Familiarity Breeds Strategy: In Silico Untangling of the Molecular Complexity on Course of Autoimmune Liver Disease-to-Hepatocellular Carcinoma Transition Predicts Novel Transcriptional Signatures

Integration of bioinformatics and machine learning approaches for the validation of pyrimidine metabolism-related genes and their implications in immunotherapy for osteoporosis

BACKGROUND

Osteoporosis (OP), the "silent epidemic" of our century, poses a significant challenge to public health, predominantly affecting postmenopausal women and the elderly. It evolves from mild symptoms to pronounced severity, stabilizing eventually. Unique among OP's characteristics is the altered metabolic profile of affected cells, particularly in pyrimidine metabolism (PyM), a crucial pathway for nucleotide turnover and pyrimidine decomposition. While metabolic adaptation is acknowledged as a therapeutic target in various diseases, the specific role of PyM genes (PyMGs) in OP's molecular response remains to be clarified.

METHODS

In pursuit of elucidating and authenticating PyMGs relevant to OP, we embarked on a comprehensive bioinformatics exploration. This entailed the integration of Weighted Gene Co-expression Network Analysis (WGCNA) with a curated list of 37 candidate PyMGs, followed by the examination of their biological functions and pathways via Gene Set Variation Analysis (GSVA). The Least Absolute Shrinkage and Selection Operator (LASSO) technique was harnessed to identify crucial hub genes. We evaluated the diagnostic prowess of five PyMGs in OP detection and explored their correlation with OP's clinical traits, further validating their expression profiles through independent datasets (GSE2208, GSE7158, GSE56815, and GSE35956).

RESULTS

Our analytical rigor unveiled five PyMGs-IGKC, TMEM187, RPS11, IGLL3P, and GOLGA8N-with significant ties to OP. A deeper dive into their biological functions highlighted their roles in estrogen response modulation, cytosolic calcium ion concentration regulation, and GABAergic synaptic transmission. Remarkably, these PyMGs emerged as potent diagnostic biomarkers for OP, distinguishing affected individuals with substantial accuracy.

CONCLUSIONS

This investigation brings to light five PyMGs intricately associated with OP, heralding new avenues for biomarker discovery and providing insights into its pathophysiological underpinnings. These findings not only deepen our comprehension of OP's complexity but also herald the advent of more refined diagnostic and therapeutic modalities.

GGT5 facilitates migration and invasion through the induction of epithelial-mesenchymal transformation in gastric cancer

BACKGROUND

Gamma-glutamyltransferase 5 (GGT5), one of the two members in the GGT family (GGT1 and GGT5), plays a crucial role in oxidative regulation, inflammation promotion, and drug metabolism. Particularly in the tumorigenesis of various cancers, its significance has been recognized. Nevertheless, GGT5's role in gastric cancer (GC) remains ambiguous. This study delves into the function and prognostic significance of GGT5 in GC through a series of in vitro experiments.

METHODS

Employing online bioinformatics analysis tools such as The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Kaplan-Meier plotter, and cBioPortal, we explored GGT5 characteristics and functions in GC. This encompassed aberrant expression, prognostic value, genomic alterations and mutations, immune cell infiltration, and associated signaling pathways. Immunohistochemistry was conducted to assess GGT5 expression in GC and adjacent normal tissues. Subsequently, univariate and multivariate logistic regression analyses were applied to investigate the associations between GGT5 and clinical characteristics. CCK8, wound healing, and migration assays were utilized to evaluate the impact of GGT5 on cell viability and migration. Additionally, Gene Set Enrichment Analysis (GSEA) and Western blot analysis were performed to scrutinize the activity of the epithelial-mesenchymal transformation (EMT) signaling pathway under GGT5 regulation.

RESULTS

GGT5 exhibits upregulation in gastric cancer, with its overexpression significantly linked to histological differentiation in GC patients (P < 0.05). Multivariate analysis indicates that elevated GGT5 expression is an independent risk factor associated with poorer overall survival in gastric cancer patients (P < 0.05). In vitro experiments reveal that downregulation of GGT5 hampers the proliferation and migration of GC cell lines. Finally, GSEA using TCGA data highlights a significant correlation between GGT5 expression and genes associated with EMT, a finding further confirmed by Western blot analysis.

CONCLUSIONS

GGT5 emerges as a promising prognostic biomarker and potential therapeutic target for GC.

Unlocking potential biomarkers bridging coronary atherosclerosis and pyrimidine metabolism-associated genes through an integrated bioinformatics and machine learning approach

BACKGROUND

This study delves into the intricate landscape of atherosclerosis (AS), a chronic inflammatory disorder with significant implications for cardiovascular health. AS poses a considerable burden on global healthcare systems, elevating both mortality and morbidity rates. The pathological underpinnings of AS involve a marked metabolic disequilibrium, particularly within pyrimidine metabolism (PyM), a crucial enzymatic network central to nucleotide synthesis and degradation. While the therapeutic relevance of pyrimidine metabolism in diverse diseases is acknowledged, the explicit role of pyrimidine metabolism genes (PyMGs) in the context of AS remains elusive. Utilizing bioinformatics methodologies, this investigation aims to reveal and substantiate PyMGs intricately linked with AS.

