Clinical Proteomic Tumor Analysis Consortium Research Articles

Circular RNA as a source of neoantigens for cancer vaccines

BackgroundThe effectiveness of somatic neoantigen-based immunotherapy is often hindered by the limited number of mutations in tumors with low to moderate mutation burden. Focusing on microsatellite-stable colorectal cancer (CRC), this...

Image-Based Subtype Classification for Glioblastoma Using Deep Learning: Prognostic Significance and Biologic Relevance.

To apply deep learning algorithms to histopathology images, construct image-based subtypes independent of known clinical and molecular classifications for glioblastoma, and produce novel insights into molecular and immune characteristics of the glioblastoma tumor microenvironment. Using whole-slide hematoxylin and eosin images from 214 patients with glioblastoma in The Cancer Genome Atlas (TCGA), a fine-tuned convolutional neural network model extracted deep learning features. Biclustering was used to identify subtypes and image feature modules. Prognostic value of image subtypes was assessed via Cox regression on survival outcomes and validated with 189 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set. Morphological, molecular, and immune characteristics of glioblastoma image subtypes were analyzed. Four distinct subtypes and modules (imClust1-4) were identified for the TCGA patients with glioblastoma on the basis of the image feature data. The glioblastoma image subtypes were significantly associated with overall survival (OS; P = .028) and progression-free survival (P = .003). Apparent association was also observed for disease-specific survival (P = .096). imClust2 had the best prognosis for all three survival end points (eg, after 25 months, imClust2 had >7% surviving patients than the other subtypes). Examination of OS in the external validation using the unseen CPTAC data set showed consistent patterns. Multivariable Cox analyses confirmed that the image subtypes carry unique prognostic information independent of known clinical and molecular predictors. Molecular and immune profiling revealed distinct immune compositions of the tumor microenvironment in different image subtypes and may provide biologic explanations for the patterns in patients' outcomes. Our image-based subtype classification on the basis of deep learning models is a novel tool to refine risk stratification in cancers. The image subtypes detected for glioblastoma represent a promising prognostic biomarker with distinct molecular and immune characteristics and may facilitate developing novel, individualized immunotherapies for glioblastoma.

CLCN3 in mediating the proliferation of human ovarian cancer cells.

Chloride channel-3 (CLCN3), a crucial component of the voltage-gated chloride channel family, is implicated in numerous physiological and pathophysiological processes. This study aimed to investigate the characteristics of CLCN3 in pancancer and its influence on the immune response through the use of a range of databases. Concurrently, we assessed the impact of CLCN3 on the proliferation of ovarian cancer (OC) cells and explored its potential mechanisms. We employed the Tumor Immune Estimation Resource (TIMER) 2.0 and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases to examine the messenger RNA (mRNA) and the protein expression of CLCN3 across various cancers. The prognostic significance of CLCN3 was evaluated using the Gene Expression Profiling Interactive Analysis 2.0 (GEPIA 2.0) database. The University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN) facilitated the analysis of CLCN3 promoter methylation levels. The association between CLCN3 expression and tumor-infiltrating immune cells was investigated using various algorithms. The cBioportal database facilitated the analysis of CLCN3 mutations and mutation sites across various cancers. The Tumor-Immune System Interactions Database (TISIDB) database was employed to explore the correlation between CLCN3 expression and immune or molecular subtypes across a variety of cancer types. We collected ovarian tissue samples, encompassing both normal ovarian and OC tissues. The human OC cell lines, SKOV3 cells and OVCAR433 cells, were cultured. CLCN3 expression was determined via reverse-transcription quantitative polymerase chain reaction (RT-qPCR), while phosphatidylinositol 3-kinase/Akt kinase (PI3K/AKT) expression was detected using Western blot. We utilized small interfering RNA (siRNA) technology to suppress CLCN3 expression. The proliferative capacity of SKOV3 and OVCAR433 cells was assessed using the Cell Counting Kit 8 (CCK-8) assay. CLCN3 demonstrated an aberrant expression in a number of cancer types and was markedly reduced in OC tissues. Poor prognosis in cervical squamous cell cancer and myeloid leukemia was linked to excessive expression of CLCN3. The examination of immune cell infiltration, which included CD8+ T cells, B cells, T regulatory cells, and cancer-associated fibroblast cells, showed a strong association with aberrant CLCN3 expression. Following the use of siRNA technology, the ability of the ovarian carcinoma cell line SKOV3 and OVCAR433 to proliferate as well as the expression of PI3K/AKT both increased. CLCN3 is a possible biomarker for immune-related processes and the prognosis of cancer, and the PI3K/AKT signaling pathway may affect OC cells' ability to proliferate.

