RNAactDrug: a comprehensive database of RNAs associated with drug sensitivity from multi-omics data

Causal relationship between inflammatory cytokines and posttraumatic stress disorder: a Mendelian randomization study and potential mechanism analysis

Background: Post-traumatic stress disorder (PTSD) is a complex condition linked to inflammation. The causality between inflammatory cytokines and PTSD risk remains unclear.Methods: We conducted a bidirectional two-sample Mendelian randomization (MR) study using genome-wide association study (GWAS) data from 41 inflammatory cytokines and PTSD. Additional analyses included differential gene expression, protein-protein interaction, and functional enrichment to explore underlying mechanisms.Results: MR analysis indicated that higher levels of stem cell factor (SCF) and interleukin-4 (IL-4) are associated with a reduced risk of PTSD. Genes POGZ and LRIG2 were identified as mediators, implicated in the TGF-beta signalling pathway.Conclusion: Our findings suggest a protective role of certain cytokines against PTSD and highlight potential molecular mediators. This knowledge could inform future therapeutic strategies for PTSD.

Integrated Bioinformatics Analysis and Cellular Experimental Validation Identify Lipoprotein Lipase Gene as a Novel Biomarker for Tumorigenesis and Prognosis in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the leading causes of death worldwide, and thus, more biomarker and therapeutic targets need to be explored. Herein, we aimed to explore new biomarkers of LUAD by integrating bioinformatics analysis with cell experiments. We firstly identified 266 druggable genes that were significantly differentially expressed between LUAD tissues and adjacent normal lung tissues. Among these genes, SMR analysis with p-value correction suggested that declining lipoprotein lipase (LPL) levels may be causally associated with an elevated risk of LUAD, which was corroborated by co-localization analysis. Analyses of clinical data showed that LPL in lung cancer tissues has considerable diagnostic value for LUAD, and elevated LPL levels were positively associated with improved patient survival outcomes. Cell experiments with an LPL activator proved these findings; the activator inhibited the proliferation and migration of lung cancer cells. Next, we found that LPL promoted the infiltration of immune cells such as DCs, IDCs, and macrophages in LUAD by mononuclear sequencing analysis and TIMER2.0. Meanwhile, patients with low levels of LPL expression demonstrated superior immunotherapeutic responses to anti-PD-1 therapy. We conclude that LPL acts as a diagnostic and prognostic marker for LUAD.

Crosstalk of lactate metabolism-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in colon adenocarcinoma

Colon adenocarcinoma (COAD) is a common malignant tumor of digestive tract and lactate metabolism has been linked to tumor development and progression. In this study, we sought to investigate the influence of lactate metabolism-related genes (LRGs) prognosis. We also aimed to identify distinct LRG-related clusters and develop a risk signature for assessing patient prognosis, immunological characteristics, and response to therapy. We analyzed data from The Cancer Genome Atlas (TCGA) to reveal the expression and mutational features of LRGs in COAD patients. In the integrated TCGA and GSE39582 cohort, consensus clustering analysis was employed to classify patients into two distinct LRG-related clusters. Using differentially expressed genes (DRGs) from these two clusters, we established a LRG-related gene cluster and prognostic signature which was used to classify patients into high-risk and low-risk groups. An validation cohort was used to validate the predictive ability of risk signature and expression of 6 candidate LRGs was confirmed through quantitative real-time PCR (qRT-PCR). Nomograms were created to visually represent the clinical value of LRG-related signature. Furthermore, we extensively examined differences in immune cell infiltration, tumor mutational load (TMB), microsatellite instability (MSI) and drug sensitivity between two risk groups. Analysis of the integrated TCGA and GSE39582 cohorts revealed two distinct LRG-related clusters and gene clusters with significant differences in overall survival (OS) and tumor microenvironment. We developed a LRG-related signature comprising 6 candidate LRGs that reliably predicted OS and qRT-PCR validation confirmed the expression of LRGs. Based on the median risk score, patients were divided into low-risk and high-risk groups, with low-risk group showing better survival. Furthermore, patients in high-risk group were more sensitive to chemotherapy and associated with higher TMB, higher proportion of MSI-H. Our study provides a valuable method for guiding clinical management and personalized treatment of COAD patients, which offers new insights into individualized treatment strategies, ultimately improving patient outcomes.

Identification of mitophagy-related biomarkers and immune infiltration in polycystic ovarian syndrome by bioinformatic study

Background

Polycystic ovarian syndrome (PCOS) is a common endocrine and metabolic disorder in women of reproductive age. In this study, we aimed to investigate the potential prognostic value of mitophagy-related genes in PCOS patients and to analyze their role in immune infiltration during PCOS pathogenesis and progression.

Methods

Training datasets were used for differential expression genes. Gene ontology annotations and Kyoto encyclopedia of genes and genomes signaling pathway enrichment analysis were performed. The potential biomarkers of mitophagy-related and immune infiltration in PCOS were screened by protein-protein interaction network using different algorithms, and the area under the curve was calculated to analyze their diagnostic value. The test datasets were used to validate the expression of hub genes, and receiver operating characteristic curves were used to evaluate the predictive effect of hub genes.

Results

Five hub genes: CTSD, IGF2R, ATP13A2, NAPA and GRN were identified as the potential diagnostic biomarkers of immune-mitophagy-related PCOS through five algorithms. GRN and NAPA were validated to be significantly different in oocytes and granulosa cells between primary and secondary follicles, respectively. Based on the single sample Gene Set Enrichment Analysis score, the infiltration of 4 immune cell types in PCOS was associated with PCOS and mitophagy. Specifically, the hub gene GRN showed a positive correlation with monocytes and plasmacytoid dendritic cells, while hub gene NAPA was negatively correlated with gamma delta T cell.

Conclusions

The current study identified immune-mitophagy-related hub genes for prognostic biomarkers of PCOS, which provided an innovative insight for the prevention and treatment of PCOS.

