A unified data repository of the National Cancer Institute (NCI)'s Genomic Data Commons (GDC) that enables data sharing across cancer genomic studies in support of precision medicine. The GDC supports several cancer genome programs at the NCI Center for Cancer Genomics (CCG), including The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and the Cancer Genome Characterization Initiative (CGCI). The GDC Data Portal provides a platform for efficiently querying and downloading high quality and complete data. The GDC also provides a GDC Data Transfer Tool and a GDC API for programmatic access.

The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA). Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the CIP TCGA Radiology Initiative.

がんに関連するゲノム研究のデータセットを検索、ダウンロード、アップデート、分析するためのポータルサイトです。TCGA （The Cancer Genome Atlas: https://cancergenome.nih.gov） およびTARGET （Therapeutically Applicable Research to Generate Effective Therapies: https://ocg.cancer.gov/programs/target） に集積されたがんゲノム研究のデータセットを中心としており、プロジェクト、実験（実験ケース、遺伝子、変異、機関）、レポジトリに対して、がんの原発部位、疾患タイプ、研究プログラム名、データの種類、実験手法などによる絞り込みができます。また、分析のページではベン図表示によるデータセットの共通性の検出やコホートによる比較が可能です。

The Cancer Genome Atlas Rectum Adenocarcinoma (TCGA-READ) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA). Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the CIP TCGA Radiology Initiative.

The Cancer Genome Atlas Ovarian Cancer (TCGA-OV) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA). Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the TCGA Ovarian Phenotype Research Group.

The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

https://wiki.cancerimagingarchive.net/display/Public/TCGA-LUAD

The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative

Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the CIP TCGA Radiology Initiative.

The Cancer Genome Atlas Sarcoma (TCGA-SARC) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA). Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the CIP TCGA Radiology Initiative.

This repository contains 411,890 unique image patches derived from histological images of colorectal cancer and gastric cancer patients in the TCGA cohort (original whole slide SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this repository are derived from formalin-fixed paraffin-embedded (FFPE) diagnostic slides ("DX" at the GDC data portal). This is explained well in this blog: http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/ Preprocessing All SVS slides were preprocessed as follows 1. automatic detection of tumor 2. resizing to 224 px x 224 px at a resolution of 0.5 µm/px 4. color normalization with the Macenko method (Macenko et al., 2009, http://wwwx.cs.unc.edu/~mn/sites/default/files/macenko2009.pdf) 5. assignment of patients to either "MSS" (microsatellite stable) or "MSIMUT" (microsatellite instable or highly mutated) 6. randomization of patients to training and testing sets (~70% and ~30%). Randomization was done on a patient level rather than on a slide or tile level 7. equilibration of training sets by undersampling (removing excess tiles in MSS class in a random way) File description 1. STAD_TRAIN_MSS - training images (~70% of all patients) for gastric (stomach) cancer TCGA patients with MSS (microsatellite stable) tumors, 50285 unique image patches; FFPE samples 2. STAD_TRAIN_MSIMUT - training images ( (~70% of all patients) for gastric (stomach) cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 50285 unique image patches; FFPE samples 3. STAD_TEST_MSS - test images (~30% of all patients) for gastric (stomach) cancer TCGA patients with MSS (microsatellite stable) tumors, 90104 unique image patches; FFPE samples 4. STAD_TEST_MSIMUT - test images ( ~30% of all patients) for gastric (stomach) cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 27904 unique image patches; FFPE samples 5. CRC_DX_TEST_MSIMUT - test images (~30% of all patients) for colorectal cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 29335 unique image patches; FFPE samples 6. CRC_DX_TEST_MSS - test images (~30% of all patients) for colorectal cancer TCGA patients with MSS (microsatellite stable) tumors, 70569 unique image patches; FFPE samples 7. CRC_DX_TRAIN_MSIMUT - training images (~70% of all patients) for colorectal cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 46704 unique image patches; FFPE samples 8. CRC_DX_TRAIN_MSS - training images (~70% of all patients) for colorectal cancer TCGA patients with MSS (microsatellite stable) tumors, 46704 unique image patches; FFPE samples

The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative

Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the CIP TCGA Radiology Initiative.

