CancerImmunityQTL: a database to systematically evaluate the impact of genetic variants on immune infiltration in human cancer

Comparative Proteomics Identified EXOSC1 as a Target Protein of Anticancer Peptide LVTX-8 in Nasopharyngeal Carcinoma Cells

Nasopharyngeal carcinoma (NPC) is a prevalent malignancy that usually occurs among the nose and throat. Due to mild initial symptoms, most patients are diagnosed in the late stage, and the recurrence rate of tumors is high, resulting in many deaths every year. Traditional radiotherapy and chemotherapy are prone to causing drug resistance and significant side effects. Therefore, searching for new bioactive drugs including anticancer peptides is necessary and urgent. LVTX-8 is a peptide toxin synthesized from the cDNA library of the spider Lycosa vittata, which is consisting of 25 amino acids. In this study, a series of in vitro cell experiments such as cell toxicity, colony formation, and cell migration assays were performed to exam the anticancer activity of LVTX-8 in NPC cells (5-8F and CNE-2). The results suggested that LVTX-8 significantly inhibited cell proliferation and migration of NPC cells. To find the potential molecular targets for the anticancer capability of LVTX-8, high-throughput proteomic and bioinformatics analysis were conducted on NPC cells. The results identified EXOSC1 as a potential target protein with significantly differential expression levels under LVTX-8+/LVTX-8- conditions. The results in this research indicate that spider peptide toxin LVTX-8 exhibits significant anticancer activity in NPC, and EXOSC1 may serve as a target protein for its anticancer activity. These findings provide a reference for the development of new therapeutic drugs for NPC and offer new ideas for the discovery of biomarkers related to NPC diagnosis. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via the iProX partner repository with the data set identifier PXD050542.

A new gene signature associated with disulfidptosis that forecasts myasthenia gravis and suggests infiltration of immune microenvironment in thymoma patients

Introduction

The mechanism of thymoma-associated myasthenia gravis (TAMG) is currently unknown, although patients with TAMG experience more severe myasthenic symptoms and have worse prognoses compared to regular thymoma patients. The objective of this research is to create a transcriptome map of TAMG using genes linked to disulfidptosis, detect possible biomarkers, and examine the disparities in the tumor immune microenvironment (TIME) among different thymoma patients. The findings will offer valuable knowledge for personalized treatment alternatives.

Methods

Thymoma samples' RNA-seq data, along with their corresponding clinical data, were acquired from the TCGA database using methods. Next, genes and disulfidptosis-related lncRNAs(DRLs) were chosen through correlation analysis. Then, a prediction model of TAMG was established by LASSO regression. Subsequent to that, an analysis of the mutation data, the tumor mutational burden (TMB), and the assessment of immune and stromal elements within the tumor microenvironment were conducted.

Results

A total of 87 patients diagnosed with thymoma were included in the study, with 29 of them having TAMG. We discovered a group of 325 lncRNAs in this sample that showed significant associations with genes related to disulfidptosis, with 25 of them displaying significantly altered expression. Moreover, utilizing LASSO regression, we constructed a predictive model incorporating 11 DRLs. The analysis revealed an area under the curve (AUC) of 0.934 (CI 0.879-0.989), a cut-off value of 0.797, along with a sensitivity of 82.8 % and specificity of 93.1 %. Furthermore, we examined the TIME in both the high-risk and low-risk groups, and observed noteworthy disparities in B cells, T cells, and APC among the two groups (p < 0.05).

Conclusion

This research offers the initial examination of genes associated with disulfidptosis and TAMG through genomic and transcriptomic analysis. Furthermore, a strong risk forecasting model was created and the significance of TIME in TAMG was also clarified. The discoveries offer significant understanding into the molecular processes of TAMG and present possible indicators for categorizing risk.