ScDrugAct: a comprehensive database to dissect tumor microenvironment cell heterogeneity contributing to drug action and resistance across human cancers

The transcriptional heterogeneity of tumor microenvironment (TME) cells is a crucial factor driving the diversity of cellular response to drug treatment and resistance. Therefore, characterizing the cells associated with drug treatment and resistance will help us understand therapeutic mechanisms, discover new therapeutic targets and facilitate precision medicine. Here, we describe a database, scDrugAct (http://bio-bigdata.hrbmu.edu.cn/scDrugAct/), which aims to establish connections among drugs, genes and cells and dissect the impact of TME cellular heterogeneity on drug action and resistance at single-cell resolution. ScDrugAct is curated with drug-cell connections between 3838 223 cells across 34 cancer types and 13 857 drugs and identifies 17 274 drug perturbation/resistance-related genes and 276 559 associations between >10 000 drugs and 53 cell types. ScDrugAct also provides multiple flexible tools to retrieve and analyze connections among drugs, genes and cells; the distribution and developmental trajectories of drug-associated cells within the TME; functional features affecting the heterogeneity of cellular responses to drug perturbation and drug resistance; the cell-specific drug-related gene network; and drug-drug similarities. ScDrugAct serves as an important resource for investigating the impact of the cellular heterogeneity of the TME on drug therapies and can help researchers understand the mechanisms of action and resistance of drugs, as well as discover therapeutic targets.

Comprehensive analysis of PDE2A: a novel biomarker for prognostic value and immunotherapeutic potential in human cancers

Phosphodiesterase 2A (PDE2A) plays a pivotal role in modulating cyclic nucleotide metabolism. Recent studies have shown that PDE2A is associated with some tumors, but its expression profiles, prognostic significance, and immunological roles in diverse cancer types remain unclear. Utilizing advanced bioinformatics tools, we performed a comprehensive analysis of PDE2A gene expression in multiple human cancers. Our study revealed that PDE2A expression was significantly reduced in the majority of cancer types and clinicopathological stages (I to IV) compared to normal tissues. Additionally, PDE2A expression was closely related to the prognosis of cancers such as stomach adenocarcinoma (STAD), ovarian serous cystadenocarcinoma (OV), and liver hepatocellular carcinoma (LIHC). Cox regression analyses indicated that PDE2A can act as an independent prognostic factor for these cancers. The level of PDE2A DNA methylation was significantly decreased in most cancers. Genetic alterations in PDE2A predominantly manifest in the form of amplifications. Moreover, infiltrating cells and immune checkpoint genes, including PDCD1, exhibited notable correlations with PDE2A expression. Significant associations were observed between PDE2A expression and tumor mutation burden as well as microsatellite instability. Single cell sequencing revealed PDE2A's crucial role in regulating differentiation and angiogenesis of cancer cells. Functional enrichment analysis emphasized the important role of PDE2A in synaptic transmission and tumor development. Aberrant expression of PDE2A influenced the sensitivity of various antitumor and chemotherapy drugs. This research provided a comprehensive analysis of PDE2A in human cancers, highlighting its potential as both a prognostic marker and an immunotherapy target for future research.

Identification of Crucial Genes and Signaling Pathways in Alectinib-Resistant Lung Adenocarcinoma Using Bioinformatic Analysis

Alectinib, a second-generation anaplastic lymphoma kinase (ALK) inhibitor, has been shown to be effective for patients with ALK-positive non-small cell lung cancer (NSCLC). However, alectinib resistance is a serious problem worldwide. To the best of our knowledge, little information is available on its molecular mechanisms using the Gene Expression Omnibus (GEO) database. In this study, the differentially expressed genes (DEGs) were selected from the gene expression profile GSE73167 between parental and alectinib-resistant human lung adenocarcinoma (LUAD) cell samples. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) annotation enrichment analyses were conducted using Database for Annotation, Visualization and Integrated Discovery (DAVID). The construction of protein-protein interaction (PPI) network was performed to visualize DEGs. The hub genes were extracted based on the analysis of the PPI network using plug-in cytoHubba of Cytoscape software. The functional roles of the key genes were investigated using Gene Expression Profiling Interactive Analysis (GEPIA), University of Alabama at Birmingham Cancer (UALCAN), Gene Set Enrichment Analysis (GSEA), and Tumor Immune Estimation Resource (TIMER) analysis. The networks of kinase, miRNA, and transcription-factor targets of SFTPD were explored using LinkedOmics. The drug sensitivity analysis of SFTPD was analyzed using the RNAactDrug database. Results showed a total of 144 DEGs were identified. Five hub genes were extracted, including mucin 5B (MUC5B), surfactant protein D (SFTPD), deleted in malignant brain tumors 1 (DMBT1), surfactant protein A2 (SFTPA2), and trefoil factor 3 (TFF3). The survival analysis using GEPIA displayed that low expression of SFTPD had a significantly negative effect on the prognosis of patients with LUAD. GSEA revealed that low expression of SFTPD was positively correlated with the pathways associated with drug resistance, such as DNA replication, cell cycle, drug metabolism, and DNA damage repair, including mismatch repair (MMR), base excision repair (BER), homologous recombination (HR), and nucleotide excision repair (NER). The SFTPD expression was negatively correlated with the drug sensitivity of alectinib according to RNAactDrug database. The expression of SFTPD was further validated in parental H3122 cells and alectinib-resistant H3122 cells by quantitative reverse transcription PCR (RT-qPCR). In conclusion, our study found that the five hub genes, especially low expression of SFTPD, are closely related to alectinib resistance in patients with LUAD.

The role of m5C RNA methylation regulators in the diagnosis and immune microenvironment of osteoarthritis

The 5-methylcytosine (m5C) is a common post-transcriptional RNA methylation modification and is involved in the pathological process of many diseases. However, little is known about the role of m5C in osteoarthritis (OA). OA gene data and the corresponding information were downloaded from the Gene Expression Omnibus database. Based on 36 m5C regulators, we constructed the landscape and diagnostic model for OA. Later, two m5C modification patterns were identified, and functional analyses were performed to evaluate whether these patterns were related to endoplasmic reticulum (ER) stress and mitochondrial autophagy. We further comprehensively analyzed the immune cell infiltration characteristics in different modification patterns in OA. We also established the post-transcriptional regulatory networks and drug-gene networks. Our findings suggested that m5C regulators were differentially expressed between OA and normal samples and could serve as novel biomarkers for the diagnosis of OA. Besides, m5C regulators may be involved in regulating ER stress, mitochondrial autophagy, and immune infiltration in OA. The m5C modification can influence the sensitivity to drugs and the potential post-transcriptional regulatory mechanisms might provide promising targets.