Pathogenic Roles for RNASET2 in Clear Cell Renal Cell Carcinoma

A specific splicing isoform of RNASET2 is associated with worse oncologic outcomes in clear cell renal cell carcinoma (ccRCC). However, the interplay between wild-type RNASET2 and its splice variant and how this might contribute to the pathogenesis of ccRCC remains poorly understood. We sought to better understand the relationship of RNASET2 in the pathogenesis of ccRCC and the interplay with a pathogenic splicing isoform (RNASET2-SV) and the tumor immune microenvironment. Using data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium, we correlated clinical variables to RNASET2 expression and the presence of a specific RNASET2-SV. Immunohistochemical staining with matched RNA sequencing of ccRCC patients was then utilized to understand the spatial relationships of RNASET2 with immune cells. Finally, in vitro studies were performed to demonstrate the oncogenic role of RNASET2 and highlight its potential mechanisms. RNASET2 gene expression is associated with higher grade tumors and worse overall survival in The Cancer Genome Atlas cohort. The presence of the RNASET2-SV was associated with increased expression of the wild-type RNASET2 protein and epigenetic modifications of the gene. Immunohistochemical staining revealed increased intracellular accumulation of RNASET2 in patients with increased RNA expression of RNASET2-SV. In vitro experiments reveal that this accumulation results in increased cell proliferation, potentially from altered metabolic pathways. RNASET2 exhibits a tumor-promoting role in the pathogenesis of ccRCC that is increased in the presence of a specific RNASET2-SV and associated with changes in the cellular localization of the protein.

CCDC71L as a novel prognostic marker and immunotherapy target via lipid metabolism in head and neck squamous cell carcinoma

BackgroundHead and neck squamous cell carcinoma (HNSCC) is the sixth most widespread cancer globally with high rate and poor prognosis. Coiled-coil domain containing 71 like (CCDC71L) exerts an important role in cellular lipid metabolic process. However, its function in HNSCC remains unclear. To this end, we examined the CCDC71L implications for prognosis and tumor microenvironment in HNSCC. Materials and methodsFirst, CCDC71L expression was explored through The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA), the Gene Expression Profiling Interactive Analysis 2 (GEPIA2), Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Gene Expression Omnibus (GEO) databases. The clinicpathological information were obtained from the dataset of TCGA. The Kaplan–Meier Plotter databases and Cox model were performed for the determination of prognostic values of CCDC71L, including the overall survival (OS), progress free interval (PFI), recurrence-free survival (RFS) and disease specific survival (DSS). Then, the potential mechanism of CCDC71L in HNSCC development was elucidated by means of Gene set enrichment analysis (GSEA) and Metascape databases. Furthermore, the relevance of CCDC71L to immune cells infiltration and immune checkpoints was assessed. The correlations among CCDC71L expression, mutational landscape and genome heterogeneity [mutant-allele tumor heterogeneity (MATH) and tumor purity] were detected by the data in TCGA. ResultsCCDC71L expression was significantly upregulated in HNSCC, and positively associated with age, gender and N stage. Higher CCDC71L expression resulted in poor OS, RFS, DSS and PFI. Multivariate Cox regression analysis showed CCDC71L would be an independent prognostic marker in patients with HNSCC. Moreover, CCDC71L and the level of macrophage and neutrophil cells infiltration were significantly correlated in HNSCC. High expression of CCDC71L was related to immune checkpoint genes, oncogene mutations and genome heterogeneity markers. ConclusionThese results implied that CCDC71L plays vital roles in HNSCC progression, which could be used as a underlying biomarker for the diagnosis and prognosis of HNSCC. Meanwhile, CCDC71L participates in immune regulation, which has a potential value for the immunotherapy of HNSCC patients.

