime cystoscopic surveillance. Developing a sensitive and specific urine-based test would significantly improve bladder cancer screening, detection, and surveillance. Experimental Design RNA-seq was used for biomarker discovery to directly assess the gene expression profile of exfoliated urothelial cells in urine derived from bladder cancer patients (n=13) and controls (n=10). Eight bladder cancer specific and 3 reference genes identified by RNA-seq were quantitated by qPCR in a training cohort of 102 urine samples. A diagnostic model based on the training cohort was constructed using multiple logistic regression. The model was further validated in an independent cohort of 101 urines. Results 418 genes were found to be differentially expressed between bladder cancer and controls. Validation of a subset of these genes was used to construct an equation for computing a probability of bladder cancer score (PBC) based on expression of 3-markers (ROBO1, WNT5A, and CDC42BPB). Setting PBC=0.45 as the cutoff for a positive test, urine testing using the 3-marker panel had overall 88% sensitivity and 92% specificity in the training cohort. The accuracy of the 3-marker panel in the independent validation cohort yielded an area under the curve of 0.87 and overall 83% sensitivity and 89% specificity. Conclusions Urine-based molecular diagnostics using this 3-marker signature could provide a valuable adjunct to cystoscopy and may lead to a reduction of unnecessary procedures for bladder cancer diagnosis.

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“…In recent years, with the development of next-generation sequencing technique and its wide application in cancer studies [ 18 ], the landscape of somatic mutations has been revealed for many types of human cancer. According to these comprehensive data sets, Mx1 mutations have been discovered in a number of common cancer types, including colorectal cancer [ 19 – 22 ], head and neck squamous cell carcinoma [ 23 ], follicular lymphoma [ 24 ], cutaneous squamous cell carcinoma [ 25 ], mantle cell lymphoma [ 26 ], embryonal rhabdomyosarcoma [ 27 ], renal cell carcinoma [ 28 ], prostate cancer [ 29 ], lung adenocarcinoma [ 30 ], melanoma [ 31 ], medulloblastoma [ 32 ], and ovarian carcinoma [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Structural analysis of tumor-related single amino acid mutations in human MxA protein

Hua

Chen

et al. 2015

Chin J Cancer

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BackgroundHuman myxovirus resistant protein A (MxA), encoded by the myxovirus resistance 1 (Mx1) gene, is an interferon (IFN)-triggered dynamin-like multi-domain GTPase involved in innate immune responses against viral infections. Recent studies suggest that MxA is associated with several human cancers and may be a tumor suppressor and a promising biomarker for IFN therapy. Mx1 gene mutations in the coding region for MxA have been discovered in many types of cancer, suggesting potential biological associations between mutations in MxA protein and corresponding cancers. In this study, we performed a systematic analysis based on the crystal structures of MxA and elucidated how these mutations specifically affect the structure and therefore the function of MxA protein.MethodsCancer-associated Mx1 mutations were collected and screened from the COSMIC database. Twenty-two unique mutations that cause single amino acid alterations in the MxA protein were chosen for the analysis. Amino acid sequence alignment was performed using Clustal W to check the conservation level of mutation sites in Mx proteins and dynamins. Structural analysis of the mutants was carried out with Coot. Structural models of selected mutants were generated by the SWISS-MODEL server for comparison with the corresponding non-mutated structures. All structural figures were generated using PyMOL.ResultsWe analyzed the conservation level of the single-point mutation sites and mapped them on different domains of MxA. Through individual structural analysis, we found that some mutations severely affect the stability and function of MxA either by disrupting the intra-/inter-molecular interactions supported by the original residues or by incurring unfavorable configuration alterations, whereas other mutations lead to gentle or no interference to the protein stability and function because of positions or polarity features. The potential clinical value of the mutations that lead to drastic influence on MxA protein is also assessed.ConclusionsAmong all of the reported tumor-associated single-point mutations, seven of them notably affect the structure and function of MxA and therefore deserve more attention with respect to potential clinical applications. Our research provides an example for systematic analysis and consequence evaluation of single-point mutations on a given cancer-related protein.

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Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling

Cited by 5 publications

References 8 publications

Systematic analyses of glutamine and glutamate metabolisms across different cancer types

Systematic analyses of glutamine and glutamate metabolisms across different cancer types

Deep Sequencing of Urinary RNAs for Bladder Cancer Molecular Diagnostics

Structural analysis of tumor-related single amino acid mutations in human MxA protein

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