TIMM9 as a prognostic biomarker in multiple cancers and its associated biological processes

TIMM9 has been identified as a mediator of essential functions in mitochondria, but its association with pan-cancer is poorly understood. We herein employed bioinformatics, computational chemistry techniques and experiments to investigate the role of TIMM9 in pan-cancer. Our analysis revealed that overexpression of TIMM9 was significantly associated with tumorigenesis, pathological stage progression, and metastasis. Missense mutations (particularly the S49L variant), copy number variations (CNV) and methylation alterations in TIMM9 were found to be associated with poor cancer prognosis. Moreover, TIMM9 was positively related with cell cycle progression, mitochondrial and ribosomal function, oxidative phosphorylation, TCA cycle activity, innate and adaptive immunity. Additionally, we discovered that TIMM9 could be regulated by cancer-associated signaling pathways, such as the mTOR pathway. Using molecular simulations, we identified ITFG1 as the protein that has the strongest physical association with TIMM9, which show a promising structural complement.

Exploring ncRNA-Drug Sensitivity Associations via Graph Contrastive Learning

Increasing evidence has shown that noncoding RNAs (ncRNAs) can affect drug efficiency by modulating drug sensitivity genes. Exploring the association between ncRNAs and drug sensitivity is essential for drug discovery and disease prevention. However, traditional biological experiments for identifying ncRNA-drug sensitivity associations are time-consuming and laborious. In this study, we develop a novel graph contrastive learning approach named NDSGCL to predict ncRNA-drug sensitivity. NDSGCL uses graph convolutional networks to learn feature representations of ncRNAs and drugs in ncRNA-drug bipartite graphs. It integrates local structural neighbours and global semantic neighbours to learn a more comprehensive representation by contrastive learning. Specifically, the local structural neighbours aim to capture the higher-order relationship in the ncRNA-drug graph, while the global semantic neighbours are defined based on semantic clusters of the graph that can alleviate the impact of data sparsity. The experimental results show that NDSGCL outperforms basic graph convolutional network methods, existing contrastive learning methods, and state-of-the-art prediction methods. Visualization experiments show that the contrastive objectives of local structural neighbours and global semantic neighbours play a significant role in contrastive learning. Case studies on two drugs show that NDSGCL is an effective tool for predicting ncRNA-drug sensitivity associations.

Development of prognostic model incorporating a ferroptosis/cuproptosis-related signature and mutational landscape analysis in muscle-invasive bladder cancer

BACKGROUND

Muscle-invasive bladder cancer (MIBC) is a prevalent and aggressive malignancy. Ferroptosis and cuproptosis are recently discovered forms of programmed cell death (PCD) that have attracted much attention. However, their interactions and impacts on MIBC overall survival (OS) and treatment outcomes remain unclear.

METHODS

Data from the TCGA-BLCA project (as the training set), cBioPortal database, and GEO datasets (GSE13507 and GSE32894, as the test sets) were utilized to identify hub ferroptosis/cuproptosis-related genes (FRGs and CRGs) and develop a prognostic signature. Differential expression analysis (DEA) was conducted, followed by univariate and multivariate Cox's regression analyses and multiple machine learning (ML) techniques to select genetic features. The performance of the ferroptosis/cuproptosis-related signature was evaluated using Kaplan-Meier (K-M) survival analysis and receiver-operating characteristics (ROC) curves. Mutational and tumour immune microenvironment landscapes were also explored. Real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) experiments confirmed the expression patterns of the hub genes, and functional assays assessed the effects of SCD knockdown on cell viability, proliferation, and migration.

RESULTS

DEA revealed dysregulated FRGs and CRGs in the TCGA MIBC cohort. SCD, DDR2, and MT1A were identified as hub genes. A prognostic signature based on the sum of the weighted expression of these genes demonstrated strong predictive efficacy in the training and test sets. Nomogram incorporating this signature accurately predicted 1-, 3-, and 5-year survival probabilities in the TCGA cohort and GSE13507 dataset. Copy number variation (CNV) and tumour immune microenvironment analysis revealed that high risk score level groups were associated with immunosuppression and lower tumour purity. The associations of risk scores with immunotherapy and chemical drugs were also explored, indicating their potential for guiding treatment for MIBC patients. The dysregulated expression patterns of three hub genes were validated by RT-qPCR experiments.

CONCLUSIONS

Targeting hub FRGs and CRGs could be a promising therapeutic approach for MIBC. Our prognostic model offers a new framework for MIBC subtyping and can inform personalized therapeutic strategies.

RAB42 overexpression correlates with poor prognosis, immune cell infiltration and chemoresistance

BACKGROUND

RAB42 (Ras-related protein 42) is a new small GTPase that controls the vesicular trafficking from endosomes to trans-Golgi network in mammalian cells. However, the role of RAB42 in multiple cancers, especially in liver hepatocellular carcinoma (LIHC), has not been well investigated.

METHODS

A variety of cancer-related databases and online tools, including TCGA, GTEx, TARGET, QUANTISEQ, EPIC, RNAactDrug, CTR-DB, TIMER algorithms and Sangerbox, were applied to explore the correlation of RAB42 expression with prognosis, immune microenvironment, immune regulatory network, RNA modification, pathway activation and drug sensitivity in pan-cancer. The prognostic, immunomodulatory and tumor-promoting effects of RAB42 were verified in various malignancies and determined by a series of in vitro cellular experiments.

RESULTS

RAB42 is significantly overexpressed in most cancers with advanced pathological stages. Its overexpression is correlated with poor survival in pan-cancer. RAB42 overexpression has a high diagnostic accuracy of various cancers (AUC > 0.80). RAB42 overexpression not only correlates with distinct stromal immune infiltration and level of immune checkpoint molecules, but also associates with weak immune cell infiltration, immunomodulatory genes expression, and immunotherapeutic response to immune checkpoint inhibitors (ICIs). Additionally, RAB42 overexpression correlates with enhanced expression of m6A RNA methylation-related genes (MRGs) and its interactors. Moreover, overexpression of RAB42 serves as a drug-resistant marker to certain chemotherapies and acts as a potential biomarker for LIHC. Notably, RAB42 overexpression or activation promotes the cellular proliferation, migration and invasion of LIHC.

CONCLUSION

Overexpressed RAB42 serves as a potential prognostic biomarker and therapeutic target in pan-cancer, especially in LIHC.