Metabolism pathway-based subtyping in endometrial cancer: An integrated study by multi-omics analysis and machine learning algorithms

Endometrial cancer (EC), the second most common malignancy in the female reproductive system, has garnered increasing attention for its genomic heterogeneity, but understanding of its metabolic characteristics is still poor. We explored metabolic dysfunctions in EC through comprehensive multi-omics analysis (RNA-seq datasets from The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), and GEO datasets; the Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteomics; CCLE metabolomics) to develop useful molecular targets for precision therapy. Unsupervised consensus clustering was performed to categorize EC patients into three metabolism-pathways-based subgroups (MPS). These MPS subgroups had distinct clinical prognoses, transcriptomic and genomic alterations, immune microenvironment landscape, and unique patterns of chemotherapy sensitivity. Moreover, the MPS2 subgroup has a better response to immunotherapy. Finally, three machine learning algorithms (LASSO, random forest, and stepwise multivariate Cox regression) were used for developing a prognostic “Metagene” signature based on metabolic molecules. Thus, a thirteen-hub-gene-based classifier was constructed to predict patients’ MPS subtype offering a more accessible and practical approach. This metabolism-based classification system can potentially enhance prognostic predictions and guide clinical strategies for immunotherapy and metabolism-targeted therapy in EC.

Cysteine induces mitochondrial reductive stress in glioblastoma through hydrogen peroxide production

Glucose and amino acid metabolism are critical for glioblastoma (GBM) growth, but little is known about the specific metabolic alterations in GBM that are targetable with FDA-approved compounds. To investigate tumor metabolism signatures unique to GBM, we interrogated The Cancer Genome Atlas for alterations in glucose and amino acid signatures in GBM relative to other human cancers and found that GBM exhibits the highest levels of cysteine and methionine pathway gene expression of 32 human cancers. Treatment of patient-derived GBM cells with the FDA-approved single cysteine compound N-acetylcysteine (NAC) reduced GBM cell growth and mitochondrial oxygen consumption, which was worsened by glucose starvation. Normal brain cells and other cancer cells showed no response to NAC. Mechanistic experiments revealed that cysteine compounds induce rapid mitochondrial H2O2 production and reductive stress in GBM cells, an effect blocked by oxidized glutathione, thioredoxin, and redox enzyme overexpression. From analysis of the clinical proteomic tumor analysis consortium (CPTAC) database, we found that GBM cells exhibit lower expression of mitochondrial redox enzymes than four other cancers whose proteomic data are available in CPTAC. Knockdown of mitochondrial thioredoxin-2 in lung cancer cells induced NAC susceptibility, indicating the importance of mitochondrial redox enzyme expression in mitigating reductive stress. Intraperitoneal treatment of mice bearing orthotopic GBM xenografts with a two-cysteine peptide induced H2O2 in brain tumors in vivo. These findings indicate that GBM is uniquely susceptible to NAC-driven reductive stress and could synergize with glucose-lowering treatments for GBM.

Quantification of eosinophilic area and its potential molecular feature in clear cell renal cell carcinoma.

