The cBioPortal for Cancer Genomics was utilized as the primary database to collect patient data and analyze survival outcomes. A total of 16 studies were organized, focusing on clinical data related to patient survival: Brain Lower Grade Glioma (TCGA, Firehose Legacy), Brain Lower Grade Glioma (TCGA, PanCancer Atlas), Diffuse Glioma (GLASS Consortium), Diffuse Glioma (GLASS Consortium, Nature 2019), Diffuse Glioma (MSK, Clin Cancer Res 2024), Glioma (MSK, Clin Cancer Res 2019), Glioma (MSK, Nature 2019), Low-Grade Gliomas (UCSF, Science 2014), Merged Cohort of LGG and GBM (TCGA, Cell 2016), Brain Tumor PDXs (Mayo Clinic, Clin Cancer Res 2020), Glioblastoma (CPTAC, Cell 2021), Glioblastoma (Columbia, Nat Med. 2019), Glioblastoma (TCGA, Cell 2013), Glioblastoma (TCGA, Nature 2008), Glioblastoma Multiforme (TCGA, Firehose Legacy), Glioblastoma Multiforme (TCGA, PanCancer Atlas). The clinical tab was selected for analysis, and survival graphs were generated for patient groups based on survival status (live and deceased groups). The p-values associated with the survival curves were carefully evaluated to ensure the best accuracy. This study's survival data encompassed 2,444 patients in the living group and 3,226 patients in the deceased group.

Solid malignancies analyzed using cBioPortal (TCGA, provisional).

Lower grade glioma (LGG) can be clinically grouped into major subtypes based on the mutation of the IDH1 and IDH2 genes and subsequently the co-deletion of the chromosome arms 1p and 19q. Chemical modification of DNA cytosine base at the cytosine-phosphate-guanine (CpG) di-nucleotide context plays an important role in mammalian cells and is altered severely in cancer including LGG. Due to the impact of IDH genes on genome-wide CpG methylation, the LGG subtypes show characteristically distinct methylation landscapes. Therefore, it is very feasible to use CpG methylation profiles to predict glioma subtypes, which are multi-class and hierarchically natured.

Here, two cohorts of LGGs with both genome-wide methylation profiles were curated from sources including Synapse TCGA Live, cBioPortal, and Gene Expression Omnibus data repositories.

• The Cancer Genome Atlas (n=507, "American Cohort")
• German Glioma Network (n=122, "German Cohort")

In both cohorts, CpG methylation was measured using the Illumina HumanMethylation450 BeadChip platform at the individual CpG level. In this Kaggle dataset, the methylation datasets have been re-processed such that:

• Instead of individual CpGs, CpG islands (CGIs), where CpG sites are densely distributed, are used as the feature unit. The value of each CGI was calculated by unweighted averaging the methylation beta-values of all available CpGs in each dataset, and then logit-transformed (i.e. M-values). The name of a CGI is simply its chromosomal coordinate on the human hg19 genome build.
• Only CGIs on chromosomes 1-22, longer than 800 nucleotides, and have at least 6 Illumina methylation probes were included

Each cohort's clinical (including subtype, the main outcome of interest) and processed CGI methylation data are organized into a CSV file. Each row is a tumor sample. Both cohorts have a column named "dummy", which is coded from the column "Subtype" where IDH-normal (wild type) = 2, IDH-mutated only = 0, and IDH mutated with co-deletion = 1. This column can be conveniently used for predictive modeling. The methylation M-values of individual CGIs (predictive features) are the 3,017 columns after the "dummy" column.

In breast cancer (BC), detecting low volumes of axillary lymph node (ALN) metastasis pre-operatively is difficult and novel biomarkers are needed. In this study, the authors compared reactive (tumour-free; n = 5) and macrometastatic (containing tumour deposits >2mm; n = 4) ALNs by combining whole section multiplex immunofluorescence with Tandem Mass Tag (TMT)-labelled Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) of the circulating perfusate.

Data access: The mass-spectrometry based proteomics data generated during the study, are publicly available in the PRIDE repository: https://identifiers.org/pride.project:PXD022722. The multiplex immunofluorescence data generated during this study, are available in the figshare repository, as part of this data record. The TCGA data analysed during the study, are available in the cBioPortal for Cancer Genomics: https://identifiers.org/cbioportal:brca_tcga. All other data supporting the findings of this study, are available as part of the supplementary files that accompany the article.

Study approval and patient consent: Patients consented for use of their tissue in the current study. The study protocol was approved by King's Health Partners Biobank (Research Ethics Committee No: 18/EE/0025).

Study aims and methodology: Lymph seems to contain higher concentrations of circulating biomarkers, particularly in the early stages of metastasis. Proteomic studies have shown that lymph reflects the pathophysiology of the tissue from which it derives, and has recently been shown to be relevant to melanoma biomarker discovery and stage prediction. To date, no such studies have been performed on lymphatic exudate from BC patients undergoing an axillary lymph node dissection (ALND) however. In this study, the authors characterised the proteome of the circulating fluid collected from perfused ALNs (perfusate) using Tandem Mass Tag (TMT) labelled mass spectrometry (MS)-based shotgun proteomics.