Identification and characterization of ferroptosis-related genes in therapy-resistant gastric cancer

Therapy resistance in gastric cancer poses ongoing challenges, necessitating the identification of ferroptosis-related genes linked to overall survival for potential therapeutic insights. The purpose of the study was to identify ferroptosis-related genes contributing to therapy resistance in gastric cancer and explore their associations with overall survival. Differentially expressed ferroptosis-related genes were identified in therapy-resistant versus therapy-responsive gastric cancer patients. Hub genes were selected from these genes. Enrichment analysis focused on oxidative stress and ROS metabolism. Validation was conducted in a TCGA stomach adenocarcinoma dataset. A hub gene-based risk model (DUSP1/TNF/NOX4/LONP1) was constructed and assessed for overall survival prediction. Associations with the tumor immune microenvironment were examined using the ESTIMATE algorithm and correlation analysis. Ten hub genes were identified, enriched in oxidative stress and ROS metabolism. Validation confirmed their aberrant expressions in the TCGA dataset. The hub gene-based risk model effectively predicted overall survival. High G6PD/TNF expression and low NOX4/SREBF1/MAPK3/DUSP1/KRAS/SIRT3/LONP1 expression correlated with stromal and immune scores. KRAS/TNF/MAPK3 expression positively correlated with immune-related SREBF1/NOX4 expression. DUSP1/NOX4/SREBF1/TNF/KRAS expression was associated with immune cell infiltration. The hub gene-based risk model (DUSP1/TNF/NOX4/LONP1) shows promise as an overall survival predictor in gastric cancer. Ferroptosis-related hub genes represent potential therapeutic targets for overcoming therapy resistance in gastric cancer treatment.

The feedback loop of EFTUD2/c-MYC impedes chemotherapeutic efficacy by enhancing EFTUD2 transcription and stabilizing c-MYC protein in colorectal cancer

BACKGROUND

Chemoresistance presents a significant obstacle in the treatment of colorectal cancer (CRC), yet the molecular basis underlying CRC chemoresistance remains poorly understood, impeding the development of new therapeutic interventions. Elongation factor Tu GTP binding domain containing 2 (EFTUD2) has emerged as a potential oncogenic factor implicated in various cancer types, where it fosters tumor growth and survival. However, its specific role in modulating the sensitivity of CRC cells to chemotherapy is still unclear.

METHODS

Public dataset analysis and in-house sample validation were conducted to assess the expression of EFTUD2 in 5-fluorouracil (5-FU) chemotherapy-resistant CRC cells and the potential of EFTUD2 as a prognostic indicator for CRC. Experiments both in vitro, including MTT assay, EdU cell proliferation assay, TUNEL assay, and clone formation assay and in vivo, using cell-derived xenograft models, were performed to elucidate the function of EFTUD2 in sensitivity of CRC cells to 5-FU treatment. The molecular mechanism on the reciprocal regulation between EFTUD2 and the oncogenic transcription factor c-MYC was investigated through molecular docking, ubiquitination assay, chromatin immunoprecipitation (ChIP), dual luciferase reporter assay, and co-immunoprecipitation (Co-IP).

RESULTS

We found that EFTUD2 expression was positively correlated with 5-FU resistance, higher pathological grade, and poor prognosis in CRC patients. We also demonstrated both in vitro and in vivo that knockdown of EFTUD2 sensitized CRC cells to 5-FU treatment, whereas overexpression of EFTUD2 impaired such sensitivity. Mechanistically, we uncovered that EFTUD2 physically interacted with and stabilized c-MYC protein by preventing its ubiquitin-mediated proteasomal degradation. Intriguingly, we found that c-MYC directly bound to the promoter region of EFTUD2 gene, activating its transcription. Leveraging rescue experiments, we further confirmed that the effect of EFTUD2 on 5-FU resistance was dependent on c-MYC stabilization.

CONCLUSION

Our findings revealed a positive feedback loop involving an EFTUD2/c-MYC axis that hampers the efficacy of 5-FU chemotherapy in CRC cells by increasing EFTUD2 transcription and stabilizing c-MYC oncoprotein. This study highlights the potential of EFTUD2 as a promising therapeutic target to surmount chemotherapy resistance in CRC patients.

Highly expressed RPLP2 inhibits ferroptosis to promote hepatocellular carcinoma progression and predicts poor prognosis

BACKGROUND

RPLP2, an integral part of ribosomal stalk, plays an important role in the tumorigenesis of various cancers. However, its specific effect on HCC remains elusive.

METHODS

TCGA, GTEx, HCCDB, HPA, UALCAN, MethSurv, TISIDB, K-M plotter, FerrDb, RNAactDrug, STRING, Cytoscape and R studio were conducted for bioinformatics analysis. RPLP2 expression level in HCC was verified by IHC and western blot. IHC was used to demonstrate the immune cell infiltration. Functional experiments including CCK8, transwell and colony formation assays, and nude mice xenograft model were performed for in vitro and in vivo validation. Western blot, IHC, CCK8 assay and detection of GSH and lipid ROS were adopted to determine the effect of RPLP2 on the ferroptosis of HCC cells.

RESULTS

Here, we demonstrate that elevated level of RPLP2 is strongly associated with advanced clinicopathologic features, and predicts poor prognosis of HCC patients. Additionally, DNA methylation level of RPLP2 decreases in HCC, and significantly correlates with patients outcome. Moreover, high RPLP2 expression level is linked closely to the unfavorable immune infiltration. Most importantly, RPLP2 positively associates with ferroptosis suppressor GPX4, and inhibition of RPLP2 could lead to the acceleration of ferroptosis to suppress tumor progression of HCC. Last, drug sensitivity analysis predicts many drugs that potentially target RPLP2.

CONCLUSION

Together, our study reveals previous unrecognized role of RPLP2 in HCC, and provides new regulatory mechanism of ferroptosis, indicating RPLP2 may be a novel therapeutic target for HCC.

Mediating oxidative stress through the Palbociclib/miR-141-3p/STAT4 axis in osteoporosis: a bioinformatics and experimental validation study