Previous studies have acknowledged the presence of eosinophilic cytoplasm in clear cell renal cell carcinoma, yet the precise quantification method and potential molecular attributes in clear cell renal cell carcinoma remain elusive. This study endeavours to precisely quantify the eosinophilic attribute and probe into the molecular mechanisms governing its presence in clear cell renal cell carcinoma. Data from cohorts of clear cell renal cell carcinoma patients who underwent nephrectomy, comprising The Cancer Genome Atlas cohort (n=475) and Sun Yat-sen University Cancer Center cohort (n=480), were aggregated to assess the eosinophilic attribute. Additionally, Omics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) (n=58) were leveraged to explore the potential molecular features associated with eosinophilic clear cell renal cell carcinoma. Employing receiver operating characteristic curve analysis, the proportion of tumour cells with eosinophilic cytoplasm was determined, leading to the classification of each cohort into distinct groups: a clear group (<5%) and an eosinophilic group (≥5%). In both cohorts, the eosinophilic feature consistently correlated with higher International Society of Urological Pathology (ISUP) grade, elevated tumor stage, and the presence of necrosis. Furthermore, the Kaplan-Meier method demonstrated that patients in the eosinophilic group exhibited shorter overall survival or disease-free survival compared with those in the clear group, a pattern reaffirmed in various stratified survival analyses. Intriguingly, within The Cancer Genome Atlas cohort, the pathological characterization of cell cytoplasm (eosinophilic vs. clear) emerged as an independent risk factor for overall survival (hazard ratio = 2.507 [95% confidence interval: 1.328-4.733], P = 0.005) or disease-free survival (hazard ratio = 1.730 [95% confidence interval: 1.062-2.818], P = 0.028) via Cox regression analysis. Moreover, multi-Omics data unveiled frequent BAP1 mutations and down-regulation of Erythroblast Transformation-Specific-Related Gene associated with the eosinophilic feature in clear cell renal cell carcinoma. Additionally, patients with low expression of Erythroblast Transformation-Specific-Related Gene showed worse overall survival (P < 0.001). The quantification of the eosinophilic feature serves as a robust predictor of clinical prognosis in clear cell renal cell carcinoma. Furthermore, the manifestation of this feature may be linked to BAP1 mutations and the down-regulation of Erythroblast Transformation-Specific-Related Gene in clear cell renal cell carcinoma. Significantly, the expression levels of Erythroblast Transformation-Specific-Related Gene manifest as an exemplary prognostic marker, providing exceptional predictive accuracy for the clinical prognosis in clear cell renal cell carcinoma.

Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution

Tumor deconvolution enables the identification of diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold-standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA sequencing) rather than protein levels. Despite the popularity of gene expression datasets, protein levels often provide a more accurate view of rare cell types. To facilitate the use, development, and reproducibility of multiomic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic datasets. Decomprolute incorporates the large-scale multiomic datasets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. http://pnnl-compbio.github.io/decomprolute.

Integrated Proteogenomic Analysis Reveals Distinct Potentially Actionable Therapeutic Vulnerabilities in Triple-Negative Breast Cancer Subtypes

Triple-negative breast cancer (TNBC) is characterized by an aggressive clinical presentation and a paucity of clinically actionable genomic alterations. Here, we utilized the Cancer Genome Atlas (TCGA) to explore the proteogenomic landscape of TNBC subtypes to see whether genomic alterations can be inferred from proteomic data. We found only 4% of the protein level changes are explained by mutations, while 21% of the protein and 35% of the transcriptomics changes were determined by copy number alterations (CNAs). We found tighter coupling between proteome and genome in some genes that are predicted to be the targets of drug inhibitors, including CDKs, PI3K, tyrosine kinase (TKI), and mTOR. The validation of our proteogenomic workflow using mass spectrometry Clinical Proteomic Tumor Analysis Consortium (MS-CPTAC) data also demonstrated the highest correlation between protein–RNA–CNA. The integrated proteogenomic approach helps to prioritize potentially actionable targets and may enable the acceleration of personalized cancer treatment.

Identification and Analysis of SND1 as an Oncogene and Prognostic Biomarker for Lung Adenocarcinoma

Transcription factor (TF) can bind specific sequences that either promotes or represses the transcription of target genes, and exerts important effects on tumorigenesis, migration, invasion. Staphylococcal nuclease-containing structural domain 1 (SND1), which is a transcriptional co-activator, is considered as a promising target for tumor therapy. However, its role in lung adenocarcinoma (LUAD) remains unclear. This study aims to explore the role of SND1 in LUAD. Data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Human Protein Atlas (HPA) database was obtained to explore the association between SND1 and the prognosis, as well as the immune cell infiltration, and subcellular localization in LUAD tissues. Furthermore, the functional role of SND1 in LUAD was verified in vitro. EdU assay, CCK-8 assay, flow cytometry, scratch assay, Transwell assay and Western blot were performed. SND1 was found to be upregulated and high expression of SND1 is correlated with poor prognosis of LUAD patients. In addition, SND1 was predominantly present in the cytoplasm of LUAD cells. Enrichment analysis showed that SND1 was closely associated with the cell cycle, as well as DNA replication, and chromosome segregation. Immune infiltration analysis showed that SND1 was closely associated with various immune cell populations, including T cells, B cells, cytotoxic cells and dendritic cells. In vitro studies demonstrated that silencing of SND1 inhibited cell proliferation, invasion and migration of LUAD cells. Besides, cell cycle was blocked at G1 phase by down-regulating SND1. SND1 might be an important prognostic biomarker of LUAD and may promote LUAD cells proliferation and migration.

