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Background: Chronic Bronchitis (CB) is a recurrent and persistent pulmonary inflammation disease. Growing evidence suggests an association between CB and Anti-neutrophil Cytoplasmic Antibody-associated Glomerulonephritis (ANCA-GN). However, the precise mechanisms underlying their association remain unclear., Aims: The purpose of this study was to further explore the molecular mechanism of the occurrence of chronic bronchitis (CB) associated with anti-neutrophil cytoplasmic antibody-associated glomerulonephritis (ANCA- GN)., Objective: Our study aimed to investigate the potential shared pathogenesis of CB-associated ANCA-GN., Methods: Datasets of ANCA (GSE108113 and GSE104948) and CB (GSE151052 and GSE162635) were obtained from the Gene Expression Omnibus (GEO) datasets. Firstly, GSE108113 and GSE151052 were analyzed to identify common differentially expressed genes (DEGs) by Limma package. Based on common DEGs, protein-protein interaction (PPI) network and functional enrichment analyses, including GO, KEGG, and GSEA, were performed. Then, hub genes were identified by degree algorithm and validated in GSE104948 and GSE162635. Further PPI network and functional enrichment analyses were performed on hub genes. Additionally, a competitive ceRNA network was constructed through miRanda and spongeScan. Transcription factors (TFs) were predicted and verified using the TRRUST database. Furthermore, the CIBERSORT algorithm was employed to explore immune cell infiltration. The Drug Gene Interaction Database (DGIDB) was utilized to predict small-molecular compounds of CB and ANCA-GN., Results: A total of 963 DEGs were identified in the integrated CB dataset, and 610 DEGs were identified in the integrated ANCA-GN dataset. Totally, we identified 22 common DEGs, of which 10 hub genes (LYZ, IRF1, PIK3CG, IL2RG, NT5E, ARG2, HBEGF, NFATC2, ALPL, and FKBP5) were primarily involved in inflammation and immune responses. Focusing on hub genes, we constructed a ceRNA network composed of 323 miRNAs and 348 lncRNAs. Additionally, five TFs (SP1, RELA, NFKB1, HIF1A, and SP3) were identified to regulate the hub genes. Furthermore, immune cell infiltration results revealed immunoregulation in CB and ANCA-GN. Finally, some small-molecular compounds (Daclizumab, Aldesleukin, and NT5E) were predicted to predominantly regulate inflammation and immunity, especially IL-2., Conclusion: Our study explores the inflammatory-immune pathways underlying CB-associated ANCA-GN and emphasizes the importance of NETs and lymphocyte differentiation, providing novel insights into the shared pathogenesis and therapeutic targets., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: It is documented that osteoarthritis can promote the progression of breast cancer (BC)., Objective: This study aims to search for the essential genes associated with breast cancer (BC) and osteoarthritis (OA), explore the relationship between epithelial-mesenchymal transition (EMT)- related genes and the two diseases, and identify the candidate drugs., Methods: The genes related to both BC and OA were determined by text mining. Protein-protein Interaction (PPI) analysis was carried out, and as a result, the exported genes were found to be related to EMT. PPI and the correlation of mRNA of these genes were also analyzed. Different kinds of enrichment analyses were performed on these genes. A prognostic analysis was performed on these genes for examining their expression levels at different pathological stages, in different tissues, and in different immune cells. Drug-gene interaction database was employed for potential drug discovery., Results: A total number of 1422 genes were identified as common to BC and OA and 58 genes were found to be related to EMT. We found that HDAC2 and TGFBR1 were significantly poor in overall survival. High expression of HDAC2 plays a vital role in the increase of pathological stages. Four immune cells might play a role in this process. Fifty-seven drugs were identified that could potentially have therapeutic effects., Conclusion: EMT may be one of the mechanisms by which OA affects BC. Using the drugs can have potential therapeutic effects, which may benefit patients with both diseases and broaden the indications for drug use., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery., Objective: Our goal here is to review the concept of scoring function space and how to explore it to develop machine learning models to address protein-ligand binding affinity., Methods: We searched the articles