Osteoporosis is a common bone disease characterized by loss of bone mass, reduced bone strength, and deterioration of bone microstructure. ROS-induced oxidative stress plays an important role in osteoporosis. However, the biomarkers and molecular mechanisms of oxidative stress are still unclear. We obtained the datasets from the Gene Expression Omnibus (GEO) database, and performed differential analysis, Venn analysis, and weighted correlation network analysis (WGCNA) analysis out the hub genes. Then, the correlation between inflammatory factors and hub genes was analyzed, and a Mendelian randomization (MR) analysis was performed on cytokines and osteoporosis outcomes. In addition, "CIBERSORT" was used to analyze the infiltration of immune cells and single-cell RNA-seq data was used to analyze the expression distribution of hub genes and cell-cell communications. Finally, we collected human blood samples for RT-qPCR and Elisa experiments, the miRNA-mRNA network was constructed using the miRBase database, the 3D structure was predicted using the RNAfold, Vfold3D database, and the drug sensitivity analysis was performed using the RNAactDrug database. We obtained three differentially expressed genes associated with oxidative stress: DBH, TAF15, and STAT4 by differential, WGCNA clustering, and Venn screening analyses, and further analyzed the correlation of these 3 genes with inflammatory factors and immune cell infiltration and found that STAT4 was significantly and positively correlated with IL-2. Single-cell data analysis showed that the STAT4 gene was highly expressed mainly in dendritic cells and monocytes. In addition, the results of RT-qPCR and Elisa experiments verified that the expression of STAT4 was consistent with the previous analysis, and a significant causal relationship between IL-2 and STAT4 SNPs and osteoporosis was found by Mendelian randomization. Finally, through miRNA-mRNA network and drug sensitivity analysis, we analyzed to get Palbociclib/miR-141-3p/STAT4 axis, which can be used for the prevention and treatment of osteoporosis. In this study, we proposed the Palbociclib/miR-141-3p/STAT4 axis for the first time and provided new insights into the mechanism of oxidative stress in osteoporosis.

Long noncoding RNA study: Genome-wide approaches

Long noncoding RNAs (lncRNAs) have been confirmed to play a crucial role in various biological processes across several species. Though many efforts have been devoted to the expansion of the lncRNAs landscape, much about lncRNAs is still unknown due to their great complexity. The development of high-throughput technologies and the constantly improved bioinformatic methods have resulted in a rapid expansion of lncRNA research and relevant databases. In this review, we introduced genome-wide research of lncRNAs in three parts: (i) novel lncRNA identification by high-throughput sequencing and computational pipelines; (ii) functional characterization of lncRNAs by expression atlas profiling, genome-scale screening, and the research of cancer-related lncRNAs; (iii) mechanism research by large-scale experimental technologies and computational analysis. Besides, primary experimental methods and bioinformatic pipelines related to these three parts are summarized. This review aimed to provide a comprehensive and systemic overview of lncRNA genome-wide research strategies and indicate a genome-wide lncRNA research system.

HGCLMDA: Predicting mRNA-Drug Sensitivity Associations via Hypergraph Contrastive Learning

The identification of drug sensitivity to mRNA interactions is crucial for drug development and disease treatment, but traditional experimental methods for verifying mRNA-drug sensitivity associations are labor-intensive and time-consuming. In this study, we present a hypergraph contrastive learning approach, HGCLMDA, to predict potential mRNA-drug sensitivity associations. HGCLMDA integrates a graph convolutional network-based method with a hypergraph convolutional network to mine high-order relationships between mRNA-drug association pairs. The proposed cross-view contrastive learning architecture improves the model's learning ability, and the inner product is used to obtain the mRNA-drug sensitivity association score. Our experiments on three mRNA-drug sensitivity association data sets show that HGCLMDA outperforms traditional graph convolutional network-based methods, graph augmentation-based contrastive learning methods, and state-of-the-art association prediction methods. The visualization experiment demonstrates the strong discrimination ability of the mRNA and drug embeddings learned by HGCLMDA, and experiments on sparse data sets showcase the performance and robustness of the method. In-depth analysis of hypergraph structures reveals a crucial role that hypergraphs play in enhancing the performance of models. The case study highlights the potential of HGCLMDA as a valuable tool for predicting mRNA-drug sensitivity associations. The interpretive analysis reveals that HGCLMDA effectively models the similarity between mRNA-mRNA and drug-drug interactions.

Cell-type-specific alternative polyadenylation promotes oncogenic gene expression in non-small cell lung cancer progression

Disrupted alternative polyadenylation (APA) is frequently involved in tumorigenesis and cancer progression by regulating the gene expression of oncogenes and tumor suppressors. However, limited knowledge of tumor-type- and cell-type-specific APA events may lead to novel APA events and their functions being overlooked. Here, we compared APA events across different cell types in non-small cell lung cancer (NSCLC) and normal tissues and identified functionally related APA events in NSCLC. We found several cell-specific 3'-UTR alterations that regulate gene expression changes showed prognostic value in NSCLC. We further investigated the function of APA-mediated 3'-UTR shortening through loss of microRNA (miRNA)-binding sites, and we identified and experimentally validated several oncogene-miRNA-tumor suppressor axes. According to our analyses, we found SPARC as an APA-regulated oncogene in cancer-associated fibroblasts in NSCLC. Knockdown of SPARC attenuates lung cancer cell invasion and metastasis. Moreover, we found high SPARC expression associated with resistance to several drugs except cisplatin. NSCLC patients with high SPARC expression could benefit more compared to low-SPARC-expression patients with cisplatin treatment. Overall, our comprehensive analysis of cell-specific APA events shed light on the regulatory mechanism of cell-specific oncogenes and provided opportunities for combination of APA-regulated therapeutic target and cell-specific therapy development.

IMOPAC: A web server for interactive multiomics and pharmacological analyses of patient-derived cancer cell lines

Large-scale multidimensional cancer genomic and pharmacological profiles have been created by several large consortium projects, including NCI-60, GDSC and DepMap, providing novel opportunities for data mining and further understanding of intrinsic therapeutic response mechanisms. However, it is increasingly challenging for experimental biologists, especially those without a bioinformatic background, to integrate, explore, and analyse these tremendous pharmacogenomics. To address this gap, IMOPAC, an interactive and easy-to-use web-based tool, was introduced to provide rapid visualizations and customizable functionalities on the basis of these three publicly available databases, which may reduce pharmacogenomic profiles from cell lines into readily understandable genetic, epigenetic, transcriptionomic, proteomic, metabolomic, and pharmacological events. The user-friendly query interface together with customized data storage enables users to interactively investigate and visualize multiomics alterations across genes and pathways and to link these alterations with drug responses across cell lines from diverse cancer types. The analyses in our portal include pancancer expression, drug-omics/pathway correlation, cancer subtypes, omics-omics (cis-/trans-regulation) correlation, fusion query analysis, and drug response prediction analysis. The comprehensive multiomics and pharmacogenomic analyses with simple clicking through IMOPAC will significantly benefit cancer precision medicine, contribute to the discoveries of potential biological mechanisms and facilitate pharmacogenomics mining in the identification of clinically actionable biomarkers for both basic researchers and clinical practitioners. IMOPAC is freely available at http://www.hbpding.com/IMOPAC.

