In Silico Study to Predict the Structural and Functional Consequences of SNPs on Biomarkers of Ovarian Cancer (OC) and BPA Exposure-Associated OC

Identification of novel protein biomarkers and therapeutic targets for ankylosing spondylitis using human circulating plasma proteomics and genome analysis

The proteome serves as the primary basis for identifying targets for treatment. This study conducted proteomic range two-sample Mendelian randomization (MR) analysis to pinpoint potential protein markers and treatment targets for ankylosing spondylitis (AS). A total of 4907 data points on circulating protein expression were collected from a large-scale protein quantitative trait locus investigation involving 35,559 individuals. Using data from a Finnish study on AS as the outcome, the dataset comprised 166,144 individuals of European ancestry (1462 cases and 164,682 controls), and causal relationships were determined through bidirectional Mendelian randomization of two samples. Proteins were further validated and identified through single-cell expression analysis, certain cells showing enriched expression levels were detected, and possible treatment targets were optimized. Increased HERC5 expression predicted by genes was related to increased AS risk, whereas the expression of the remaining five circulating proteins, AIF1, CREB3L4, MLN, MRPL55, and SPAG11B, was negatively correlated with AS risk. For each increase in gene-predicted protein levels, the ORs of AS were 2.11 (95% CI 1.44-3.09) for HERC5, 0.14 (95% CI 0.05-0.41) for AIF1, 0.48 (95% CI 0.34-0.68) for CREB3L4, 0.54 (95% CI 0.42-0.68) for MLN, 0.23 (95% CI 0.13-0.38) for MRPL55, and 0.26 (95% CI 0.17-0.39) for SPAG11B. The hypothesis of a reverse causal relationship between these six circulating proteins and AS is not supported. Three of the six protein-coding genes were expressed in both the AS and healthy control groups, while CREB3L4, MLN, and SPAG11B were not detected. Increased levels of HERC5 predicted by genes are related to increased AS risk, whereas the levels of the remaining five circulating proteins, AIF1, CREB3L4, MLN, MRPL55, and SPAG11B, negatively correlate with AS risk. HERC5, AIF1, and MRPL55 are potential therapeutic targets for AS. This study advanced the field by employing a novel combination of proteomic range two-sample MR analysis and single-cell expression analysis to identify potential protein markers and therapeutic targets for AS. This approach enabled a comprehensive understanding of the causal relationships between circulating proteins and AS, which has not been extensively explored in previous studies.

Establishing Molecular Subgroups of CD8+ T Cell-Associated Genes in the Ovarian Cancer Tumour Microenvironment and Predicting the Immunotherapy Response

BACKGROUND

The mechanism by which infiltrating CD8+ T lymphocytes in the tumour microenvironment influence the survival of patients with ovarian cancer (OC) remains unclear.

METHODS

To identify biomarkers to optimise OC treatment, 13 immune-cell-line-associated datasets, RNA sequencing data, and clinical data from the GEO, TCGA, and the ICGC were collected. Gene expression in OC was assessed using quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) staining.

RESULTS

We identified 520 genes and three immunological clusters (IC1, IC2, and IC3) associated with CD8+ T cells. Higher IFN scores, immune T cell lytic activity, and immune cell infiltration and upregulated expression of immune-checkpoint-related genes indicated that IC3 is more responsive to immunotherapy, whereas IC1 and IC2 have a poorer prognosis. A 10-gene signature, including SEMA4F, CX3CR1, STX7, PASK, AKIRIN2, HEMGN, GBP5, NSG1, and CXorf65, was constructed, and a multivariate Cox regression analysis revealed a significant association between the 10-gene signature-based risk model and overall survival (p < 0.001). A nomogram was constructed with age and the 10-gene signature. Consistent with the bioinformatics analysis, IHC and qRT-PCR confirmed the accuracy of the signatures in OC tissue samples. The predictive ability of the risk model was demonstrated using the Imvigor210 immunotherapy dataset.

CONCLUSIONS

The development of a novel gene signature associated with CD8+ T cells could facilitate more accurate prognostics and prediction of the immunotherapeutic response of patients with OC.