ALNs were harvested from 10 BC patients and perfused ex vivo at 37˚C as described previously (Research Ethics Committee No: 18/EE/0025). The following are described in more detail in the related article: patient cohort and ALN harvest, perfusate collection, multiplex immunofluorescence (MIF), proteomic analysis, neutrophil quantification, The Cancer Genome Atlas (TCGA) BC proteomics analysis and statistical analysis.

Data supporting the figures, tables, supplementary figures and supplementary tables in the related article:

Data supporting figure 1 and supplementary figure 1: REPLICANT Metastatic Lymph Node Multiplex IF.xlsx and REPLICANT Reactive Lymph Nodes Multiplex IF.xlsx, both in .xlsx file format. The files are part of this figshare data record. The data files contain cell density counts from REPLICANT reactive (tumour-free) lymph nodes and metastatic lymph nodes, generated algorithmically from multiplex immunofluorescence (Vectra).

Data supporting figures 2 and 3; and table 1: Proteomics data publicly available in the PRIDE repository: https://identifiers.org/pride.project:PXD022722.

Data supporting figure 4 and supplementary figure 2: TCGA data publicly available at cBioPortal for Cancer Genomics: https://identifiers.org/cbioportal:brca_tcga.

Data supporting figure 5: Supplementary Tables 4 and 5.xlxs. The data are available as part of the supplementary files that accompany the article.

Data supporting figure 6: Supplementary Table 6.xlxs. The data are available as part of the supplementary files that accompany the article.

Data supporting supplementary table 1: Proteomics data publicly available in the PRIDE repository: https://identifiers.org/pride.project:PXD022722.

Data supporting supplementary table 2: TCGA data publicly available at cBioPortal for Cancer Genomics: https://identifiers.org/cbioportal:brca_tcga.

Data supporting supplementary table 3: Proteomics data publicly available in the PRIDE repository: https://identifiers.org/pride.project:PXD022722 and supplementary files of article https://doi.org/10.1002/pmic.200800303.

Data supporting supplementary table 4: Proteomics data publicly available in the PRIDE repository: https://identifiers.org/pride.project:PXD022722 and supplementary files of article https://doi.org/10.1002/path.3959.

Data supporting supplementary table 5: Proteomics data publicly available in the PRIDE repository: https://identifiers.org/pride.project:PXD022722 and supplementary files of article https://doi.org/10.1016/j.neo.2017.10.009.

Data supporting supplementary table 6: Proteomics data publicly available in the PRIDE repository: https://identifiers.org/pride.project:PXD022722, and Supplementary Tables 4 and 5.xlxs.

Possible correlation between AR- and UPR-associated gene expression was assessed in the global gene expression data available in the TCGA Prostate Adenocarcinoma cohort (n = 190) (http://www.cbioportal.org/public-portal/index.do). Tumors were stratified according to AR status into three groups, that is ARlow (n = 60), ARmedium (n = 70), and ARhigh (n = 60). The levels of UPR gene expression in the three groups were compared using Pearson's correlation analysis by the R software and presented as a heatmap. There were significant differences between the three groups (Supplementary Table S2).. List of tagged entities: Tumors, , real-time PCR (bao:BAO_0002084)

Summary

This metadata record provides details of the data supporting the claims of the related article: “Signaling of MK2 Sustains Robust AP1 Activity for Triple Negative Breast Cancer Tumorigenesis through Direct Phosphorylation of JAB1”.

The related study showed that p38MAPK signalling pathway regulation of activator protein 1 (AP1) activity involves both MAPKAPK2 (MK2) and JAB1, a known JUN binding protein.

Type of data: signalling pathway activity

Subject of data: antibodies; Eukaryotic cell lines (ATCC); Mus musculus (Foxn1nu, female, Catalog# 007850, The Jackson laboratory)

Data access

The following files underlying the figures in the related manuscript are openly available with this data record:

• Fig.1 data.xlsx (Fig.1a,1b of the related article)

• Fig.2 data.xlsx (Fig.2c,2d and 2e)

• Fig.3 data.xlsx (Fig.3e and 3f)

• Fig.4 data.xlsx (Fig.4f,4g and 4f)

• Fig.5 data.xlsx (Fig.5b,5c and 5d)

• Fig.6 data.xlsx

• Fig.7 data.xlsx (Fig. 7e and 7f)

• Fig.8 data.xlsx (Fig.8a and 8c)

All other data supporting the related study can be found in the supplementary information file of the related article, and the corresponding author can make any materials available upon request. Un-cropped gels and western blots for Fig. to Fig.5 were included in Supplementary Materials (Fig.S11).”