EDEM2 is a diagnostic and prognostic biomarker and associated with immune infiltration in glioma: A comprehensive analysis

Glioma is a highly common pathological brain tumor. Misfolded protein response, which is strongly associated with the growth of cancerous tumors, is mediated by the gene, endoplasmic reticulum degradation-enhancing alpha-mannosidase-like protein 2. However, this gene has not been linked to glioma. To assess the same, we used The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and Genotype-Tissue Expression datasets. The gene was overexpressed in gliomas. This overexpression was linked to unfavorable clinical characteristics, such as the World Health Organization grade, isocitrate dehydrogenase mutation, and the combined loss of the short arm chromosome 1 and the long arm of chromosome 19. Quantitative polymerase chain reaction experiments and immunohistochemistry on clinical samples from our institution verified the gene's expression and clinical importance. The Human Protein Atlas website verified the messenger ribonucleic acid expression of the gene in glioma cell lines, and immunohistochemistry verified the presence of its protein. A previous survival study indicated that its high expression is substantially related to a bad prognosis. It was identified as an independent predictor of primary glioma prognosis using multivariate Cox regression analysis. To forecast individual survival, we created a nomogram based on this (concordance-index = 0.847). Additionally, functional annotation demonstrated its major role in the control of the extracellular matrix and immune system. The scratch assay and transwell migration assay confirmed the decreased invasive ability of U251 glioma cells with the gene knockdown. Its increased expression was found to be related to the extent of macrophage infiltration using the CIBERSORT, ESTIMATE, Single-sample Gene Set Enrichment Analysis, and Tumor Immune Single-Cell Hub (TISCH) algorithms. The Tumor Immune Dysfunction and Exclusion algorithm revealed that the gene can accurately predict the response of immunotherapy (area under the receiver operating characteristic curve = 0.857). Further, isocitrate dehydrogenase 1 mutation is typically more frequent when the gene expression is high. Finally, five medicines targeting this gene were discovered utilizing the molecular docking program and drug sensitivity analysis of the RNAactDrug website. Low expression of the gene inhibited glioma cell invasion. Therefore, the gene is helpful for the diagnosis, prognosis, and case-specific immunotherapy of glioma.

A novel method to identify and characterize personalized functional driver lncRNAs in cancer samples

Cancer is a highly heterogeneous disease, and different individuals of the same cancer type can display different therapeutic effects and prognosis. Genetic variation of long non-coding RNA is the key factor driving tumor development, and plays an important role in genetic and biological heterogeneity. Therefore, it is of great significance to identify lncRNA as a driving factor in the non-coding region and explain its function in tumors for revealing the pathogenesis of cancer. In this study, we developed an integrated method to identify Personalized Functional Driver lncRNAs (PFD-lncRNAs) by integrating the DNA copy number data, gene expression data, and the biological subpathways information. Then, we applied the method to identify 2695 PFD-lncRNAs in 5334 samples across 19 cancer types. We performed an analysis of the association between PFD-lncRNAs and drug sensitivity, which provides medication guidance in disease therapy and drug discovery in the individual. Our research is of great significance for elucidating the biological roles of lncRNA genetic variation in cancer, revealing the related mechanism of cancer, and providing novel insights for individualized medicine.

Comprehensive Multiomic Analysis Identified TUBA1C as a Potential Prognostic Biological Marker of Immune-Related Therapy in Pan-Cancer

TUBA1C is correlated with an unfavourable prognosis and the infiltration of immune cells in several cancers. However, its function as a significant biomarker for the prognosis of immunotherapy in pan-cancer remains unclear. This study aims at assessing the role of TUBA1C in pan-cancer at multiple levels, including mutations, gene expression, methylation, m6A methylation, and immune cell infiltration levels. Data retrieved from major public databases, such as TCGA, GEO, GTEx, GSCA, CancerSEA, HPA, and RNAactDrugs, revealed that TUBA1C expression was high in 33 cancer types. Survival analysis revealed that TUBA1C was a poor prognostic factor for 12 tumour types, and mutations, CNVs, and methylation affected the prognosis of some cancer types. Furthermore, TUBA1C was found to be related to immune-related genes, immune cell infiltration, and the immune microenvironment. In addition, the sensitivity of 10 anticancer drugs was associated with high TUBA1C expression. Therefore, TUBA1C may serve as a viable prognostic biomarker for immunotherapy of pan-cancer.

Prognostic analysis and validation of diagnostic marker genes in patients with osteoporosis

Backgrounds

As a systemic skeletal dysfunction, osteoporosis (OP) is characterized by low bone mass and bone microarchitectural damage. The global incidences of OP are high.

Methods

Data were retrieved from databases like Gene Expression Omnibus (GEO), GeneCards, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Gene Expression Profiling Interactive Analysis (GEPIA2), and other databases. R software (version 4.1.1) was used to identify differentially expressed genes (DEGs) and perform functional analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression and random forest algorithm were combined and used for screening diagnostic markers for OP. The diagnostic value was assessed by the receiver operating characteristic (ROC) curve. Molecular signature subtypes were identified using a consensus clustering approach, and prognostic analysis was performed. The level of immune cell infiltration was assessed by the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm. The hub gene was identified using the CytoHubba algorithm. Real-time fluorescence quantitative PCR (RT-qPCR) was performed on the plasma of osteoporosis patients and control samples. The interaction network was constructed between the hub genes and miRNAs, transcription factors, RNA binding proteins, and drugs.

Results

A total of 40 DEGs, eight OP-related differential genes, six OP diagnostic marker genes, four OP key diagnostic marker genes, and ten hub genes (TNF, RARRES2, FLNA, STXBP2, EGR2, MAP4K2, NFKBIA, JUNB, SPI1, CTSD) were identified. RT-qPCR results revealed a total of eight genes had significant differential expression between osteoporosis patients and control samples. Enrichment analysis showed these genes were mainly related to MAPK signaling pathways, TNF signaling pathway, apoptosis, and Salmonella infection. RT-qPCR also revealed that the MAPK signaling pathway (p38, TRAF6) and NF-kappa B signaling pathway (c-FLIP, MIP1β) were significantly different between osteoporosis patients and control samples. The analysis of immune cell infiltration revealed that monocytes, activated CD4 memory T cells, and memory and naïve B cells may be related to the occurrence and development of OP.

Conclusions

We identified six novel OP diagnostic marker genes and ten OP-hub genes. These genes can be used to improve the prognostic of OP and to identify potential relationships between the immune microenvironment and OP. Our research will provide insights into the potential therapeutic targets and pathogenesis of osteoporosis.