available in PubMed related to the scoring function space. We also utilized crystallographic structures found in the protein data bank (PDB) to represent the protein space., Results: The application of systems-level approaches to address receptor-drug interactions allows us to have a holistic view of the process of drug discovery. The scoring function space adds flexibility to the process since it makes it possible to see drug discovery as a relationship involving mathematical spaces., Conclusion: The application of the concept of scoring function space has provided us with an integrated view of drug discovery methods. This concept is useful during drug discovery, where we see the process as a computational search of the scoring function space to find an adequate model to predict receptor-drug binding affinity., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Aims: We aimed to classify molecular subtypes and establish a prognostic gene signature based on miRNAs for the prognostic prediction and therapeutic response in Stomach adenocarcinoma (STAD)., Background: STAD is a common diagnosed gastrointestinal malignancy and its heterogeneity is a big challenge that influences prognosis and precision therapies. Present study was designed to classify molecular subtypes and construct a prognostic gene signature based on miRNAs for the prognostic prediction and therapeutic response in STAD., Objective: The objective of this study is to investigate the molecular subtypes and prognostic model for STAD., Methods: A STAD specific miRNA-messenger RNA (mRNA) competing endogenous RNA (ceRNA) network was generated using the RNA-Seq and miRNA expression profiles from The Cancer Genome Atlas (TCGA) database, in which miRNA-related mRNAs were screened. Molecular subtypes were then determined using miRNA-related genes. Through univariate Cox analysis and multivariate regression analysis, a prognostic model was established in GSE84437 Train dataset and validated in GSE84437 Test, TCGA, GSE84437 and GSE66229 datasets. Immunotherapy datasets were employed for assessing the performance of the risk model. Finally, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was applied to validate the expression of hub genes used for the risk score signature., Results: We constructed a ceRNA network containing 84 miRNAs and 907 mRNAs and determined two molecular subtypes based on 26 genes from the intersection of TCGASTAD and GSE84437 datasets. Subtype S2 had poor prognosis, lower tumor mutational burden, higher immune score and lower response to immunotherapy. Subtype S1 was more sensitive to Sorafenib, Pyrimethamine, Salubrinal, Gemcitabine, Vinorelbine and AKT inhibitor VIII. Next, a five-gene signature was generated and its robustness was validated in Test and external datasets. This risk model also had a good prediction performance in immunotherapy datasets., Conclusion: This study promotes the underlying mechanisms of miRNA-based genes in STAD and offers directions for classification. A five-gene signature accurately predicts the prognosis and helps therapeutic options., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Objective: Genomic instability can drive clonal evolution, continuous modification of tumor genomes, and tumor genomic heterogeneity. The molecular mechanism of genomic instability still needs further investigation. This study aims to identify novel genome instabilityassociated lncRNAs (GI-lncRNAs) and investigate the role of genome instability in pan-Renal cell carcinoma (RCC)., Materials and Methods: A mutator hypothesis was employed, combining the TCGA database of somatic mutation (SM) information, to identify GI-lncRNAs. Subsequently, a training cohort (n = 442) and a testing cohort (n = 439) were formed by randomly dividing all RCC patients. Based on the training cohort dataset, a multivariate Cox regression analysis lncRNAs risk model was created. Further validations were performed in the testing cohort, TCGA cohort, and different RCC subtypes. To confirm the relative expression levels of lncRNAs in HK-2, 786-O, and 769-P cells, qPCR was carried out. Functional pathway enrichment analyses were performed for further investigation., Results: A total of 170 novel GI-lncRNAs were identified. The lncRNA prognostic risk model was constructed based on LINC00460, AC073218.1, AC010789.1, and COLCA1. This risk model successfully differentiated patients into distinct risk groups with significantly different clinical outcomes. The model was further validated in multiple independent patient cohorts. Additionally, functional