Proteogenomic data and resources for pan-cancer analysis

lncRNAlyzr: Enrichment Analysis for lncRNA Sets

lncRNAs make up a large portion of the human genome affecting many biological processes in normal physiology and diseases. However, human lncRNAs are understudied compared to protein-coding genes. While there are many tools for performing gene set enrichment analysis for coding genes, few tools exist for lncRNA enrichment analysis. lncRNAlyzr is a webserver application designed for lncRNAs enrichment analysis. lncRNAlyzr has a database containing 33 lncRNA set libraries created by computing correlations between lncRNAs and annotated coding gene sets. After users submit a set of lncRNAs to lncRNAlyzr, the enrichment analysis results are visualized as ball-and-stick subnetworks where nodes are lncRNAs connected to enrichment terms from across selected lncRNA set libraries. To demonstrate lncRNAlyzr, it was used to analyze the effects of knocking down the lncRNA CYTOR in K562 cells. Overall, lncRNAlyzr is an enrichment analysis tool for lncRNAs aiming to further our understanding of lncRNAs functional modules. lncRNAlyzr is available from: https://lncrnalyzr.maayanlab.cloud.

Integrative multi-omics analysis reveals the LncRNA 60967.1-PLCD4-ATRA axis as a key regulator of colorectal cancer progression and immune response

Colorectal cancer (CRC) is a major global health concern, characterized by high morbidity and mortality rates. CRC progression involves intricate molecular networks that remain incompletely understood. In this study, we conducted an integrative multi-omics analysis of transcriptomic, proteomic, and metabolomic profiles from CRC tissues and matched normal adjacent tissues (NATs). Our analysis revealed 1,394 differentially expressed long non-Coding RNAs (lncRNAs), 2,788 genes, 548 proteins, and 91 metabolites. A significant interaction network comprising 22 lncRNAs, 14 mRNAs/proteins, and 9 metabolites was identified, among which lncRNA 60967.1 emerged as a pivotal regulator. Functional validation demonstrated that lncRNA 60967.1 is markedly downregulated in CRC cell lines and patient tissues. Overexpression of lncRNA 60967.1 restored expression of the tumor suppressor PLCD4 and increased levels of all-trans retinoic acid (ATRA). This modulation enhanced IFN-γ-induced apoptosis and increased expression of the IFN-γ receptor subunit IFNGR1, thereby partially reversing IFN-γ resistance. In murine models, lncRNA 60967.1 overexpression promoted immune cell infiltration and synergized with anti-PD-1 therapy to inhibit tumor growth. Collectively, our findings uncover a novel lncRNA-mRNA/protein-metabolite network, the lncRNA 60967.1-PLCD4-ATRA axis, that plays a critical role in CRC progression and immune modulation, offering promising therapeutic targets for improved treatment efficacy.

A unified pan-cancer proteome atlas

In this issue of Cancer Cell, Knol et al. present the Pan-Cancer Proteome Atlas (TPCPA), a proteomic resource developed using single-shot data-independent acquisition mass spectrometry (DIA-MS). TPCPA provides proteome-scale quantifications of 999 tumors across 22 cancer types in a unified manner, for discovering tumor biology, biomarkers, and therapeutic targets.

Comprehensive evaluation of phosphoproteomic-based kinase activity inference

Kinases regulate cellular processes and are essential for understanding cellular function and disease. To investigate the regulatory state of a kinase, numerous methods have been developed to infer kinase activities from phosphoproteomics data using kinase-substrate libraries. However, few phosphorylation sites can be attributed to an upstream kinase in these libraries, limiting the scope of kinase activity inference. Moreover, inferred activities vary across methods, necessitating evaluation for accurate interpretation. Here, we present benchmarKIN, an R package enabling comprehensive evaluation of kinase activity inference methods. Alongside classical perturbation experiments, benchmarKIN introduces a tumor-based benchmarking approach utilizing multi-omics data to identify highly active or inactive kinases. We used benchmarKIN to evaluate kinase-substrate libraries, inference algorithms and the potential of adding predicted kinase-substrate interactions to overcome the coverage limitations. Our evaluation shows most computational methods perform similarly, but the choice of library impacts the inferred activities with a combination of manually curated libraries demonstrating superior performance in recapitulating kinase activities. Additionally, in the tumor-based evaluation, adding predicted targets from NetworKIN further boosts the performance. We then demonstrate how kinase activity inference aids characterize kinase inhibitor responses in cell lines. Overall, benchmarKIN helps researchers to select reliable methods for identifying deregulated kinases.

Ontolomics-P: Advancing Proteomics Data Interpretation through GPT-4o Reannotated Topic Ontology and Data-Driven Analysis

The interpretation of proteomics data often relies on functional enrichment analysis, such as Gene Ontology (GO) enrichment, to uncover the biological functions of proteins, as well as the examination of protein expression patterns across data sets like the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. However, conventional approaches to functional enrichment frequently produce extensive and redundant term lists, complicating interpretation and synthesis. Moreover, the absence of specialized tools tailored to proteomics researchers limits the efficient exploration of protein expression within specific biological contexts. To address these challenges, we developed Ontolomics-P, a user-friendly web-based tool designed to advance proteomics data interpretation. Ontolomics-P integrates topic modeling using latent Dirichlet allocation (LDA) with GO semantic similarity analysis, enabling the consolidation of redundant terms into coherent topics. These topics are further refined and reannotated using the GPT-4o language model, creating a novel topics database that provides precise and interpretable insights into shared biological functions. Additionally, Ontolomics-P incorporates quantitative proteomic data from 10 diverse cancer types archived in the CPTAC database, allowing for a comprehensive exploration of protein expression profiles from a data-driven perspective. Through detailed case studies, we demonstrate the tool's capacity to streamline workflows, simplify interpretation, and provide actionable biological insights. Ontolomics-P represents a significant advancement in proteomics data analysis, offering innovative solutions for functional annotation, quantitative exploration, and visualization, ultimately empowering researchers to accelerate discoveries in systems biology and beyond.