Identification and Verification of Potential Biomarkers in Gastric Cancer By Integrated Bioinformatic Analysis

Background: Gastric cancer (GC) is a common cancer with high mortality. This study aimed to identify its differentially expressed genes (DEGs) using bioinformatics methods.

Methods: DEGs were screened from four GEO (Gene Expression Omnibus) gene expression profiles. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. A protein–protein interaction (PPI) network was constructed. Expression and prognosis were assessed. Meta-analysis was conducted to further validate prognosis. The receiver operating characteristic curve (ROC) was analyzed to identify diagnostic markers, and a nomogram was developed. Exploration of drugs and immune cell infiltration analysis were conducted.

Results: Nine up-regulated and three down-regulated hub genes were identified, with close relations to gastric functions, extracellular activities, and structures. Overexpressed Collagen Type VIII Alpha 1 Chain (COL8A1), Collagen Type X Alpha 1 Chain (COL10A1), Collagen Triple Helix Repeat Containing 1 (CTHRC1), and Fibroblast Activation Protein (FAP) correlated with poor prognosis. The area under the curve (AUC) of ADAM Metallopeptidase With Thrombospondin Type 1 Motif 2 (ADAMTS2), COL10A1, Collagen Type XI Alpha 1 Chain (COL11A1), and CTHRC1 was >0.9. A nomogram model based on CTHRC1 was developed. Infiltration of macrophages, neutrophils, and dendritic cells positively correlated with COL8A1, COL10A1, CTHRC1, and FAP. Meta-analysis confirmed poor prognosis of overexpressed CTHRC1.

Conclusion: ADAMTS2, COL10A1, COL11A1, and CTHRC1 have diagnostic values in GC. COL8A1, COL10A1, CTHRC1, and FAP correlated with worse prognosis, showing prognostic and therapeutic values. The immune cell infiltration needs further investigations.

JMJD8 Is an M2 Macrophage Biomarker, and It Associates With DNA Damage Repair to Facilitate Stemness Maintenance, Chemoresistance, and Immunosuppression in Pan-Cancer

BACKGROUND

JMJD8 has recently been identified as a cancer-related gene, but current studies provide limited information. We aimed to clarify its roles and the potential mechanisms in pan-cancer.

METHODS

Pan-cancer bulk sequencing data and online web tools were applied to analyze JMJD8's correlations with prognosis, genome instability, cancer stemness, DNA repair, and immune infiltration. Moreover, single-cell datasets, SpatialDB database, and multiple fluorescence staining were used to validate the association between JMJD8 expression and M2 macrophages. Further, we utilized ROCplotter and cMap web tool to analyze the therapeutic responses and screened JMJD8-targeted compounds, respectively, and we used AlphaFold2 and Discovery Studio to conduct JMJD8 homology modeling and molecular docking.

RESULTS

We first noticed that JMJD8 was an oncogene in many cancer types. High JMJD8 was associated with lower genome stability. We then found that high JMJD8 correlated with high expression of mismatch repair genes, stemness, homologous repair gene signature in more than 9 cancers. ESTIMATE and cytokine analyses results presented JMJD8's association with immunosuppression. Also, immune checkpoint CD276 was positively relevant to JMJD8. Subsequently, we validated JMJD8 as the M2 macrophage marker and showed its connection with other immunosuppressive cells and CD8+ T-cell depression. Finally, potential JMJD8-targeted drugs were screened out and docked to JMJD8 protein.

CONCLUSION

We found that JMJD8 was a novel oncogene, and it correlated with immunosuppression and DNA repair. JMJD8 was highly associated with immune checkpoint CD276 and was an M2 macrophage biomarker in many cancers. This study will reveal JMJD8's roles in pan-cancer and its potential as a novel therapeutic target.

CNNLSTMac4CPred: A Hybrid Model for N4-Acetylcytidine Prediction

N4-Acetylcytidine (ac4C) is a highly conserved post-transcriptional and an extensively existing RNA modification, playing versatile roles in the cellular processes. Due to the limitation of techniques and knowledge, large-scale identification of ac4C is still a challenging task. RNA sequences are like sentences containing semantics in the natural language. Inspired by the semantics of language, we proposed a hybrid model for ac4C prediction. The model used long short-term memory and convolution neural network to extract the semantic features hidden in the sequences. The semantic and the two traditional features (k-nucleotide frequencies and pseudo tri-tuple nucleotide composition) were combined to represent ac4C or non-ac4C sequences. The eXtreme Gradient Boosting was used as the learning algorithm. Five-fold cross-validation over the training set consisting of 1160 ac4C and 10,855 non-ac4C sequences obtained the area under the receiver operating characteristic curve (AUROC) of 0.9004, and the independent test over 469 ac4C and 4343 non-ac4C sequences reached an AUROC of 0.8825. The model obtained a sensitivity of 0.6474 in the five-fold cross-validation and 0.6290 in the independent test, outperforming two state-of-the-art methods. The performance of semantic features alone was better than those of k-nucleotide frequencies and pseudo tri-tuple nucleotide composition, implying that ac4C sequences are of semantics. The proposed hybrid model was implemented into a user-friendly web-server which is freely available to scientific communities: http://47.113.117.61/ac4c/ . The presented model and tool are beneficial to identify ac4C on large scale.

Comprehensive Analysis of the Prognostic Signature of Mutation-Derived Genome Instability-Related lncRNAs for Patients With Endometrial Cancer

Background: Emerging evidence shows that genome instability-related long non-coding RNAs (lncRNAs) contribute to tumor–cell proliferation, differentiation, and metastasis. However, the biological functions and molecular mechanisms of genome instability-related lncRNAs in endometrial cancer (EC) are underexplored.

Methods: EC RNA sequencing and corresponding clinical data obtained from The Cancer Genome Atlas (TCGA) database were used to screen prognostic lncRNAs associated with genomic instability via univariate and multivariate Cox regression analysis. The genomic instability-related lncRNA signature (GILncSig) was developed to assess the prognostic risk of high- and low-risk groups. The prediction performance was analyzed using receiver operating characteristic (ROC) curves. The immune status and mutational loading of different risk groups were compared. The Genomics of Drug Sensitivity in Cancer (GDSC) and the CellMiner database were used to elucidate the relationship between the correlation of prognostic lncRNAs and drug sensitivity. Finally, we used quantitative real-time PCR (qRT-PCR) to detect the expression levels of genomic instability-related lncRNAs in clinical samples.