PancanQTLv2.0: a comprehensive resource for expression quantitative trait loci across human cancers

Expression quantitative trait locus (eQTL) analysis is a powerful tool used to investigate genetic variations in complex diseases, including cancer. We previously developed a comprehensive database, PancanQTL, to characterize cancer eQTLs using The Cancer Genome Atlas (TCGA) dataset, and linked eQTLs with patient survival and GWAS risk variants. Here, we present an updated version, PancanQTLv2.0 (https://hanlaboratory.com/PancanQTLv2/), with advancements in fine-mapping causal variants for eQTLs, updating eQTLs overlapping with GWAS linkage disequilibrium regions and identifying eQTLs associated with drug response and immune infiltration. Through fine-mapping analysis, we identified 58 747 fine-mapped eQTLs credible sets, providing mechanic insights of gene regulation in cancer. We further integrated the latest GWAS Catalog and identified a total of 84 592 135 linkage associations between eQTLs and the existing GWAS loci, which represents a remarkable ∼50-fold increase compared to the previous version. Additionally, PancanQTLv2.0 uncovered 659516 associations between eQTLs and drug response and identified 146948 associations between eQTLs and immune cell abundance, providing potentially clinical utility of eQTLs in cancer therapy. PancanQTLv2.0 expanded the resources available for investigating gene expression regulation in human cancers, leading to advancements in cancer research and precision oncology.

Palmitoylation landscapes across human cancers reveal a role of palmitoylation in tumorigenesis

BACKGROUND

Protein palmitoylation, which is catalyzed by palmitoyl-transferase and de-palmitoyl-transferase, plays a crucial role in various biological processes. However, the landscape and dynamics of protein palmitoylation in human cancers are not well understood.

METHODS

We utilized 23 palmitoyl-acyltransferases and seven de-palmitoyl-acyltransferases as palmitoylation-related genes for protein palmitoylation analysis. Multiple publicly available datasets were employed to conduct pan-cancer analysis, examining the transcriptome, genomic alterations, clinical outcomes, and correlation with c-Myc (Myc) for palmitoylation-related genes. Real-time quantitative PCR and immunoblotting were performed to assess the expression of palmitoylation-related genes and global protein palmitoylation levels in cancer cells treated with Myc depletion or small molecule inhibitors. Protein docking and drug sensitivity analyses were employed to predict small molecules that target palmitoylation-related genes.

RESULTS

We identified associations between palmitoylation and cancer subtype, stage, and patient survival. We discovered that abnormal DNA methylation and oncogenic Myc-driven transcriptional regulation synergistically contribute to the dysregulation of palmitoylation-related genes. This dysregulation of palmitoylation was closely correlated with immune infiltration in the tumor microenvironment and the response to immunotherapy. Importantly, dysregulated palmitoylation was found to modulate canonical cancer-related pathways, thus influencing tumorigenesis. To support our findings, we performed a proof-of-concept experiment showing that depletion of Myc led to reduced expression of most palmitoylation-related genes, resulting in decreased global protein palmitoylation levels. Through mass spectrometry and enrichment analyses, we also identified palmitoyl-acyltransferases ZDHHC7 and ZDHHC23 as significant contributors to mTOR signaling, DNA repair, and immune pathways, highlighting their potential roles in tumorigenesis. Additionally, our study explored the potential of three small molecular (BI-2531, etoposide, and piperlongumine) to modulate palmitoylation by targeting the expression or activity of palmitoylation-related genes or enzymes.

CONCLUSIONS

Overall, our findings underscore the critical role of dysregulated palmitoylation in tumorigenesis and the response to immunotherapy, mediated through classical cancer-related pathways and immune cell infiltration. Additionally, we propose that the aforementioned three small molecule hold promise as potential therapeutics for modulating palmitoylation, thereby offering novel avenues for cancer therapy.

Genetic Variants That Impact Alternative Polyadenylation in Cancer Represent Candidate Causal Risk Loci

Alternative polyadenylation (APA) is emerging as a major mechanism of posttranscriptional regulation. APA can impact the development and progression of cancer, suggesting that the genetic determinants of APA might play an important role in regulating cancer risk. Here, we depicted a pan-cancer atlas of human APA quantitative trait loci (apaQTL), containing approximately 0.7 million apaQTLs across 32 cancer types. Systematic multiomics analyses indicated that cancer apaQTLs could contribute to APA regulation by altering poly(A) motifs, RNA-binding proteins (RBP), and chromatin regulatory elements and were preferentially enriched in genome-wide association studies (GWAS)-identified cancer susceptibility loci. Moreover, apaQTL-related genes (aGene) were broadly related to cancer signaling pathways, high mutational burden, immune infiltration, and drug response, implicating their potential as therapeutic targets. Furthermore, apaQTLs were mapped in Chinese colorectal cancer tumor tissues and then screened for functional apaQTLs associated with colorectal cancer risk in 17,789 cases and 19,951 controls using GWAS-ChIP data, with independent validation in a large-scale population consisting of 6,024 cases and 10,022 controls. A multi-ancestry-associated apaQTL variant rs1020670 with a C>G change in DNM1L was identified, and the G allele contributed to an increased risk of colorectal cancer. Mechanistically, the risk variant promoted aberrant APA and facilitated higher usage of DNM1L proximal poly(A) sites mediated by the RBP CSTF2T, which led to higher expression of DNM1L with a short 3'UTR. This stabilized DNM1L to upregulate its expression, provoking colorectal cancer cell proliferation. Collectively, these findings generate a resource for understanding APA regulation and the genetic basis of human cancers, providing insights into cancer etiology.