Explainable Machine Learning Identifies Factors for Dosage Compensation in Aneuploid Human Cancer Cells

Aneuploidy, a hallmark of cancer, leads to widespread changes in chromosome copy number, altering the abundance of hundreds or thousands of proteins. How-ever, evidence suggests that levels of proteins encoded on affected chromosomes are often buffered toward their abundances observed in diploid cells. Despite its preval-ence, the molecular mechanisms driving this protein dosage compensation remain largely unknown. It is unclear whether all proteins are buffered to a similar degree, what factors determine buffering, and whether dosage compensation varies across different cell lines or tumor types. Moreover, its potential adaptive advantage and therapeutic relevance remain unexplored. Here, we established a novel approach to quantify protein dosage buffering in a gene copy number-dependent manner, show-ing that dosage compensation is widespread but variable in cancer cell lines and in vivo tumor samples. By developing multifactorial machine learning models, we identify mean gene dependency, protein complex participation, haploinsufficiency, and mRNA decay as key predictors of buffering. We also show that dosage com-pensation can affect oncogenic potential and that higher buffering correlates with reduced proteotoxic stress and increased drug resistance. These findings highlight protein dosage compensation as a crucial regulatory mechanism and a potential therapeutic target in aneuploid cancers.

Protein lipidation in the tumor microenvironment: enzymology, signaling pathways, and therapeutics

Protein lipidation is a pivotal post-translational modification that increases protein hydrophobicity and influences their function, localization, and interaction network. Emerging evidence has shown significant roles of lipidation in the tumor microenvironment (TME). However, a comprehensive review of this topic is lacking. In this review, we present an integrated and in-depth literature review of protein lipidation in the context of the TME. Specifically, we focus on three major lipidation modifications: S-prenylation, S-palmitoylation, and N-myristoylation. We emphasize how these modifications affect oncogenic signaling pathways and the complex interplay between tumor cells and the surrounding stromal and immune cells. Furthermore, we explore the therapeutic potential of targeting lipidation mechanisms in cancer treatment and discuss prospects for developing novel anticancer strategies that disrupt lipidation-dependent signaling pathways. By bridging protein lipidation with the dynamics of the TME, our review provides novel insights into the complex relationship between them that drives tumor initiation and progression.

Melanoma Proteomics Unveiled: Harmonizing Diverse Data Sets for Biomarker Discovery and Clinical Insights via MEL-PLOT

Using several melanoma proteomics data sets we created a single analysis platform that enables the discovery, knowledge build, and validation of diagnostic, predictive, and prognostic biomarkers at the protein level. Quantitative mass-spectrometry-based proteomic data was obtained from five independent cohorts, including 489 tissue samples from 394 patients with accompanying clinical metadata. We established an interactive R-based web platform that enables the comparison of protein levels across diverse cohorts, and supports correlation analysis between proteins and clinical metadata including survival outcomes. By comparing differential protein levels between metastatic, primary tumor, and nonmalignant samples in two of the cohorts, we identified 274 proteins showing significant differences among the sample types. Further analysis of these 274 proteins in lymph node metastatic samples from a third cohort revealed that 45 proteins exhibited a significant effect on patient survival. The three most significant proteins were HP (HR = 4.67, p = 2.8e-06), LGALS7 (HR = 3.83, p = 2.9e-05), and UBQLN1 (HR = 3.2, p = 4.8e-05). The user-friendly interactive web platform, accessible at https://www.tnmplot.com/melanoma, provides an interactive interface for the analysis of proteomic and clinical data. The MEL-PLOT platform, through its interactive capabilities, streamlines the creation of a comprehensive knowledge base, empowering hypothesis formulation and diligent monitoring of the most recent advancements in the domains of biomedical research and drug development.

GRP78 Nanobody-Directed Immunotoxin Activates Innate Immunity Through STING Pathway to Synergize Tumor Immunotherapy

The lack of targetable antigens poses a significant challenge in developing effective cancer-targeted therapies. Cell surface translocation of endoplasmic reticulum (ER) chaperones, such as glucose-regulated protein 78 (GRP78), during malignancy, drug resistance, and ER stress induced by therapies, offers a promising pan-cancer target. To target GRP78, nanobody C5, identified from a phage library and exhibiting high affinity for human and mouse GRP78, is utilized to develop the Pseudomonas exotoxin (PE) immunotoxin C5-PE38. C5-PE38 induced ER stress, apoptosis and immunogenic cell death in targeted cells and showed antitumor efficacy against colorectal cancer and melanoma models without obvious toxicity. Mechanistically, transcriptome profiling showed that C5-PE38 reshaped the tumor immune microenvironment with enhanced innate and adaptive immune response and response to interferon beta. Moreover, C5-PE38-induced cell death could trans-activate STING pathway in dendritic cells and macrophages, promoting CD8+ T cell infiltration. It also sensitizes both primary and metastatic melanomas to anti-PD1 therapy, partly through STING activation. Overall, this study unveils a feasible GRP78 nanobody-directed therapy strategy for single or combinatorial cancer intervention. This work finds that C5-PE38-induced cell death stimulates STING-dependent cytosolic DNA release to promote antitumor immunity, a mechanism not previously reported for PE38, providing valuable insights for its clinical use.

Precision proteogenomics reveals pan-cancer impact of germline variants

We investigate the impact of germline variants on cancer patients' proteomes, encompassing 1,064 individuals across 10 cancer types. We introduced an approach, "precision peptidomics," mapping 337,469 coding germline variants onto peptides from patients' mass spectrometry data, revealing their potential impact on post-translational modifications, protein stability, allele-specific expression, and protein structure by leveraging the relevant protein databases. We identified rare pathogenic and common germline variants in cancer genes potentially affecting proteomic features, including variants altering protein abundance and structure and variants in kinases (ERBB2 and MAP2K2) impacting phosphorylation. Precision peptidome analysis predicted destabilizing events in signal-regulatory protein alpha (SIRPA) and glial fibrillary acid protein (GFAP), relevant to immunomodulation and glioblastoma diagnostics, respectively. Genome-wide association studies identified quantitative trait loci for gene expression and protein levels, spanning millions of SNPs and thousands of proteins. Polygenic risk scores correlated with distal effects from risk variants. Our findings emphasize the contribution of germline genetics to cancer heterogeneity and high-throughput precision peptidomics.

Multi-omics analysis reveals CMTR1 upregulation in cancer and roles in ribosomal protein gene expression and tumor growth

BACKGROUND

CMTR1 (cap methyltransferase 1), a key nuclear mRNA cap methyltransferase, catalyzes 2'-O-methylation of the first transcribed nucleotide, a critical step in mRNA cap formation. Previous studies have implicated CMTR1 in embryonic stem cell differentiation and immune responses during viral infection; however, its role in cancer biology remains largely unexplored. This study aims to elucidate CMTR1's function in cancer progression and evaluate its potential as a novel therapeutic target in certain cancer types.

