Current Bioinformatics (Volume 21 - Issue 4)

A Survey of Trends in Biomolecule Recognition for Sensing and Machine Learning Combined with Heterogeneous Information

2026-03-25

Biomolecule sensing for recognition is exhibited as the fundamental upstream step concerning target identification during the metabolism of individual life. Nevertheless, it is always a complicated work that leverages both in vitro and in vivo experiments to discriminate the corresponding interaction, affinity, structure, activity, and toxicity concerning target biomolecules. Simultaneously, biological investigation with intelligent computing has extended to bio-sequence analysis and biomedical image processing, especially biomolecule identification in multi-view and multimodal. This review presents a panorama of contemporary development among biomolecular omics and computing biological sensing, machine learning scenarios, and heterogeneous information with multi-view, multi-modal, structured, and unstructured text and biomedical images. After being given the background, the concept and database of biomolecule interaction, affinity, and structure are introduced. Then, the machine learning paradigms in bioinformatics and biomedical engineering are demonstrated according to epigenetics-centered or pharmacogenomics. Next, the multi-view or multi- modal learning algorithms and optimization strategies with structured and unstructured data formats, including texts and biomedical images are listed in detail. By comparing and analyzing the state-of-the-art works, this study has summarized the advantages of existing methods in target biomolecule identification and the challenges. Finally, future developments are prospected, including the trend of research in robustness, data augmentation, generalized model delineated, and acceleration.

Multiple Approaches to Identifying Key Genes Linked to the Anti-inflammatory Effects of Ginsenosides

2026-03-25

Ginsenoside is a naturally occurring active ingredient in ginseng, which mainly consists of four components, including Rb1, Rb2, Rc, and Rd, which are considered to be an important part of ginseng's medicinal effects. Ginsenosides can enhance the anti-fatigue ability of the body, regulate immune function, improve cardiovascular function, and have anti-aging, antioxidant, and neuroprotective effects. In recent years, many studies have found that ginsenosides have anti-inflammatory properties and are used in the treatment of many inflammatory diseases, such as endodontitis, bronchitis, and many others. Ginsenosides reduce inflammation by suppressing the release of inflammatory mediators, modulating inflammatory signaling pathways, scavenging free radicals, and modulating the immune system in a variety of ways. However, existing studies have not investigated the specific genes underlying the inflammation-reducing properties of ginsenosides. In this study, we analyzed two publicly accessible datasets from the GEO database (GSE255672 and GSE173990) to investigate the molecular basis of the antiinflammatory effects of ginsenosides. This study aims to advance our understanding of how ginsenosides exert their anti-inflammatory properties, providing preliminary findings for identifying gene targets for their anti-inflammatory effects, thereby enhancing our understanding of their biological function and identifying new therapeutic pathways in the management of inflammation. It paves the way for further research of ginsenosides and therapeutic application of inflammationrelated diseases.

Innovative Insights into Liver Cancer: Multi-Omics Reveals Critical Subtypes and Hub Genes

2026-03-25

Introduction/Objective: Hepatocellular carcinoma (HCC) is a highly heterogeneous malignant tumor, characterized by elevated mortality rates and poor diagnostic outcomes. Accurate identification of cancer subtypes is crucial for guiding personalized treatment and improving patient prognosis.

Methods: A method for precisely identifying HCC subtypes by integrating multi-omics data was presented. This approach combines the GRACES dimensionality reduction technique with the hMKL subtype identification model to analyze data from 266 HCC patients.

Results: We identified two subtypes more accurately, both significantly associated with overall survival. Their respective three-year mortality rates were 55.9% and 27.9%. Additionally, we observed significant differences in the activity of five pathways between these two subtypes, along with notable variations in the abundance and status of seven types of immune cells. Through further determination of the PPI network and centrality indicators, 13 up-regulated hub genes and 14 downregulated hub genes were identified.

Discussion: Based on the above results, we compared and discussed the hub genes with the textual data, examined differences in gene upregulation and downregulation, and evaluated findings from other bioinformatics analyses to identify potential biomarkers.

Conclusion: Limited research on ENPP3 and C3 in HCC suggests their potential as biomarkers. Additionally, low expression levels of PIK3R1, KDR, and CYP3A5, along with high expression levels of EGLN3 and EPO, may indicate a higher risk of liver cancer in patients. Single-gene survival analysis highlighted the significant impact of highly expressed genes on HCC prognosis, with PKM, RRM2, and EPO playing crucial roles in the risk scores.