JAB1 expression in different breast cancer subtypes were downloaded from https://tcga.xenahubs.net/download/TCGA.BRCA.sampleMap/HiSeqV2.gz, and https://tcga.xenahubs.net/download/TCGA.BRCA.sampleMap/BRCA_clinicalMatrix.gz. For analysis of p38MAPK activity in breast cancer, Reverse Phase Protein Array (RPPA) z score and corresponding clinical data from TCGA Breast Cancer Invasive Carcinoma, PanCancer Atlas were first downloaded through cBioportal (https://www.cbioportal.org/).

Corresponding author(s) for this study

Shuang Huang, Department of Anatomy and Cell Biology, University of Florida College of Medicine, Gainesville, FL 32610. E-mail: shuanghuang@ufl.edu

Study approval

University of Florida Institutional Animal Care and Use Committee.

Genetic alterations in K-ras and p53 are thought to be critical in pancreatic cancer development and progression. However, K-ras and p53 expression in pancreatic adenocarcinoma have not been systematically examined in The Cancer Genome Atlas (TCGA) Data Portal. Information regarding K-ras and p53 alterations, mRNA expression data, and protein/protein phosphorylation abundance was retrieved from The Cancer Genome Atlas (TCGA) databases, and analyses were performed by the cBioPortal for Cancer Genomics. The mutual exclusivity analysis showed that events in K-ras and p53 were likely to co-occur in pancreatic adenocarcinoma (Log odds ratio = 1.599, P = 0.006). The graphical summary of the mutations showed that there were hotspots for protein activation. In the network analysis, no solid association between K-ras and p53 was observed in pancreatic adenocarcinoma. In the survival analysis, neither K-ras nor p53 were associated with both survival events. As in the data mining study in the TCGA databases, our study provides a new perspective to understand the genetic features of K-ras and p53 in pancreatic adenocarcinoma.

Background: As a transcription factor, Zinc finger protein ZIC2 can interact with various DNAs and proteins. Current studies have shown that ZIC2 plays an oncogene role in various cancers. In this study, we systematically characterize the prevalence and predictive value of ZIC2 expression across multiple cancer types.Methods: We mined several public databases, including Oncomine, the Cancer Genome Atlas (TCGA), cBioPortal, Kaplan-Meier Plotter and PrognoScan to evaluated the differentially expressed ZIC2 between tumor samples and normal control samples in pan-cancner, and then explored the association between ZIC2 expression and patient survival, prognosis and clinicopathologic stage. We also analyzed the relationship between tumor mutation burden (TMB), microsatellite instability (MSI), tumor microenvironment, tumor- and immune-related genes and ZIC2 expression. Finally, we explored the potential signaling pathway mechanism through gene set enrichment analysis (GSEA).Results: ZIC2 expression was higher in most cancer tissues compared with adjacent normal tissues. High ZIC2 expression was associated with worse prognosis and a higher clinicopathologic stage. ZIC2 expression was strongly associated with the TMB, MSI, tumor microenvironment and tumor- and immune-related genes. The GSEA revealed that multiple tumor- and immune-related pathways were differentially enriched in ZIC2 high or low expression phenotype.Conclusion: ZIC2 expression may be a potential prognostic molecular biomarker of poor survival in pan-cancer and may act as an oncogene with a strong effect in the processes of tumorigenesis and progression.

Objective: Liquid-liquid phase separation (LLPS) is a functional unit formed by specific molecules. It lacks a membrane and has been reported to play a crucial role in tumor drug resistance and growth by regulating gene expression and drug distribution. However, whether LLPS could be used to predict cancer prognosis was not clear. This study aimed to construct a prognostic model for breast cancer based on LLPS-correlated genes (LCGs).Methods: LCGs were identified using the PhaSepDB, gene expression profile and clinical characteristics of breast cancer were obtained from TCGA and cBioportal. The PanCancer Atlas (TCGA) cohort was used as the training cohort to construct the prognostic model, while the Nature 2012 and Nat Commun 2016 (TCGA) cohort and GEO data were used as test cohort to perform external verification. Data analysis was performed with R4.2.0 and SPSS20.0.Results: We identified 140 prognosis-related LCGs (pLCGs) (p< 0.01) in all cohorts, 240 pLCGs (p< 0.01) in the luminal cohort, and 28 pLCGs (p< 0.05) in the triple-negative breast cancer (TNBC) cohort. Twelve genes in all cohorts (training cohort: 5/10-year ROC values were 0.76 and 0.77; verification cohort: 5/10-year ROC values were 0.61 and 0.58), eight genes in the luminal cohort (training cohort: 5/10-year ROC values were 0.79 and 0.75; verification cohort: 5/10-year ROC values were 0.62 and 0.62), and four genes in the TNBC cohort (training cohort: 5/10-year ROC values were 0.73 and 0.79; verification cohort: 5/10-year ROC values were 0.55 and 0.54) were screened out to construct the prognostic prediction model. The 17-gene risk-score was constructed in all cohorts (1/3/5-year ROC values were 0.88, 0.83, and 0.81), and the 11-gene risk-score was constructed in the luminal cohort (1/3/5-year ROC values were 0.67, 0.85 and 0.84), and the six-gene risk-score was constructed in the TNBC cohort (1/3/5-year ROC value were 0.87, 0.88 and 0.81). Finally, the risk-score and clinical factors were applied to construct nomograms in all cohorts (1/3/5-year ROC values were 0.89, 0.79 and 0.75, C-index = 0.784), in the luminal cohort (1/3/5-year ROC values were 0.84, 0.83 and 0.85, C-index = 0.803), and in the TNBC cohort (1/3/5-year ROC values were 0.95, 0.84 and 0.77, C-index = 0.847).Discussion: This study explored the roles of LCGs in the prediction of breast cancer prognosis.