METHODS

We conducted a comprehensive multi-omics analysis of CMTR1 across various human cancers using TCGA and CPTAC datasets. Functional studies were performed using CRISPR-mediated knockout and siRNA knockdown in human and mouse basal-like breast cancer models. Transcriptomic and pathway enrichment analyses were carried out in CMTR1 knockout/knockdown models to identify CMTR1-regulated genes. In silico screening and biochemical assays were employed to identify novel CMTR1 inhibitors.

RESULTS

Multi-omics analysis revealed that CMTR1 is significantly upregulated at the mRNA, protein, and phosphoprotein levels across multiple cancer types in the TCGA and CPTAC datasets. Functional studies demonstrated that CMTR1 depletion significantly inhibits tumor growth both in vitro and in vivo. Transcriptomic analysis of CMTR1 knockout cells revealed that CMTR1 primarily regulates ribosomal protein genes and other transcripts containing 5' Terminal Oligopyrimidine (TOP) motifs. Additionally, CMTR1 affects the expression of snoRNA host genes and snoRNAs, suggesting a broader role in RNA metabolism. Mechanistic studies indicated that CMTR1's target specificity is partly determined by mRNA structure, particularly the presence of 5'TOP motifs. Finally, through in silico screening and biochemical assays, we identified several novel CMTR1 inhibitors, including N97911, which demonstrated in vitro growth inhibition activity in breast cancer cells.

CONCLUSIONS

Our findings establish CMTR1 as an important player in cancer biology, regulating critical aspects of RNA metabolism and ribosome biogenesis. The study highlights CMTR1's potential as a therapeutic target in certain cancer types and provides a foundation for developing novel cancer treatments targeting mRNA cap methylation.

Proteomic-based stemness score measures oncogenic dedifferentiation and enables the identification of druggable targets

Cancer progression and therapeutic resistance are closely linked to a stemness phenotype. Here, we introduce a protein-expression-based stemness index (PROTsi) to evaluate oncogenic dedifferentiation in relation to histopathology, molecular features, and clinical outcomes. Utilizing datasets from the Clinical Proteomic Tumor Analysis Consortium across 11 tumor types, we validate PROTsi's effectiveness in accurately quantifying stem-like features. Through integration of PROTsi with multi-omics, including protein post-translational modifications, we identify molecular features associated with stemness and proteins that act as active nodes within transcriptional networks, driving tumor aggressiveness. Proteins highly correlated with stemness were identified as potential drug targets, both shared and tumor specific. These stemness-associated proteins demonstrate predictive value for clinical outcomes, as confirmed by immunohistochemistry in multiple samples. The findings emphasize PROTsi's efficacy as a valuable tool for selecting predictive protein targets, a crucial step in customizing anti-cancer therapy and advancing the clinical development of cures for cancer patients.

Proteomic Analysis of 442 Clinical Plasma Samples From Individuals With Symptom Records Revealed Subtypes of Convalescent Patients Who Had COVID-19

After the coronavirus disease 2019 (COVID-19) pandemic, the postacute effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have gradually attracted attention. To precisely evaluate the health status of convalescent patients with COVID-19, we analyzed symptom and proteome data of 442 plasma samples from healthy controls, hospitalized patients, and convalescent patients 6 or 12 months after SARS-CoV-2 infection. Symptoms analysis revealed distinct relationships in convalescent patients. Results of plasma protein expression levels showed that C1QA, C1QB, C2, CFH, CFHR1, and F10, which regulate the complement system and coagulation, remained highly expressed even at the 12-month follow-up compared with their levels in healthy individuals. By combining symptom and proteome data, 442 plasma samples were categorized into three subtypes: S1 (metabolism-healthy), S2 (COVID-19 retention), and S3 (long COVID). We speculated that convalescent patients reporting hair loss could have a better health status than those experiencing headaches and dyspnea. Compared to other convalescent patients, those reporting sleep disorders, appetite decrease, and muscle weakness may need more attention because they were classified into the S2 subtype, which had the most samples from hospitalized patients with COVID-19. Subtyping convalescent patients with COVID-19 may enable personalized treatments tailored to individual needs. This study provides valuable plasma proteomic datasets for further studies associated with long COVID.

Health position paper and redox perspectives - Bench to bedside transition for pharmacological regulation of NRF2 in noncommunicable diseases

Nuclear factor erythroid 2-related factor 2 (NRF2) is a redox-activated transcription factor regulating cellular defense against oxidative stress, thereby playing a pivotal role in maintaining cellular homeostasis. Its dysregulation is implicated in the progression of a wide array of human diseases, making NRF2 a compelling target for therapeutic interventions. However, challenges persist in drug discovery and safe targeting of NRF2, as unresolved questions remain especially regarding its context-specific role in diseases and off-target effects. This comprehensive review discusses the dualistic role of NRF2 in disease pathophysiology, covering its protective and/or destructive roles in autoimmune, respiratory, cardiovascular, and metabolic diseases, as well as diseases of the digestive system and cancer. Additionally, we also review the development of drugs that either activate or inhibit NRF2, discuss main barriers in translating NRF2-based therapies from bench to bedside, and consider the ways to monitor NRF2 activation in vivo.

Depletion of tumor-derived CXCL5 improves T cell infiltration and anti-PD-1 therapy response in an obese model of pancreatic cancer

BACKGROUND

CXCR1/2 inhibitors are being implemented with immunotherapies in PDAC clinical trials. CXC-ligands are a family of cytokines responsible for stimulating these receptors; while typically secreted by activated immune cells, fibroblasts, and even adipocytes, they are also secreted by immune-evasive cancer cells. CXC-ligand release is known to occur in response to inflammatory stimuli. Adipose tissue is an endocrine organ and a source of inflammatory signaling peptides. Importantly, adipose-derived cytokines and chemokines are implicated as potential drivers of tumor cell immune evasion; cumulatively, these findings suggest that targeting CXC-ligands may be beneficial in the context of obesity.

METHODS

RNA-sequencing of human PDAC cell lines was used to assess influences of adipose conditioned media on the cancer cell transcriptome. The adipose-induced secretome of PDAC cells was validated with ELISA for induction of CXCL5 secretion. Human tissue data from CPTAC was used to correlate IL-1β and TNF expression with both CXCL5 mRNA and protein levels. CRISPR-Cas9 was used to knockout CXCL5 from a murine PDAC KPC cell line to assess orthotopic tumor studies in syngeneic, diet-induced obese mice. Flow cytometry and immunohistochemistry were used to compare the immune profiles between tumors with or without CXCL5. Mice-bearing CXCL5 competent or deficient tumors were monitored for differential tumor size in response to anti-PD-1 immune checkpoint blockade therapy.