and pathway enrichment analyses revealed that GI-lncRNAs play a crucial role in GI. Furthermore, the assessments of immune response, drug sensitivity, and cancer stemness revealed a significant relationship between GI-lncRNAs and tumor microenvironment infiltration, mutational burden, microsatellite instability, and drug resistance., Conclusions: In this study, we discovered four novel GI-lncRNAs and developed a novel signature that effectively predicted clinical outcomes in pan-RCC. The findings provide valuable insights for pan-RCC immunotherapy and shed light on potential underlying mechanisms., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: Head and neck squamous cell carcinoma (HNSC) is the seventh most common cancer worldwide. Although there are several options for the treatment of HNSC, there is still a lack of better biomarkers to accurately predict the response to treatment and thus be more able to correctly treat the therapeutic modality., Methods: First, we typed cases from the TCGA-HNSC cohort into subtypes by a Bayesian non-negative matrix factorization (BayesNMF)-based consensus clustering approach. Subsequently, genomic and proteomic data from HNSC cell lines were integrated to identify biomarkers of response to targeted therapies and immunotherapies. Finally, associations between HNSC subtypes and CD8 T-cell-associated effector molecules, common immune checkpoint genes, were compared to assess the potential of HNSC subtypes as clinically predictive immune checkpoint blockade therapy., Results: The 500 HNSC cases from TCGA were put through a consensus clustering approach to identify six HNSC expression subtypes. In addition, subtypes with unique proteomics and dependency profiles were defined based on HNSC cell line histology and proteomics data. Subtype 4 (S4) exhibits hyperproliferative and hyperimmune properties, and S4-associated cell lines show specific vulnerability to ADAT2, EIF5AL1, and PAK2. PD-L1 and CASP1 inhibitors have therapeutic potential in S4, and we have also demonstrated that S4 is more responsive to immune checkpoint blockade therapy., Conclusion: Overall, our HNSC typing approach identified robust tumor-expressing subtypes, and data from multiple screens also revealed subtype-specific biology and vulnerabilities. These HNSC expression subtypes and their biomarkers will help develop more effective therapeutic strategies., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: Bladder cancer (BLCA) is a commonly diagnosed cancer worldwide that exhibits high rates of recurrence and metastasis. Immunotherapy is increasingly being recognised in the clinical management of bladder cancer. In addition, the prospect of developing Natural Killer (NK) cell-related immunotherapy is promising in BLCA., Methods: We established and verified a prognostic signature based on NK cell-related gene expression. We then calculated the NKscore of BLCA samples and correlated it with the clinical outcomes, molecular subtypes of BLCA, tumour microenvironment (TME), and predicted efficacy of immune checkpoint inhibitors (ICI) and chemotherapy drugs to thoroughly explore the implications of the NKscore. Finally, the role of the NK signature gene HECTD1 in BLCA was verified by Quantitative Real-time PCR, Cell Counting Kit-8 Assay (CCK-8), Transwell Assay and Colony Formation Experiment., Results: We analysed NK cell-associated genes and identified six genes with significant prognostic relevance. A high NK score significantly represents a worse prognosis. NKscore was significantly correlated with seven types of classical molecular subtype classifications of BLCA. In addition, NKscore positively correlates with NK-related immune checkpoints, suggesting that emerging NK cell immune checkpoint inhibitors, such as monalizumab, may have potential therapeutic promise for patients with high NKscore. The results of the T cell inflamed score (TIS) and tumour immune dysfunction exclusion (TIDE) score confirmed the suitability of immunotherapy for patients with a high NK score. Likewise, patients with a high NK score may be more suitable for several significant chemotherapeutic drugs. Functional experiments showed that the knockdown of HECTD1 significantly attenuated the proliferation, migration, and invasion ability of tumour cells., Conclusion: To sum up, the capability of our signature to predict prognosis and immunotherapy response was robust. Hopefully, these results will provide new insights for BLCA research and patient immunotherapy., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)