Background: Mitophagy is correlated with tumor initiation and development of malignancy. However, HCC heterogeneity with reference to mitophagy has yet not been systematically explored.Materials and Methods: Mitophagy-related, glycolysis-related, and cholesterol biosynthesis-related gene sets were obtained from the Reactome database. Mitophagy-related and metabolism-related subtypes were identified using the ConsensusClusterPlus algorithm. Univariate Cox regression was analysis was performed to identify prognosis-related mitophagy regulators. Principal component analysis (PCA) was used to create composite measures of the prognosis-related mitophagy regulators (mitophagyscore). Individuals with a mitophagyscore higher or lower than the median value were classified in high- or low-risk groups. Kaplan-Meier survival and ROC curve analyses were utilized to evaluate the prognostic value of the mitophagyscore. The nomogram and calibration curves were plotted using the“rms” R package. The package “limma” was used for differential gene expression analysis. Differentially expressed genes (DEGs) between high- and low-risk groups were used as queries in the CMap database. R package “pRRophetic” and Genomics of Drug Sensitivity in Cancer (GDSC) database were used to determine the sensitivity of 21 previously reported anti-HCC drugs.Results: Three distinct HCC subtypes with different mitophagic accumulation (low, high, and intermediate mitophagy subtypes) were identified. High mitophagy subtype had the worst outcome and highest glycolysis level. The lowest degree of hypoxia and highest cholesterol biosynthesis was observed in the low mitophagy subtype; oncogenic dedifferentiation level in the intermediate mitophagy subtype was the lowest. Mitophagyscore could serve as a novel prognostic indicator for HCC.High-risk patients had a poorer prognosis (log-rank test, p < 0.001). The area under the ROC curve for mitophagyscore in 1-year survival was 0.77 in the TCGA cohort and 0.75 in the ICGC cohort. Nine candidate small molecules which were potential drugs for HCC treatment were identified from the CMap database. A decline in the sensitivity towards 21 anti-HCC drugs was observed in low-risk patients by GDSC database. We also identified a novel key gene, SPP1, which was highly associated with different mitophagic subtypes.Conclusion: Based on bioinformatic analyses, we systematically examined the HCC heterogeneity with reference to mitophagy and observed three distinct HCC subtypes having different prognoses and metabolic patterns.

Pancreatic cancer (PC) is the fourth leading cause of cancer death with an overall 5-year survival rate of < 5%, a statistic that has changed little in almost 50 years. A deeper understanding of the underlying molecular pathophysiology is expected to advance the urgent need to develop novel therapeutic and early detection strategies for this disease. Genomic characterisation of PC has previously relied on targeted PCR based exome sequencing of small cohorts of mixed primary and metastatic lesions propagated as xenografts or cell lines (Jones et al, Science 321:1801-1806), leaving the true mutational spectrum of the clinical disease largely unresolved. Here we use exome sequencing (https://www.ebi.ac.uk/ega/studies/EGAS00001000154) and copy number analysis (not submitted) to define genomic aberrations in a prospectively accrued clinical cohort (n = 142) of early (Stage I and II) pancreatic adenocarcinoma. Detailed analysis of 99 informative tumours identified 1982 non-silent mutations and 1628 significant CNV events, and defined 439 significantly mutated genes based on stringent Significant Mutated Gene or GISTIC analysis. Integration with functional data from in vitro shRNA and in vivo Sleeping Beauty-mediated somatic mutagenesis screens provided supportive evidence for 184 of these as candidate driver mutations. Pathway based analysis recapitulated clustering of mutations in core signalling pathways in PC, and identified multiple new components in each, particularly in DNA damage repair mechanisms (ATM, TOP2A, TLM, RPA1). We also identified frequent somatic aberrations in genes involved in novel mechanisms including chromatin modification (SWI/SNF complex members, SETD2, EPC1), and axon guidance (Semaphorin, Slit, Netrin and Ephrin signalling), extending the number of core perturbed pathways in PC. Aberrant expression of axon guidance genes co- segregated with poor patient survival, and in animal models was associated with disease development and progression, further implicating perturbation of the axon guidance pathway as a novel mechanism important in PC. This dataset includes gene expression data from 90 primary tumour samples, 88 of which were used in this manuscript for survival analysis. Much of this data is also available through the International Cancer Genome Consortium (ICGC) Data Portal (http://dcc/icgc.org), under the project code: "Pancreatic Cancer (QCMG, AU)". Access to the strictly restricted clinical data must be made through the ICGC Data Access Compliance Office (http://www.icgc.org/daco). This dataset contains expression array data from 90 primary pancreatic ductal adenocarcinoma samples. One sample is present with two biological replicates, all others have 1 biological replicate.

