The Clinical Proteomic Tumor Analysis Consortium (CPTAC) analyzes cancer biospecimens by mass spectrometry, characterizing and quantifying their constituent proteins, or proteome. Proteomic analysis for each CPTAC study is carried out independently by Proteomic Characterization Centers (PCCs) using a variety of protein fractionation techniques, instrumentation, and workflows. Mass spectrometry and related data files are organized into datasets by study, sub-proteome, and analysis site.

This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium Lung Adenocarcinoma (CPTAC-LUAD) cohort. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.

Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name "CPTAC-cancertype". Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available. For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. Pathology imaging is collected as part of the CPTAC qualification workflow.

All CPTAC cohorts are released as either a single combined cohort, or split into Discovery and Confirmatory where applicable. There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term "discovery proteomics" is in reference to "untargeted" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term “targeted proteomics” refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein in vivo. Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the CPTAC Imaging Proteomics page.

CPTAC Imaging Special Interest Group

You can join the CPTAC Imaging Special Interest Group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses! Artifacts from previous webinars such as slide decks and video recordings can be found on the CPTAC SIG Webinars page.

On July 22, 2020 Shankha Satpathy presented about the consortium's proteogenomic analyses of CPTAC-LUAD. This deep dive into the LUAD genomic and proteomic datasets will help researchers better understand how these can be correlated with features derived from the imaging data. (Download the slides)

This project contains raw data, intermediate files and results used to create the integrated map of protein expression in human cancer (including data from cell lines and tumours). The map is based on joint reanalysis of 11 large-scale quantitative proteomics studies. The datasets were primarily retrieved from the PRIDE database, as well as MassIVE database and CPTAC data portal. The raw files were manually curated in order to capture mass spectrometry acquisition parameters, experimental design and sample characteristics. The raw files were jointly processed with MaxQuant computational platform using standard settings (see Data Processing Protocol). Due to size of the data, the processing was done in two batches denoted as “celllines” and “tumours” analysis. In total, using a 1% peptide spectrum match and protein false discovery rates, the analysis allowed identification of 21,580 protein groups in the cell lines dataset (MQ search results available in ‘txt-celllines’ folder), and 13,441 protein groups in the tumours dataset (MQ search results available in ‘txt-tumours’ folder).

This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute Imaging Data Commons (IDC) [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: CPTAC-COAD. You can use the manifests included in this Zenodo record to download the content of the collection following the Download instructions below.

Collection description

This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium CPTAC Colon Adenocarcinoma cohort. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics.

Please see the CPTAC-COAD wiki page to learn more about the images and to obtain any supporting metadata for this collection.

Files included

A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, collection_id-idc_v8-aws.s5cmd corresponds to the contents of the collection_id collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.

1. cptac_coad-idc_v7-aws.s5cmd: manifest of files available for download from public IDC Amazon Web Services buckets
2. cptac_coad-idc_v7-gcs.s5cmd: manifest of files available for download from public IDC Google Cloud Storage buckets
3. cptac_coad-idc_v7-dcf.dcf: Gen3 manifest (for details see https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids)

Note that manifest files that end in -aws.s5cmd reference files stored in Amazon Web Services (AWS) buckets, while -gcs.s5cmd reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.

Download instructions

Each of the manifests include instructions in the header on how to download the included files.

To download the files using .s5cmd manifests:

1. install idc-index package: pip install --upgrade idc-index
2. download the files referenced by manifests included in this dataset by passing the .s5cmd manifest file: idc download manifest.s5cmd.

To download the files using .dcf manifest, see manifest header.

Acknowledgments

Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.

References

[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence. RadioGraphics (2023). https://doi.org/10.1148/rg.230180

This file includes the pointer to the 42 patient ids and zip file names of the 84 genomic and proteomic datasets used for the paper "Gil, Y, Garijo, D, Ratnakar, V, Mayani, R, Adusumilli, A, Srivastava, R, Boyce, H, Mallick,P. Towards Continuous Scientific Data Analysis and Hypothesis Evolution", accepted in AAAI 2017.

