Cancer Proteome Atlas Research Articles

Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe)

We propose a new method for the simultaneous selection and estimation of multivariate sparse additive models with correlated errors. Our method called Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe) simultaneously selects among null, linear, and smooth non-linear effects for each predictor while incorporating joint estimation of the sparse residual structure among responses, with the motivation that accounting for inter-response correlation structure can lead to improved accuracy in variable selection and estimation efficiency. CoMPAdRe is constructed in a computationally efficient way that allows the selection and estimation of linear and non-linear covariates to be conducted in parallel across responses. Compared to single-response approaches that marginally select linear and non-linear covariate effects, we demonstrate in simulation studies that the joint multivariate modeling leads to gains in both estimation efficiency and selection accuracy, of greater magnitude in settings where signal is moderate relative to the level of noise. We apply our approach to protein-mRNA expression levels from multiple breast cancer pathways obtained from The Cancer Proteome Atlas and characterize both mRNA-protein associations and protein-protein subnetworks for each pathway. We find non-linear mRNA-protein associations for the Core Reactive, EMT, PIK-AKT, and RTK pathways. Supplementary Materials are available online.

Comparative profiling of whole-cell and exosome samples reveals protein signatures that stratify breast cancer subtypes

Identifying novel breast cancer biomarkers will improve patient stratification, enhance therapeutic outcomes, and help develop non-invasive diagnostics. We compared the proteomic profiles of whole-cell and exosomal samples of representative breast cancer cell subtypes to evaluate the potential of extracellular vesicles as non-invasive disease biomarkers in liquid biopsies. Overall, differentially-expressed proteins in whole-cell and exosome samples (which included markers for invasion, metastasis, angiogenesis, and drug resistance) effectively discriminated subtypes; furthermore, our results confirmed that the proteomic profile of exosomes reflects breast cancer cell-of-origin, which underscores their potential as disease biomarkers. Our study will contribute to identifying biomarkers that support breast cancer patient stratification and developing novel therapeutic strategies. We include an open, interactive web tool to explore the data as a molecular resource that can explain the role of these protein signatures in breast cancer classification.Graphical (A) We quantified proteomic profiles of breast cancer cells (BCCs) and breast cancer-derived exosomes (BCDEs) samples from four breast cancer cell lines representative of common breast cancer subtypes – Her2-positive (Her2, MDA-MB-453), Luminal A (LA, MCF7), Luminal B (LB, ZR-75), and triple-negative (TN, MDA-MB-231). (B) We independently performed four comparisons for BCCs and BCDEs, comparing each cell line against the remaining three. (C) We identified differentially-expressed proteins (DEPs) for BCCs and BCDEs, defined protein signatures, and functionally analyzed resulting networks. Through pathway inference analysis (PIA), we identified Kyoto Encyclopedia of genes and genomes (KEGG) subpaths and biological Gene Ontology (GO) terms that displayed differential activation. (D) We validated our proteomic signature using the Cancer Genome Atlas (TCGA) and the Cancer Proteome Atlas (TCPA) databases, verified that differentially-activated pathways in BCDEs caused a corresponding response in receptor cells, and confirmed that the BCDE proteomic signature reflects their cell-of-origin and identifies candidate disease biomarkers in liquid biopsies