Investigating the Unique Transcriptional miRNA-mRNA Regulatory Network of ALK-positive Lung Adenocarcinoma Using Machine Learning Methods

2026-03-25

Introduction: Non-small Cell Lung Cancer (NSCLC) is characterized by key gene mutations, such as EGFR, KRAS, and ALK. ALK rearrangement occurs in 3–5% of patients with nonsmall cell lung adenocarcinoma and is related to different clinical characteristics. Although ALK tyrosine kinase inhibitors have shown efficacy, drug resistance remains a challenge. This current study aims to determine the unique molecular characteristics of ALK-positive lung adenocarcinoma to improve detection and prognosis.

Methods: GSE128311 integrates expression profiling data by array from GSE128309 and noncoding RNA profiling data by array from GSE128310, including 42 patients with ALK-positive lung adenocarcinoma and 35 patients with ALK-negative lung adenocarcinoma. This data was analyzed by eight feature ranking algorithms, yielding eight feature lists. These lists were fed into incremental feature selection to extract essential features.

Results: Key differentially expressed genes and miRNAs were identified, and functional enrichment analysis was carried out.

Discussion: Results of the imbalance of the cell cycle pathway, FOXM1 transcription factor network, and immune response process in ALK-positive tumors were emphasized. It is worth noting that CX3CL1, MMS22L, DSG3, RUFY1, miR-652-5p, and miR-1288 are potentially important markers. Gene set enrichment analysis revealed the low expression of the cell cycle pathway in ALK-positive samples.

Conclusion: This comprehensive computational analysis provides new insights into the molecular basis of ALK-positive lung adenocarcinoma and determines promising biomarkers for further research.

Analysis of Alternative Splicing Heterogeneity during Early Stages of Mouse Embryonic Development

2026-03-25

Introduction: Pre-mRNA alternative splicing (AS) is a prevalent phenomenon in mammals, playing a crucial role in various biological processes such as embryonic development, tissue differentiation, and disease pathogenesis. Despite the advancements in single-cell RNA sequencing (scRNA-seq) technology, the extent of AS heterogeneity at the transcript level during early mouse embryonic development remains largely unexplored.

Methods: The BRIE2 and expedition were employed to identify and quantify splicing events. Cell clustering was performed with Scanpy based on Percent Spliced In (PSI) values and gene expression levels. Then, marker AS events and differential AS events were detected by the Wlicocon rank-sum test and BRIE2's Mode-2 quantification mode. GO and KEGG enrichment analysis were conducted by ClusterProfiler.

Results: The results suggested substantial heterogeneity in AS events and elucidated PSI values as a critical index of cell heterogeneity during early mouse embryonic development, shedding light on the regulatory mechanisms underlying these processes. By examining marker and differential AS events, the study provided a comprehensive understanding of the dynamic changes in splicing patterns throughout early mouse embryonic development.

Discussion: This study revealed the heterogeneity of AS and elucidated its implications during early mouse embryonic development by analyzing AS at the single-cell level. However, the results are theoretical and lack experimental validation.

Conclusion: The findings offer critical insights into studying mouse embryonic development from the perspective of RNA cellular heterogeneity, emphasizing the importance of AS in shaping cellular diversity and developmental processes.

Genome-wide Analysis of Ovarian Cancer-specific circRNAs in Alternative Splicing Regulation

2026-03-25

Introduction: Ovarian cancer (OC) is a fatal female reproductive system cancer with a high mortality rate and is hard to detect at an early stage. Recent studies have indicated that alternative splicing plays an important role in OC progression by activating genes and pathways involved in tumorigenesis. Circular RNAs (circRNAs) have also been found to play a regulating role in tumor progression and present their potential ability in alternative splicing regulation. However, the underlying mechanism by which circRNAs regulate alternative splicing events (ASEs) in OC remains unclear.

Methods: In this study, we performed a comprehensive transcriptomic study on the RNA-seq data of our collected tumor and normal samples from OC patients, aiming to investigate the regulatory roles of OC-specific circRNAs in aberrant splicing events and their underlying pathways in tumorigenesis.

Results: We conducted a genome-wide regulatory network with strong correlations from 300 differentially expressed (DE) circRNAs and 1,150 aberrant ASEs, mediated by 31 DE SFs. Analyses of this network revealed that dysregulation of circRNAs may lead to aberrant ASEs that are closely involved in ovarian tumorigenesis. In addition, two crucial circRNAs, circ_AKT3 (hsa_circ_0000199) and circ_GSK3B (hsa_circ_0008797), were identified due to their significant roles in the network and associations with multiple tumor-related functional pathways.

Discussion: These findings suggest that OC-specific circRNAs may participate in tumor progression by indirectly regulating groups of ASEs through multiple SFs, rather than through direct interaction. Subnetwork analyses centered on the two hub circRNAs revealed that their associated ASEs are functionally clustered and involved in coordinated biological processes relevant to tumor biology.

Conclusion: This study provides novel insights into the regulatory pathways by which circRNAs are involved in OC progression, offering clues for discovering diagnostic biomarkers and therapeutic targets.