METHODS

A set of 41 candidate PyMGs was scrutinized through differential expression analysis. GSEA and GSVA were employed to illuminate potential biological pathways and functions associated with the identified PyMGs. Simultaneously, Lasso regression and SVM-RFE were utilized to distill core genes and assess the diagnostic potential of four quintessential PyMGs (CMPK1, CMPK2, NT5C2, RRM1) in discriminating AS. The relationship between key PyMGs and clinical presentations was also explored. Validation of the expression levels of the four PyMGs was performed using the GSE43292 and GSE9820 datasets.

RESULTS

This investigation identified four PyMGs, with NT5C2 and RRM1 emerging as key players, intricately linked to AS pathogenesis. Functional analysis underscored their critical involvement in metabolic processes, including pyrimidine-containing compound metabolism and nucleotide biosynthesis. Diagnostic evaluation of these PyMGs in distinguishing AS showcased promising results.

CONCLUSION

In conclusion, this exploration has illuminated a constellation of four PyMGs with a potential nexus to AS pathogenesis. These findings unveil emerging biomarkers, paving the way for novel approaches to disease monitoring and progression, and providing new avenues for therapeutic intervention in the realm of atherosclerosis.

RNA sequencing reveals differential long noncoding RNA expression profiles in bacterial and viral meningitis in children

BACKGROUND

We aimed to investigate the involvement of long non-coding RNA (lncRNA) in bacterial and viral meningitis in children.

METHODS

The peripheral blood of five bacterial meningitis patients, five viral meningitis samples, and five healthy individuals were collected for RNA sequencing. Then, the differentially expressed lncRNA and mRNA were detected in bacterial meningitis vs. controls, viral meningitis vs. healthy samples, and bacterial vs. viral meningitis patients. Besides, co-expression and the competing endogenous RNA (ceRNA) networks were constructed. Receiver operating characteristic curve (ROC) analysis was performed.

RESULTS

Compared with the control group, 2 lncRNAs and 32 mRNAs were identified in bacterial meningitis patients, and 115 lncRNAs and 54 mRNAs were detected in viral meningitis. Compared with bacterial meningitis, 165 lncRNAs and 765 mRNAs were identified in viral meningitis. 2 lncRNAs and 31 mRNAs were specific to bacterial meningitis, and 115 lncRNAs and 53 mRNAs were specific to viral meningitis. The function enrichment results indicated that these mRNAs were involved in innate immune response, inflammatory response, and immune system process. A total of 8 and 1401 co-expression relationships were respectively found in bacterial and viral meningitis groups. The ceRNA networks contained 1 lncRNA-mRNA pair and 4 miRNA-mRNA pairs in viral meningitis group. GPR68 and KIF5C, identified in bacterial meningitis co-expression analysis, had an area under the curve (AUC) of 1.00, while the AUC of OR52K2 and CCR5 is 0.883 and 0.698, respectively.

CONCLUSIONS

Our research is the first to profile the lncRNAs in bacterial and viral meningitis in children and may provide new insight into understanding meningitis regulatory mechanisms.

A novel prognostic signature and therapy guidance for hepatocellular carcinoma based on STEAP family

BACKGROUND

The six-transmembrane epithelial antigen of prostate (STEAP) family members are known to be involved in various tumor-related biological processes and showed its huge potential role in tumor immunotherapy.

METHODS

Biological differences were investigated through Gene set enrichment analysis (GSEA) and tumor microenvironment analysis by CIBERSORT. Tumor mutation burden (TMB), immunotherapy response and chemotherapeutic drugs sensitivity were estimated in R.

RESULTS

We established a prognostic signature with the formula: risk score = STEAP1 × 0.3994 + STEAP4 × (- 0.7596), which had a favorable concordance with the prediction. The high-risk group were enriched in cell cycle and RNA and protein synthesis related pathways, while the low-risk group were enriched in complement and metabolic related pathways. And the risk score was significantly correlated with immune cell infiltration. Most notably, the patients in the low-risk group were characterized with increased TMB and decreased tumor immune dysfunction and exclusion (TIDE) score, indicating that these patients showed better immune checkpoint blockade response. Meanwhile, we found the patients with high-risk were more sensitive to some drugs related to cell cycle and apoptosis.

CONCLUSIONS

The novel signature based on STEAPs may be effective indicators for predicting prognosis, and provides corresponding clinical treatment recommendations for HCC patients based on this classification.

