HNCDrugResDb: a platform for deciphering drug resistance in head and neck cancers.

Drug resistance poses a significant obstacle to the success of anti-cancer therapy in head and neck cancers (HNCs). We aim to develop a platform for visualizing and analyzing molecular expression alterations associated with HNC drug resistance. Through data mining, we convened differentially expressed molecules and context-specific signaling events involved in drug resistance. The driver genes, interaction networks and transcriptional regulations were explored using bioinformatics approaches. A total of 2364 differentially expressed molecules were identified in 78 distinct drug-resistant cells against 14 anti-cancer drugs, comprising 1131 mRNAs, 746 proteins, 62 lncRNAs, 257 miRNAs, 1 circRNA, and 166 post-translational modifications. Among these, 255 molecules were considerably, the signature driver genes of HNC drug resistance. Further, we also developed a landscape of signaling pathways and their cross-talk with diverse signaling modules involved in drug resistance. Additionally, a publicly-accessible database named "HNCDrugResDb" was designed with browse, query, and pathway explorer options to fetch and enrich molecular alterations and signaling pathways altered in drug resistance. HNCDrugResDb is also enabled with a Drug Resistance Analysis tool as an initial platform to infer the likelihood of resistance based on the expression pattern of driver genes. HNCDrugResDb is anticipated to have substantial implications for future advancements in drug discovery and optimization of personalized medicine approaches.

Delineating protein biomarkers for gastric cancers: A catalogue of mass spectrometry-based markers and assessment of their suitability for targeted proteomics applications.

Gastric cancer (GC) is a global health concern. To facilitate improved management of GCs, protein biomarkers have been identified through mass spectrometry-based proteomics platforms. In order to exhibit clinical utility of such data, we congregated over 6800 differentially regulated proteins in GCs from proteomics studies and recorded the mass spectrometry platforms, association of the protein with infectious agents, protein identifiers, sample size and clinical characters of samples used with details on validation. Development of targeted proteomics methods is the cornerstone for pursuing these markers into clinical utility. Therefore, we developed Protein Biomarker Matrix for Gastric Cancer (PBMGC), a simple catalogue of robustness of each protein. This analysis yielded the identification of robust tissue, serum, and urine diagnostic and prognostic protein biomarker panels which can be further tested for their clinical utility. We also ascertained proteotypic tryptic peptides of 5631 proteins suitable for developing multiple reaction monitoring (MRM) assays. Extensive characterization of these peptides was carried out to record peptide ions, mass/charge and enhanced specific peptide features. With the vision of catering to proteomics researchers, the data generated through this analysis has been catalogued at Gastric Cancer Proteomics DataBase (GCPDB) (https://ciods.in/gcpdb/). Users can browse and download the data and improve GCPDB by submitting recently published data. SIGNIFICANCE: Mass spectrometry-based proteomics platforms have accumulated substantial data on proteins differentially regulated in gastric cancer (GC) clinical samples. The utility of such data in clinical applications is limited by search for suitable biomarker panels for assessment of GCs. We assembled over 6800 differentially regulated proteins in GCs from proteomics studies and recorded the corresponding details including mass spectrometry platforms, status on the association of the protein with infectious agents, protein identifiers from different databases, sample size and clinical characters of samples used in test and control conditions along with details on their validation. Towards the vision of utilizing these markers in clinical assays, Protein Biomarker Matrix for Gastric Cancer (PBMGC) was developed and clinically relevant multi-protein panels were identified. We also demonstrated identification and characterization of tryptic proteotypic tryptic peptides of 5631 proteins biomarkers of GCs which are suitable for development of MRM assays in a SCIEX QTRAP instrument. Aimed to caterproteomics researchers, the data generated through this analysis has been catalogued at Gastric Cancer Proteomics DataBase (GCPDB) (https://ciods.in/gcpdb/). The users can browse and download details on different markers and improve GCPDB by submitting recently published data. Such an analysis could lay a cornerstone for building more such resources or conduct such analysis in different clinical conditions to uptake and develop targeted proteomics as the method of choice for clinical applications.

Mining proteomics data to extract post-translational modifications associated with gastric cancer.

Gastric cancers are highly heterogeneous, deep-seated tumours associated with late diagnosis and poor prognosis. Post-translational modifications (PTMs) of proteins are known to be well-associated with oncogenesis and metastasis in most cancers. Several enzymes which drive PTMs have also been used as theranostics in cancers of the breast, ovary, prostate and bladder. However, there is limited data on PTMs in gastric cancers. Considering that experimental protocols for simultaneous analysis of multiple PTMs are being explored, a data-driven approach involving reanalysis of mass spectrometry-derived data is useful in cataloguing altered PTMs. We subjected publicly available mass spectrometry data on gastric cancer to an iterative searching strategy for fetching PTMs including phosphorylation, acetylation, citrullination, methylation and crotonylation. These PTMs were catalogued and further analyzed for their functional enrichment through motif analysis. This value-added approach delivered identification of 21,710 unique modification sites on 16,364 modified peptides. Interestingly, we observed 278 peptides corresponding to 184 proteins to be differentially abundant. Using bioinformatics approaches, we observed that majority of these altered PTMs/proteins belonged to cytoskeletal and extracellular matrix proteins, which are known to be perturbed in gastric cancer. The dataset derived by this mutiPTM investigation can provide leads to further investigate the potential role of altered PTMs in gastric cancer management.