Stabilizing the Proteomes of Acute Myeloid Leukemia Cells: Implications for Cancer Proteomics

Previous work has shown that inhibition of abundant myeloid azurophil granule-associated serine proteases (ELANE [neutrophil elastase], PRTN3 [protease 3], and CTSG [Cathepsin G]) is required to stabilize some proteins in myeloid cells. We therefore hypothesized that effective inhibition of these proteases may be necessary for quantitative proteomic analysis of samples containing myeloid cells. To test this hypothesis, we thawed viably preserved acute myeloid leukemia cells from cryovials in the presence or the absence of diisopropyl fluorophosphate (DFP), a cell-permeable and irreversible serine protease inhibitor. Global proteomic analysis was performed, using label-free and isobaric peptide-labeling quantitation. The presence of DFP resulted in an increase of tryptic peptides (14–57%) and proteins (9–31%). In the absence of DFP, 11 to 31% of peptide intensity came from nontryptic peptides; 52 to 75% had cleavage specificity consistent with activities of ELANE–PRTN3. Treatment with DFP reduced the intensity of nontryptic peptides to 4-8% of the total. ELANE inhibition was 95%, based on diisopropyl phosphate modification of active site serine residue. Overall, the relative abundance of 20% of proteins was significantly altered by DFP treatment. These results suggest that active myeloid serine proteases, released during sample processing, can skew quantitative proteomic measurements. Finally, significant ELANE activity was also detected in Clinical Proteomics Tumor Analysis Consortium datasets of solid tumors (many of which have known myeloid infiltration). In the pancreatic cancer dataset, the median percentage of nontryptic intensity detected across patient samples was 34%, with many patient samples having more than half of their detected peptide intensity from nontryptic cleavage events consistent with ELANE-PRTN3 cleavage specificity. Our study suggests that in vitro cleavage of proteins by myeloid serine proteases may be relevant for proteomic studies of any tumor that contains infiltrating myeloid cells.

Population-based high-dimensional analyses identify multiple intrinsic characters for cancer vaccines against lung squamous cell carcinoma.

In lung squamous cell carcinoma (LUSC), current cancer vaccines show promising effects, despite a lack of benefit for a large number of patients. We first identified the tumor antigens into shared and private antigens, and determined the population by clustering analysis in public datasets. For vaccine development, The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were collected. WGCNA method was furthermore applied to construct a consensus gene co-expression network based on TCGA and CPTAC datasets. The main analyses in bulk sequencing included survival, clinical features, tumor microenvironment (TME), and pathways enrichment. In addition, single-cell RNA (scRNA) analysis of cancer epithelium dissected consensus subtype. We identified the ideal population for cancer vaccines, and candidate neoantigens including AOC1, COL5A2, LGI2, and POSTN. According to subtype analysis, Lung squamous 1 (LSQ1) type exhibited a higher tumor mutational load (TMB) and copy number but no immune infiltration, whereas lung squamous 2 (LSQ2) tumors had a higher global methylation level and more fibroblasts but had less stemness. Meanwhile, trajectory analysis further revealed that the evolution of TME influenced prognosis. We emphasized specific pathways or targets with the potential for combination immunotherapy by consensus network and single-cell RNA analyses. Anti-androgen therapy has been validated in vitro experiments of LUSC as proof of concept. In conclusion,LSQ1 was linked to immune exclusion and might be utilized for vaccination, while LSQ2 was linked to immune dysfunction and could be used for programmed cell death protein 1 (PD1) blocking therapy.

Systematic characterization of m6A proteomics across 12 cancer types: a multi-omics integration study.