The prognostic significance of collagen VI in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by poor prognosis and lack of effective diagnostic and prognostic biomarkers. A rich and activated desmoplastic tumor stroma is considered a hallmark of PDAC, and therefore stromal components could be a source of possible new prognostic biomarkers. Expression of collagen VI (COL6) has been found to associate with prognosis in many forms of cancer but has been less extensively studied in PDAC. Some previous studies indicate that COL6 expression is upregulated in PDAC, and it could have prognostic value. In this study the expression of COL6 was analyzed by immunohistochemistry in tissue microarrays (TMAs) containing tumor tissue samples from 164 (n = 164) PDAC patients who had undergone surgical resection. Scoring results were combined with clinical data, and the prognostic significance of COL6 was estimated with Kaplan-Meier survival estimates and multivariable Cox regression analysis. COL6 protein expression patterns were further investigated in The Cancer Proteome Atlas (TCPA) dataset (n = 103) and in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset (n = 138). COL6 mRNA expression in PDAC tissue was investigated using The Cancer Genome Atlas (TCGA) by forming a dataset containing gene expression data and corresponding clinical variables of 168 (n = 168) PDAC patients. In this large PDAC TMA cohort we could not find statistically significant (p < 0.05) differences in survival when comparing patients with high and low protein expression of any of the analyzed COL6 α-chains (α1(VI): HR 0.90, 95% CI 0.64-1.28; α2(VI): HR 1.28, 95% CI 0.86-1.89; α3(VI): HR 0.91, 95%CI 0.64-1.29). Similar results were obtained when assessing expression of COL6 in public data from TCPA, CPTAC and TCGA. In contrast with previous studies and some other cancer forms, we did not find any association of COL6 tissue expression and PDAC survival on protein or mRNA level.

EPPK1 as a Prognostic Biomarker in Type I Endometrial Cancer and Its Correlation with Immune Infiltration.

Approximately 20% of patients with type I endometrial cancer (EC) of the uterus experience recurrence and metastasis. However, existing data do not provide sufficient evidence for the utility of protein levels as prognostic biomarkers in type I EC. This study aims to determine whether epiplakin1 (EPPK1) and progesterone receptor (PR) play a role in the recurrence and metastasis of type I EC. Following the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) for assessing the quality of biomarker research results, a retrospective analysis was conducted on clinical information and tissue samples of type I EC patients. Protein expression data and clinical data for type I EC were downloaded from The Cancer Proteome Atlas (TCPA) database. We utilized the Kaplan-Meier (K-M) method and Cox proportional hazards regression analyses to evaluate whether epiplakin1 (EPPK1), progesterone receptor (PR) and certain clinical parameters can serve as independent prognostic factors. The Immune Cell Abundance Identifier (ImmuCellAI) and Cancer Immunome Atlas (TCIA) were employed to predict responses to immunotherapy. Immunohistochemistry was carried out to assess the expression of EPPK1 in type I EC. Type I EC patients with high EPPK1 and low PR expression had higher International Federation of Gynecology and Obstetrics (FIGO) stage, recurrence, and metastasis rates. Furthermore, EPPK1 was identified as an independent prognostic factor, and low expression of EPPK1 was predominantly observed in the POLE ultramutated (POLEmut) group, indicating a favorable prognosis. Additionally, the high EPPK1 expression group had a lower Immune Prognostic Score (IPS), suggesting that the high-expression group may not benefit from immune checkpoint inhibitors. High expression of EPPK1 is an independent prognostic factor in type I EC patients with low PR expression. It can identify a subgroup of patients at high risk of recurrence. A more aggressive treatment approach is recommended for these patients.

Abstract 4931: The Cancer Proteome Atlas: Expanded datasets and empowered with an AI-driven chatbot

Abstract Reverse-phase protein arrays (RPPAs) represent a powerful functional proteomic approach to elucidate cancer-related molecular mechanisms and develop novel cancer therapies. To facilitate community-based investigation of the large-scale protein expression data generated by this platform, we have developed a user-friendly, open-access bioinformatic resource, The Cancer Proteome Atlas (TCPA, http://tcpaportal.org), which contains several applications. The first one focuses on RPPA data of patient tumors, which contains &amp;gt;8,000 samples of 32 cancer types from The Cancer Genome Atlas (TCGA) and other independent patient cohorts. The second application focuses on the RPPA data of &amp;gt;1,500 cancer cell lines with publicly available, high-quality DNA, RNA and drug screening data. The third one focuses on perturbed RPPA profiles of &amp;gt;14,000 samples given drug treatments. We recently expanded our RPPA protein set from 200 to 500 proteins Importantly, this highly selected set is enriched in therapeutic targets and biomarkers and covers key oncogenic pathways such as PI3K/AKT, RAS/MAPK, Src/FAK, TGFβ/SMAD, JAK/STAT, epithelial-mesenchymal transition, DNA damage repair, Hippo, Notch, cell cycle, apoptosis, and immune-oncology. Using this upgraded RPPA platform, we generated an harmonized, expanded RPPA dataset of 8000 TCGA patient samples and 1000 CCLE cell line samples. To further address the informatic challenges of analyzing such large, diverse datasets, in addition to the GUI interfaces, we recently developed a chatbot through which users can analyze the RPPA data through human nature languages and obtain the results and related analytic reports without a learning curve. Such a chatbot empowers a broad research community to explore high-quality RPPA datasets and generate testable hypotheses in an effective and intuitive manner, representing the direction of next-generation data analytics. Citation Format: Han Liang, Wei Liu, Jun Li, Yitao Tang. The Cancer Proteome Atlas: Expanded datasets and empowered with an AI-driven chatbot [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4931.