The datasets itself are not published due to their size and access conditions. They can be retrieved with the provided ids from TCGA (https://gdc-portal.nci.nih.gov/legacy-archive/search/f) and CPTAC (https://cptac-data-portal.georgetown.edu/cptac/s/S022) archives.

These patient ids are a subset of the nearly 90 samples used in "Zhang, B., Wang, J., Wang, X., Zhu, J., Liu, Q., et al. Proteogenomic characterization of human colon and rectal cancer. Nature 513,382–387", in order to test the system described in the AAAI 2017 paper. More samples were not included in the analysis due to time constraints.

This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute Imaging Data Commons (IDC) [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: CPTAC-BRCA. You can use the manifests included in this Zenodo record to download the content of the collection following the Download instructions below.

Collection description

This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium CPTAC Breast Invasive Carcinoma cohort. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.

Please see the CPTAC-BRCA wiki page to learn more about the images and to obtain any supporting metadata for this collection.

Files included

A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, collection_id-idc_v8-aws.s5cmd corresponds to the contents of the collection_id collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.

1. cptac_brca-idc_v10-aws.s5cmd: manifest of files available for download from public IDC Amazon Web Services buckets
2. cptac_brca-idc_v10-gcs.s5cmd: manifest of files available for download from public IDC Google Cloud Storage buckets
3. cptac_brca-idc_v10-dcf.dcf: Gen3 manifest (for details see https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids)

Note that manifest files that end in -aws.s5cmd reference files stored in Amazon Web Services (AWS) buckets, while -gcs.s5cmd reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.

Download instructions

Each of the manifests include instructions in the header on how to download the included files.

To download the files using .s5cmd manifests:

1. install idc-index package: pip install --upgrade idc-index
2. download the files referenced by manifests included in this dataset by passing the .s5cmd manifest file: idc download manifest.s5cmd.

To download the files using .dcf manifest, see manifest header.

Acknowledgments

Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.

References

[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence. RadioGraphics (2023). https://doi.org/10.1148/rg.230180

This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium Cutaneous Melanoma (CPTAC-CM) cohort. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.

Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name "CPTAC-cancertype". Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available. For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. Pathology imaging is collected as part of the CPTAC qualification workflow.

All CPTAC cohorts are released as either a single combined cohort, or split into Discovery and Confirmatory where applicable. There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term "discovery proteomics" is in reference to "untargeted" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term “targeted proteomics” refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein in vivo. Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the CPTAC Imaging Proteomics page.

CPTAC Imaging Special Interest Group

You can join the CPTAC Imaging Special Interest Group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses! Artifacts from previous webinars such as slide decks and video recordings can be found on the CPTAC SIG Webinars page.

This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multiforme (CPTAC-GBM) cohort. The GBM confirmatory cohort was supplemented with retrospective samples from CHOP (Children’s hospital of Philadelphia)-Upenn. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.

Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name "CPTAC-cancertype". Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available. For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. Pathology imaging is collected as part of the CPTAC qualification workflow.

All CPTAC cohorts are released as either a single combined cohort, or split into Discovery and Confirmatory where applicable. There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term "discovery proteomics" is in reference to "untargeted" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term “targeted proteomics” refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein in vivo. Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the CPTAC Imaging Proteomics page.

CPTAC Imaging Special Interest Group

You can join the CPTAC Imaging Special Interest Group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses! Artifacts from previous webinars such as slide decks and video recordings can be found on the CPTAC SIG Webinars page.

On May 13, 2020 Liang-Bo Wang and Runyu Hong presented the consortium's proteogenomic analyses of the CPTAC Glioblastoma (GBM) cohort. This deep dive into the GBM genomic and proteomic datasets will help researchers better understand how these can be correlated with features derived from the imaging data. (Download the slides)