Summary This metadata record provides details of the data supporting the claims of the related article: “A subset of lung cancer cases shows robust signs of homologous recombination deficiency associated genomic mutational signatures”. The related study analysed all available whole genome sequencing data from the TCGA lung adenocarcinoma (LUAD) and squamous lung cancer (LUSC) cohorts and determined which of a list of mutational signatures were present in these cases, analysing whole genome and whole exome data to estimate the frequency of potentially homologous recombination (HR) deficient lung cancer cases. Type of data: single nucleotide variation; binary alignment maps Subject of data: Eukaryotic cell lines; Homo sapiens Population characteristics: lung cancer cases Recruitment: Cancer cell lines were sourced from Cancer Cell Line Encyclopedia, Genomics of Drug Sensitivity in Cancer data portal. The exceptional responder was identified as part of a larger ongoing study to understand the determinants of treatment response to platinum based therapy. Data access The results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga, and the LUAD and LUSC data are available at ICGC (https://dcc.icgc.org/) and GDC (https://portal.gdc.cancer.gov/) data portals. A comprehensive list of the file names underlying the figures and supplementary materials of the related article, along with direct links to the data in the above sources, is provided in the file ‘Diossy_et_al_2021_underlying_data_list.xlsx’, which is included with this data record. Sample single nucleotide variation analysis of a stage IVA lung squamous carcinoma case with a durable (> 20 months), symptom-free survival in response to platinum-based treatment (H75T) has been deposited in the European Variation Archive under accession https://identifiers.org/ebi/bioproject:PRJEB45238. Corresponding author(s) for this study Zoltan Szallasi, Computational Health Informatics Program (CHIP) Boston Children’s Hospital, Harvard Medical School, 300 Longwood Ave., Boston Massachusetts, USA, 02215, e-mail: Zoltan.szallasi@childrens.harvard.edu, +1-617-355-2179. Study approval The Hungarian Scientific and Research Ethics Committee of the Medical Research Council, No 2285-1/2019/EUIG és 2307-3/2020/EUIG has approved the study.

Background: Mitophagy is closely related to cancer initiation and progression. However, heterogeneity with reference to mitophagy remains unexplored in pancreatic adenocarcinoma (PAAD).Materials and methods: We used Reactome database to download the mitophagy-related, glycolysis-related and cholesterol biosynthesis-related signaling pathways. Unsupervised clustering using the “ConsensusClusterPlus” R package was performed to identify molecular subtypes related to mitophagy and metabolism. Prognosis-related mitophagy regulators were identified by univariate Cox regression analysis. Receiver operating characteristics (ROC) and Kaplan-Meier (K-M) survival analyses were used to assess the diagnostic and prognostic role of the hub genes and prognosis risk model. Weighted gene co-expression network analysis (WGCNA) was utilized for screening the mitophagy subtype-related hub genes. Metascape was utilized to carry out functional enrichment analysis. The “glmnet” R package was utilised for LASSO, and the “e1071” R package was utilised for SVM. Chemotherapeutic drug sensitivity was estimated using the R package “pRRophetic” and Genomics of Drug Sensitivity in Cancer (GDSC) database. The nomogram was established by the “rms” R package.Results: Three distinct mitophagy subtypes (low, high and intermediate) of PAAD were identified based on the landscape of mitophagy regulators. The high mitophagy subtype had the worst prognosis, highest mRNA expression-based stemness index scores and most hypoxic environment compared to the other subtypes. Additionally, glycolysis and cholesterol biosynthesis were significantly elevated. Three mitophagy subtype-specific gene signatures (CAST, CCDC6, and ERLIN1) were extracted using WGCNA and machine learning. Moreover, PAAD tumours were insensitive to Erlotinib, Sunitinib and Imatinib in the high mitophagy subtype and high CAST, CCDC6, and ERLIN1 expressed subtypes. Furthermore, CAST, CCDC6, and ERLIN1 affected immune cell infiltration (M1 and CD8Tcm), resulting in the altered prognosis of patients with PAAD. A nomogram was constructed to screen patients with the low mitophagy subtype, which showed a higher sensitivity to chemotherapeutic agents.Conclusion: Based on various bioinformatics tools and databases, the PAAD heterogeneity regarding mitophagy was systematically examined. Three different PAAD subtypes having different outcomes, metabolism patterns and chemosensitivity were observed. Moreover, three novel biomarkers that are closely associated with mitophagy and have the potential to guide individualised treatment regimens in PAAD were obtained.