BackgroundLung cancer is the leading cause of mortality in cancer patients. N7-methylguanosine (m7G) modification as a translational regulation pattern has been reported to participate in multiple types of cancer progression, but little is known in lung cancer. This study attempts to explore the role of m7G-related proteins in genetic and epigenetic variations in lung adenocarcinoma, and its relationship with clinical prognosis, immune infiltration, and immunotherapy.MethodsSequencing data were obtained from the Genomic Data Commons (GDC) Data Portal and Gene Expression Omnibus (GEO) databases. Consensus clustering was utilized to distinguish m7G clusters, and responses to immunotherapy were also evaluated. Moreover, univariate and multivariate Cox and Least absolute shrinkage and selection operator LASSO Cox regression analyses were used to screen independent prognostic factors and generated risk scores for constructing a survival prediction model. Multiple cell types such as epithelial cells and immune cells were identified to verify the bulk RNA results. Short hairpin RNA (shRNA) Tet-on plasmids, Clustered Regularly Interspaced Short Palindromic Repeats CRISPR/Cas9 for knockout plasmids, and nucleoside diphosphate linked to moiety X-type motif 4 (NUDT4) overexpression plasmids were constructed to inhibit or promote tumor cell NUDT4 expression, then RT-qPCR, Cell Counting Kit-8 CCK8 proliferation assay, and Transwell assay were used to observe tumor cell biological functions.ResultsFifteen m7G-related genes were highly expressed in tumor samples, and 12 genes were associated with poor prognosis. m7G cluster-B had lower immune infiltration level, worse survival, and samples that predicted poor responses to immunotherapy. The multivariate Cox model showed that NUDT4 and WDR4 (WD repeat domain 4) were independent risk factors. Single-cell m7G gene set variation analysis (GSVA) scores also had a negative correlation tendency with immune infiltration level and T-cell Programmed Death-1 PD-1 expression, but the statistics were not significant. Knocking down and knocking out the NUDT4 expression significantly inhibited cell proliferation capability in A549 and H1299 cells. In contrast, overexpressing NUDT4 promoted tumor cell proliferation. However, there was no difference in migration capability in the knockdown, knockout, or overexpression groups.ConclusionsOur study revealed that m7G modification-related proteins are closely related to the tumor microenvironment, immune cell infiltration, responses to immunotherapy, and patients’ prognosis in lung adenocarcinoma and could be useful biomarkers for the identification of patients who could benefit from immunotherapy. The m7G modification protein NUDT4 may be a novel biomarker in promoting the progression of lung cancer.

The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative

Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the TCGA Renal Phenotype Research Group.

Information about the dataset files:

1) pancan_rnaseq_freeze.tsv.gz: Publicly available gene expression data for the TCGA Pan-cancer dataset. File: PanCanAtlas EBPlusPlusAdjustPANCAN_IlluminaHiSeq_RNASeqV2.geneExp.tsv was processed using script process_sample_freeze.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [http://api.gdc.cancer.gov/data/3586c0da-64d0-4b74-a449-5ff4d9136611] [https://doi.org/10.1016/j.celrep.2018.03.046]

2) pancan_mutation_freeze.tsv.gz: Publicly available Mutational information for TCGA Pan-cancer dataset. File: mc3.v0.2.8.PUBLIC.maf.gz was processed using script process_sample_freeze.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [http://api.gdc.cancer.gov/data/1c8cfe5f-e52d-41ba-94da-f15ea1337efc] [https://doi.org/10.1016/j.celrep.2018.03.046]

3) pancan_GISTIC_threshold.tsv.gz: Publicly available Gene- level copy number information of the TCGA Pan-cancer dataset. This file is processed using script process_copynumber.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. The files copy_number_loss_status.tsv.gz and copy_number_gain_status.tsv.gz generated from this data are used as inputs in our Galaxy pipeline. [https://xenabrowser.net/datapages/?cohort=TCGA%20Pan-Cancer%20(PANCAN)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443] [https://doi.org/10.1016/j.celrep.2018.03.046]