Deleted in Liver Cancer-1 (DLC1), a member of the RhoGAP family of proteins, functions as a tumor suppressor in several cancers including breast cancer. However, its clinical relevance is unclear in breast cancer. In this study, expression of DLC1 was correlated with prognosis using publicly available breast cancer gene expression datasets and quantitative Reverse Transcription PCR in cohorts of Estrogen Receptor-positive (ER+) breast cancer. Low expression of DLC1 correlates with poor prognosis in patients with ER+ breast cancer with further decrease in metastatic lesions. The Cancer Genome Atlas (TCGA) data showed that down regulation of DLC1 is not due to methylation or mutations. To seek further insights in understanding the role of DLC1 in ER+ breast cancer, we stably overexpressed DLC1-full-length (DLC1-FL) in T-47D breast cancer cells; this inhibited cell colony formation significantly in vitro compared to its control counterpart. Label-free global proteomic and TiO2 phosphopeptide enrichment assays (ProteomeXchange identifier PXD008220) showed that 205 and 122 phosphopeptides were unique to DLC1-FL cells and T-47D-control cells, respectively, whereas 6,726 were quantified by phosphoproteomics analysis in both conditions. The top three significant clusters of differentially phosphopeptides identified by DAVID pathway analysis represent cell-cell adhesion, mRNA processing and splicing, and transcription regulation. Phosphoproteomics analysis documented an inverse relation between DLC1 expression and several phosphopeptides including epithelial cell transforming sequence 2 (ECT2). Decreased phosphorylation of ECT2 at the residue T359, critical for its active conformational change, was validated by western blot. In addition, the ECT2 T359-containing phosphopeptide was detected in both basal and luminal patient-derived breast cancers breast cancer phosphoproteomics data on the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Assay portal. Together, for the first time, this implicates ECT2 phosphorylation in breast cancer, which has been proposed as a therapeutic target in lung cancer. In conclusion, this data suggests that low expression of DLC1 is associated with poor prognosis. Targeting ECT2 phosphopeptides could provide a promising mechanism for controlling poor prognosis seen in DLC1low ER+ breast cancer.

Background: The pseudouridine synthases (PUSs) have been reported to be associated with cancers. However, their involvement in hepatocellular carcinoma (HCC) has not been well documented. Here, we assess the roles of PUSs in HCC.Methods: RNA sequencing data of TCGA-LIHC and LIRI-JP were downloaded from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), respectively. GSE36376 gene expression microarray was downloaded from the Gene Expression Omnibus (GEO). Proteomics data for an HBV-related HCC cohort was obtained from the CPTAC Data Portal. The RT-qPCR assay was performed to measure the relative mRNA expression of genes in clinical tissues and cell lines. Diagnostic efficiency was evaluated by the ROC curve. Prognostic value was assessed using the Kaplan-Meier curve, Cox regression model, and time-dependent ROC curve. Copy number variation (CNV) was analyzed using the GSCA database. Functional analysis was carried out with GSEA, GSVA, and clusterProfiler package. The tumor microenvironment (TME) related analysis was performed using ssGSEA and the ESTIMATE algorithm.Results: We identified 7 PUSs that were significantly upregulated in HCC, and 5 of them (DKC1, PUS1, PUS7, PUSL1, and RPUSD3) were independent risk factors for patients’ OS. Meanwhile, the protein expression of DKC1, PUS1, and PUS7 was also upregulated and related to poor survival. Both mRNA and protein of these PUSs were highly diagnostic of HCC. Moreover, the CNV of PUS1, PUS7, PUS7L, and RPUSD2 was also associated with prognosis. Further functional analysis revealed that PUSs were mainly involved in pathways such as genetic information processing, substance metabolism, cell cycle, and immune regulation.Conclusion: PUSs may play crucial roles in HCC and could be used as potential biomarkers for the diagnosis and prognosis of patients.

This pathway incorporates the most important proteins for breast cancer. The Rp score from the Connectivity-Maps (C-Maps) webserver was used to determine the rank of the most important proteins in breast cancer. These proteins were then used to determine the most important pathways involved in breast cancer by using the Human Pathway Database (HPD). The pathways retrieved from the Human Pathway Database were from several sources such as Protein Lounge, BioCarta, KEGG, and NCI-Nature. The pathways were then annotated. Protein-protein relations for the most important proteins for breast cancer were determined by annotating the pathways and by literature review. The protein-protein interactions are mapped onto this pathway. Proteins on this pathway have targeted assays available via the CPTAC Assay Portal.