Additional file 2: Additional Table 1 BIRC5 high impact mutations according to ICGC Data Portal. Additional Table 2 BIRC5 pathogenic mutations according to COSMIC. Additional Table 3 Top 100 genes co-expressed with BIRC5 for each cancer type. Additional Table 4 BIRC5 expression and clinicopathological features.

ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: BRCA-EU.

Homologous repair deficiency (HRD) impedes double-strand break repair, which is a common driver of carcinogenesis. Positive HRD status can be used as theranostic markers of response to platinum- and PARP inhibitor-based chemotherapies. Here, we aimed to fully investigate the therapeutic and prognostic potential of HRD in pancreatic adenocarcinoma (PAAD) and identify effective biomarkers related to HRD using comprehensive bioinformatics analysis. The HRD score was defined as the unweighted sum of the LOH, TAI, and LST scores, and it was obtained based on the previous literature. To characterize PAAD immune infiltration subtypes, the “ConsensusClusterPlus” package in R was used to conduct unsupervised clustering. A WGCNA was conducted to elucidate the gene coexpression modules and hub genes in the HRD-related gene module of PAAD. The functional enrichment study was performed using Metascape. LASSO analysis was performed using the “glmnet” package in R, while the random forest algorithm was realized using the “randomForest” package in R. The prognostic variables were evaluated using univariate Cox analysis. The prognostic risk model was built using the LASSO approach. ROC curve and KM survival analyses were performed to assess the prognostic potential of the risk model. The half-maximal inhibitory concentration (IC50) of the PARP inhibitors was estimated using the “pRRophetic” package in R and the Genomics of Drug Sensitivity in Cancer database. The “rms” package in R was used to create the nomogram. A high HRD score indicated a poor prognosis and an advanced clinical process in PAAD patients. PAAD tumors with high HRD levels revealed significant T helper lymphocyte depletion, upregulated levels of cancer stem cells, and increased sensitivity to rucaparib, Olaparib, and veliparib. Using WGCNA, 11 coexpression modules were obtained. The red module and 122 hub genes were identified as the most correlated with HRD in PAAD. Functional enrichment analysis revealed that the 122 hub genes were mainly concentrated in cell cycle pathways. One novel HRD-related gene signature consisting of CKS1B, HJURP, and TPX2 were screened via LASSO analysis and a random forest algorithm, and they were validated using independent validation sets. No direct association between HRD and CKS1B, HJURP, or TPX2 has not been reported in the literature so far. Thus, these findings indicated that CKS1B, HJURP, and TPX2 have potential as diagnostic and prognostic biomarkers for PAAD. We constructed a novel HRD-related prognostic model that provides new insights into PAAD prognosis and immunotherapy. Based on bioinformatics analysis, we comprehensively explored the therapeutic and prognostic potential of HRD in PAAD. One novel HRD-related gene signature consisting of CKS1B, HJURP, and TPX2 were identified through the combination of WGCNA, LASSO analysis and a random forest algorithm. A novel HRD-related risk model that can predict clinical prognosis and immunotherapeutic response in PAAD patients was constructed.

BackgroundImmunological-related genes (IRGs) play a critical role in the immune microenvironment of tumors. Our study aimed to develop an IRG-based survival prediction model for hepatocellular carcinoma (HCC) patients and to investigate the impact of IRGs on the immune microenvironment.MethodsDifferentially expressed IRGs were obtained from The Genomic Data Commons Data Portal (TCGA) and the immunology database and analysis portal (ImmPort). The univariate Cox regression was used to identify the IRGs linked to overall survival (OS), and a Lasso-regularized Cox proportional hazard model was constructed. The International Cancer Genome Consortium (ICGC) database was used to verify the prediction model. ESTIMATE and CIBERSORT were used to estimate immune cell infiltration in the tumor immune microenvironment (TIME). RNA sequencing was performed on HCC tissue specimens to confirm mRNA expression.ResultsA total of 401 differentially expressed IRGs were identified, and 63 IRGs were found related to OS on the 237 up-regulated IRGs by univariate Cox regression analyses. Finally, five IRGs were selected by the LASSO Cox model, including SPP1, BIRC5, STC2, GLP1R, and RAET1E. This prognostic model demonstrated satisfactory predictive value in the ICGC dataset. The risk score was an independent predictive predictor for OS in HCC patients. Immune-related analysis showed that the immune infiltration level in the high-risk group was higher, suggesting that the 5-IRG signature may play an important role in mediating immune escape and immune resistance in the TIME of HCC. Finally, we confirmed the 5-IRG signature is highly expressed in 65 HCC patients with good predictive power.ConclusionWe established and verified a new prognosis model for HCC patients based on survival-related IRGs, and the signature could provide new insights into the prognosis of HCC.