HPV-negative head and neck cancers with adverse pathological features carry specific molecular changes that are associated with survival.

Adverse pathological features following surgery in head and neck squamous cell carcinoma (HNSCC) are strongly associated with survival and guide adjuvant therapy. We investigated molecular changes associated with these features. We downloaded data from the Cancer Genome Atlas and Cancer Proteome Atlas HNSCC cohorts. We compared tumors positive versus negative for perineural invasion (PNI), lymphovascular invasion (LVI), extracapsular spread (ECS), and positive margins (PSM), with multivariable analysis. All pathological features were associated with poor survival, as were the following molecular changes: low cyclin E1 (HR = 1.7) and high PKC-alpha (HR = 1.8) in tumors with PNI; six of 13 protein abundance changes with LVI; greater tumor hypoxia and high Raptor (HR = 2.0) and Rictor (HR = 1.6) with ECS; and low p38 (HR = 2.3), high fibronectin (HR = 1.6), low annexin A1 (HR = 3.1), and high caspase-9 (HR = 1.6) abundances with PSM. Pathological features in HNSCC carry specific molecular changes that may explain their poor prognostic associations.

The Genetic, Pharmacogenomic, and Immune Landscapes Associated with Protein Expression across Human Cancers.

Comprehensive characterization of the relationship between protein expression and the genetic, pharmacogenomic, and immune landscape of tumors across cancer types provides a foundation for investigating the role of protein expression in cancer development and treatment.

The Weight-Based Feature Selection (WBFS) Algorithm Classifies Lung Cancer Subtypes Using Proteomic Data.

Feature selection plays an important role in improving the performance of classification or reducing the dimensionality of high-dimensional datasets, such as high-throughput genomics/proteomics data in bioinformatics. As a popular approach with computational efficiency and scalability, information theory has been widely incorporated into feature selection. In this study, we propose a unique weight-based feature selection (WBFS) algorithm that assesses selected features and candidate features to identify the key protein biomarkers for classifying lung cancer subtypes from The Cancer Proteome Atlas (TCPA) database and we further explored the survival analysis between selected biomarkers and subtypes of lung cancer. Results show good performance of the combination of our WBFS method and Bayesian network for mining potential biomarkers. These candidate signatures have valuable biological significance in tumor classification and patient survival analysis. Taken together, this study proposes the WBFS method that helps to explore candidate biomarkers from biomedical datasets and provides useful information for tumor diagnosis or therapy strategies.

Functional Proteomic Profiling Analysis in Four Major Types of Gastrointestinal Cancers.

Gastrointestinal (GI) cancer accounts for one in four cancer cases and one in three cancer-related deaths globally. A deeper understanding of cancer development mechanisms can be applied to cancer medicine. Comprehensive sequencing applications have revealed the genomic landscapes of the common types of human cancer, and proteomics technology has identified protein targets and signalling pathways related to cancer growth and progression. This study aimed to explore the functional proteomic profiles of four major types of GI tract cancer based on The Cancer Proteome Atlas (TCPA). We provided an overview of functional proteomic heterogeneity by performing several approaches, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), t-stochastic neighbour embedding (t-SNE) analysis, and hierarchical clustering analysis in oesophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ) tumours, to gain a system-wide understanding of the four types of GI cancer. The feature selection approach, mutual information feature selection (MIFS) method, was conducted to screen candidate protein signature subsets to better distinguish different cancer types. The potential clinical implications of candidate proteins in terms of tumour progression and prognosis were also evaluated based on TCPA and The Cancer Genome Atlas (TCGA) databases. The results suggested that functional proteomic profiling can identify different patterns among the four types of GI cancers and provide candidate proteins for clinical diagnosis and prognosis evaluation. We also highlighted the application of feature selection approaches in high-dimensional biological data analysis. Overall, this study could improve the understanding of the complexity of cancer phenotypes and genotypes and thus be applied to cancer medicine.