The modification patterns of N6-methyladenosine (m6A) regulators and interacting genes are deeply involved in tumors. However, the effect of m6A modification patterns on human proteomics remains largely unknown. We evaluated the molecular characteristics and clinical relevance of m6A modification proteomics patterns among 1013 pan-cancer samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). More than half of the m6A proteins were expressed at higher levels in tumor tissues and presented oncogenic characteristics. Furthermore, we performed multi-omics analyses integrating with transcriptomics data of m6A regulators and interactive coding and non-coding RNAs and developed a m6A multi-omics signature to identify potential m6A modification target proteins across global proteomics. It was significantly associated with overall survival in nine cancer types, tumor mutation burden (P = 0.01), and immune checkpoints including PD-L1 (P = 4.9 × 10-8) and PD-1 (P < 0.01). We identified 51 novel proteins associated with the multi-omics signature (PFDR < 0.05). These proteins were functional through pathway enrichment analyses. The protein with the highest hit frequency was CHORDC1, which was significantly up-regulated in tumor tissues in nine cancer types. Its higher abundance was significantly associated with a poorer prognosis in seven cancer types. The identified m6A target proteins might provide infomation for the study of molecular mechanism of cancer.

A pan-cancer analysis to determine the prognostic analysis and immune infiltration of HSPA5.

Heat shock 70kDa protein 5 (HSPA5), also known as GRP78, is widely expressed in most malignant cells and has been shown to have a significant role in the spread of most malignancies by transferring them to the cell membrane. High-level HSPA5 may serve as an independent prognostic marker for various malignancies due to its ability to accelerate tumor growth and migration, inhibit cell apoptosis and closely connect to prognosis. Therefore, it is crucial to examine HSPA5 using pan-cancer research, which might result in the discovery of novel cancer treatment targets. The GTEx and TCGA databases have both provided evidence of the expression of various amounts of HSPA5 in various tissues. The Clinical Proteomics Tumor Analysis Consortium (CPTAC) evaluated the levels of HSPA5 protein expression, while qPCR investigations also evaluated the expression of HSPA5 mRNA in certain tumors. HSPA5 was studied using the Kaplan-Meier method to examine how it influences overall survival and disease-free survival in malignancies. GEPIA2 was used to investigate the correlation between HSPA5 expression and the clinical stage of cancer. The tumor-immune system interaction database (TISIDB) examined the expression of HSPA5 in association with molecular and tumor immune subtypes. The co-expressed genes of HSPA5 were extracted from the STRING database, and the top 5 co-expressed genes of HSPA5 in 33 cancers were identified using the TIMER database. Further research examined the relationship between tumor mutations and HSPA5. Microsatellite Instability (MSI) and Tumor Mutation Burden (TMB) were the primary areas of interest. The association between HSPA5 mRNA expression and immune infiltration was also explored using the TIMER database. Additionally, through the Linkedomics database, we examined the enrichment of GO and KEGG for HSPA5 in glioblastoma. Finally, the Cluster Analyzer tool was used to carry out a GSEA functional enrichment investigation. HSPA5 mRNA expression was found to be greater in all 23 tumor tissues than in the equivalent normal tissues, and high HSPA5 expression appeared to be strongly related to a poor prognosis in the majority of cancers, as observed by survival plots. In the tumour clinical stage display map, HSPA5 showed differential expression in most tumours. HSPA5 is strongly associated with Tumor Mutation Burden (TMB) and Microsatellite Instability (MSI). Cancer-associated Fibroblasts (CAFs) infiltration was strongly associated with HSPA5, as were nine immunological subtypes of malignancy and seven molecular subtypes of malignancy. According to the results of GO and KEGG enrichment analyses, HSPA5 in GBM is mostly involved in neutrophil-mediated immunological and collagen metabolic activities. Additionally, GSEA enrichment analyses of HSPA5 and associated genes demonstrated a substantial link between HSPA5 and the immunological milieu of tumors, cell division and nervous system regulation. By using qPCR, we were able to further corroborate the enhanced expression in the GBM, COAD, LUAD and CESC cell lines. Our bioinformatics research leads us to hypothesize that HSPA5 may be involved in immune infiltration as well as tumor growth and progression. Additionally, it was found that differentially expressed HSPA5 is linked to a poor prognosis for cancer, with the neurological system, the tumor immunological microenvironment and cytokinesis being potential contributing factors. As a result, HSPA5 mRNA and the associated protein might be used as therapeutic targets and possible prognostic markers for a range of malignancies.