Tumor immune microenvironment is associated with tumor progression. However, previous studies have not fully explored the breast cancer (BC) immune microenvironment. All the data analyzed in this study were obtained from the open-access database, including The Cancer Genome Atlas, Gene Expression Omnibus (TCGA), and cBioPortal databases. R software v4.0 and SPSS 13.0 were used to perform all the statistical analysis. Firstly, the clinical and expression profile information of TCGA, GSE20685, GSE20711, GSE48390, GSE58812, and METABRIC cohorts was collected. Then, 53 immune terms were quantified using the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. A prognosis model based on HER2_Immune_PCA, IL12_score, IL13_score, IL4_score, and IR7_score was established, which showed great prognosis prediction efficiency in both training group and validation group. A nomogram was then established for a better clinical application. Clinical correlation showed that elderly BC patients might have a higher riskscore. Pathway enrichment analysis showed that the pathway of oxidative phosphorylation, E2F targets, hedgehog signaling, adipogenesis, DNA repair, glycolysis, heme metabolism, and mTORC1 signaling was activated in the high-risk group. Moreover, Tumor Immune Dysfunction and Exclusion and Genomics of Drug Sensitivity in Cancer analysis showed that low-risk patients might be more sensitive to PD-1 therapy, cisplatin, gemcitabine, paclitaxel, and sunitinib. Finally, four genes, XCL1, XCL2, TNFRSF17, and IRF4, were identified for risk group classification. In summary, our signature is a useful tool for the prognosis and prediction of the drug sensitivity of BC.

Tumor immune microenvironment is associated with tumor progression. However, previous studies have not fully explored the breast cancer (BC) immune microenvironment. All the data analyzed in this study were obtained from the open-access database, including The Cancer Genome Atlas, Gene Expression Omnibus (TCGA), and cBioPortal databases. R software v4.0 and SPSS 13.0 were used to perform all the statistical analysis. Firstly, the clinical and expression profile information of TCGA, GSE20685, GSE20711, GSE48390, GSE58812, and METABRIC cohorts was collected. Then, 53 immune terms were quantified using the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. A prognosis model based on HER2_Immune_PCA, IL12_score, IL13_score, IL4_score, and IR7_score was established, which showed great prognosis prediction efficiency in both training group and validation group. A nomogram was then established for a better clinical application. Clinical correlation showed that elderly BC patients might have a higher riskscore. Pathway enrichment analysis showed that the pathway of oxidative phosphorylation, E2F targets, hedgehog signaling, adipogenesis, DNA repair, glycolysis, heme metabolism, and mTORC1 signaling was activated in the high-risk group. Moreover, Tumor Immune Dysfunction and Exclusion and Genomics of Drug Sensitivity in Cancer analysis showed that low-risk patients might be more sensitive to PD-1 therapy, cisplatin, gemcitabine, paclitaxel, and sunitinib. Finally, four genes, XCL1, XCL2, TNFRSF17, and IRF4, were identified for risk group classification. In summary, our signature is a useful tool for the prognosis and prediction of the drug sensitivity of BC.

Background: The polypyrimidine tract-binding protein (PTBP) nuclear ribonucleoprotein family of proteins, including PTBP1, PTBP2 and PTBP3, regulate the process of cell proliferation, differentiation, apoptosis and carcinogenesis. PTBPs exhibit oncogenic effects in certain tumors. However, the role of PTBPs in pan-cancer remains unclear. Our study examined the clinical significance and mechanism of PTBPs in pan-cancer.Methods: We compared the expression of PTBPs in paired and unpaired tissue samples from the Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression, Kaplan–Meier curves, and time-dependent receiver operating characteristic (ROC) curves were used to assess the prognostic significance of PTBPs in pan-cancer. The cBioPortal database also identified genomic abnormalities in PTBPs. TISIDB, TCGA, and Cellminer were used to investigate the relationship between PTBP expression and immune subtypes, immune checkpoint (ICP) genes, tumor mutational burden (TMB), microsatellite instability (MSI), tumor-infiltrating immune cells, and chemosensitivity. cBioPortal was used to search for PTBP co-expressing genes in pan-cancer, and GO and KEGG enrichment analyses were performed to search for PTBP-related signaling pathways.Results:PTBPs were shown to be widely upregulated in human tumor tissues. PTBP1 showed good prognostic value in ACC, KIRP, and LGG; PTBP2 in ACC and KICH; and PTBP3 in ACC, LGG, and PAAD, with AUC >0.7. PTBPs were differentially expressed in tumor immune subtypes and had a strong correlation with tumor-infiltrating lymphocytes (TILs) in the tumor microenvironment (TME). In addition, PTBP expressions were related to ICP, TMB, and MSI, suggesting that these three PTBPs may be potential tumor immunotherapeutic targets and predict the efficacy of immunotherapy. Enrichment analysis of co-expressed genes of PTBPs showed that they may be involved in alternative splicing, cell cycle, cellular senescence, and protein modification.Conclusion: PTBPs are involved in the malignant progression of tumors. PTBP1, PTBP2 and PTBP3 may be potential biomarkers for prognosis and immunotherapy in pan-cancer and may be novel immunotherapeutic targets.

