Current Bioinformatics - Online First

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 non-small 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.

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.

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

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.

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

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.

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.

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 down-regulated hub genes were identified.

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.

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.

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.

Disease is a major threat to life, and extensive efforts have been made over the past centuries to develop effective treatments. Identifying drug-disease and disease-target associations is crucial for therapeutic advancements, whereas drug-target associations facilitate the design of more effective treatment strategies. However, traditional experimental approaches for identifying these associations are costly and time-consuming. Numerous computational models have been developed to predict drug-target, drug-disease, and disease-target associations. However, these models are designed individually and cannot directly predict drug-target-disease associations, which involve interconnections among drugs, targets, and diseases. Such triple associations provide deeper insights into disease mechanisms and therapeutic interventions by capturing high-order associations.

This study proposes a computational model named PDTDAHN to predict drug-target-disease triple associations.

Six association types retrieved from public databases are used to construct a heterogeneous network comprising drugs, targets, and diseases. The network embedding algorithm Mashup is applied to extract features for drugs, targets, and diseases, which are then combined to represent each drug-target-disease association. The classification model is trained using LightGBM.

Cross-validation on eight datasets demonstrates the high performance of PDTDAHN, with AUROC and AUPR exceeding 0.9. This model outperforms previous models based on pairwise association predictions.

The proposed model effectively predicts drug-target-disease triple associations.

This study aimed to study breast cancer, the most common cancer affecting women worldwide, using one primary and two metastatic breast tumor cell lines to identify therapeutic drugs.

Investigating the changes in gene expression triggered by drugs offers a robust method for uncovering potential new treatments. Through the analysis of the impacts of drugs on gene activity, scientists can unravel the molecular mechanisms within cells, comprehend the effects of drugs, identify chances for drug repositioning, and foresee patient outcomes to treatments.

Our approach has involved two main strategies: analyzing drug-perturbed gene expression profiles and leveraging drug-induced gene expression profiles. Firstly, we have assessed how drugs affect the expression of target genes in a dose-dependent manner, determining whether they inhibit or activate gene expression. This analysis could inform the identification of new potential drugs. Secondly, we have grouped drugs based on their expression profiles to explore potential synergistic effects.

Our methodology has involved quantifying gene profile changes relative to drug dosage, categorizing effects as up-regulating or down-regulating, and employing functional enrichment with cancer hallmark annotations to predict drugs with potential for cancer treatment. Additionally, we have determined the optimal number of drug groups with similar effects on gene expression and explored their mechanisms of action through cancer hallmark annotations.

By analyzing dose-dependent gene expression, we have found that seven, three, and five drugs may induce similar sets of up-regulated and down-regulated genes in Hs-578-T, MCF7, and MDA-MB-231 cell lines, respectively. Clustering and functional enrichment analyses have suggested a shared molecular mechanism of action among these drug candidates.

We have thus categorized drugs with opposing gene expression profiles and proposed new drug candidates for breast cancer treatment based on cancer hallmark annotations. Moreover, our study has uncovered synergistic drug combinations, including those utilizing FDA-approved drugs, for primary and metastatic breast cancer cell lines.

Genetic information about organisms' traits is stored and encoded in deoxyribonucleic acid (DNA) sequences. The fundamental inquiry into the storage mechanisms of this genetic information within genomes has long been of interest to geneticists and biophysicists.

The objective of this study was to investigate the distribution of coding sequence (CDS) lengths in species genomes across different kingdoms.

In this study, we used the maximum entropy principle and the gamma distribution model based on a comprehensive dataset including viruses, archaea, bacteria, and eukaryote species.

Our study result revealed unique patterns in CDS length distributions among kingdoms and CDS lengths exhibit a right-skewed distribution, with varying preferences among kingdoms. Eukaryotes displayed bimodal distributions, with CDS sequences longer than those of prokaryotes. Fitting the gamma distribution model revealed differences in shape and scale parameters among kingdoms, with eukaryotes exhibiting larger scale parameters, indicating longer CDS sequences. Additionally, analysis of moments highlighted the complexity of eukaryotic genomes relative to prokaryotes.

This study result deepens our understanding of genome evolution and provides valuable insights for biological research.

G-protein coupled receptors (GPCRs) represent a large family of membrane proteins, distinguished by their seven-transmembrane helical structures. These receptors play a pivotal role in numerous physiological processes. Nowadays, many researchers have proposed computational methods to identify GPCRs. In the past, we introduced a powerful method, EMCBOW-GPCR, which was designed for this purpose. However, the feature extraction technique employed is susceptible to out-of-vocabulary errors, indicating the potential for enhanced accuracy in GPCR identification.

To solve the challenges, we propose a novel approach termed GPCR-AFPN. This method leverages the FastText algorithm to effectively extract features from protein sequences. Additionally, it employs a powerful deep neural network as the predictive model to improve prediction accuracy.

To validate the efficacy of the proposed GPCR-AFPN method, we conducted five-fold cross-validation and independent tests, respectively. The experimental results indicate that GPCR-AFPN outperforms existing methods.

Overall, our proposed method, GPCR-AFPN, can improve the accuracy of GPCR identification. For the convenience of researchers interested in applying our latest advancements, a user-friendly webserver for GPCR-AFPN is available at www.lzzzlab.top/gpcrafpn/, and the corresponding code can be downloaded at https://github.com/454170054/GPCR-AFPN.

Echinococcosis, a parasitic disease caused by the larvae of the Echinococcus parasite, poses a serious threat to human health. Medication is an indispensable means of treatment for Echinococcosis; however, due to the less satisfactory efficacy of single drugs, identifying effective drug combinations for the treatment of Echinococcosis is essential. Yet, current predictive models for drug synergy in Echinococcosis face accuracy challenges due to data scarcity, method limitations, and insufficient feature representation.

This work aims to design an end-to-end method to predict drug synergistic combinations, which enables efficient and accurate identification of drug combinations against Echinococcosis.

In this work, an end-to-end method, named DSPE, is proposed for predicting anti-Echinococcosis drug synergistic combinations. In DSPE, a dataset of Echinococcosis drug synergistic combinations is constructed by retrieving and extracting information from related scientific articles. Further, DSPE employs a residual graph attention network to deeply analyze drug characteristics and their interactions, thereby enhancing the performance of deep learning models. It also explores the protein-protein interaction network related to Echinococcosis, using node2vec combined with an attention mechanism to efficiently encode disease features. Finally, it predicts the synergy of drug combinations based on the Bliss score by integrating drug combinations and disease features.

Experimental evidence shows that DSPE outperforms five state-of-the-art algorithms in predicting drug combination effects by leveraging disease-target information and single-agents for the treatment.

DSPE improves prediction accuracy and addresses the issue of data scarcity for new diseases, offering new insights and methods for the development of treatment plans for parasitic diseases in the future.