A Clinician's Guide to Bioinformatics for Next-Generation Sequencing

Biological clock regulation by the PER gene family: a new perspective on tumor development

The Period (PER) gene family is one of the core components of the circadian clock, with substantial correlations between the PER genes and cancers identified in extensive researches. Abnormal mutations in PER genes can influence cell function, metabolic activity, immunity, and therapy responses, thereby promoting the initiation and development of cancers. This ultimately results in unequal cancers progression and prognosis in patients. This leads to variable cancer progression and prognosis among patients. In-depth studies on the interactions between the PER genes and cancers can reveal novel strategies for cancer detection and treatment. In this review, we aim to provide a comprehensive overview of the latest research on the role of the PER gene family in cancer.

Pan-cancer analysis reveals potential immunological and prognostic roles of COA6 in human cancers and preliminary exploration of COA6 in bladder cancer

BACKGROUND

Cytochrome C oxidase assembly factor 6 (COA6) is significantly involved in the progression of cancer and is aberrantly expressed in disease. Nevertheless, the comprehensive analysis of COA6 using many omics techniques, and its impact on the prognosis and immunological microenvironment of cancer patients, remains unexplored.

METHODS

We gathered data from 33 cancer cases and conducted a thorough analysis of abnormalities in COA6 gene expression. This analysis included examining its relevance to disease, its diagnostic and prognostic value, pathway enrichment, the immune microenvironment, its association with immune checkpoints, and its ability to predict patient response to immunotherapy and natural small molecule drugs that target the COA6 protein. Ultimately, we examined the function of COA6 in bladder cancer by in vitro research.

RESULTS

Our study revealed significant variations in gene expression and identified COA6 as a potential diagnostic biomarker for cancer. COA6 was also discovered to have a crucial function in pan-cancer involving the tumor microenvironment. COA6 has a strong correlation with well-known immunological checkpoints, including TMB and MSI. Molecular docking identified natural small chemical inhibitors that specifically target the COA6 protein. Ultimately, scientific evidence has verified that suppressing the expression of the COA6 gene hinders the growth and infiltration of bladder cancer cells.

CONCLUSIONS

Our study emphasizes the significant potential of COA6 as a predictive and immunotherapeutic response biomarker. This finding may lead to future investigation into the mechanism of tumor infiltration and the therapeutic possibilities of COA6 in cancer.

Super enhancer lncRNAs: a novel hallmark in cancer

Super enhancers (SEs) consist of clusters of enhancers, harboring an unusually high density of transcription factors, mediator coactivators and epigenetic modifications. SEs play a crucial role in the maintenance of cancer cell identity and promoting oncogenic transcription. Super enhancer lncRNAs (SE-lncRNAs) refer to either transcript from SEs locus or interact with SEs, whose transcriptional activity is highly dependent on SEs. Moreover, these SE-lncRNAs can interact with their associated enhancer regions in cis and modulate the expression of oncogenes or key signal pathways in cancers. Inhibition of SEs would be a promising therapy for cancer. In this review, we summarize the research of SE-lncRNAs in different kinds of cancers so far and decode the mechanism of SE-lncRNAs in carcinogenesis to provide novel ideas for the cancer therapy.

Current challenges and practical aspects of molecular pathology for non-small cell lung cancers