1.Clinical DataTranscript data and Clinicopathological information was downloaded from The Cancer Genome Atlas (TCGA) database (https://portal.Gdc.cancer.gov/), including 41 cases of para-tumor, 473 cases of CRC tumor and 452 clinical cases.

The survival and transcriptional data of 250 CRC cases were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). The transcript dataset GSE161158 uploaded in November 2020 by Moffitt Cancer Research Center, University of Miami was used (10). Lists of immune related genes were download from ImmPort (https://www.immport. org/home) and Innate DB (https://www.innatedb.ca/). KEGG (http://www.gsea-msigdb.org/gsea/index.jsp) gene sets and all Gene Ontology (GO) gene sets were used as Gene Symbols. Gene mutation information was downloaded from cBioPortal (http://www.cbioportal.org/).2.Murine Data2.1 IRGPI Genes Expression in CRC Murine ModelSPF Balb/c male mice, 6 - 8 weeks old, body mass (20 ± 5) g, purchased from Huaxing Experimental Animal Farm of Huiji District (Zhengzhou City, China), experimental animal license NO. is SCXK (Yu) 2019-0002. All animal experiments was approved by the Experimental Mouse Ethics Committee of Nanjing University of Traditional Chinese Medicine (NO. 202010A026).The above mice were randomly divided into: Control group (C), CRC Model group (M)，10 per group. Five Balb/c male mice were taken as tumor-bearing mice, and 1×107 CT26 cells were subcutaneously injected into the left axilla, and sacrificed one week later. The subcutaneous tumor was removed under sterile conditions, placed in sterile PBS, and disintegrated into several 1 mm3 masses. Under sterile conditions, the two groups of mice were dissected to expose the colon, the 1 mm3 tumor mass was fixed to the colon of the CRC Model group with tissue glue, while nothing was fixed in the Control group, and then the abdomen of the two groups of mice was sutured. After 3 days of postoperative recovery, mice were weighed, and Micro-CT scans were performed on the 26th day (under the condition of isoflurane respiratory anesthesia), and on the 27th day, the mice were sacrificed after anesthesia with 2 % sodium pentobarbital. Total RNA was extracted from the colon of the Control group and the tumor tissue of the CRC Model group by FastPure Cell/Tissue Total RNA Isolation Kit (Vazyme, China, Cat#RC101-01) , and after reverse transcribed into cDNA using HiScript® Ⅲ RT SuperMix for q PCR (Vazyme, China, Cat#R323-01), Real-Time PCR was used to detect the expression of IRCPI genes in each group using BlastaqTM Green 2× qPCR MasterMix (abm, Canada, Cat#G891). The primer sequences are shown in Supplementary Table S4.2.2 Immune Infiltration in CRC murine model The liver, colon, tumor, and mesentery of paraffin-embedded mice were sectioned, and then stained with hematoxylin-eosin (HE staining), and photographed with an upright white light photographic microscope (Nikon, Japan, Eclipse Ci-L).

TIME immune cells were detected by flow cytometry (FCM). PBMCs were extracted by RBC lysate (FcMACS, China, Cat#FMS-RBC500). At least 5×106 cell suspensions(100 μL) were incubated with FC blocker at 4 ℃ for 10 min, then Anti-Human/Mouse CD11b FITC Antibody (PeproTech, USA, Cat#03221-50)、PE-Cy™7 Rat Anti-Mouse CD86 Antibody (BD Pharmingen™, USA, Cat#560582) and Alexa Fluor® 488 Anti-Mouse CD206 Antibody (Biolegend, USA, Cat#141710) were used to marked Macrophages; Alexa Fluor® 488 anti-mouse CD19 Antibody (Invitrogen, USA, REF#11-0193-81) and PE/Cy7 anti-mouse/rat/human CD27 Antibody (Biolegend, USA, Cat#124216) were used to marked B cells; Anti-Mouse CD4 APC-Cyanine7 (PeproTech, USA, Cat#06122-87)、Anti-Mouse CD8a FITC Antibody (PeproTech, USA, Cat#10122-50)、Anti-Mouse CD25 APC Antibody (PeproTech, USA, Cat#07312-80) and Anti-Mouse/Rat FOXP3 PE Antibody (PeproTech, USA, Cat#83422-60) were used to marked T cells, and PBMCs monochromic tubes were made respectively. The cells were detected on the Amnis FlowSight flow cytometer (Merck Millipore, USA), and immunocyte subsets were analyzed using the IDEAS software (Merck Millipore, USA). Supplement Fig.S7 visualized the analysis strategies for IRGPI immunocyte subsets by Flow cytometry.