SIGNIFICANCE

Cancer risk is mediated by alternative polyadenylation quantitative trait loci, including the rs1020670-G variant that promotes alternative polyadenylation of DNM1L and increases colorectal cancer risk.

Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective

Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.

Advanced Glycation End Products' Receptor DNA Methylation Associated with Immune Infiltration and Prognosis of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Background

Advanced glycation end products' receptor (AGER) is a multiligand receptor that interacts with a wide range of ligands. Previous studies have shown that abnormal AGER expression is closely related to immune infiltration and tumorigenesis. However, the AGER DNA methylation relationship between prognosis and infiltrating immune cells in LUAD and LUSC is still unclear.

Methods

AGER expression in pan-cancer was obtained by using the UALCAN databases. Kaplan-Meier plotter showed the correlation of AGER mRNA expression levels and clinicopathological parameters. The protein expression levels for AGER were derived from Human Protein Atlas Database Analysis. The copy number, somatic mutation, and DNA methylation of AGER were presented with UCSC Xena database. TIMER platform and TISIDB website were used to show the correlation between AGER expression and tumor immune cell infiltration level.

Results

The expression level of AGER was significantly reduced in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Low expression of AGER was significantly correlated with histology, stage, lymph node metastasis, and tumor protein 53 (TP53) mutation and could be used as a potential indicator of poor prognosis of LUAD and LUSC. Moreover, AGER expression was positively correlated with the infiltrating immune cells. Further analysis showed that copy number variation (CNV), mutation, and DNA methylation were involved in AGER downregulation. In addition, we also found that hypermethylated AGER was significantly correlated with tumor-infiltrating lymphocytes.

Conclusion

AGER may be a candidate for the prognostic biomarker of LUAD and LUSC related to tumor immune microenvironment.

Large-scale integration of the non-coding RNAs with DNA methylation in human cancers

Characterizing influences of DNA methylation (DNAm) on non-coding RNAs (ncRNAs) is important to understand the mechanisms of gene regulation and cancer outcome. In our study, we describe the results of ncRNA quantitative trait methylation sites (ncQTM) analyses on 8,545 samples from The Cancer Genome Atlas (TCGA), 763 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and 516 samples from Genotype-Tissue Expression (GTEx) to identify the significant associations between DNAm sites and ncRNAs (miRNA, long non-coding RNA [lncRNA], small nuclear RNA [snRNA], small nucleolar RNA [snoRNA], and rRNA) across 32 cancer types. With more than 22 billion tests, we identify 302,764 cis-ncQTMs (6.28% of all tested) and 79,841,728 trans-ncQTMs (1.15% of all tested). Most DNAm sites (70.6% on average) are in trans association, while only 25.2% DNAm sites are in cis association. Further, we develop a subtype named ncm_cluster based on cancer-specific ncRNAs thatis associated with tumor microenvironment, clinical outcome, and biological pathways. To comprehensively describe the ncQTM patterns, we developed a database named Pancan-ncQTM (http://bigdata.njmu.edu.cn/Pancan-ncQTM/).