RESULTS

Human adipose tissue conditioned media stimulates CXCL5 secretion from PDAC cells via either IL-1β or TNF; neutralization of both is required to significantly block the release of CXCL5 from tumor cells. Ablation of CXCL5 from tumors promoted an enriched immune phenotype with an unanticipatedly increased number of exhausted CD8 T cells. Application of anti-PD-1 treatment to control tumors failed to alter tumor growth, yet treatment of CXCL5-deficient tumors showed response by significantly diminished tumor mass.

CONCLUSIONS

In summary, our findings show that both TNF and IL-1β can stimulate CXCL5 release from PDAC cells in vitro, which correlates with expression in patient data. CXCL5 depletion in vivo alone is sufficient to promote T cell infiltration into tumors, increasing efficacy and requiring checkpoint blockade inhibition to alleviate tumor burden.

Deciphering the dark cancer phosphoproteome using machine-learned co-regulation of phosphosites

Mass spectrometry-based phosphoproteomics offers a comprehensive view of protein phosphorylation, yet our limited knowledge about the regulation and function of most phosphosites hampers the extraction of meaningful biological insights. To address this challenge, we integrate machine learning with phosphoproteomic data from 1195 tumor specimens spanning 11 cancer types to construct CoPheeMap, a network that maps the co-regulation of 26,280 phosphosites. By incorporating network features from CoPheeMap into a second machine learning model, namely CoPheeKSA, we achieve superior performance in predicting kinase-substrate associations. CoPheeKSA uncovers 24,015 associations between 9399 phosphosites and 104 serine/threonine kinases, shedding light on many unannotated phosphosites and understudied kinases. We validate the accuracy of these predictions using experimentally determined kinase-substrate specificities. Through the application of CoPheeMap and CoPheeKSA to phosphosites with high computationally predicted functional significance and those associated with cancer, we demonstrate their effectiveness in systematically elucidating phosphosites of interest. These analyses unveil dysregulated signaling processes in human cancer and identify understudied kinases as potential therapeutic targets.

Inflammatory reprogramming of the solid tumor microenvironment by infiltrating clonal hematopoiesis is associated with adverse outcomes

Clonal hematopoiesis (CH)-the expansion of somatically mutated hematopoietic cells-is common in solid cancers. CH is associated with systemic inflammation, but its impact on tumor biology is underexplored. Here, we report the effects of CH on the tumor microenvironment (TME) using 1,550 treatment-naive patient samples from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) cohort. CH is present in 18.3% of patients, with one-third of CH mutations also detectable in tumor-derived DNA from the same individual (CH-Tum), reflecting CH-mutant leukocyte infiltration. Across cancers, the presence of CH-Tum is associated with worse survival outcomes. Molecular analyses reveal an association between CH-Tum and an immune-rich, inflammatory TME that is notably distinct from age-related gene expression changes. These effects are most prominent in glioblastoma, where CH correlates with pronounced macrophage infiltration, inflammation, and an aggressive, mesenchymal phenotype. Our findings demonstrate that CH shapes the TME, with potential applications as a biomarker in precision oncology.

Molecular Phenotyping With Proteomics

This JAMA Insights explores the capability of proteomics to analyze thousands of proteins in patient samples, which could improve clinicians’ understanding of and ability to treat a wide range of diseases.

Tumor Prognostic Risk Model Related to Monocytes/Macrophages in Hepatocellular Carcinoma Based on Machine Learning and Multi-Omics

Tumor-associated macrophages (TAMs) are crucial in hepatocellular carcinoma (HCC) development and invasion. This study explores monocyte/ macrophage-associated gene expression profiles in HCC, constructs a prognostic model based on these genes, and examines its relationship with drug resistance and immune therapy responses. Single-cell RNA sequencing(scRNA-seq) data from 10 HCC tissue biopsy samples, totaling 24,597 cells, were obtained from the GEO database to identify monocyte/macrophage-associated genes. A prognostic model was constructed and validated using external datasets and Western blot. Relationships between the model, clinical correlates, drug sensitivity, and immune therapy responses were investigated. From scRNA-seq data, 2,799 monocyte/macrophage marker genes were identified. Using the TCGA dataset, a prognostic model based on the single-gene UQCRH was constructed, stratifying patients into high-risk and low-risk groups based on overall survival rates. High-risk group patients showed reduced survival rates and higher UQCRH expression in tumor tissues. Western blot analysis further confirmed the elevated expression of UQCRH in HCC cell lines. Spatial transcriptomics analysis revealed that high UQCRH expression co-localized with malignant cells in the tumor tissue. Drug sensitivity analysis revealed that the high-risk group had lower sensitivity to sorafenib and axitinib. Immune therapy response analysis indicated poorer outcomes in the high-risk group, with more pronounced APC inhibition and a weaker IFN-II response. Clinical indicator analysis showed a positive correlation between high UQCRH expression and tumor invasion. Enrichment analysis of UQCRH and associated molecules indicated involvement in oxidative phosphorylation and mitochondrial electron transport. This study introduces a prognostic model for HCC patients based on monocyte/macrophage marker genes. The single-gene model predicts HCC patient survival and treatment outcomes, identifying high-risk individuals with varying drug sensitivities and immune suppression states.

Diagnostic, prognostic, and immunological roles of NCAPG in pan-cancer: A bioinformatics analysis

Growing studies have shown that non-SMC condensin I complex subunit G (NCAPG) was highly expressed in a variety of tumors and was involved in the progression of multitumors, but the role of NCAPG in tumorigenesis is not fully understood. Our study purposed to systematically investigate the role of NCAPG across cancer types. Interacting molecules with NCAPG were analyzed using searching bioinformatics websites including Search Tool for the Retrieval of Interacting Genes/Proteins, GeneMANIA, and Global Positioning System-Prot. NCAPG-related diseases were acquired using the Open Targets Platform. The interaction of NCAPG and 14 cancer functional states was achieved using the CancerSEA website. The databases including the University of California Santa Cruz Xena, Genotype-Tissue Expression, The Cancer Genome Atlas Program, Human Protein Atlas, and XIANTAO Academic were used to interpret the expression of NCAPG. Correlations between NCAPG expression and immune infiltration and immune-related molecules were analyzed by using Tumor Immune Estimation Resource Version 2 and Tumor and Immune System Interaction Database databases. NCAPG expression was significantly upregulated in most cancer types. NCAPG was identified as a marker of diagnostic value and prognostic significance in most cancer types. NCAPG expression was related to immune cell infiltration and immune-related molecules across various cancers, especially kidney renal clear cell carcinoma and thyroid carcinoma. Furthermore, NCAPG expression could affect the enrichment and decrease immune cell infiltration to influence prognosis in kidney renal clear cell carcinoma but was devoid of evidence in thyroid carcinoma. NCAPG was a prospective marker for the diagnosis and prognosis of pan-cancer. Our results suggested that NCAPG was a potential cancer biomarker for the diagnosis and prognosis of pan-cancer. NCAPG might affect the immune microenvironment, which could be applied in the development of new-targeted drugs for immunotherapy.