Pan-cancer analysis revealing the multidimensional expression and prognostic and immunologic roles of TGFB1 in cancer.

This study aimed to perform an integrated pan-cancer analysis to characterize the expression patterns, prognostic value, genetic alterations, and immunologic roles of transforming growth factor beta 1 (TGFB1) across diverse human cancer types. Bioinformatics analyses were conducted using multiple public databases including The Cancer Genome Atlas, Genotype-Tissue Expression, Clinical Proteomic Tumor Analysis Consortium, TIMER2, GEPIA2, cBioPortal, StringDB, and others. Differential expression, survival, immune correlation, and protein interaction network analyses were performed. TGFB1 was overexpressed in several tumor types compared with that in normal tissues. High TGFB1 expression was associated with an advanced stage and poorer prognosis in certain cancers. TGFB1 mutations occurred in 1.3% of 10,967 cases surveyed. TGFB1 expression correlated with tumor-infiltrating immune cells and immunotherapy-related genes. This comprehensive multi-omics analysis revealed the complex expression and prognostic landscape of TGFB1 across cancers. TGFB1 is emerging as a potential immunotherapeutic target in certain contexts. Further research should elucidate its multifaceted tumor-promoting and tumor-suppressive mechanisms. Our pan-cancer analysis provides new insights into TGFB1 as a prognostic biomarker and immunotherapeutic target in human cancers, and our findings may guide future preclinical and clinical investigations of TGFB1-directed therapies.

Molecular Expression and Prognostic Implications of Krüppel-Like Factor 3 (KLF3) in Clear Cell Renal Cell Carcinoma.

A major subtype of renal cancer is clear cell renal cell carcinoma (ccRCC). Krüppel-like factor 3 (KLF3) dysfunction is also revealed leading to poor prognosis in multiple cancer types. However, dysregulation and molecular dynamics of KLF3 underlying ccRCC progression still remains elusive. Here KLF3 gene and protein expressions in ccRCC were explored using data cohorts from The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA), Clinical Proteomic Tumor Analysis Consortium (CPTAC) and verified them in our patient cohort. Correlations of KLF3 expression with clinicopathological features, epigenetic modification, and immune microenvironment characteristics were further investigated. KLF3 was significantly down-regulated expressed in ccRCC tissues compared to adjacent normal controls. Adverse pathological parameters and poor prognosis were associated with lower expression of KLF3. Mechanically, KLF3 regulation was mainly attributed to CpG island methylation. KLF3-high expression subgroup was significantly enriched in cell signaling pathways most associated with EMT markers, angiogenesis, inflammatory response, apoptosis, TGF-β, degradation of ECM, G2M checkpoint, and PI3K-AKT-mTOR. Based on GDSC database, KLF3 upregulation was identified to be associated with higher sensitivities towards PI3K-Akt-mTOR pathway inhibitors such as PI-103, PIK-93, and OSI-027. In addition, patients with down-regulated KLF3 expressions were found more sensitive towards Trametinib, Cetuximab, and Erlotinib. Collectively, our findings suggest that KLF3 may act as a suitable biomarker for prognosis prediction, tumor microenvironment (TME) phenotype identification, thereby helping ccRCC patients to make better therapeutic decisions.

