Recent Trends in Cancer Genomics and Bioinformatics Tools Development

Anashkina, Anastasia A.; Leberfarb, Elena Yu; Orlov, Yuriy L.

doi:10.3390/ijms222212146

Cited by 24 publications

(23 citation statements)

References 43 publications

(53 reference statements)

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“…Recently, with the rapid development of bioinformatics, an increasing number of computing methods have been developed and used in tumor research to promote tumor diagnosis and treatment technology (Anashkina et al, 2021;Rogers et al, 2021). To investigate the molecular function of NFE2L3 in pan-cancer, we performed a comprehensive and integrated analysis of NFE2L3 by combining multiple bioinformatic approaches.…”

Section: Discussionmentioning

confidence: 99%

Multi-Omics Analysis of Molecular Characteristics and Carcinogenic Effect of NFE2L3 in Pan-Cancer

et al. 2022

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NFE2L3, also known as NFE2L3, is a nuclear transcription factor associated with the pathogenesis and progression of human tumors. To systematically and comprehensively investigate the role of NFE2L3 in tumors, a pan-cancer analysis was performed using multi-omics data, including gene expression analysis, diagnostic and prognostic analysis, epigenetic methylation analysis, gene alteration analysis, immune feature analysis, functional enrichment analysis, and tumor cell functional status analysis. Furthermore, the molecular mechanism of NFE2L3 in liver hepatocellular carcinoma (LIHC) was explored. The relationship between NFE2L3 expression and survival prognosis of patients with LIHC was analyzed and a nomogram prediction model was constructed. Our study showed that NFE2L3 expression was upregulated in most cancers, suggesting that NFE2L3 may play an important role in promoting cancer progression. NFE2L3 expression is closely related to DNA methylation, genetic alteration, immune signature, and tumor cell functional status in pan-cancers. Furthermore, NFE2L3 was demonstrated to be an independent risk factor for LIHC, and the nomogram model based on NFE2L3 expression had good prediction efficiency for the overall survival of patients with LIHC. In summary, our study indicated that NFE2L3 may be an important molecular biomarker for the diagnosis and prognosis of pan-cancer. NFE2L3 is expected to be a potential molecular target for the treatment of tumors.

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Section: Discussionmentioning

confidence: 99%

Multi-Omics Analysis of Molecular Characteristics and Carcinogenic Effect of NFE2L3 in Pan-Cancer

et al. 2022

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“…Microarray analyses offer an effective means of identifying genes that are differentially expressed in a particular pathological or physiological context, thereby providing a basis for efforts to understand the molecular etiology of a particular biological state. Many prior research efforts have explored differences in gene expression pro les as a function of disease subtype [19][20][21]. Jang et al, for example, conducted the molecular strati cation of non-small cell lung cancer patients based upon patterns of transcriptomic gene expression, ultimately leading to the identi cation of several immune-related molecular subtypes for this particular cancer that were suited to predicting patients to PD-1 blockade-based immunotherapeutic intervention.…”

Section: Discussionmentioning

confidence: 99%

Gene expression profiling-based identification of different influenza patient molecular subgroups

Chen

Hua

2022

Preprint

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Background Influenza infections routinely circulate throughout the world, resulting in extensive patient morbidity and mortality. Even so, the molecular mechanisms underlying patient responses to influenza remain poorly understood. The present study was thus developed with the goal of classifying influenza patients into different molecular subgroups based on gene expression profiling data and assessing the relationship between these subgroups and disease severity. Methods The GSE101702 and GSE21802 microarray datasets were downloaded from the Gene Expression Omnibus, and the influenza cases in these datasets were classified via consensus clustering into differential gene expression profile-based subgroups. Transcriptomic differences among these subgroups were assessed via weighted gene co-expression network analysis (WGCNA), and enrichment analyses for individual WGCNA modules were then performed. Results In total, 143 influenza patients were separated into two distinct molecular subgroups through a consensus clustering approach. Patients in subgroup II exhibited increased influenza disease severity independent of age. In total, 8 subgroup-specific WCGNA modules were defined, and functional enrichment analyses revealed that relative to patients in subgroup I, those in subgroup II exhibited significant upregulation of immune response and inflammation-related pathways together with ribosome biogenesis pathway downregulation. Conclusions The transcriptomic classification of influenza patients performed herein was linked to disease severity, offering insight into the molecular pathogenesis of severe influenza infections.