Using feature selection and Bayesian network identify cancer subtypes based on proteomic data

The Cancer Proteome Atlas (TCPA) project collects reverse-phase protein arrays (RPPA)-based proteome datasets from nearly 8000 samples across 32 cancer types. This study aims to investigate the pan-cancer proteome signature and identify cancer subtypes of glioma, kidney cancer, and lung cancer based on TCPA data. We first visualized the tumor clustering models using t-distributed stochastic neighbour embedding (t-SNE) and bi-clustering heatmap. Then, three feature selection methods (pyHSICLasso, XGBoost, and Random Forest) were performed to select protein features for classifying cancer subtypes in training dataset, and the LibSVM algorithm was empolyed to test classification accuracy in the validation dataset. Clustering analysis revealed that different kinds of tumors have relatively distinct proteomic profiling based on tissue or origin. We identified 20, 10, and 20 protein features with the highest accuracies in classifying subtypes of glioma, kidney cancer, and lung cancer, respectively. The predictive abilities of the selected proteins were confirmed by receiving operating characteristic (ROC) analysis. Finally, the Bayesian network was utilized to explore the protein biomarkers that have direct causal relationships with cancer subtypes. Overall, we highlight the theoretical and technical applications of machine learning based feature selection approaches in the analysis of high-throughput biological data, particularly for cancer biomarker research. SignificanceFunctional proteomics is a powerful approach for characterizing cell signaling pathways and understanding their phenotypic effects on cancer development. The TCPA database provides a platform to explore and analyze TCGA pan-cancer RPPA-based protein expression. With the advent of the RPPA technology, the availability of high-throughput data in TCPA platform has made it possible to use machine learning methods to identify protein biomarkers and further differentiate subtypes of cancer based on proteomic data. In this study, we highlight the role of feature selection and Bayesian network in discovery protein biomarker for classifying cancer subtypes based on functional proteomic data. The application of machine learning methods in the analysis of high-throughput biological data, particularly for cancer biomarker researches, which have potential clinical values in developing individualized treatment strategies.

Abstract 5372: Supervised manifold learning and classification; application to expression data visualization and cancer prediction

Abstract Background: Manifold Learning (ML) has become essential in recent years for expression data visualization and dimension reduction. ML operates under the assumption that the data points lie on a manifold embedded in Euclidean space. The goal of ML is to learn an inverse embedding function to decipher the manifold structure. Cancer classification is a widely investigated application of expression profile data (e.g., miRNA) currently in the literature. The non-linear ML techniques commonly applied to expression data, such as t-distributed Stochastic Neighborhood Embedding (TSNE), Isomap, and Locally Linear Embedding (LLE), are intended for visualization, and cannot easily be applied to classification or unseen data, more generally. Such methods are also unsupervised, and do not factor in class information. Conventional linear ML methods, such as Principal Component Analysis (PCA), while applicable to unseen data and easy to use, are limited and can learn only linear manifolds. Results: We introduce a novel, non-linear ML technique, which incorporates class memberships, and is readily applicable to unseen data and classification. Specifically, we construct a series of neighborhood graphs which describe the manifold structure locally within each cluster and among the cluster centroids. Our objective function uses the neighborhood graph information to preserve the class separations and manifold structure in reduced dimension space, and we train a neural network to learn an explicit, inverse embedding function. This allows for fast visualization and classification of unseen data. The technique is compared against similar methods from the literature which use neural networks for ML, and is shown to offer improved cancer classification performance on expression data from The Cancer Genome Atlas (TCGA) and The Cancer Proteome Atlas (TCPA). Conclusion: We present a new, supervised ML technique designed specifically for classification, which can be efficiently applied to unseen data. The results show promise on multiple, large-scale expression data sets, and thus warrant further research into supervised ML methods for cancer classification and expression data visualization. Citation Format: James Webber, Kevin Elias. Supervised manifold learning and classification; application to expression data visualization and cancer prediction. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5372.