Additional file 4.Table S1: SLC31A1 genetic alteration types in TCGA datasets from cBioportal. Table S2. SLC31A1 genetic mutations summary in TCGA datasets from cBioportal. Table S3. SLC31A1 genetic alteration profile. Table S4: The source of the mutation of SLC31A1. Table S5: Top 100 genes with similar expression patterns to the SLC3A1 gene from all tumor types of TCGA datasets by GEPIA2. Table S6: GO cellular component (CC) enrichment analysis of 100 SLC31A1-correlated genes. Table S7: GO biological process (BP) enrichment analysis of 100 SLC31A1-correlated genes. Table S8: GO molecular function (MF) enrichment analysis of 100 SLC31A1-correlated genes. Table S9: KEGG enrichment analysis of 100 SLC31A1-correlated genes.

BackgroundThe emergence of immune checkpoint inhibitors (ICIs) has significantly improved the clinical outcomes of patients with metastatic melanoma. However, survival benefits are only observed in a subset of patients. The fibroblast growth factor receptor (FGFR) family genes are frequently mutated in melanoma, yet their impacts on the efficacy of ICIs remain unclear. Our study aimed to explore the association of FGFR mutations with ICIs efficacy in metastatic melanoma.MethodsThe Cancer Genome Atlas (TCGA) data (PanCancer Atlas, skin cutaneous melanoma (SKCM), n = 448) in cBioPortal were collected as a TCGA cohort to investigate the association between FGFR mutations and prognosis of melanoma patients. To explore the impact of FGFR mutations on the efficacy of ICIs in melanoma, clinical and tumor whole-exome sequencing (WES) data of four ICI-treated studies from cBioPortal were consolidated as an ICIs-treated cohort. Moreover, the relationship between FGFR mutations and immunogenicity (tumor mutation burden (TMB), neo-antigen load (NAL), mismatch repair (MMR)-related genes and DNA damage repair (DDR)-related genes) of melanoma was evaluated utilizing data from the ICIs-treated cohort. The influence of FGFR mutations on the tumor immune microenvironment (TIME) of melanoma was also analyzed using the TCGA cohort.ResultsIn the TCGA cohort, survival in melanoma patients with or without FGFR mutations was nearly equivalent. In the ICIs-treated cohort, patients with FGFR mutations had better survival than those without (median overall survival: 60.00 vs. 31.00 months; hazard ratio: 0.58, 95% CI: 0.42-0.80; P = 0.0051). Besides, the objective response rate was higher for patients harboring FGFR mutations (55.56%) compared to wild-type patients (22.40%) (P = 0.0076). Mechanistically, it was revealed that FGFR mutations correlated with increased immunogenicity (e.g., TMB, NAL, MMR-related gene mutations and DDR-related gene mutations). Meanwhile, FGFR mutant melanoma tended to exhibit an enhanced antitumor TIME compared with its wild-type counterparts.ConclusionsOur study demonstrated that FGFR mutations is a promising biomarker in stratifying patients with advanced melanoma who might benefit from ICIs therapy.

Triple-negative breast cancer (TNBC) is a heterogeneous disease that lacks both effective patient stratification strategies and therapeutic targets. Whilst elevated levels of the MET receptor tyrosine kinase are associated with TNBCs and predict poor clinical outcome, the functional role of MET in TNBC is still poorly understood. In this study, the authors utilized an established Met-dependent transgenic mouse model of TNBC, human cell lines, and patient-derived xenografts to investigate the role of MET in TNBC tumourigenesis.

Data access: Processed RNA sequencing datasets generated during the study, are available in Gene expression Omnibus: https://identifiers.org/geo:GSE162272. The raw RNA sequencing data are available in Sequence Read Archive: https://identifiers.org/ncbi/insdc.sra:SRP294504. All other datasets generated and analysed during the study (including tumoursphere formation assays, tumoursphere proliferation assays, immunohistochemistry data, quantitative RT-PCR, in vivo inhibitor treatments (including tumour volume calculations), flow cytometry data and immunofluorescence data) are publicly available in the figshare repository as part of this data record. The publicly available TCGA data analysed during the study are available in cBioPortal for Cancer Genomics: https://identifiers.org/cbioportal:brca_tcga_pub. Microarray data from the MMTV-Metmt;Trp53fl/+;Cre tumours analysed during the study, are available in Gene Expression Omnibus: https://identifiers.org/geo:GSE41601. RNA sequencing data from breast cancer pairs of primary tumors and PDXs, analysed during the study, are also available in Gene Expression Omnibus: https://identifiers.org/geo:GSE142767. Uncropped Western blots are part of the supplementary files that accompany the article.