Deciphering functional tumor-immune crosstalk through highly multiplexed imaging and deep visual proteomics

Deciphering the intricate tumor-immune interactions within the microenvironment is crucial for advancing cancer immunotherapy. Here, we introduce mipDVP, an advanced approach integrating highly multiplexed imaging, single-cell laser microdissection, and sensitive mass spectrometry to spatially profile the proteomes of distinct cell populations in a human colorectal and tonsil cancer with high sensitivity. In a colorectal tumor-a representative cold tumor-we uncovered spatial compartmentalization of an immunosuppressive macrophage barrier that potentially impedes T cell infiltration. Spatial proteomic analysis revealed distinct functional states of T cells in different tumor compartments. In a tonsil cancer sample-a hot tumor-we identified significant proteomic heterogeneity among cells influenced by proximity to cytotoxic T cell subtypes. T cells in the tumor parenchyma exhibit metabolic adaptations to hypoxic regions. Our spatially resolved, highly multiplexed strategy deciphers the complex cellular interplay within the tumor microenvironment, offering valuable insights for identifying immunotherapy targets and predictive signatures.

Inferring kinase-phosphosite regulation from phosphoproteome-enriched cancer multi-omics datasets

Phosphorylation in eukaryotic cells plays a key role in regulating cell signaling and disease progression. Despite the ability to detect thousands of phosphosites in a single experiment using high-throughput technologies, the kinases responsible for regulating these sites are largely unidentified. To solve this, we collected the quantitative data at the transcriptional, protein, and phosphorylation levels of 10 159 samples from 23 tumor datasets and 15 adjacent normal tissue datasets. Our analysis aimed to uncover the potential impact and linkage of kinase-phosphosite (KPS) pairs through experimental evidence in publications and prediction tools commonly used. We discovered that both experimentally validated and tool-predicted KPS pairs were enriched in groups where there is a significant correlation between kinase expression/phosphorylation level and the phosphorylation level of phosphosite. This suggested that a quantitative correlation could infer the KPS interconnections. Furthermore, the Spearman's correlation coefficient for these pairs were notably higher in tumor samples, indicating that these regulatory interactions are particularly pronounced in tumors. Consequently, building on the KPS correlations of different datasets as predictive features, we have developed an innovative approach that employed an oversampling method combined with and XGBoost algorithm (SMOTE-XGBoost) to predict potential kinase-specific phosphorylation sites in proteins. Moreover, the computed correlations and predictions of kinase-phosphosite interconnections were integrated into the eKPI database (https://ekpi.omicsbio.info/). In summary, our study could provide helpful information and facilitate further research on the regulatory relationship between kinases and phosphosites.

PKN2 Is a Dependency of the Mesenchymal-like Cancer Cell State

Cancer cells exploit a mesenchymal-like transcriptional state (MLS) to survive drug treatments. Although the MLS is well characterized, few therapeutic vulnerabilities targeting this program have been identified. In this study, we systematically identify the dependency network of mesenchymal-like cancers through an analysis of gene essentiality scores in ∼800 cancer cell lines, nominating a poorly studied kinase, PKN2, as a top therapeutic target of the MLS. Coessentiality relationships, biochemical experiments, and genomic analyses of patient tumors revealed that PKN2 promotes mesenchymal-like cancer growth through a PKN2-SAV1-TAZ signaling mechanism. Notably, pairing genetic PKN2 inhibition with clinically relevant targeted therapies against EGFR, KRAS, and BRAF suppresses drug resistance by depleting mesenchymal-like drug-tolerant persister cells. These findings provide evidence that PKN2 is a core regulator of the Hippo tumor suppressor pathway and highlight the potential of PKN2 inhibition as a generalizable therapeutic strategy to overcome drug resistance driven by the MLS across cancer contexts. Significance: This work identifies PKN2 as a core member of the Hippo signaling pathway, and its inhibition blocks YAP/TAZ-driven tumorigenesis. Furthermore, this study discovers PKN2-TAZ as arguably the most selective dependency of mesenchymal-like cancers and supports specific inhibition of PKN2 as a provocative strategy to overcome drug resistance in diverse cancer contexts. See related commentary by Shen and Tan, p. 458.

Integrated Analysis of Proteome and Transcriptome Profiling Reveals Pan-Cancer-Associated Pathways and Molecular Biomarkers

Understanding dysregulated genes and pathways in cancer is critical for precision oncology. Integrating mass spectrometry-based proteomic data with transcriptomic data presents unique opportunities for systematic analyses of dysregulated genes and pathways in pan-cancer. Here, we compiled a comprehensive set of datasets, encompassing proteomic data from 2404 samples and transcriptomic data from 7752 samples across 13 cancer types. Comparisons between normal or adjacent normal tissues and tumor tissues identified several dysregulated pathways including mRNA splicing, interferon pathway, fatty acid metabolism, and complement coagulation cascade in pan-cancer. Additionally, pan-cancer upregulated and downregulated genes (PCUGs and PCDGs) were also identified. Notably, RRM2 and ADH1B, two genes which belong to PCUGs and PCDGs, respectively, were identified as robust pan-cancer diagnostic biomarkers. TNM stage-based comparisons revealed dysregulated genes and biological pathways involved in cancer progression, among which the dysregulation of complement coagulation cascade and epithelial-mesenchymal transition are frequent in multiple types of cancers. A group of pan-cancer continuously upregulated and downregulated proteins in different tumor stages (PCCUPs and PCCDPs) were identified. We further constructed prognostic risk stratification models for corresponding cancer types based on dysregulated genes, which effectively predict the prognosis for patients with these cancers. Drug prediction based on PCUGs and PCDGs as well as PCCUPs and PCCDPs revealed that small molecule inhibitors targeting CDK, HDAC, MEK, JAK, PI3K, and others might be effective treatments for pan-cancer, thereby supporting drug repurposing. We also developed web tools for cancer diagnosis, pathologic stage assessment, and risk evaluation. Overall, this study highlights the power of combining proteomic and transcriptomic data to identify valuable diagnostic and prognostic markers as well as drug targets and treatments for cancer.