4) mutation_burden_freeze.tsv.gz: Publicly available Mutational information for TCGA Pan-cancer dataset mc3.v0.2.8.PUBLIC.maf.gz was processed using script process_sample_freeze.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [https://github.com/greenelab/pancancer/][http://api.gdc.cancer.gov/data/1c8cfe5f-e52d-41ba-94da-f15ea1337efc] [https://doi.org/10.1016/j.celrep.2018.03.046]

5) sample_freeze.tsv or sample_freeze_version4_modify.tsv: The file lists the frozen samples as determined by TCGA PanCancer Atlas consortium along with raw RNAseq and mutation data. These were previously determined and included for all downstream analysis All other datasets were processed and subset according to the frozen samples.[https://github.com/greenelab/pancancer/]

6) vogelstein_cancergenes.tsv: compendium of OG and TSG used for the analysis. [https://github.com/greenelab/pancancer/]

7) CCLE_DepMap_18Q1_maf_20180207.txt.gz Publicly available Mutational data for CCLE cell lines from Broad Institute Cancer Cell Line Encyclopedia (CCLE) / DepMap Portal. [https://depmap.org/portal/download/api/download/external?file_name=ccle%2FCCLE_DepMap_18Q1_maf_20180207.txt]

8) ccle_rnaseq_genes_rpkm_20180929.gct.gz: Publicly available Expression data for 1019 cell lines (RPKM) from Broad Institute Cancer Cell Line Encyclopedia (CCLE) / DepMap Portal. [https://depmap.org/portal/download/api/download/external?file_name=ccle%2Fccle_2019%2FCCLE_RNAseq_genes_rpkm_20180929.gct.gz]

9) CCLE_MUT_CNA_AMP_DEL_binary_Revealer.gct: Publicly available merged Mutational and copy number alterations that include gene amplifications and deletions for the CCLE cell lines. This data is represented in the binary format and provided by the Broad Institute Cancer Cell Line Encyclopedia (CCLE) / DepMap Portal. [https://data.broadinstitute.org/ccle_legacy_data/binary_calls_for_copy_number_and_mutation_data/CCLE_MUT_CNA_AMP_DEL_binary_Revealer.gct]

10) GDSC_cell_lines_EXP_CCLE_names.csv.gz Publicly available RMA normalized expression data for Genomics of Drug Sensitivity in Cancer(GDSC) cell-lines. File gdsc_cell_line_RMA_proc_basalExp.csv was downloaded. This data was subsetted to 389 cell lines that are common among CCLE and GDSC. All the GDSC cell line names were replaced with CCLE cell line names for further processing. [https://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources//Data/preprocessed/Cell_line_RMA_proc_basalExp.txt.zip]

11) GDSC_CCLE_common_mut_cnv_binary.csv.gz: A subset of merged Mutational and copy number alterations that include gene amplifications and deletions for common cell lines between GDSC and CCLE. This file is generated using CCLE_MUT_CNA_AMP_DEL_binary_Revealer.gct and a list of common cell lines.

12) gdsc1_ccle_pharm_fitted_dose_data.txt.gz: Pharmacological data for GDSC1 cell lines. [ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/current_release/GDSC1_fitted_dose_response_15Oct19.xlsx]

13) gdsc2_ccle_pharm_fitted_dose_data.txt.gz: Pharmacological data for GDSC2 cell lines. [ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/current_release/GDSC2_fitted_dose_response_15Oct19.xlsx]

14) compounds.csv: list of pharmacological compounds tested for our analysis

15) tcga_dictonary.tsv: list of cancer types used in the analysis.

16) seg_based_scores.tsv: Measurement of total copy number burden, Percent of genome altered by copy number alterations. This file was used as part of the Pancancer analysis by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [https://github.com/greenelab/pancancer/]

17) sign.csv: file with original values assigned for tumor [1] or normal [-1] for given external samples (GSE69822)

18) vlog_trans.csv: variant stabilized log transformed expression values for given external samples (GSE69822)

19 path_genes.csv: file with list of ERK/RAS/PI3K pathway genes used in the analysis.