Clear cell renal cell carcinoma (ccRCC), the most common histological subtype of kidney cancer, shows poor prognosis, and non-sensitivity to radiotherapy or chemotherapy. The lncRNA small nucleolar RNA host gene 12 (SNHG12) has been revealed to play a carcinogenic role in various neoplasms, but the underlying mechanism in ccRCC is still unclear. To explore the potential role of SNHG12 in ccRCC, the data downloaded from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) Data Portal was used to compare the expression of SNHG12 in tumors and adjacent normal tissues. MRNA microarray and quantitative real-time PCR revealed that SNHG12 was overexpressed in the ccRCC tissues and cell lines. Functional inhibition of SNHG12 suppressed the viability and mobility of ccRCC cells. Mechanistically, dual luciferase assay and RNA immunoprecipitation (RIP) assay showed that miR-129-5p could bind to SNHG12 directly. There was a negative relationship between SNHG12 and miR-129-5p. What's more, we used bioinformatics-based prediction software to predict the target genes of miR-129-5p. Through data analysis and experimental verification, we found MDM4, a regulatory factor in p53 pathway, was involved in this ceRNA network. Our findings demonstrated that SNHG12 served as a sponge for miR-129-5p to regulate the expression of MDM4 and p53 pathway in the development of ccRCC.

Background: Hepatocellular carcinoma (HCC) is notorious for its high mortality and incidence. Accumulating evidence confirms that chromatin regulators (CRs) have a significant impact on cancer. Therefore, exploring the mode of action and prognostic value of CRs is imminent for the treatment of hepatocellular carcinoma.Method: Transcriptome and clinical data for this study have been downloaded from TCGA (https://portal.gdc.cancer.gov/) and ICGC (https://dcc.icgc.org/). Univariate analysis was used to screen CRs with prognostic value, and our prognostic risk score signature was developed using least absolute shrinkage along with selection operator (lasso) Cox regression analysis. The CRs-based prognostic model was constructed in the TCGA dataset, and low-risk HCC patients had a better prognosis, which was finally validated in the ICGC dataset. We used the receiver operating characteristic curve to identify the accuracy of the prediction model and establish a line chart to prove the clinical effectiveness of the model. We also discussed the differences in drug sensitivity via CellMiner database, tumor immune microenvironment via ssGSEA algorithm, and clinical characteristics among different risk groups.Results: A prognostic model consisting of seven CRs was constructed and verified in HCC patients. Furthermore, we found that this risk score prognostic signature could independently predict the prognosis of HCC patients. Functional enrichment analysis revealed that CRs are mainly associated with cancer-related signaling pathways and metabolic pathways. In addition, immune cell abundance correlates with risk score levelsConclusion: In brief, we systematically explored the mode of action of CRs in HCC patients and established a reliable prognostic prediction model.

This database provided five datasets. The first dataset contained experimentally verified tumor-immune cell interactions which included interacting gene symbols, cell types, interaction types, cancer types, species and other detailed information. The second dataset consisted of integrated ligand-receptor interactions including ligands, receptors, functions and sources. The third dataset presented significant interacting experimentally verified tumor-immune cell interactions inferred via Interaction Intensity module based on cancer types from TCGA (https://portal.gdc.cancer.gov/), ICGC (https://dcc.icgc.org/) and EMBL-EBI Expression Atlas (https://www.ebi.ac.uk/gxa/home). The fourth dataset contained significant interacting integrated ligand-receptor interactions predicted by TItalk based on cancer types from TCGA, ICGC and EMBL-EBI Expression Atlas. The fifth dataset consisted of predicted tumor-immune cell interactions inferred by five algorithms based on 32 scRNA-seq datasets and union of ligand-receptor interactions. These datasets can be downloaded from the Download module in TICCom.