Results: GILncSig was built using five lncRNAs (AC007389.3, PIK3CD-AS2, LINC01224, AC129507.4, and GLIS3-AS1) associated with genomic instability, and their expression levels were verified using qRT-PCR. Further analysis revealed that risk score was negatively correlated with prognosis, and the ROC curve demonstrated the higher accuracy of GILncSig. Patients with a lower risk score had higher immune cell infiltration, a higher immune score, lower tumor purity, higher immunophenoscores (IPSs), lower mismatch repair protein expression, higher microsatellite instability (MSI), and a higher tumor mutation burden (TMB). Furthermore, the level of expression of prognostic lncRNAs was significantly related to the sensitivity of cancer cells to anti-tumor drugs.

Conclusion: A novel signature composed of five prognostic lncRNAs associated with genome instability can be used to predict prognosis, influence immune status, and chemotherapeutic drug sensitivity in EC.

A Comprehensive Multiomics Analysis Identified Ubiquilin 4 as a Promising Prognostic Biomarker of Immune-Related Therapy in Pan-Cancer

Recently, it was reported that ubiquilin 4 (UBQLN4) alteration was associated with genomic instability in some cancers. However, whether UBQLN4 is a valuable biomarker for the prognosis of immunotherapy in pan-cancer was not identified. We evaluated the biologic and oncologic significance of UBQLN4 in pan-cancer at multiomics level, such as expression, mutation, copy number variation (CNV), methylation, and N⁶-methyladenosine (m⁶A) methylation. These omics data were obtained from several public databases, including Oncomine, The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), the Genotype-Tissue Expression (GTEx), the Human Protein Atlas (HPA), Gene Set Cancer Analysis (GSCA), m⁶A-Atlas, CancerSEA, and RNAactDrug. We found that UBQLN4 mRNA and protein were overexpressed in most cancer types, and the expression, mutation, CNV, and methylation of UBQLN4 were associated with the prognosis of some cancers. Mechanistically, UBQLN4 was involved in angiogenesis, DNA damage, apoptosis, and the pathway of PI3K/AKT and TSC/mTOR. Moreover, UBQLN4 mRNA was significantly correlated with immune checkpoints, tumor mutational burden (TMB), microsatellite instability (MSI), and mismatch repair (MMR). And, the correlation among UBQLN4 mRNA, CNV, and methylation and immune microenvironment was also identified. Furthermore, UBQLN4 was associated with the sensitivity of chemotherapy and targeted drugs at multiomics level. In conclusion, UBQLN4 was a promising prognostic biomarker of immune-related therapy in pan-cancer.

A Novel RNA-Binding Protein-Based Nomogram for Predicting Survival of Patients with Gastric Cancer

Background

We attempted to develop a prognostic model and characterize molecular subtypes for gastric cancer on the basis of ribonucleic acid (RNA)-binding proteins (RBPs).

Material/Methods

RNA sequence data of gastric cancer were obtained from The Cancer Genome Atlas. Univariate Cox regression analysis was used to screen survival-related RBPs, followed by least absolute shrinkage and selection operator Cox modeling. Overall and stratified survival analysis was carried out between high and low risk score groups, followed by receiver operator characteristic curve construction. Univariate and multivariate survival analysis was applied to assess its independent prognostic potential. A nomogram was constructed by combining age and the risk score, which was verified by calibration curves and decision curve analyses for 1-, 3-, and 5-year survival. Molecular subtypes were identified using nonnegative matrix factorization method. Clinical features of the identified subtypes were characterized on prognosis, drug sensitivity, and immune infiltration. An external Gene Expression Omnibus dataset was used to verify the above findings.

Results

On the basis of 44 survival-related RBPs, a robust prognostic 15-RBP signature was constructed. Patients with high risk score had a poorer prognosis than those with low risk score. The risk score had good performance in predicting clinical outcomes for 1-, 3-, and 5-year survival. The signature was effectively independent of other clinical features. The nomogram model combining age and the 15-RBP prognostic model exhibited better practicality and reliability for prognosis. RBP expression data were utilized to define 2 distinct molecular subtypes obviously related to survival outcomes, chemotherapeutic drug sensitivity, and immune infiltration.

Conclusions

Our study provides a nomogram model that consists of age and a 15-RBP signature and identifies 2 molecular subtypes for gastric cancer that possess potential value for preclinical, clinical, and translational research on gastric cancer.

RMDisease: a database of genetic variants that affect RNA modifications, with implications for epitranscriptome pathogenesis

Deciphering the biological impacts of millions of single nucleotide variants remains a major challenge. Recent studies suggest that RNA modifications play versatile roles in essential biological mechanisms, and are closely related to the progression of various diseases including multiple cancers. To comprehensively unveil the association between disease-associated variants and their epitranscriptome disturbance, we built RMDisease, a database of genetic variants that can affect RNA modifications. By integrating the prediction results of 18 different RNA modification prediction tools and also 303,426 experimentally-validated RNA modification sites, RMDisease identified a total of 202,307 human SNPs that may affect (add or remove) sites of eight types of RNA modifications (m6A, m5C, m1A, m5U, Ψ, m6Am, m7G and Nm). These include 4,289 disease-associated variants that may imply disease pathogenesis functioning at the epitranscriptome layer. These SNPs were further annotated with essential information such as post-transcriptional regulations (sites for miRNA binding, interaction with RNA-binding proteins and alternative splicing) revealing putative regulatory circuits. A convenient graphical user interface was constructed to support the query, exploration and download of the relevant information. RMDisease should make a useful resource for studying the epitranscriptome impact of genetic variants via multiple RNA modifications with emphasis on their potential disease relevance. RMDisease is freely accessible at: www.xjtlu.edu.cn/biologicalsciences/rmd.

Systems Biology Approaches for Therapeutics Development Against COVID-19

Understanding the systems biology approaches for promoting the development of new therapeutic drugs is attaining importance nowadays. The threat of COVID-19 outbreak needs to be vanished for global welfare, and every section of research is focusing on it. There is an opportunity for finding new, quick, and accurate tools for developing treatment options, including the vaccine against COVID-19. The review at this moment covers various aspects of pathogenesis and host factors for exploring the virus target and developing suitable therapeutic solutions through systems biology tools. Furthermore, this review also covers the extensive details of multiomics tools i.e., transcriptomics, proteomics, genomics, lipidomics, immunomics, and in silico computational modeling aiming towards the study of host-virus interactions in search of therapeutic targets against the COVID-19.