Current Bioinformatics - Current Issue

Multi-omics data integration has transformed personalized medicine, providing a comprehensive understanding of disease mechanisms and informed precision therapeutic options. Multi-omics data generated for the same samples/patients can help in getting insights into the flow of biological information at several levels, thereby providing in-depth information regarding the molecular mechanisms underlying pathological conditions. Multi-omics integration plays a pivotal role in personalized medicine by providing comprehensive insights into the complex biological systems of individual patients. This review provides a comprehensive account of the current and future progress brought into multi-omics methodologies, promising to refine diagnostics and therapeutic strategy by integrating genomic, transcriptomic analyses, proteomics approaches and metabolome screens.

A literature search was performed in PubMed using keywords like genomics, proteomics, transcriptomics, metabolomics, multi-omics, and precision medicine to identify published research articles. A thorough review of all results was then conducted, and their results and conclusions were compiled and summarized.

By analyzing various omics layers, such as genomics, transcriptomics, proteomics, and metabolomics, multi-omics approaches enable the identification of patient-specific molecular traits and the discovery of new clinical therapeutics for diseases. Integration of various data types augments diagnostics, optimizes therapeutic regimens and supports personalized medicine according to an individual patient profile.

Integration of multi-omics data and its applications in various fields, such as cancer research, helps in optimizing patient-specific treatment and improvement of patient health. With time, as these technologies reach more people, they stand to democratize precision medicine and hopefully bridge health disparities. In conclusion, the present review highlights multiomics data integration as a transformative step towards personalized medicine and ultimately changing patient care from empirical-based to precision or individualized.

The heterogeneity in tumours poses significant challenges to the accurate prediction of cancer stages, necessitating the expertise of highly trained medical professionals for diagnosis. Over the past decade, the integration of deep learning into medical diagnostics, particularly for predicting cancer stages, has been hindered by the black-box nature of these algorithms, which complicates the interpretation of their decision-making processes.

This study seeks to mitigate these issues by leveraging the complementary attributes found within functional genomics datasets (including mRNA, miRNA, and DNA methylation) and stained histopathology images. We introduced the Extended Squeeze- and-Excitation Multiheaded Attention (ESEMA) model, designed to harness these modalities. This model efficiently integrates and enhances the multimodal features, capturing biologically pertinent patterns that improve both the accuracy and interpretability of cancer stage predictions.

Our findings demonstrate that the explainable classifier utilised the salient features of the multimodal data to achieve an area under the curve (AUC) of 0.9985, significantly surpassing the baseline AUCs of 0.8676 for images and 0.995 for genomic data.

Furthermore, the extracted genomics features were the most relevant for cancer stage prediction, suggesting that these identified genes are promising targets for further clinical investigation.

Celiac Disease (CD) is a common autoimmune disorder caused by the activation of CD4+ T cells that specifically target gluten and CD8+ T cells, further causing cell death inside the epithelial layer despite no available established biomarkers of CD diagnosis. This work aimed to compare scRNA-seq and transcriptome data to find novel gene biomarkers linked to T cells that might potentially be utilized for the diagnosis and assessment of CD.

Collecting the scRNA and RNAseq datasets from the NCBI database, the Seurat package of R studio, and the statistical analysis tool GREIN server were employed to identify Differentially Expressed Genes (DEGs). Then, DAVID, FunRich, STRING, and NetworkAnalyst tools were utilized to explore significant pathways, key hub proteins, and gene regulators.

After integrating genes and conducting a comparative analysis, a total of 115 genes were identified as DEGs. Exosomes, MHC class II receptor activity, immune response, interferon gamma signaling, and bystander B cell activation within the immune system pathways were the significant Gene Ontology (GO) and metabolic pathways identified. Besides, eleven topological algorithms discovered two hub proteins, namely HLA-DRA and HLA-DRB1, from the PPI network. Through the analysis of the regulatory network, we have identified four crucial Transcription Factors (TFs), including YY1, FOXC1, GATA2, and USF2, and seven significant miRNAs (hsa-mir-129-2-3p, and hsa-mir-155-5p, etc.) in transcriptionally and post-transcriptionally regulated. Validation of hub proteins and transcription factors using Receiver Operating Characteristic (ROC) analysis indicates the acceptable value of the Area Under the Curve (AUC).

This study utilized single-cell RNA sequencing and transcriptomics data analysis to define unique protein biomarkers associated with T cells throughout the progression of CD. Furthermore, wet lab studies will be needed to validate the potential hub proteins, TFs, and miRNAs as clinical biomarkers.

An uncommon neurological condition known as Guillain-Barre syndrome (GBS) develops when the body's immunological system unintentionally targets peripheral nerves.

This work aimed to compare scRNA-seq and transcriptome data to find novel gene biomarkers linked to CD4⁺ T cells and B cells that might potentially be utilized for the diagnosis and assessment of GBS. It aimed to employ scRNA-seq data and bioinformatics tools analysis to identify cell-specific biomarkers for GBS diagnosis and prognosis.

scRNA-seq and microarray datasets from the GEO database were utilized to identify differentially expressed genes (DEGs). Pathway enrichment, identification of potential hub genes, and gene regulatory studies were employed using FunRich, DAVID, STRING, and NetworkAnalyst tools.

After integrating the DEGs and performing a comparative analysis, it was discovered that there were 84 DEGs shared between scRNA-seq and microarray datasets. The presence of signal transduction, immune system, cytokine signaling, NOD-like receptor signaling, and focal adhesion was detected in the most significant gene ontology and metabolic pathways. After generating a protein-protein interaction (PPI) network, we used eleven topological algorithms of the cytoHubba plugin for identifying six key hub genes, including CDC42, PTPRC, SRSF1, HNRNPA2B1, NIPBL, and FOS. Several crucial transcription factors (CHD1, IRF1, FOXC1, GATA2, YY1, E2F1, and CREB1) and two significant microRNAs (hsa-mir-20a-5p and hsa-mir-16-5p) were also discovered as hub gene regulators. The receiver operating characteristics (ROC) curve was used to evaluate the prognostic, expression, and diagnostic capabilities of the six major hub genes, indicating a good scoring value.

Finally, functional enrichment pathway analysis, PPI, and regulatory networks analysis demonstrated the critical functions of the identified key hub genes. After further wet lab research is validated, our research work may offer useful predicted potential biomarkers for the diagnosis and prognosis of GBS.