Secernin-2 Stabilizes Histone Methyltransferase KMT2C to Suppress Progression and Confer Therapeutic Sensitivity to PARP Inhibition in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a difficulty and bottleneck in the clinical treatment of breast cancer due to a lack of effective therapeutic targets. Herein, we first report that secernin 2 (SCRN2), an uncharacterized gene in human cancer, acts as a novel tumor suppressor in TNBC to inhibit cancer progression and enhance therapeutic sensitivity to poly(ADP-ribose) polymerase (PARP) inhibition both in vitro and in vivo. SCRN2 is downregulated in TNBC through chaperone-mediated autophagic degradation, and its downregulation is associated with poor patient prognosis. Moreover, SCRN2 impedes the proteasomal degradation of histone-lysine N-methyltransferase 2C (KMT2C) by recruiting Bcl2-associated athanogene 2 to block the interaction of KMT2C with E3 ubiquitin-protein ligase CHIP. Consistently, SCRN2 transcriptionally activates Bcl2-modifying factor by amplifying histone H3 monomethylation at lysine 4 at its enhancer, thereby inducing intrinsic apoptosis. Notably, KMT2C knockdown restores the impaired TNBC progression caused by SCRN2 overexpression both in vitro and in vivo. Furthermore, SCRN2 decreases the expression of key DNA repair-related genes and induces endogenous DNA damage, thus conferring therapeutic sensitivity of TNBC cells to PARP inhibition.   Collectively, these findings identify SCRN2 as a novel suppressor of TNBC, reveal its mechanism of action, and highlight its potential role in TNBC therapy.

Proteogenomic analysis reveals adaptive strategies for alleviating the consequences of aneuploidy in cancer

Aneuploidy is prevalent in cancer and associates with fitness advantage and poor patient prognosis. Yet, experimentally induced aneuploidy initially leads to adverse effects and impaired proliferation, suggesting that cancer cells must adapt to aneuploidy. We performed in vitro evolution of cells with extra chromosomes and obtained cell lines with improved proliferation and gene expression changes congruent with changes in aneuploid cancers. Integrated analysis of cancer multi-omics data and model cells revealed increased expression of DNA replicative and repair factors, reduced genomic instability, and reduced lysosomal degradation. We identified E2F4 and FOXM1 as transcription factors strongly associated with adaptation to aneuploidy in vitro and in cancers and validated this finding. The adaptation to aneuploidy also coincided with specific copy number aberrations that correlate with poor patient prognosis. Chromosomal engineering mimicking these aberrations improved aneuploid cell proliferation, while loss of previously present extra chromosomes impaired it. The identified common adaptation strategies suggest replication stress, genomic instability, and lysosomal stress as common liabilities of aneuploid cancers.

Advancements in proteogenomics for preclinical targeted cancer therapy research

Advancements in molecular characterization technologies have accelerated targeted cancer therapy research at unprecedented resolution and dimensionality. Integrating comprehensive multi-omic molecular profiling of a tumor, proteogenomics, marks a transformative milestone for preclinical cancer research. In this paper, we initially provided an overview of proteogenomics in cancer research, spanning genomics, transcriptomics, and proteomics. Subsequently, the applications were introduced and examined from different perspectives, including but not limited to genetic alterations, molecular quantifications, single-cell patterns, different post-translational modification levels, subtype signatures, and immune landscape. We also paid attention to the combined multi-omics data analysis and pan-cancer analysis. This paper highlights the crucial role of proteogenomics in preclinical targeted cancer therapy research, including but not limited to elucidating the mechanisms of tumorigenesis, discovering effective therapeutic targets and promising biomarkers, and developing subtype-specific therapies.

The role of Prolyl 3-Hydroxylase 1 (P3H1) in tumor development and prognosis: a pan-cancer analysis with validation in colonic adenocarcinoma

BACKGROUND

Cancer is a multifaceted disease characterized by unregulated cell proliferation, evasion of apoptosis, and metastasis. Recent studies have highlighted the importance of extracellular matrix remodeling and post-translational modifications in tumorigenesis. Prolyl 3-hydroxylase 1 (P3H1), an enzyme involved in collagen hydroxylation, has gained attention for its role in cancer progression.

METHODS

This study investigates P3H1 expression, prognostic value, and functional relevance across multiple human cancers using a combination of bioinformatic and experimental approaches.

RESULTS

Using The Cancer Genome Atlas (TCGA) data from TIMER2.0 and UALCAN databases, we observed a significant upregulation of P3H1 mRNA and protein in various cancers. Prognostic analysis using GEPIA2 and KM plotter revealed that high P3H1 expression correlates with poorer overall survival in colon adenocarcinoma (COAD), kidney renal clear cell carcinoma (KIRC), and liver hepatocellular carcinoma (LIHC). Further, genetic and promoter methylation analyses showed low mutation frequencies and reduced methylation of P3H1 in specific cancer types. Functional and pathway enrichment analyses indicated that P3H1 is involved in collagen formation, endoplasmic reticulum activity, and pathways such as ECM-receptor interaction and PI3K-Akt signaling. Validation by enzyme linked immunosorbent assay in COAD patient serum samples demonstrated significantly elevated P3H1 levels compared to healthy controls, with an AUC approaching 1.0 by receiver operating characteristic (ROC) curve analysis. This suggests its potential as a diagnostic biomarker. Additionally, functional experiments were conducted in COAD cells to assess P3H1's role in tumorigenesis. Knockdown of P3H1 in HCT116 cells resulted in a significant reduction in cell proliferation, colony formation, and migratory abilities of these cells.

CONCLUSION

These findings emphasize P3H1's relevance in COAD, KIRC, and LIHC pathogenesis and possible utility in clinical diagnosis.

NetworkCommons: bridging data, knowledge, and methods to build and evaluate context-specific biological networks

SUMMARY

We present NetworkCommons, a platform for integrating prior knowledge, omics data, and network inference methods, facilitating their usage and evaluation. NetworkCommons aims to be an infrastructure for the network biology community that supports the development of better methods and benchmarks, by enhancing interoperability and integration.

AVAILABILITY AND IMPLEMENTATION

NetworkCommons is implemented in Python and offers programmatic access to multiple omics datasets, network inference methods, and benchmarking setups. It is a free software, available at https://github.com/saezlab/networkcommons, and deposited in Zenodo at https://doi.org/10.5281/zenodo.14719118.