The continuing evolution of treatment options in thoracic oncology requires the pathologist to regularly update diagnostic algorithms for management of tumor samples. It is essential to decide on the best way to use tissue biopsies, cytological samples, as well as liquid biopsies to identify the different mandatory predictive biomarkers of lung cancers in a short turnaround time. However, biological resources and laboratory member workforce are limited and may be not sufficient for the increased complexity of molecular pathological analyses and for complementary translational research development. In this context, the surgical pathologist is the only one who makes the decisions whether or not to send specimens to immunohistochemical and molecular pathology platforms. Moreover, the pathologist can rapidly contact the oncologist to obtain a new tissue biopsy and/or a liquid biopsy if he/she considers that the biological material is not sufficient in quantity or quality for assessment of predictive biomarkers. Inadequate control of algorithms and sampling workflow may lead to false negative, inconclusive, and incomplete findings, resulting in inappropriate choice of therapeutic strategy and potentially poor outcome for patients. International guidelines for lung cancer treatment are based on the results of the expression of different proteins and on genomic alterations. These guidelines have been established taking into consideration the best practices to be set up in clinical and molecular pathology laboratories. This review addresses the current predictive biomarkers and algorithms for use in thoracic oncology molecular pathology as well as the central role of the pathologist, notably in the molecular tumor board and her/his participation in the treatment decision-making. The perspectives in this setting will be discussed.

[Advances in next-generation sequencing technology to analyze the microbiome of patients with chronic sinusitis]

Chronic rhinosinusitis （CRS） is a chronic inflammatory disease of the sinus mucosa, and the pathogenesis of CRS has not been fully elucidated, and the impact of dysbiosis of the microbiome in the nasal cavity and even in the gut on the pathogenesis of CRS remains controversial. Next-generation sequencing technology, a culture-independent high-throughput sequencing method, contributes to a comprehensive understanding of the CRS microbiome. This article reviews the progress of research on the relevance of bacteria and other microorganisms to CRS and the microbial characteristics of the sinus and intestinal tract of patients with CRS, introduces next-generation sequencing technologies for the study of the CRS microbiome, and discusses the therapeutic prospects of CRS and the possibility of probiotic therapy.

Update on HLA-B*15:02 allele associated with adverse drug reactions

HLA alleles, part of the major histocompatibility complex, are strongly associated with adverse drug reactions (ADRs). This review focuses on HLA-B*15:02 and explores its association with ADRs in various ethnic populations and with different drugs, aiming to provide insights into the safe clinical use of drugs and minimize the occurrence of ADRs. Furthermore, the review explores the potential mechanisms by which HLA-B*15:02 may be associated with ADRs, aiming to gain new insights into drug modification and identification of haptens. In addition, it analyzes the frequency of the HLA-B*15:02, genotyping methods, cost-effectiveness and treatment measures for adverse reactions, thereby providing a theoretical basis for formulating clinical treatment plans.

Mitochondria-Related Candidate Genes and Diagnostic Model to Predict Late-Onset Alzheimer's Disease and Mild Cognitive Impairment

Background

Late-onset Alzheimer's disease (LOAD) is the most common type of dementia, but its pathogenesis remains unclear, and there is a lack of simple and convenient early diagnostic markers to predict the occurrence.

Objective

Our study aimed to identify diagnostic candidate genes to predict LOAD by machine learning methods.

Methods

Three publicly available datasets from the Gene Expression Omnibus (GEO) database containing peripheral blood gene expression data for LOAD, mild cognitive impairment (MCI), and controls (CN) were downloaded. Differential expression analysis, the least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature elimination (SVM-RFE) were used to identify LOAD diagnostic candidate genes. These candidate genes were then validated in the validation group and clinical samples, and a LOAD prediction model was established.

Results

LASSO and SVM-RFE analyses identified 3 mitochondria-related genes (MRGs) as candidate genes, including NDUFA1, NDUFS5, and NDUFB3. In the verification of 3 MRGs, the AUC values showed that NDUFA1, NDUFS5 had better predictability. We also verified the candidate MRGs in MCI groups, the AUC values showed good performance. We then used NDUFA1, NDUFS5 and age to build a LOAD diagnostic model and AUC was 0.723. Results of qRT-PCR experiments with clinical blood samples showed that the three candidate genes were expressed significantly lower in the LOAD and MCI groups when compared to CN.

Conclusion

Two mitochondrial-related candidate genes, NDUFA1 and NDUFS5, were identified as diagnostic markers for LOAD and MCI. Combining these two candidate genes with age, a LOAD diagnostic prediction model was successfully constructed.