Advances in Genomic Data and Biomarkers: Revolutionizing NSCLC Diagnosis and Treatment

Non-small cell lung cancer (NSCLC) is a significant public health concern with high mortality rates. Recent advancements in genomic data, bioinformatics tools, and the utilization of biomarkers have improved the possibilities for early diagnosis, effective treatment, and follow-up in NSCLC. Biomarkers play a crucial role in precision medicine by providing measurable indicators of disease characteristics, enabling tailored treatment strategies. The integration of big data and artificial intelligence (AI) further enhances the potential for personalized medicine through advanced biomarker analysis. However, challenges remain in the impact of new biomarkers on mortality and treatment efficacy due to limited evidence. Data analysis, interpretation, and the adoption of precision medicine approaches in clinical practice pose additional challenges and emphasize the integration of biomarkers with advanced technologies such as genomic data analysis and artificial intelligence (AI), which enhance the potential of precision medicine in NSCLC. Despite these obstacles, the integration of biomarkers into precision medicine has shown promising results in NSCLC, improving patient outcomes and enabling targeted therapies. Continued research and advancements in biomarker discovery, utilization, and evidence generation are necessary to overcome these challenges and further enhance the efficacy of precision medicine. Addressing these obstacles will contribute to the continued improvement of patient outcomes in non-small cell lung cancer.

Identification of risk for ovarian disease enhanced by BPB or BPAF exposure

The ban on bisphenol A (BPA) has led to a rapid increase in the use of BPA analogs, and they are increasingly being detected in the natural environment and biological organisms. Studies have pointed out that BPA analogs can lead to adverse health outcomes. However, their interference with ovarian tissue has not been fully elucidated. In this study, seven- to eight-week-old CD-1 mice were exposed to corn oil containing 300 μg/kg/day bisphenol B (BPB) or bisphenol AF (BPAF) through oral gavage, and ovarian tissues were collected at 14 and 28 days of exposure. Ovarian toxicity was evaluated by the ovarian index, ovarian area, and follicle number. mRNA-seq was used to identify differentially expressed genes (DEGs) and infer the association of DEGs with ovarian diseases. BPB or BPAF exposure induced morphological changes in ovarian tissue in CD-1 mice. In addition, Gene Ontology (GO) analysis revealed disturbances in biological processes (BP) associated with steroid biosynthetic process (GO:0006694) and cellular calcium ion homeostasis (GO:0006874). Subsequently, regulatory networks of BPA analogs (BPB or BPAF)-DEGs-ovarian diseases were constructed. Importantly, the expression levels of DEGs and transcription factors (TFs) associated with ovarian disease were altered. BPB or BPAF exposure causes damage to ovarian morphology through the synergistic effects of multiple biological processes and may be associated with altered mRNA expression profiles as a risk factor for ovarian diseases.

Identification and in silico analysis of a spectrum of SLC4A11 variations in Indian familial and sporadic cases of congenital hereditary endothelial dystrophy

Background

Congenital hereditary endothelial dystrophy (CHED) is a rare form of corneal dystrophy caused by SLC4A11 gene variations. This study aims to find the genetic alterations in SLC4A11, in two Indian familial CHED cases with affected members n = 3 and n = 2 respectively and five sporadic CHED cases using direct sequencing, followed by in silico analysis and characterization of the identified variants.

Results

All three affected members of the first CHED family were identified with a novel homozygous c.1514C > G (p.Ser489Trp) variation while second family showed presence of a compound heterozygous variation c.529A > C (p.Arg161Arg) + c.2461insT (p.Val805fs). Among five sporadic cases, two showed novel changes, homozygous c.1487G > T (p.Ser480Ile) and c.620-2A > G, while the other one had previously reported homozygous c.2653C > T (p.Arg869Cys) variation. The remaining two cases did not reveal the presence of SLC4A11-related pathogenic variations. The identified variations were excluded from the Indian control (n = 80). In silico analysis using homology-based protein modeling and pathogenicity prediction tools, which revealed these alterations as pathogenic, changing their protein stability, local flexibility, residue contact clashes, and the hydrogen bond interactions.

Conclusions

This study contributed to the CHED mutational spectrum, adding four novel variations and confirming a previously reported one. It demonstrates different type of variations in CHED cases, including coding, non-coding, homozygous, synonymous, and compound heterozygous variations. The identified variations revealed different degrees of pathogenic effects in silico. Moreover, two sporadic cases could not be identified with pathogenic variation emphasizing the involvement of other genes or genetic mechanisms.