Germline rare deleterious variant load alters cancer risk, age of onset and tumor characteristics

Recent studies show that rare, deleterious variants (RDVs) in certain genes are critical determinants of heritable cancer risk. To more comprehensively understand RDVs, we performed the largest-to-date germline variant calling analysis in a case-control setting for a multi-cancer association study from whole-exome sequencing data of 20,789 participants, split into discovery and validation cohorts. We confirm and extend known associations between cancer risk and germline RDVs in specific gene-sets, including DNA repair (OR = 1.50; p-value = 8.30e-07; 95% CI: 1.28-1.77), cancer predisposition (OR = 1.51; p-value = 4.58e-08; 95% CI: 1.30-1.75), and somatic cancer drivers (OR = 1.46; p-value = 4.04e-06; 95% CI: 1.24-1.72). Furthermore, personal RDV load in these gene-sets associated with increased risk, younger age of onset, increased M1 macrophages in tumor and, increased tumor mutational burden in specific cancers. Our findings can be used towards identifying high-risk individuals, who can then benefit from increased surveillance, earlier screening, and treatments that exploit their tumor characteristics, improving prognosis.

Pancan-MNVQTLdb: systematic identification of multi-nucleotide variant quantitative trait loci in 33 cancer types

Multi-nucleotide variants (MNVs) are defined as clusters of two or more nearby variants existing on the same haplotype in an individual. Recent studies have identified millions of MNVs in human populations, but their functions remain largely unknown. Numerous studies have demonstrated that single-nucleotide variants could serve as quantitative trait loci (QTLs) by affecting molecular phenotypes. Therefore, we propose that MNVs can also affect molecular phenotypes by influencing regulatory elements. Using the genotype data from The Cancer Genome Atlas (TCGA), we first identified 223 759 unique MNVs in 33 cancer types. Then, to decipher the functions of these MNVs, we investigated the associations between MNVs and six molecular phenotypes, including coding gene expression, miRNA expression, lncRNA expression, alternative splicing, DNA methylation and alternative polyadenylation. As a result, we identified 1 397 821 cis-MNVQTLs and 402 381 trans-MNVQTLs. We further performed survival analysis and identified 46 173 MNVQTLs associated with patient overall survival. We also linked the MNVQTLs to genome-wide association studies (GWAS) data and identified 119 762 MNVQTLs that overlap with existing GWAS loci. Finally, we developed Pancan-MNVQTLdb (http://gong_lab.hzau.edu.cn/mnvQTLdb/) for data retrieval and download. Pancan-MNVQTLdb will help decipher the functions of MNVs in different cancer types and be an important resource for genetic and cancer research.

Identification of Essential Tumor-Infiltrating Immune Cells and Relevant Genes in Left-Sided and Right-Sided Colon Cancers

Simple Summary

Differences in oncogenes between left-sided colon cancer and right-sided colon cancer have been reported in-depth. Tumor-infiltrating immune cells and relevant genes between left-sided and right-sided colon cancers are unclear. Bioinformatic analysis was used to identify these hub immune cells and relevant genes. Colon cancer outcomes are associated with changes in MDSC infiltration, and therefore LCP1, ITGB2, and IKZF1 may be novel targets for immunotherapy.

Abstract

Backgrounds: Colorectal cancer is the third most prevalent cancer worldwide. A right-sided colon cancer patient typically has a worse prognosis than one who has a left-sided colon cancer. There is an unclear understanding of how left-sided colon cancer differs from right-sided colon cancer in tumor-infiltrating immune cells (TIICs) and relevant genes. Methods: The Cancer Genome Atlas provided RNA-seq data and clinical information regarding colon adenocarcinoma. We conducted a single-sample gene set enrichment analysis (ssGSEA) to quantify the level of 24 immune cells infiltrating the tissues. Based on an analysis of univariate Cox regression, immune cell types associated with survival were identified. Weighted gene co-expression network analysis (WGCNA) was used to identify hub genes related to location and critical immune cells. Based on the Search Tool for the Retrieval of Interacting Genes (STRING), interaction potential was predicted among the hub genes. Hub genes that influence outcomes through immune infiltration were identified using the least absolute shrinkage and selection operator (LASSO). Then, we used the TISIDB database (a repository portal for tumor–immune system interactions) to validate the correlation between hub genes and immune cell infiltration. Finally, immunohistochemical assays were conducted to determine the levels of proteins expressed by critical TIICs and cancer cells. Results: Colon cancers on the right side of the body had higher levels of myeloid-derived suppressor cells (MDSCs) than on the left side. There were three key genes: LCP1, ITGB2, and IKZF1. It was found that their expression was linked to poor prognosis and an increased level of MDSC infiltration. An immunohistochemical study confirmed these findings. Conclusions: There is a higher rate of MDSC infiltration in right-sided colon cancer when compared with left-sided colon cancer. COAD outcomes are associated with changes in MDSC infiltration, and therefore LCP1, ITGB2, and IKZF1 may be novel targets for immunotherapy.