Purine metabolism-related genes and immunization in thyroid eye disease were validated using bioinformatics and machine learning

Thyroid eye disease (TED), an autoimmune inflammatory disorder affecting the orbit, exhibits a range of clinical manifestations. While the disease presentation can vary, cases that adhere to a prototypical pattern typically commence with mild symptoms that subsequently escalate in severity before entering a phase of stabilization. Notably, the metabolic activity of cells implicated in the disease substantially deviates from that of healthy cells, with purine metabolism representing a critical facet of cellular material metabolism by supplying components essential for DNA and RNA synthesis. Nevertheless, the precise involvement of Purine Metabolism Genes (PMGs) in the defensive mechanism against TED remains largely unexplored. The present study employed a bioinformatics approach to identify and validate potential PMGs associated with TED. A curated set of 65 candidate PMGs was utilized to uncover novel PMGs through a combination of differential expression analysis and a PMG dataset. Furthermore, GSEA and GSVA were employed to explore the biological functions and pathways associated with the newly identified PMGs. Subsequently, the Lasso regression and SVM-RFE algorithms were applied to identify hub genes and assess the diagnostic efficacy of the top 10 PMGs in distinguishing TED. Additionally, the relationship between hub PMGs and clinical characteristics was investigated. Finally, the expression levels of the identified ten PMGs were validated using the GSE58331 and GSE105149 datasets. This study revealed ten PMGs related with TED. PRPS2, PFAS, ATIC, NT5C1A, POLR2E, POLR2F, POLR3B, PDE3A, ADSS, and NTPCR are among the PMGs. The biological function investigation revealed their participation in processes such as RNA splicing, purine-containing chemical metabolism, and purine nucleotide metabolism. Furthermore, the diagnostic performance of the 10 PMGs in differentiating TED was encouraging. This study was effective in identifying ten PMGs linked to TED. These findings provide light on potential new biomarkers for TED and open up possibilities for tracking disease development.

The HBV web: An insight into molecular interactomes between the hepatitis B virus and its host en route to hepatocellular carcinoma

Hepatitis B virus (HBV) is a major aetiology associated with the development and progression of hepatocellular carcinoma (HCC), the most common primary liver malignancy. Over the past few decades, direct and indirect mechanisms have been identified in the pathogenesis of HBV-associated HCC which include altered signaling pathways, genome integration, mutation-induced genomic instability, chromosomal deletions and rearrangements. Intertwining of the HBV counterparts with the host cellular factors, though well established, needs to be systemized to understand the dynamics of host-HBV crosstalk and its consequences on HCC progression. Existence of a vast array of protein-protein and protein-nucleic acid interaction databases has led to the uncoiling of the compendia of genes/gene products associated with these interactions. This review covers the existing knowledge about the HBV-host interplay and brings it down under one canopy emphasizing on the HBV-host interactomics; and thereby highlights new strategies for therapeutic advancements against HBV-induced HCC.

In Silico Integration of Transcriptome and Interactome Predicts an ETP-ALL-Specific Transcriptional Footprint that Decodes its Developmental Propensity

Early T precursor acute lymphoblastic leukemia (ETP-ALL) exhibits poor clinical outcomes and high relapse rates following conventional chemotherapeutic protocols. Extensive developmental flexibility of the multipotent ETP-ALL blasts with considerable intra-population heterogeneity in terms of immunophenotype and prognostic parameters might be a target for novel therapeutic interventions. Using a public gene expression dataset (GSE28703) from NCBI GEO DataSets with 12 ETP-ALL and 40 non-ETP-ALL samples, such heterogeneity was found to be reflected in their transcriptome as well. Hub genes were identified from the STRING-derived functional interaction network of genes showing differential expression between ETP-ALL and non-ETP-ALL as well as variable expression across ETP-ALL. Nine genes (KIT, HGF, NT5E, PROM1, CD33, ANPEP, CDH2, IL1B, and CXCL2) among the hubs were further validated as possible diagnostic ETP-ALL markers using another gene expression dataset (GSE78132) with 17 ETP-ALL and 27 non-ETP-ALL samples. Linear dimensionality reduction analysis with the expression levels of the hub genes in ETP-ALL revealed their divergent inclinations towards different hematopoietic lineages, proposing them as novel indicators of lineage specification in the incompletely differentiated ETP-ALL blasts. This further led to the formulation of a personalized lineage score calculation algorithm, which uncovered a considerable B-lineage-bias in a substantial fraction of ETP-ALL subjects from the GSE28703 and GSE78132 cohorts. In addition, STRING-derived physical interactome of the potential biomarkers displayed complete segregation of the B-lineage-skewed markers from other lineage-associated factors, highlighting their distinct functionality and possible druggability in ETP-ALL. A panel of these biomarkers might be useful in pinpointing the dominant lineage specification programmes in the ETP-ALL blasts on a personalized level, urging the development of novel lineage-directed precision therapies as well as repurposing of existing therapies against leukemia of different hematopoietic lineages; which might overcome the drawbacks of conventional chemotherapy.