Study approval and patient consent: All human participants provided informed consent for this study and tissue was collected at McGill University Health Center in accordance with the protocols approved by the research ethics board (SUR-99-780). All animal studies linked to this protocol were approved by the McGill University Animal Care Committee (2014-7514). The Biobank protocol (05-006) and the protocol to generate PDX from biobank tissues (14-168) were both approved by Jewish General Hospital ethics committee.

Study aims and methodology: In the present study, the authors assayed tumour-initiating cells (TIC) properties to directly investigate the role of Met in tumour initiation and identify FGFR1 signaling as a key convergent pathway with Met for the maintenance of TICs.

Primary mouse cell lines were established by dissociation of MMTV-Metmt, Trp53fl/+;Cre, and MMTV-Metmt;Trp53fl/+;Cre mammary tumours as previously described. Additionally, the following cell lines were used during the study: BT-20, HCC70, HCC1937, HCC1954, HCC1395, MDA-MB-468, MDA-MB-436, MDA-MB-157, MDA-MB-231, BT-549, and Hs578T.

The following are described in more detail in the published article: cell culture, patient-derived xenografts, antibodies and reagents, lentiviral infection, tumoursphere formation assays, tumoursphere proliferation assays, Western blot analysis, quantitative RT-PCR, immunohistochemistry, RNA sequencing, in vivo limiting dilution assay, in vivo inhibitor treatments, flow cytometry, immunofluorescence, tumour dissociation, analysis of gene expression data, and statistical analysis.

Data supporting the figures, supplementary figures and supplementary tables in the article:

This data record consists of a total of 38 data files in the following file formats: .xlsx, .pdf, .csv, .txt, .png and tiff.

A list of all the datasets generated during the study, are included in the file Sung, V. et al.xlsx.

Ferroptosis is an iron-dependent programmed cell death process. Although ferroptosis inducers hold promising potential in the treatment of breast cancer, the specific role and mechanism of the ferroptosis-related gene EMC2 in breast cancer have not been entirely determined. The potential roles of EMC2 in different tumors were explored based on The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Gene Expression Profiling Interactive Analysis 2 (GEPIA2), Tumor Immune Estimation Resource (TIMER), Shiny Methylation Analysis Resource Tool (SMART), starBase, and cBioPortal for cancer genomics (cBioPortal) datasets. The expression difference, mutation, survival, pathological stage, DNA methylation, non-coding RNAs (ncRNAs), and immune cell infiltration related to EMC2 were analyzed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to identify the differences in biological processes and functions among different related genes. The expression levels of core prognostic genes were then verified in breast invasive carcinoma samples using immunohistochemistry and breast invasive carcinoma cell lines using real-time polymerase chain reaction. High expression levels of EMC2 were observed in most cancer types. EMC2 expression in breast cancer tissue samples correlated with poor overall survival. EMC2 was mutated and methylated in a variety of tumors and affected survival. The LINC00665-miR-410-3p axis was identified as the most potential upstream ncRNA-related pathway of EMC2 in breast cancer. EMC2 levels were significantly positively correlated with tumor immune cell infiltration, immune cell biomarkers, and immune checkpoint expression. Our study offers a comprehensive understanding of the oncogenic roles of EMC2 across different tumors. The upregulation of EMC2 expression mediated by ncRNAs is related to poor prognosis and tumor immune infiltration in breast cancer.

Background: Cuprotosis is a new form of programmed cell death induced by copper. We explored the correlation of cuprotosis with clear cell renal cell carcinoma (ccRCC) and constructed a cuprotosis-related signature to predict the prognosis of patients with ccRCC.Methods: The clinical and transcriptomic data of ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA), cBioPortal, and GEO databases, and cuprotosis-related gene sets were contained in the previous study. A cuprotosis-related signature was developed based on data from TCGA and verified by data from cBioPortal and GEO databases. The immune cell infiltrates and the corresponding signature risk scores were investigated. Two independent cohorts of clinical trials were analyzed to explore the correlation of the signature risk score with immune therapy response.Results: A signature containing six cuprotosis-related genes was identified and can accurately predict the prognosis of ccRCC patients. Patients with downregulated copper-induced programmed death had a worse overall survival (hazard ratio: 1.90, 95% CI: 1.39–2.59, p < 0.001). The higher signature risk score was significantly associated with male gender (p = 0.026), higher tumor stage (p < 0.001), and higher histological grade (p < 0.001). Furthermore, the signature risk score was positively correlated with the infiltration of B cells, CD8+ T cells, NK cells, Tregs, and T cells, whereas it was negatively correlated with eosinophils, mast cells, and neutrophils. However, no correlation between cuprotosis and response to anti-PD-1 therapy was found.Conclusion: We established a cuprotosis signature, which can predict the prognosis of patients with ccRCC. Cuprotosis was significantly correlated with immune cell infiltrates in ccRCC.