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“…High-throughput sequencing often produces millions of sequencing reads at a time, and the alignment or assembly of these reads allows determination of various mutations at the genomic level, accurate gene expression quantification at the transcriptomic level, and identification of histone or DNA modification at the epigenomic level. The resulting accumulation of enormous multi-omics information has opened up a new era of finding effective disease markers and studying their roles in disease occurrence and development (Anashkina et al, 2021).Using high-throughput sequencing, various markers of chronic diseases have been developed at all omics levels, which have been used for diagnosis and classification of diseases, prediction of treatment effects, and prevention of diseases (Voronova et al, 2020;Glukhov et al, 2021). The chronic diseases include cancer, heart disease, diabetes, arthritis.…”

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Editorial: High-Throughput Sequencing-Based Investigation of Chronic Disease Markers and Mechanisms

et al. 2022

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Editorial on the Research Topic High-throughput sequencing-based investigation of chronic disease markers and mechanismsThis "High-throughput sequencing-based investigation of chronic disease markers and mechanisms" issue focuses on the genomics studies on cancer and chronic diseases. With the recent development of sequencing technology and the rapid reduction of sequencing costs, high-throughput sequencing (including second and third-generation sequencing) is revolutionizing basic life science research and clinical research. High-throughput sequencing often produces millions of sequencing reads at a time, and the alignment or assembly of these reads allows determination of various mutations at the genomic level, accurate gene expression quantification at the transcriptomic level, and identification of histone or DNA modification at the epigenomic level. The resulting accumulation of enormous multi-omics information has opened up a new era of finding effective disease markers and studying their roles in disease occurrence and development (Anashkina et al., 2021).Using high-throughput sequencing, various markers of chronic diseases have been developed at all omics levels, which have been used for diagnosis and classification of diseases, prediction of treatment effects, and prevention of diseases (Voronova et al., 2020;Glukhov et al., 2021). The chronic diseases include cancer, heart disease, diabetes, arthritis. The quickly and massively acquired multi-omics data, together with newly developed algorithms, provide excellent opportunities for the identification of more reliable biomarkers. This Research Topic aimed at 1) developing new chronic disease markers at four levels (i.e., genome, epigenome, transcriptome, and translatome) with the help of highthroughput sequencing, and 2) delineating potential marker-related mechanisms for chronic disease occurrence or development. More specifically, this Research Topic contains contributions including: 1) Identification of novel biomarkers and prediction signatures for chronic disease detection or prognosis prediction using high-throughput sequencing; 2) Analysis the possible pathological causes of markers as well as the potential roles they play in disease initiation and development; 3) Applications of new high-throughput sequencing techniques facilitating the development of more effective biomarkers of chronic disease;

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Recent Trends in Cancer Genomics and Bioinformatics Tools Development

Abstract: We overview recent research trends in cancer genomics, bioinformatics tools development and medical genetics, based on results discussed in papers collections “Medical Genetics, Genomics and Bioinformatics” (https://www [...]

Cited by 24 publications

References 43 publications

Multi-Omics Analysis of Molecular Characteristics and Carcinogenic Effect of NFE2L3 in Pan-Cancer

Multi-Omics Analysis of Molecular Characteristics and Carcinogenic Effect of NFE2L3 in Pan-Cancer

Gene expression profiling-based identification of different influenza patient molecular subgroups

Editorial: High-Throughput Sequencing-Based Investigation of Chronic Disease Markers and Mechanisms

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