Identification of acetyl-CoA carboxylase alpha as a prognostic and targeted candidate for hepatocellular carcinoma.

The de novo lipogenesis has been a longstanding observation in hepatocellular carcinoma (HCC). However, the prognostic value and carcinogenic roles of the enzyme Acetyl-CoA carboxylase alpha (ACACA) in HCC remains unknown. The proteins with remarkable prognostic significance were screened out from The Cancer Proteome Atlas Portal (TCPA) database. Furthermore, the expression characteristics and prognostic value of ACACA were evaluated in multiple databases and the local HCC cohort. The loss-of-function assays were performed to uncover the potential roles of ACACA in steering malignant behaviors of HCC cells. The underlying mechanisms were conjectured by bioinformatics and validated in HCC cell lines. ACACA was identified as a crucial factor of HCC prognosis. Bioinformatics analyses showed that HCC patients with higher expression of ACACA protein or mRNA levels had poor prognosis. Knockdown of ACACA remarkably crippled the proliferation, colony formation, migration, invasion, epithelial-mesenchymal transition (EMT) process of HCC cells and induced the cell cycle arrest. Mechanistically, ACACA might facilitate the malignant phenotypes of HCC through aberrant activation of Wnt/β-catenin signaling pathway. In addition, ACACA expression was associated with the dilute infiltration of immune cells including plasmacytoid DC (pDC) and cytotoxic cells by utilization of relevant database analysis. ACACA could be a potential biomarker and molecular target for HCC.

AXL upregulates c‑Myc expression through AKT and ERK signaling pathways in breast cancers.

Breast cancer (BC) is common worldwide. c-Myc and AXL are both overexpressed in BC, promoting its progression. The present study aimed to investigate the role of AXL in c-Myc expression in BC. Overexpression of AXL increased c-Myc expression while knockdown of AXL decreased c-Myc expression as determined by western blot analysis. Pharmaceutical inhibition of AXL also suppressed c-Myc expression. AKT and ERK inhibitor LY294002 and U0126 suppressed c-Myc expression, respectively. AXL overexpression which activates AKT and ERK signaling, upregulates c-Myc expression, while kinase-dead AXL which cannot activate AKT and ERK signaling, does not upregulate c-Myc expression, emphasizing the important role of these two signaling pathways in c-Myc upregulation. Finally, expression data of BC tissues from The Cancer Proteome Atlas displayed an association between AXL and c-Myc. Taken together, the present study revealed that AXL upregulates c-Myc expression through AKT and ERK signaling pathways in BC.

Tumor Androgen Receptor Protein Level Is Positively Associated with a Better Overall Survival in Melanoma Patients.

Androgen receptor (AR) is expressed in numerous tissues and serves important biologic functions in skin, prostate, immune, cardiovascular, and neural systems, alongside sexual development. Several studies have associated AR expression and patient survival in various cancers, yet there are limited studies examining the relationship between AR expression and cutaneous melanoma. This study used genomics and proteomics data from The Cancer Proteome Atlas (TCPA) and The Cancer Genome Atlas (TCGA), with 470 cutaneous melanoma patient data points. Cox regression analyses evaluated the association between AR protein level with overall survival and revealed that a higher level of AR protein was positively associated with a better overall survival (OS) (p = 0.003). When stratified by sex, the AR association with OS was only significant for both sexes. The multivariate Cox models with justifications of sex, age of diagnosis, stage of disease, and Breslow depth of the tumor confirmed the AR-OS association in all patients. However, the significance of AR was lost when ulceration was included in the model. When stratified by sex, the multivariate Cox models indicated significant role of AR in OS of female patients but not in males. AR-associated genes were identified and enrichment analysis revealed shared and distinct gene network in male and female patients. Furthermore, AR was found significantly associated with OS in RAS mutant subtypes of melanoma but not in BRAF, NF1, or triple-wild type subtypes of melanoma. Our study may provide insight into the well-known female survival advantage in melanoma patients.