Bioinformatics analysis and experimental validation of tumorigenic role of PPIA in gastric cancer

Gastric cancer (GC) is a malignant tumor with high incidence rate and mortality. Due to the lack of effective diagnostic indicators, most patients are diagnosed in late stage and have a poor prognosis. An increasing number of studies have proved that Peptidylprolyl isomerase A (PPIA) can play an oncogene role in various cancer types. However, the precise mechanism of PPIA in GC is still unclear. Herein, we analyzed the mRNA levels of PPIA in pan-cancer. The prognostic value of PPIA on GC was also evaluated using multiple databases. Additionally, the relationship between PPIA expression and clinical factors in GC was also examined. We further confirmed that PPIA expression was not affected by genetic alteration and DNA methylation. Moreover, the upstream regulator miRNA and lncRNA of PPIA were identified, which suggested that LINC10232/miRNA-204-5p/PPIA axis might act as a potential biological pathway in GC. Finally, this study revealed that PPIA was negatively correlated with immune checkpoint expression, immune cell biomarkers, and immune cell infiltration in GC.

Recent advance of next-generation sequencing in patients with lung cancer

INTRODUCTION

Precision medicine based on the driver genes mutation status is the current systemic therapeutic paradigm in patients with lung cancer. Next-generation sequencing (NGS) has emerged as a powerful platform for molecular diagnosis by virtue of high-throughput and massively parallel sequencing. Liquid biopsy also enabled the dynamic monitoring and comprehensive profiling of lung cancer in a noninvasive manner. However, challenges remain in the field of technology and clinical applications, especially in the era of immunotherapy.

AREAS COVERED

Here, we update the role of NGS in the context of lung cancer screening, molecular diagnosis, predictive and prognostic biomarkers, and guiding personalized treatment.

EXPERT OPINION

The NGS application for actable genomic alternation has greatly changed the therapeutic landscape in patients with lung cancer including perioperative setting and advanced stage. Meanwhile, emerging evidence has shown the potential of other applications such as early screening and detection, and MRD. However, challenges remain such as the lack of standardized protocols across different platforms and bioinformatics analysis pipelines, and the complexity of interpreting and leveraging numerous genomic mutation messages for therapy selection. Future research is needed to overcome these challenges and expand the applications of NGS to other aspects such as immunotherapy.

Identification of GNG7 as a novel biomarker and potential therapeutic target for gastric cancer via bioinformatic analysis and in vitro experiments

Gastric cancer (GC) is one of the most common malignancies with unfavorable prognoses. The present study aimed to identify novel biomarkers or potential therapeutic targets in GC via bioinformatic analysis and in vitro experiments. The Gene Expression Omnibus and The Cancer Genome Atlas databases were used to screen the differentially expressed genes (DEGs). After protein-protein interaction network construction, both module and prognostic analyses were performed to identify prognosis-related genes in GC. The expression patterns and functions of G protein γ subunit 7 (GNG7) in GC were then visualized in multiple databases and further verified using in vitro experiments. A total of 897 overlapping DEGs were detected and 20 hub genes were identified via systematic analysis. After accessing the prognostic value of the hub genes using the online server Kaplan-Meier plotter, a six-gene prognostic signature was identified, which was also significantly correlated with the process of immune infiltration in GC. The results of open-access database analyses suggested that GNG7 is downregulated in GC; this downregulation was associated with tumor progression. Furthermore, the functional enrichment analysis unveiled that the GNG7-coexpressed genes or gene sets were closely correlated with the proliferation and cell cycle processes of GC cells. Finally, in vitro experiments further confirmed that GNG7 overexpression inhibited GC cell proliferation, colony formation, and cell cycle progression and induced apoptosis. As a tumor suppressor gene, GNG7 suppressed the growth of GC cells via cell cycle blockade and apoptosis induction and thus may be used as a potential biomarker and therapeutic target for GC.