Mass-spectrometry-based proteomics: from single cells to clinical applications

Mass-spectrometry (MS)-based proteomics has evolved into a powerful tool for comprehensively analysing biological systems. Recent technological advances have markedly increased sensitivity, enabling single-cell proteomics and spatial profiling of tissues. Simultaneously, improvements in throughput and robustness are facilitating clinical applications. In this Review, we present the latest developments in proteomics technology, including novel sample-preparation methods, advanced instrumentation and innovative data-acquisition strategies. We explore how these advances drive progress in key areas such as protein-protein interactions, post-translational modifications and structural proteomics. Integrating artificial intelligence into the proteomics workflow accelerates data analysis and biological interpretation. We discuss the application of proteomics to single-cell analysis and spatial profiling, which can provide unprecedented insights into cellular heterogeneity and tissue architecture. Finally, we examine the transition of proteomics from basic research to clinical practice, including biomarker discovery in body fluids and the promise and challenges of implementing proteomics-based diagnostics. This Review provides a broad and high-level overview of the current state of proteomics and its potential to revolutionize our understanding of biology and transform medical practice.

Pan-cancer secreted proteome and skeletal muscle regulation: insight from a proteogenomic data-driven knowledge base

Large-scale, pan-cancer analysis is enabled by data driven knowledge bases that link tumor molecular profiles with phenotypes. A debilitating cancer-related phenotype is skeletal muscle loss, or cachexia, which occurs partly from tumor products secreted into circulation. Using the LinkedOmicsKB knowledge base assembled from the Clinical Proteomics Tumor Analysis Consortium proteogenomic analysis, along with catalogs of human secretome proteins, ligand-receptor pairs and molecular signatures, we sought to identify candidate pan-cancer proteins secreted to blood that could regulate skeletal muscle phenotypes in multiple solid cancers. Tumor proteins having significant pan-cancer associations with muscle were referenced against secretome proteins secreted to blood from the Human Protein Atlas, then verified as increased in paired tumor vs. normal tissues in pan-cancer manner. This workflow revealed seven secreted proteins from cancers afflicting kidneys, head and neck, lungs and pancreas that classified as protein-binding activity modulator, extracellular matrix protein or intercellular signaling molecule. Concordance of these biomarkers with validated molecular signatures of cachexia and senescence supported relevance to muscle and cachexia disease biology, and high tumor expression of the biomarker set associated with lower overall survival. In this article, we discuss avenues by which skeletal muscle and cachexia may be regulated by these candidate pan-cancer proteins secreted to blood, and conceptualize a strategy that considers them collectively as a biomarker signature with potential for refinement by data analytics and radiogenomics for predictive testing of future risk in a non-invasive, blood-based panel amenable to broad uptake and early management.

PhosNetVis: A web-based tool for fast kinase-substrate enrichment analysis and interactive 2D/3D network visualizations of phosphoproteomics data

Protein phosphorylation involves the reversible modification of a protein (substrate) residue by another protein (kinase). Liquid chromatography-mass spectrometry studies are rapidly generating massive protein phosphorylation datasets across multiple conditions. Researchers then must infer kinases responsible for changes in phosphosites of each substrate. However, tools that infer kinase-substrate interactions (KSIs) are not optimized to interactively explore the resulting large and complex networks, significant phosphosites, and states. There is thus an unmet need for a tool that facilitates user-friendly analysis, interactive exploration, visualization, and communication of phosphoproteomics datasets. We present PhosNetVis, a web-based tool for researchers of all computational skill levels to easily infer, generate, and interactively explore KSI networks in 2D or 3D by streamlining phosphoproteomics data analysis steps within a single tool. PhostNetVis lowers barriers for researchers by rapidly generating high-quality visualizations to gain biological insights from their phosphoproteomics datasets. It is available at https://gumuslab.github.io/PhosNetVis/.

DCAF13-mediated K63-linked ubiquitination of RNA polymerase I promotes uncontrolled proliferation in Breast Cancer

Hyperactivation of ribosome biogenesis (RiBi) drives cancer progression, yet the role of RiBi-associated proteins (RiBPs) in breast cancer (BC) is underexplored. In this study, we perform a comprehensive multi-omics analysis and reveal that assembly and maturation factors (AMFs), a subclass of RiBPs, are upregulated at both RNA and protein levels in BC, correlating with poor patient outcomes. In contrast, ribosomal proteins (RPs) do not show systematic upregulation across various cancers, including BC. We further demonstrate that the oncogenic activation of a top AMF candidate in BC, DCAF13, enhances Pol I transcription and promotes proliferation in BC cells both in vitro and in vivo. Mechanistically, DCAF13 promotes Pol I transcription activity by facilitating the K63-linked ubiquitination of RPA194. This process stimulates global protein synthesis and cell growth. Our findings uncover a modification of RPA194 that regulates Pol I activity; this modification is dysregulated in BC, contributing to cancer progression.

Bioinformatics approaches to multi-omics analysis of the potential of CDKN2A as a biomarker and therapeutic target for uterine corpus endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is a significant cause of cancer-related mortality among women worldwide. Prior research has demonstrated an association between cyclin-dependent kinase inhibitor 2 A (CDKN2A) and various tumors. As a member of the INK4 family, CDKN2A is involved in cell cycle regulation by controlling CDKs. In the present study, bioinformatics was used to analyze public datasets. The expression levels, signaling pathways, and copy number variations of CDKN2A in UCEC were explored, along with its immune cell subset associations. CDKN2A expression was found to be elevated in UCEC, particularly in the signaling pathways involved in cell proliferation and inflammation. Analysis of somatic copy number alterations in the TCGA (The Cancer Genome Atlas)-UCEC dataset revealed a connection between CDKN2A and drug metabolism in UCEC. Assessment of the relationship between CDKN2A and genes involved in immunotherapy for UCEC patients showed a negative correlation between CDKN2A and CD8+ T cell activity, as well as IL-2 and TP53. Collectively, these insights suggest that CDKN2A may be a potential biomarker for prognosis and treatment strategies in UCEC.

Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics

Large-scale omics profiling has uncovered a vast array of somatic mutations and cancer-associated proteins, posing substantial challenges for their functional interpretation. Here we present a network-based approach centered on FunMap, a pan-cancer functional network constructed using supervised machine learning on extensive proteomics and RNA sequencing data from 1,194 individuals spanning 11 cancer types. Comprising 10,525 protein-coding genes, FunMap connects functionally associated genes with unprecedented precision, surpassing traditional protein-protein interaction maps. Network analysis identifies functional protein modules, reveals a hierarchical structure linked to cancer hallmarks and clinical phenotypes, provides deeper insights into established cancer drivers and predicts functions for understudied cancer-associated proteins. Additionally, applying graph-neural-network-based deep learning to FunMap uncovers drivers with low mutation frequency. This study establishes FunMap as a powerful and unbiased tool for interpreting somatic mutations and understudied proteins, with broad implications for advancing cancer biology and informing therapeutic strategies.