Construction and validation of a protein-associated prognostic model for gastrointestinal cancer.

This paper aims to identify the hub prognosis-related proteins (PAPs) and construct a prognosis model to GIC patients for clinical application. Protein expression data of GIC were obtained from The Cancer Proteome Atlas (TCPA) and downloaded the clinicopathological data from The Cancer Genome Atlas database (TCGA). Besides, hub proteins were filtrated out via univariate and multivariate Cox regression analysis. Moreover, survival analysis and nomogram were used to predict overall survival (OS). We used the calibration curves to assess the predictive and actual survival rate consistency. The consistency index (C-index) was used to evaluate the prognostic ability of the predictive model. Furthermore, functional enrichment analysis and protein co-expression of PAPs were used to explore their roles in GIC. Finally, a prognosis model was conducted based on ten PAPs (CYCLIND1, DVL3, NCADHERIN, SYK, ANNEXIN VII, CD20, CMET, RB, TFRC, and PREX1). The risk score calculated by the model was an independent prognostic predictor. Compared with the high-risk subgroup, the low-risk subgroup had better OS. In the TCGA cohort, the area under the curve value of the receiver operating characteristic curve of the prognostic model was 0.692. The expression of proteins and risk score had a significant association with the clinicopathological characteristics of GIC. Besides, nomogram based on GIC clinicopathological feature and risk score could properly predict the OS of individual GIC patients. The C-index is 0.71 in the TCGA cohort and 0.73 in the GEO cohort, respectively. The results indicate that the risk score is an independent prognostic biomarker and is related to the malignant clinical features of GIC patients. Besides, several PAPs, associated with the survival and clinicopathological characteristics of GIC might be potential biomarkers for GIC diagnosis and treatment.

Machine learning multi-omics analysis reveals cancer driver dysregulation in pan-cancer cell lines compared to primary tumors

Cancer cell lines have been widely used for decades to study biological processes driving cancer development, and to identify biomarkers of response to therapeutic agents. Advances in genomic sequencing have made possible large-scale genomic characterizations of collections of cancer cell lines and primary tumors, such as the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA). These studies allow for the first time a comprehensive evaluation of the comparability of cancer cell lines and primary tumors on the genomic and proteomic level. Here we employ bulk mRNA and micro-RNA sequencing data from thousands of samples in CCLE and TCGA, and proteomic data from partner studies in the MD Anderson Cell Line Project (MCLP) and The Cancer Proteome Atlas (TCPA), to characterize the extent to which cancer cell lines recapitulate tumors. We identify dysregulation of a long non-coding RNA and microRNA regulatory network in cancer cell lines, associated with differential expression between cell lines and primary tumors in four key cancer driver pathways: KRAS signaling, NFKB signaling, IL2/STAT5 signaling and TP53 signaling. Our results emphasize the necessity for careful interpretation of cancer cell line experiments, particularly with respect to therapeutic treatments targeting these important cancer pathways.

Large-Scale Proteomics Data Reveal Integrated Prognosis-Related Protein Signatures and Role of SMAD4 and RAD50 in Prognosis and Immune Infiltrations of Prostate Cancer Microenvironment.

The online version contains supplementary material available at 10.1007/s43657-022-00070-1.