State Heritage Register Items.7/8/17 - Edited to include the Byron Bay Railway Station yard area as per the online State Heritage Register Plan 2735 gazetted 2/4/99. (Sue Green)25/2/19 - Edited to include Cape Byron Lightstation SRH 02023 Lots 1, 2 & 3 DP 847753. Government Gazette No 17 22/2/19 (#E2019/13532). X Y cooridnates listed on OEH Plan 3210 were used to create shape file, with additional vertices added so that the shape matched the Georeferenced Map (#E2019/13587) (Sue Green)

This data set comprises one tar file containing all files necessary for the running of the proactivWorkflow package.

• Directory TCGA_Kidney_Junction_Files contains processed junction files (BED) of KIRC normal and tumour samples (Source: The Cancer Genome Atlas Research Network, https://portal.gdc.cancer.gov/)
• Directory Yan_Junction_Files contains processed junction files (BED) of human preimplantation embryos and human embryonic stem cells (Source: GSE36552, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36552)
• gencode.v19.annotation.gtf: GENCODE annotations (Human Release 19)
  (Source: GENCODE, https://www.gencodegenes.org/human/release_19.html)
• gencode.v34.annotation.gtf: GENCODE annotations (Human Release 34)
  (Source: GENCODE, https://www.gencodegenes.org/human/release_34.html)
• condition.tcga.rds: a character vector of the conditions for each KIRC sample
• condition.yan.rds: a character vector of the conditions for each embryo sample
• junctionFilePaths.tcga.rds: a character vector of the junction file paths for each KIRC sample
• junctionFilePaths.yan.rds: a character vector of the junction file paths for each embryo sample
• promoterAnnotation.gencodev19.rds: promoter annotation object corresponding to GENCODE annotation (Human Release 19)
• promoterAnnotation.gencodev34.rds: promoter annotation object corresponding to GENCODE annotation (Human Release 34)

BackgroundIn reference to previous studies, necroptosis played an important role in cancer development. Our team decided to explore the potential prognostic values of long non-coding RNAs (lncRNAs) associated with necroptosis in bladder urothelial carcinoma (BLCA) and their relationship with the tumor microenvironment (TME) and the immunotherapeutic response for accurate dose.MethodsTo obtain the required data, bladder urothelial carcinoma transcriptome data were searched from Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/). We used co-expression analysis, differential expression analysis, and univariate Cox regression to screen out prognostic lncRNAs associated with necroptosis in BLCA. Then the least absolute shrinkage and selection operator (LASSO) was conducted to construct the necroptosis-associated lncRNAs model. Based on this model, we also performed the Kaplan–Meier analysis and time-dependent receiver operating characteristics (ROC) to estimate the prognostic power of risk score. Multivariate and univariate Cox regression analysis were performed to build up a nomogram. Calibration curves, and time-dependent ROC were also conducted to evaluate nomogram. Principal component analysis (PCA) revealed a difference between high- and low-risk groups. In addition, we explored immune analysis, gene set enrichment analyses (GSEA), and evaluation of the half-maximal inhibitory concentration (IC50) in constructed model. Finally, the entire samples were divided into three clusters based on model of necroptosis-associated lncRNAs to further compare immunotherapy in cold and hot tumors.ResultsA model was built up based on necroptosis-associated lncRNAs. The model revealed good consistence between calibration plots and prognostic prediction. The area of 1-, 3-, and 5-year OS under the ROC curve (AUC) were 0.707, 0.679, and 0.675. Risk groups could be helpful for systemic therapy due to the markedly diverse IC50 between risk groups. To our delight, clusters could effectively identify cold and hot tumors, which would be beneficial to accurate mediation. Clusters 2 and 3 were considered the hot tumor, which was more sensitive to immunotherapeutic drugs.ConclusionsThe outcomes of our study suggested that necroptosis-associated lncRNAs could effectively predict patients with BLCA prognosis, which may be helpful for distinguishing the cold and hot tumors and improving individual treatment of BLCA.

