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.

Long non-coding RNAs (lncRNAs) are a category of more extended RNA strands that lack protein-coding abilities. Although they are not involved in the translation of proteins, studies have shown that they play essential regulatory functions in cells, regulating gene expression and cell biological processes. However, it is both costly and inefficient to determine the associations between lncRNAs and diseases through biological experiments. Therefore, there is an urgent need to develop convenient and fast computational methods to predict lncRNA-disease associations (LDAs) more efficiently.

Predicting disease-associated lncRNAs can help explore the mechanisms of action of lncRNAs in diseases, and this is crucial for early intervention and treatment of diseases.

In this paper, we propose an enhanced heterogeneous graph representation method for predicting LDAs, named GCGALDA. The GCGALDA first obtains the topological structure features of nodes by a biased random walk. Based on this, the neighboring nodes of a node are weighted using the attention mechanism to further mine the semantic association relationships between nodes in the graph data. Then, a graph convolution network (GCN) is used to transfer the neighborhood features of the node to the central node and combine them with the node's features so that the final node representation contains not only structural information but also semantic association information. Finally, the association score between lncRNA and disease is obtained by multilayer perceptron (MLP).

As evidenced by the experimental findings, the GCGALDA outperforms other advanced models in terms of prediction accuracy on openly accessible databases. In addition, case studies on several human diseases further confirm the predictive ability of the GCGALDA.

In conclusion, the proposed GCGALDA model extracts multi-perspective features, such as topology, semantic association, and node attributes, obtains high-quality heterogeneous graph node representations, and effectively improves the performance of the LDA prediction model.

Cancers routinely exhibit chromosomal instability that results in copy number variants (CNVs), namely changes in the abundance of genomic material. Unfortunately, the detection of these variants in cancer genomes is difficult.

We present Ploidetect, a software package that effectively identifies CNVs within whole-genome sequenced tumors. Ploidetect utilizes a coarse-to-fine segmentation approach which yields highly contiguous segments while allowing for focal CNVs to be detected with high sensitivity.

We benchmark Ploidetect against popular CNV tools using synthetic data, cell line data, and real-world metastatic tumor data and demonstrate strong performance in all tests. We show that high quality CNVs from Ploidetect enable the identification of recurrent homozygous deletions and genes associated with chromosomal instability in a multi-cancer cohort of 687 patients. Using highly contiguous CNV calls afforded by Ploidetect, we also demonstrate the use of segment N50 as a novel metric for the measurement of chromosomal instability within tumor biopsies.

We propose that the increasingly accurate determination of CNVs is critical for their productive study in cancer, and our work demonstrates advances made possible by progress in this regard.

Since each dimension of a tensor can store different types of genomics data, compared to matrix methods, utilizing tensor structure can provide a deeper understanding of multi-dimensional data while also facilitating the discovery of more useful information related to cancer. However, in reality, there are issues such as insufficient utilization of prior knowledge in multi-omics data and limitations in the recovery of low-tubal-rank tensors. Therefore, the method proposed in this article was developed.

e: In this paper, we proposed a low transformed tubal rank tensor model (LTTRT) using a spatial-tubal constraint to accurately partition different types of cancer samples and provide reliable theoretical support for the identification, diagnosis, and treatment of cancer.

In the LTTRT method, the transformed tensor nuclear norm based on the transformed tensor singular value decomposition is characterized by the low-rank tensor, which can explore the global low-rank property of the tensor, resolving the challenge of the tensor nuclear norm-based method not achieving the lowest tubal rank. Additionally, the introduction of weighted total variation regularization is conducive to extracting more information from sequencing data in both spatial and tubal dimensions, exploring cross-correlation features of multiple genomic data, and addressing the problem of overlooking prior knowledge from various perspectives. In addition, the L1-norm is used to improve sparsity. A symmetric Gauss‒Seidel-based alternating direction method of multipliers (sGS-ADMM) is used to update the LTTRT model iteratively.

The experiments of sample clustering on multiple integrated cancer multi-omics datasets show that the proposed LTTRT method is better than existing methods. Experimental results validate the effectiveness of LTTRT in accurately partitioning different types of cancer samples.

The LTTRT method achieves precise segmentation of different types of cancer samples.

With the rapid evolution of single-cell RNA sequencing technology, the study of cellular heterogeneity in complex tissues has reached an unprecedented resolution. One critical task of the technology is cell-type annotation. However, challenges persist, particularly in annotating novel cell types.

Current methods rely heavily on well-annotated reference data, using correlation comparisons to determine cell types. However, identifying novel cells remains unstable due to the inherent complexity and heterogeneity of scRNA-seq data and cell types. To address this problem, we propose scADCA, a method based on anomaly detection, for identifying novel cell types and annotating the entire dataset.

The convolutional modules and fully connected networks are integrated into an autoencoder, and the reference dataset is trained to obtain the reconstruction errors. The threshold based on these errors can distinguish between novel and known cells in the query dataset. After novel cells are identified, a multinomial logistic regression model fully annotates the dataset.

Using a simulation dataset, three real scRNA-seq pancreatic datasets, and a real scRNA-seq lung cancer cell line dataset, we compare scADCA with six other cell-type annotation methods, demonstrating competitive performance in terms of distinguished accuracy, full accuracy, -score, and confusion matrix.

In conclusion, the scADCA method can be further improved and expanded to achieve better performance and application effects in cell type annotation, which is helpful to improve the accuracy and reliability of cytology research and promote the development of single-cell omics.