Construction and validation of a prognostic risk model for breast cancer based on protein expression

Breast cancer (BRCA) is the primary cause of mortality among females globally. The combination of advanced genomic analysis with proteomics characterization to construct a protein prognostic model will help to screen effective biomarkers and find new therapeutic directions. This study obtained proteomics data from The Cancer Proteome Atlas (TCPA) dataset and clinical data from The Cancer Genome Atlas (TCGA) dataset. Kaplan–Meier and Cox regression analyses were used to construct a prognostic risk model, which was consisted of 6 proteins (CASPASE7CLEAVEDD198, NFKBP65-pS536, PCADHERIN, P27, X4EBP1-pT70, and EIF4G). Based on risk curves, survival curves, receiver operating characteristic curves, and independent prognostic analysis, the protein prognostic model could be viewed as an independent factor to accurately predict the survival time of BRCA patients. We further validated that this prognostic model had good predictive performance in the GSE88770 dataset. The expression of 6 proteins was significantly associated with the overall survival of BRCA patients. The 6 proteins and encoding genes were differentially expressed in normal and primary tumor tissues and in different BRCA stages. In addition, we verified the expression of 3 differential proteins by immunohistochemistry and found that CDH3 and EIF4G1 were significantly higher in breast cancer tissues. Functional enrichment analysis indicated that the 6 genes were mainly related to the HIF-1 signaling pathway and the PI3K-AKT signaling pathway. This study suggested that the prognosis-related proteins might serve as new biomarkers for BRCA diagnosis, and that the risk model could be used to predict the prognosis of BRCA patients.

The Prediction of a 3-Protein-Based Model on the Prognosis of Head and Neck Squamous Cell Carcinoma.

Background Head and neck squamous cell carcinoma (HNSCC) is one of the commonest malignant tumors. Using high-throughput genomic methods, RNA-based diagnostic and prognostic models for HNSCC with potential clinical value have been developed. However, the clinical utility and reproducibility of these models are uncertain. Because the complex regulatory processes occurring after mRNA is transcribed, the abundance of proteins in a cell can never be fully predicted or explained by their corresponding mRNA expression. We aimed to assume and verify a novel protein signature for checking the HNSCC patients' prognosis. Methods The functional proteomic data of 332 HNSCC cases were collected from The Cancer Proteome Atlas (TCPA), and the related follow-up and clinical data were acquired from The Cancer Genome Atlas (TCGA). This study adopted multivariate and univariate Cox regression analysis, Akaike Information Criterion, receiver operating characteristic (ROC) analysis, and Kaplan-Meier method. Results Patients' clinical features in both sets were comparable (all, P > 0.05). The area under the ROC curve (AUC) for the 3-protein signature (X4EBP1_pT37T46, HER3_pY1289, and NF2) in the test set was 0.655 and in the combined cohort (all 332 patients combined) was 0.699. In addition, the 3-protein signature exhibited better predictive value for the survival of HNSCC patients as in comparison with conventional clinical factors like age, gender, tumor stage, and smoking history (TNM stage). Conclusion The 3-protein signature developed in this study exhibits good performance in predicting the overall survival of with HNSCC patients. The 3-protein signature exhibited better predictive value for survival than conventional clinical factors just like gender, TNM stage, smoking history, and age.

Comprehensive analysis of novel prognosis-related proteomic signature effectively improve risk stratification and precision treatment for patients with cervical cancer.

Cervical cancer (CC) is one of the most common types of malignant female cancer, and its incidence and mortality are not optimistic. Protein panels can be a powerful prognostic factor for many types of cancer. The purpose of our study was to investigate a proteomic panel to predict the survival of patients with common CC. The protein expression and clinicopathological data of CC were downloaded from The Cancer Proteome Atlas and The Cancer Genome Atlas database, respectively. We selected the prognosis-related proteins (PRPs) by univariate Cox regression analysis and found that the results of functional enrichment analysis were mainly related to apoptosis. We used Kaplan-Meier analysis and multivariable Cox regression analysis further to screen PRPs to establish a prognostic model, including BCL2, SMAD3, and 4EBP1-pT70. The signature was verified to be independent predictors of OS by Cox regression analysis and the area under curves. Nomogram and subgroup classification were established based on the signature to verify its clinical application. Furthermore, we looked for the co-expressed proteins of three-protein panel as potential prognostic proteins. A proteomic signature independently predicted OS of CC patients, and the predictive ability was better than the clinicopathological characteristics. This signature can help improve prediction for clinical outcome and provides new targets for CC treatment.