Regulatory pattern of abnormal promoter CpG island methylation in the glioblastoma multiforme classification

Glioblastoma (GBM) is characterized by extensive genetic and phenotypic heterogeneity. However, it remains unexplored primarily how CpG island methylation abnormalities in promoter mediate glioblastoma typing. First, we presented a multi-omics scale map between glioblastoma sample clusters constructed based on promoter CpG island (PCGI) methylation-driven genes, using datasets including methylation profiles, expression profiles, and single-cell sequencing data from multiple highly annotated public clinical cohorts. Second, we identified differences in the tumor microenvironment between the two glioblastoma sample clusters and resolved key signaling pathways between cell clusters at the single-cell level based on comprehensive comparative analyses to investigate the reasons for survival differences between two of these clusters. Finally, we developed a diagnostic map and a prediction model for glioblastoma, and compared theoretical differences of drug sensitivity between two glioblastoma sample clusters. In summary, this study established a classification system for dissecting promoter CpG island methylation heterogeneity in glioblastoma and provides a new perspective for the diagnosis and treatment of glioblastoma.

MiR-192-5p/RB1/NF-κBp65 signaling axis promotes IL-10 secretion during gastric cancer EMT to induce Treg cell differentiation in the tumour microenvironment

BACKGROUND

Regulatory T (Treg) cells are important components of the tumour microenvironment (TME) that play roles in gastric cancer (GC) metastasis. Although tumour cells that undergo epithelial-mesenchymal transition (EMT) regulate Treg cell function, their regulatory mechanism in GC remains unclear.

METHODS

The miR-192-5p was identified by examining three Gene Expression Omnibus GC miRNA expression datasets. RNA immunoprecipitation (RIP) and dual-luciferase reporter assays were conducted to identify interactions between miR-192-5p and RB1. The role of miR-192-5p/RB1 in GC progression was evaluated based on EdU incorporation, wound healing and Transwell assays. An in vitro co-culture assay was performed to measure the effect of miR-192-5p/RB1 on Treg cell differentiation. In vivo experiments were conducted to explore the role of miR-192-5p in GC progression and Treg cell differentiation.

RESULTS

MiR-192-5p was overexpressed in tumour and was associated with poor prognosis in GC. MiR-192-5p bound to the RB1 3'-untranslated region, resulting in GC EMT, proliferation, migration and invasion. MiR-192-5p/RB1 mediated interleukin-10 (IL-10) secretion by regulating nuclear factor-kappaBp65 (NF-κBp65), affecting Treg cell differentiation. NF-κBp65, in turn, promoted miR-192-5p expression and formed a positive feedback loop. Furthermore, in vivo experiments confirmed that miR-192-5p/RB1 promotes GC growth and Treg cell differentiation.

CONCLUSION

Collectively, our studies indicate that miR-192-5p/RB1 promotes EMT of tumour cells, and the miR-192-5p/RB1/NF-κBp65 signaling axis induces Treg cell differentiation by regulating IL-10 secretion in GC. Our results suggest that targeting miR-192-5p/RB1/NF-κBp65 /IL-10 may pave the way for the development of new immune treatments for GC.

Identification of Ligand-Receptor Pairs Associated With Tumour Characteristics in Clear Cell Renal Cell Carcinoma