IProPhos: A Web-Based Interactive Platform for Integrated Proteome and Phosphoproteome Analysis

Large-scale omics studies have generated a wealth of mass spectrometry–based proteomics data, which provide additional insights into disease biology spanning genomic boundaries. However, there is a notable lack of web-based analysis and visualization tools that facilitate the reutilization of these data. Given this challenge, we present iProPhos, a user-friendly web server to deliver interactive and customizable functionalities. iProPhos incorporates a large number of samples, including 1444 tumor samples and 746 normal samples across 12 cancer types, sourced from the Clinical Proteomic Tumor Analysis Consortium. Additionally, users can also upload their own proteomics/phosphoproteomics data for analysis and visualization. In iProPhos, users can perform profiling plotting and differential expression, patient survival, clinical feature–related, and correlation analyses, including protein–protein, mRNA-protein, and kinase-substrate correlations. Furthermore, functional enrichment, protein–protein interaction network, and kinase-substrate enrichment analyses are accessible. iProPhos displays the analytical results in interactive figures and tables with various selectable parameters. It is freely accessible at http://longlab-zju.cn/iProPhos without login requirement. We present two case studies to demonstrate that iProPhos can identify potential drug targets and upstream kinases contributing to site-specific phosphorylation. Ultimately, iProPhos allows end-users to leverage the value of big data in cancer proteomics more effectively and accelerates the discovery of novel therapeutic targets.

Insulin-like growth factor 2 mRNA-binding protein 2 is a therapeutic target in ovarian cancer.

Ovarian cancer (OC) is a fatal gynecologic disease. The most common treatment for OC patients is surgery combined with chemotherapy but most patients at advanced stages eventually develop relapse due to chemoresistance. This study examined the role and function of insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) in OC. We observed that the expression of IGF2BP2 mRNA and protein was up-regulated in OC cells and tissues using quantitative real-time polymerase chain reaction (qRT-PCR) and western blot, respectively. An increase in IGF2BP2 expression at mRNA and protein levels was verified by the analyses of The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), respectively. Gene Expression Omnibus (GEO) and Cancer Cell Line Encyclopedia (CCLE) databases were applied to analyze the expression and clinical value of IGF2BP2. Gene set enrichment analysis (GSEA), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) analyses explored biological functions and the involvement of IGF2BP2 in cell growth. Indeed, the knockdown of IGF2BP2 resulted in the inhibition of OC cell proliferation evaluated by the Cell Counting Kit-8 assay. Genomic amplification of IGF2BP2 partly accounted for its overexpression. High expression of IGF2BP2 was associated with signal transducer and activator of transcription 1 (STAT1) and drug sensitivity and was correlated with an unfavorable survival outcome in OC patients. Furthermore, the responsiveness of chemotherapy and immunotherapy were analyzed using the "pRRophetic" R package and The Cancer Immune Atlas (TCIA) database, respectively. The low expression of IGF2BP2 was associated with chemoresistance but with high tumor microenvironment scores and tumor-infiltrating immune cells, suggesting that immunotherapy may apply in chemoresistant patients. The alteration of IGF2BP2 expression may respond to chemotherapy and immunotherapy. Thus, IGF2BP2 shows potential as a therapeutic target and diagnostic biomarker for OC.

Inhibition of SF3B1 improves the immune microenvironment through pyroptosis and synergizes with αPDL1 in ovarian cancer

Ovarian cancer is resistant to immune checkpoint blockade (ICB) treatment. Combination of targeted therapy and immunotherapy is a promising strategy for ovarian cancer treatment benefit from an improved immune microenvironment. In this study, Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) cohorts were used to screen prognosis and cytotoxic lymphocyte infiltration-associated genes in upregulated genes of ovarian cancer, tissue microarrays were built for further verification. In vitro experiments and mouse (C57/BL6) ovarian tumor (ID8) models were built to evaluate the synergistic effect of the combination of SF3B1 inhibitor and PD-L1 antibody in the treatment of ovarian cancer. The results show that SF3B1 is shown to be overexpressed and related to low cytotoxic immune cell infiltration in ovarian cancer. Inhibition of SF3B1 induces pyroptosis in ovarian cancer cells and releases mitochondrial DNA (mtDNA), which is englobed by macrophages and subsequently activates them (polarization to M1). Moreover, pladienolide B increases cytotoxic immune cell infiltration in the ID8 mouse model as a SF3B1 inhibitor and increases the expression of PD-L1 which can enhance the antitumor effect of αPDL1 in ovarian cancer. The data suggests that inhibition of SF3B1 improves the immune microenvironment of ovarian cancer and synergizes ICB immunotherapy, which provides preclinical evidence for the combination of SF3B1 inhibitor and ICB to ovarian cancer treatment.