BackgroundCircular RNAs (circRNAs) are now under hot discussion as novel promising biomarkers for patients with hepatocellular carcinoma (HCC). The purpose of our study is to identify several competing endogenous RNA (ceRNA) networks related to the prognosis and progression of HCC and to further investigate the mechanism of their influence on tumor progression.MethodsFirst, we obtained gene expression data related to liver cancer from The Cancer Genome Atlas (TCGA) database (http://www.portal.gdc.cancer.gov/), including microRNA (miRNA) sequence, RNA sequence, and clinical information. A co-expression network was constructed through the Weighted Correlation Network Analysis (WGCNA) software package in R software. The differentially expressed messenger RNAs (DEmRNAs) in the key module were analyzed with the Database for Annotation Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/summary.jsp) to perform functional enrichment analysis including Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The data of miRNA expression and clinical information downloaded from TCGA were utilized for survival analysis to detach the prognostic value of the DEmiRNAs of the key module.ResultsThe 201 differentially expressed miRNAs (DEmiRNAs) and 3,783 DEmRNAs were preliminarily identified through differential expression analysis. The co-expression networks of DEmiRNAs and DEmRNAs were constructed with WGCNA. Further analysis confirmed four miRNAs in the most significant module (blue module) were associated with the overall survival (OS) of patients with liver cancer, including hsa-miR-92b-3p, hsa-miR-122-3p, hsa-miR-139-5p, and hsa-miR-7850-5p. DAVID was used for functional enrichment analysis of 286 co-expressed mRNAs. The GO analysis results showed that the top enriched GO terms were oxidation–reduction process, extracellular exosome, and iron ion binding. In KEGG pathway analysis, the top three enriched terms included metabolic pathways, fatty acid degradation, and valine, leucine, and isoleucine degradation. In addition, we intersected the miRNA–mRNA interaction prediction results with the differentially expressed and prognostic mRNAs. We found that hsa-miR-92b-3p can be related to CPEB3 and ACADL. By overlapping the data of predicted circRNAs by circBank and differentially expressed circRNAs of GSE94508, we screened has_circ_0077210 as the upstream regulatory molecule of hsa-miR-92b-3p. Hsa_circ_0077210/hsa-miR-92b-3p/cytoplasmic polyadenylation element binding protein-3 (CPEB3) and acyl-Coenzyme A dehydrogenase, long chain (ACADL) were validated in HCC tissue.ConclusionOur research provides a mechanistic elucidation of the unknown ceRNA regulatory network in HCC. Hsa_circ_0077210 might serve a momentous therapeutic role to restrain the occurrence and development of HCC.

Additional file 2. Table S1. Databases and accession numbers. The original RNA-seq data were downloaded from the recount2 website ( http://idies.jhu.edu/recount/data/fc_rc/rse_fc_TCGA_prostate.Rdata ) and the database of Genomic and Phenotypes (dbGaP) under accession number phs000178.v11.p8.c1, phs000915.v2.p2.c1. The original clinical information of both cohorts is deposited in the CbioPortal ( https://www.cbioportal.org ) and GDCPortal ( https://portal.gdc.cancer.gov )

We aimed to explore the tumor mutational burden (TMB) and immune infiltration in HCC and investigate new biomarkers for immunotherapy. Transcriptome and gene mutation data were downloaded from the GDC portal, including 374 HCC samples and 50 matched normal samples. Furthermore, we divided the samples into high and low TMB groups, and analyzed the differential genes between them with GO, KEGG, and GSEA. Cibersort was used to assess the immune cell infiltration in the samples. Finally, univariate and multivariate Cox regression analyses were performed to identify differential genes related to TMB and immune infiltration, and a risk prediction model was constructed. We found 10 frequently mutated genes, including TP53, TTN, CTNNB1, MUC16, ALB, PCLO, MUC, APOB, RYR2, and ABCA. Pathway analysis indicated that these TMB-related differential genes were mainly enriched in PI3K-AKT. Cibersort analysis showed that memory B cells (p = 0.02), CD8+ T cells (p = 0.09), CD4+ memory activated T cells (p = 0.07), and neutrophils (p = 0.06) demonstrated a difference in immune infiltration between high and low TMB groups. On multivariate analysis, GABRA3 (p = 0.05), CECR7 (p < 0.001), TRIM16 (p = 0.003), and IL7R (p = 0.04) were associated with TMB and immune infiltration. The risk prediction model had an area under the curve (AUC) of 0.69, suggesting that patients with low risk had better survival outcomes. Our study demonstrated for the first time that CECR7, GABRA3, IL7R, and TRIM16L were associated with TMB and promoted antitumor immunity in HCC.