Background: The pseudouridine synthases (PUSs) have been reported to be associated with cancers. However, their involvement in hepatocellular carcinoma (HCC) has not been well documented. Here, we assess the roles of PUSs in HCC.Methods: RNA sequencing data of TCGA-LIHC and LIRI-JP were downloaded from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), respectively. GSE36376 gene expression microarray was downloaded from the Gene Expression Omnibus (GEO). Proteomics data for an HBV-related HCC cohort was obtained from the CPTAC Data Portal. The RT-qPCR assay was performed to measure the relative mRNA expression of genes in clinical tissues and cell lines. Diagnostic efficiency was evaluated by the ROC curve. Prognostic value was assessed using the Kaplan-Meier curve, Cox regression model, and time-dependent ROC curve. Copy number variation (CNV) was analyzed using the GSCA database. Functional analysis was carried out with GSEA, GSVA, and clusterProfiler package. The tumor microenvironment (TME) related analysis was performed using ssGSEA and the ESTIMATE algorithm.Results: We identified 7 PUSs that were significantly upregulated in HCC, and 5 of them (DKC1, PUS1, PUS7, PUSL1, and RPUSD3) were independent risk factors for patients’ OS. Meanwhile, the protein expression of DKC1, PUS1, and PUS7 was also upregulated and related to poor survival. Both mRNA and protein of these PUSs were highly diagnostic of HCC. Moreover, the CNV of PUS1, PUS7, PUS7L, and RPUSD2 was also associated with prognosis. Further functional analysis revealed that PUSs were mainly involved in pathways such as genetic information processing, substance metabolism, cell cycle, and immune regulation.Conclusion: PUSs may play crucial roles in HCC and could be used as potential biomarkers for the diagnosis and prognosis of patients.

This dataset consists of reference segmentations for 91 abdominal CTs delineating the pancreas and pancreas adenocarcinoma (PDA). The CTs were downloaded from freely available public archives for pancreas AI applications- The Cancer Imaging Archive (TCIA) CPTAC-PDA (n=60) and Medical Segmentation Decathlon (n=420) and evaluated by two radiologists for image quality, phase of image acquisition, presence of biliary stents and etiology of the pancreatic lesion. Volumetric pancreas and PDA segmentations was done on the portal-venous phase CTs on 3D Slicer software, by a radiologist with 4-year post residency experience. The PDA segmentations were subsequently verified by an expert abdominal radiologist with 11-years post residency experience. Segmentations were performed only on CTs that did not have expert segmentations as a part of original dataset.

For the CPTAC-PDA dataset, segmentations were performed on 42 out of 60 CTs after excluding 18 CTs for the following reasons: presence of biliary stent (n=10), missing slices through pancreas (n=4), post-pancreatectomy status (n=1), incorrect region of scan (n=1) and unavailable portal-venous phase (n=2).

For the MSD dataset, segmentations were performed on 49 out of 139 ‘testing’ subset CTs after excluding 90 CTs for the following reasons: presence of biliary stent (n=38), non-PDA pathology [n=36 for IPMN, n=14 for PNET) and post-treatment status (n=2).

The uploaded files are in NIfTI format. In addition, a metadata has been uploaded containing the following information for each segmented CT: location of tumor in pancreas, T-stage, single largest diameter of tumor, presence or absence of pancreatic atrophy, dilatation of main pancreatic duct and common bile duct.

This dataset has been referenced and elaborated in the paper-https://pubmed.ncbi.nlm.nih.gov/33840636/.

The most frequently altered genes in glioblastoma. This pathway originally accompanied the 2008 Nature publication on the comprehensive genomic characterization of human glioblastoma genes and core pathways by TCGA, The Cancer Genome Atlas (see Bibliography). Assembled from literature and public pathway database resources, this representation can easily be kept up to date at WikiPathways.org. Sources: cBio Cancer Genomics Portal Proteins on this pathway have targeted assays available via the CPTAC Assay Portal

PPAR alpha (also known as NR1C1) is a nuclear receptor that is involved with transcriptional regulation of genes involved in beta-oxidation, metabolism, fatty acid transport, etc. Proteins on this pathway have targeted assays available via the CPTAC Assay Portal.