The tumour microenvironment (TME) of clear cell renal cell carcinoma (ccRCC) comprises multiple cell types, which promote tumour progression and modulate drug resistance and immune cell infiltrations via ligand-receptor (LR) interactions. However, the interactions, expression patterns, and clinical relevance of LR in the TME in ccRCC are insufficiently characterised. This study characterises the complex composition of the TME in ccRCC by analysing the single-cell sequencing (scRNA-seq) data of patients with ccRCC from the Gene expression omnibus database. On analysing the scRNA-seq data combined with the cancer genome atlas kidney renal clear cell carcinoma (TCGA-KIRC) dataset, 46 LR-pairs were identified that were significantly correlated and had prognostic values. Furthermore, a new molecular subtyping model was proposed based on these 46 LR-pairs. Molecular subtyping was performed in two ccRCC cohorts, revealing significant differences in prognosis between the subtypes of the two ccRCC cohorts. Different molecular subtypes exhibited different clinicopathological features, mutational, pathway, and immune signatures. Finally, the LR.score model that was constructed using ten essential LR-pairs that were identified based on LASSO Cox regression analysis revealed that the model could accurately predict the prognosis of patients with ccRCC. In addition, the differential expression of ten LR-pairs in tumour and normal cell lines was identified. Further functional experiments showed that CX3CL1 can exert anti-tumorigenic role in ccRCC cell line. Altogether, the effects of immunotherapy were connected to LR.scores, indicating that potential medications targeting these LR-pairs could contribute to the clinical benefit of immunotherapy. Therefore, this study identifies LR-pairs that could be effective biomarkers and predictors for molecular subtyping and immunotherapy effects in ccRCC. Targeting LR-pairs provides a new direction for immunotherapy regimens and prognostic evaluations in ccRCC.

Integrated Multichip Analysis and WGCNA Identify Potential Diagnostic Markers in the Pathogenesis of ST-Elevation Myocardial Infarction

Background

ST-elevation myocardial infarction (STEMI) is a myocardial infarction (MI) with ST-segment exaltation of electrocardiogram (ECG) caused by vascular occlusion of the epicardium. However, the diagnostic markers of STEMI remain little.

Methods

STEMI raw microarray data are acquired from the Gene Expression Omnibus (GEO) database. Based on GSE60993 and GSE61144, differentially expressed genes (DEGs) are verified via R software, and key modules associated with pathological state of STEMI are verified by weighted correlation network analysis (WGCNA). Take the intersection gene of key module and DEGs to perform the pathway enrichment analyses by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Construct the protein-protein interaction (PPI) network by Cytoscape. Then, select and identify the diagnostic biomarkers of STEMI by least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Finally, assess the infiltration of immune cells of STEMI by CIBERSORT and analyze the correlation between diagnostic markers and infiltrating immune cells.

Results

We get 710 DEGs in the STEMI group and 376 genes associated with STEMI in blue module. 92 intersection genes were concentrated in 30 GO terms and 2 KEGG pathways. 28 hub genes involved in the development of STEMI. Moreover, upregulated ALOX5AP (AUC = 1.00) and BST1 (AUC = 1.00) are confirmed as diagnostic markers of STEMI. CD8+T cells, regulatory T (Treg) cells, resting natural killer (NK) cells, M0 macrophages, resting mast cells, and neutrophils are related to the procession of STEMI. Moreover, ALOX5AP and BST1 are positively related to resting NK cells, M0 macrophages, and neutrophils, while ALOX5AP and BST1 are negatively related to CD8+ T cells, Treg cells, and resting mast cells.

Conclusion

ALOX5AP and BST1 may be the diagnostic markers of STEMI. Immune cell infiltration plays a key role in the development of STEMI.

In silico tools and databases for designing cancer immunotherapy

Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.

A proteogenomic portrait of lung squamous cell carcinoma

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.

CanImmunother: a manually curated database for identification of cancer immunotherapies associating with biomarkers, targets, and clinical effects

As immunotherapy is evolving into an essential armamentarium against cancers, numerous translational studies associated with relevant biomarkers, targets, and clinical effects have been reported in recent years. However, a large amount of associated experimental data remains unexplored due to the difficulty in accessibility and utilization. Here, we established a comprehensive high-quality database for cancer immunotherapy called CanImmunother (http://www.biomedical-web.com/cancerit/) through manual curation on 4515 publications. CanImmunother contains 3267 experimentally validated associations between 218 cancer sub-types across 34 body parts and 484 immunotherapies with 642 biomarkers, 108 targets, and 121 control therapies. Each association was manually curated by professional curators, incorporated with valuable annotation and cross references, and assigned with an association score for prioritization. To help clinicians and researchers in identifying and discovering better cancer immunotherapy and their respective biomarkers and targets, CanImmunother offers user-friendly web applications including search, browse, excel table, association prioritization, and network visualization. CanImmunother presents a landscape of experimental cancer immunotherapy association data, serving as a useful resource to improve our insight and to facilitate further discovery of advanced immunotherapy options for cancer patients.