Summary

This metadata record provides details of the data supporting the claims of the related manuscript: “RIPK1 is a negative mediator in Aquaporin 1-driven triple-negative breast carcinoma progression and metastasis”.

The related study reports the aberrant expression of Aquaporin 1 (AQP1) and receptor-interacting protein kinase 1 (RIPK1) in triple-negative breast carcinoma (TNBC) that are associated with different prognoses, then validates the interaction of AQP1 and RIPK1 and the suppressive effect of RIPK1 on AQP1-driven TNBC progression and metastasis, and finally identifies the underlying mechanism of TNBC cell death resistance that AQP1 binds to the D324 site of RIPK1 and facilitates RIPK1 cleavage by promoting the caspase-8/RIPK1 negative feedback loop.

Type of data: mass spectrometry

Subject of data: Homo sapiens; Eukaryotic cell lines; Mus musculus

Population characteristics: human patients were female, diagnosed with TNBC and average age 45.4 years; seven-week-old female BALB/c mice

Recruitment: consecutively recruited between May 1, 2012 and April 30, 2013 at Tianjin Medical University Cancer Institute and Hospital and the First Affiliated Hospital of Xiamen University

Data access

The public data resources used in the related study are openly available from the following sources: the Oncomine database (http://www.oncomine.org), the Cancer Genome Atlas (TCGA, https://identifiers.org/cbioportal:brca_tcga), Genotype-Tissue Expression (GTEx, https://gtexportal.org), and the Gene Expression Omnibus data repository (GEO, https://identifiers.org/geo:GSE1456, https://identifiers.org/geo:GSE6532, and https://identifiers.org/geo:GSE7390).

The majority of the GraphPad Prism files underlying the figures and supplementary figures of the related article are openly available as part of this data record. However, several are saved in institutional storage and are not publicly available to protect the patient privacy. These may be available from the corresponding author upon reasonable request.

All the uncropped western blots generated during this study are available in Supplementary Figure 6.

Corresponding author(s) for this study

Fanxin Zeng, Ph.D., Department of Clinical Research Center, Dazhou Central Hospital, No.56 Nanyuemiao St, Tongchuan District, Dazhou, 635000, China, Phone: +86- 818-2381051, E-mail: zengfx@pku.edu.cn.

Huiwen Ren, Ph.D., Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, No.22 Qixiangtai Rd. Heping District, Tianjin, 300070, China, Phone: +86-22-83336668, E-mail: renhuiwen@tmu.edu.cn.

Study approval

The study conformed to the Ethical Guidelines of the Helsinki Declaration, and was approved by the Ethics Committee of Tianjin Medical University Cancer Institute and Hospital, Tianjin, and the Ethics Committee of the First Affiliated Hospital of Xiamen University, Xiamen, People’s Republic of China.

Background: The polypyrimidine tract-binding protein (PTBP) nuclear ribonucleoprotein family of proteins, including PTBP1, PTBP2 and PTBP3, regulate the process of cell proliferation, differentiation, apoptosis and carcinogenesis. PTBPs exhibit oncogenic effects in certain tumors. However, the role of PTBPs in pan-cancer remains unclear. Our study examined the clinical significance and mechanism of PTBPs in pan-cancer.Methods: We compared the expression of PTBPs in paired and unpaired tissue samples from the Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression, Kaplan–Meier curves, and time-dependent receiver operating characteristic (ROC) curves were used to assess the prognostic significance of PTBPs in pan-cancer. The cBioPortal database also identified genomic abnormalities in PTBPs. TISIDB, TCGA, and Cellminer were used to investigate the relationship between PTBP expression and immune subtypes, immune checkpoint (ICP) genes, tumor mutational burden (TMB), microsatellite instability (MSI), tumor-infiltrating immune cells, and chemosensitivity. cBioPortal was used to search for PTBP co-expressing genes in pan-cancer, and GO and KEGG enrichment analyses were performed to search for PTBP-related signaling pathways.Results:PTBPs were shown to be widely upregulated in human tumor tissues. PTBP1 showed good prognostic value in ACC, KIRP, and LGG; PTBP2 in ACC and KICH; and PTBP3 in ACC, LGG, and PAAD, with AUC >0.7. PTBPs were differentially expressed in tumor immune subtypes and had a strong correlation with tumor-infiltrating lymphocytes (TILs) in the tumor microenvironment (TME). In addition, PTBP expressions were related to ICP, TMB, and MSI, suggesting that these three PTBPs may be potential tumor immunotherapeutic targets and predict the efficacy of immunotherapy. Enrichment analysis of co-expressed genes of PTBPs showed that they may be involved in alternative splicing, cell cycle, cellular senescence, and protein modification.Conclusion: PTBPs are involved in the malignant progression of tumors. PTBP1, PTBP2 and PTBP3 may be potential biomarkers for prognosis and immunotherapy in pan-cancer and may be novel immunotherapeutic targets.