Helicobacter pylori regulated microRNA map of human gastric cells.

BACKGROUND: Helicobacter pylori is an infection of concern for its chronic colonization leading to peptic ulcers and gastric cancer. In recent times, microRNAs have been extensively studied to understand their role in the pathogenesis of this bacteria in diverse contexts of gastric diseases. The current analysis reports the microRNA-mRNA interactions that are associated with effective survival and virulence of this pathogen. MATERIALS AND METHODS: We convened differentially regulated human microRNAs responsive to H. pylori infection (HP-hDEmiRs) at different multiplicity of infection and time points in human gastric cell lines through retrospective data mining of experimental studies. In view of the molecular disparity of clinical samples and animal models, data from tissue, serum/plasma, urine, and ascites were excluded. Further, we utilized diverse bioinformatics approaches to retrieve experimentally validated, high-confidence targets of the HP-hDEmiRs to analyze the microRNA-mRNA interactions that are relevant to H. pylori pathogenesis. RESULTS: A total of 39 HP-hDEmiRs that showed unidirectional expression of either overexpression or downregulation were identified to modulate 23 targets explicitly studied under this infection. We also identified 476 experimentally validated targets regulated by at least 4 of the HP-hDEmiRs. In addition to the pathways prior-associated with H. pylori infection, the microRNA-mRNA interactome analysis identified several cellular processes and pathways highly associated with cell cycle, cell division, migration, and carcinogenesis. CONCLUSION: This study generated a platform to study the mechanisms utilized by this pathogen using microRNAs as surrogate.

A multi-cellular molecular signaling and functional network map of C-C motif chemokine ligand 18 (CCL18): a chemokine with immunosuppressive and pro-tumor functions.

The C-C Motif Chemokine Ligand 18 (CCL18) is a beta-chemokine sub-family member with immunomodulatory functions in primates. CCL18-dependent migration and epithelial-to-mesenchymal transition of oral squamous cell carcinoma, squamous cell carcinoma of head and neck, breast cancer, hepatocellular carcinoma, non-small cell lung carcinoma, ovarian cancer, pancreatic ductal carcinoma and bladder cancer cells are well-established. In the tumor niche, tumor-associated macrophages produce CCL18 and its overexpression is correlated with reduced patient survival in multiple cancers. Although multiple receptors including C-C chemokine receptor type 3 (CCR3), type 6 (CCR6), type 8 (CCR8) and G-protein coupled estrogen receptor (GPER1) are reported for CCL18, the Phosphatidylinositol Transfer Protein, Membrane-Associated 3 (PITPNM3) receptor is currently considered as its predominant receptor. Characterization of the molecular events and check points associated with the immunosuppressive and cancer progression support functions induced by CCL18 for their potential towards therapeutic applications is an area of active research. Hence, in this study, we assembled 917 signaling events reported to be induced by CCL18 through their studied receptors in diverse cell types as an integrated knowledgebase for reference, data integration and gene-set enrichment analysis of global transcriptomic and/or proteomics datasets.

Omics Data Mining for multiPTMs in Oral Cancer: Cellular Proteome and Secretome of Chronic Tobacco-Treated Oral Keratinocytes.

Oral cancer is common worldwide but lacks robust diagnostics and therapeutics. Lifestyle factors, such as tobacco chewing and smoking, are significantly associated with oral cancers. Mapping the changes in the global proteome, secretome and post-translational modifications (PTMs) during tobacco exposure of oral keratinocytes hold great potential for understanding the mechanisms of oral carcinogenesis, not to mention for innovation toward clinical interventions in the future. On the other hand, although advances in mass spectrometry (MS)-based techniques have enabled the deep mining of complex proteomes, a large portion of the mass spectrometric data remains unassigned. These unassigned spectral data can be researched for multiple post-translational modifications (multiPTMs). Using data mining of publicly available proteomics data, we report, in this study, a multiPTM analysis of high-resolution MS-derived datasets on cellular proteome and secretome of chronic tobacco-treated oral keratinocytes. We identified 800 PTM sites in 496 proteins. Among them, 43 PTM sites in 37 proteins were found to be differentially expressed, accounting for their protein-level expression. Enrichment analysis of the proteins with altered phosphosite expression and the known kinases of these phosphosites discovered the overrepresentation of certain biological processes such as splicing and hemidesmosome assembly. These findings contribute to a deeper understanding of omics level changes in chronic tobacco-treated oral keratinocytes, and by extension, pathophysiology of oral cancers.