The Pregnane X receptor (PXR, a.k.a. NR1I2) is a nuclear receptor whose primary function is sensing xenobiotics. It regulates the gene expression of genes that encode proteins involved in detoxification and clearance of xenobiotics. Proteins on this pathway have targeted assays available via the CPTAC Assay Portal.

BackgroundWe have previously reported that human cytomegalovirus (HCMV) infection could promote the progression of glioma. Here we discovered a stress-induced nuclear protein ZC3H11A (ZC3) through high-throughput sequencing after HCMV infection, which has been reported recently by our research group in regulating mRNA export under stress conditions. And also, a thorough analysis of ZC3 in pan-cancer and the omics data of ZC3 are yet to be conducted.MethodsThe transcriptomes of glioma cells after HCMV infection were assessed by RNA sequencing. ZC3 mRNA and protein level following HCMV infection were validated and measured by qRT-PCR and Western-blot. The RNA sequencing and protein expression information of ZC3 across pan-cancer were analyzed and visualized by R packages. The localization of ZC3 protein was assessed by IHC images from HPA. The ZC3 proteomics and transcriptomics data in different cancers were extracted through the CPTAC data portal, and comparisons were conducted with a Python script. The genetic alteration, survival prognosis, immune infiltration analysis of ZC3 in pan-cancer were analyzed by cBioPortal, TCGA, and TIMER2 databases. The protein interaction networks were revealed by STRING, GEPIA2 and TCGA.ResultsGenes in mRNA processing pathways were upregulated after HCMV infection and ZC3 expression in mRNA and protein level was validated. We also discovered that the status of ZC3 were generally at high levels in cancers, although varied among different cancer types. ZC3 protein in tumor cells localized to the nuclear whereas in normal cells it was mainly found in cytoplasmic/membranous. However, from ZC3 proteomics and transcriptomics data in some cancer types, the increase in ZC3 protein was not accompanied by a significant elevation in mRNA level. Additionally, our analysis indicated that elevated ZC3 expression was primarily linked to a negative prognosis in majority cancers but still varied depending on the cancer types. Our annotation analysis suggested that ZC3-related proteins are mainly involved in mRNA processing clusters.ConclusionWe demonstrated that ZC3 significantly impacted by HCMV infection in gliomas. Furthermore, we identified a set of genes exhibiting analogous expression patterns to ZC3H11A in TCGA pan-cancer cohorts, implying a potential functional role for ZC3H11A in mRNA processing. Our study provided valuable insights into the role of a new mRNA export protein ZC3 in HCMV infection and pan-cancer progression. These results lay the foundation for our next research on the regulatory mechanism of ZC3 in virus-infected tumors.

The transcription factors involved in adipogenesis are shown in the current pathway. Adipogensis is the biological proces of cell differentation in which preadipocytes are converted into adipocytes. Proteins on this pathway have targeted assays available via the CPTAC Assay Portal.

The liver X receptor (LXR) is a nuclear receptor involved in the regulation of liver-specific processes, such as cholesterol, fatty acid and glucose homeostasis. Proteins on this pathway have targeted assays available via the CPTAC Assay Portal.

SMARCB1 is a core subunit protein of the SWI/SNF chromatin remodeling complex, which interact with transcription factors at promoters and enhancers to modulate gene expression. Renal medullary carcinomas have been found to be deficient in SMARCB1 (BAF47) due to mutations. This pathway represents a summary of target genes and pathways implicated in the tumor suppression activity of SMARCB1. This pathway is modeled after figure 2 of "Oncogenic roles of SMARCB1/INI1 and its deficient tumors" by Kohashi and Oda, https://www.ncbi.nlm.nih.gov/pubmed/28109176. Inactivating mutation of SMARCB1 in renal medullary carcinoma is indicated. Proteins on this pathway have targeted assays available via the CPTAC Assay Portal