Identification of Protein Kinase Drug Targets Using Activity Estimation in Clinical Phosphoproteomics

This chapter describes protein kinase (will be termed here kinase) activity estimation methods and their application to clinical cancer phosphoproteomics datasets, proposing a novel approach for identification of protein kinases as therapeutic targets. Despite significant advances in genomics-based target identification, clinical proteomics and phosphoproteomics remain underutilized. We highlight the growing availability of proteomics data from projects like Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Proteomics Identifications Database (PRIDE), and review key kinase activity estimation algorithms, including PTM-SEA, KSEA, Rokai, KStar, and Kinome Atlas. Applying these methods on clinical phosphoproteomic data, we demonstrate the identification of hyperactivated kinases in specific cancer indications and highlight HER2 and EGFR as benchmarks. Our description underscores the potential of integrating kinase activity estimation with clinical phosphoproteomics to uncover new therapeutic targets and develop precision oncology therapies.

Towards routine proteome profiling of FFPE tissue: insights from a 1,220-case pan-cancer study

Proteome profiling of formalin-fixed paraffin-embedded (FFPE) specimens has gained traction for the analysis of cancer tissue for the discovery of molecular biomarkers. However, reports so far focused on single cancer entities, comprised relatively few cases and did not assess the long-term performance of experimental workflows. In this study, we analyze 1220 tumors from six cancer entities processed over the course of three years. Key findings include the need for a new normalization method ensuring equal and reproducible sample loading for LC-MS/MS analysis across cohorts, showing that tumors can, on average, be profiled to a depth of >4000 proteins and discovering that current software fails to process such large ion mobility-based online fractionated datasets. We report the first comprehensive pan-cancer proteome expression resource for FFPE material comprising 11,000 proteins which is of immediate utility to the scientific community, and can be explored via a web resource. It enables a range of analyses including quantitative comparisons of proteins between patients and cohorts, the discovery of protein fingerprints representing the tissue of origin or proteins enriched in certain cancer entities.

Translationally Controlled Tumor Protein Enhances Angiogenesis in Ovarian Tumors by Activating Vascular Endothelial Growth Factor Receptor 2 Signaling

Translationally controlled tumor protein (TCTP) is a regulatory protein that plays pivotal roles in cellular processes including the cell cycle, apoptosis, microtubule stabilization, embryo development, stress responses, and cancer. However, the molecular mechanism by which it promotes tumor angiogenesis is still unclear. In this study, we explored the mechanisms underlying stimulation of angiogenesis by a novel TCTP. Recombinant TCTP enhanced vascular endothelial growth factor (VEGF)-induced endothelial cell migration, capillary-like tubular structure formation, and cell proliferation by interacting with VEGF receptor 2 (VEGFR-2) in vitro. In contrast, we showed that TCTP knockdown (using short interfering [si]TCTP) led to a decrease in ovarian tumor cells. We also examined the expression of VEGF and hypoxia inducible factor 1 (HIF-1α), an important angiogenic factor. The expression of VEGF as well as HIF-1α was dramatically decreased by siTCTP. Mechanistically, siTCTP inhibited VEGFR-2 tyrosine phosphorylation and phosphorylation of its downstream targets PI3K, Akt, and mTOR. Collectively, these findings indicate that TCTP can promote proliferation and angiogenesis via the VEGFR-2/PI3K and mTOR signaling pathways in ovarian tumor cells, providing new insight into the mechanism behind the involvement of TCTP in tumor angiogenesis.

An Overview of Analytical Approaches to Cancer Proteogenomics

The molecular landscape of human cancers involves multiple omics layers of complexity, from genome to proteome and beyond. Cancer proteogenomics involves the integration of protein expression patterns with somatic DNA alterations. Recently, advances in mass spectrometry-based proteomic profiling technologies have enabled the generation of combined proteomic and multi-omic data for thousands of human tumors across dozens of studies. These data in the public domain can be utilized to give us a more complete picture of cancer-specific pathways and processes and identify gene candidates for therapeutic targeting. Many proteogenomic studies are ongoing involving various cancer types according to tissue or cell of origin, including studies to predict response to therapy. In addition, pan-cancer analyses across multiple studies can identify molecular commonalities, differences, and emergent themes across tumor lineages. Data integration can determine which gene alterations at the transcriptome level are translated to the protein level. A wealth of knowledge and analytical approaches developed historically to integrate gene transcription with genomic data can be readily applied to proteogenomic analyses. Here is provided an overview of higher-level analyses of proteogenomic datasets. Such analyses include defining proteomic subtypes of cancer, exploring the impact of somatic mutations and epigenetic modifications on protein expression, cataloging proteomic correlates of more aggressive disease or drug response, and identifying enriched pathways.

Protocol for using Multiomics2Targets to identify targets and driver kinases for cancer cohorts profiled with multi-omics assays

The availability of multi-omics data applied to profile cancer cohorts is rapidly increasing. Here, we present a protocol for Multiomics2Targets, a computational pipeline that can identify driver cell signaling pathways, protein kinases, and cell-surface targets for immunotherapy. We describe steps for preparing the data, uploading files, and tuning parameters. We then detail procedures for running the workflow, visualizing the results, and exporting and sharing reports containing the analysis. For complete details on the use and execution of this protocol, please refer to Deng et al.1.

PhosNetVis: A web-based tool for fast kinase-substrate enrichment analysis and interactive 2D/3D network visualizations of phosphoproteomics data

Protein phosphorylation involves the reversible modification of a protein (substrate) residue by another protein (kinase). Liquid chromatography-mass spectrometry studies are rapidly generating massive protein phosphorylation datasets across multiple conditions. Researchers then must infer kinases responsible for changes in phosphosites of each substrate. However, tools that infer kinase-substrate interactions (KSIs) are not optimized to interactively explore the resulting large and complex networks, significant phosphosites, and states. There is thus an unmet need for a tool that facilitates user-friendly analysis, interactive exploration, visualization, and communication of phosphoproteomics datasets. We present PhosNetVis, a web-based tool for researchers of all computational skill levels to easily infer, generate and interactively explore KSI networks in 2D or 3D by streamlining phosphoproteomics data analysis steps within a single tool. PhostNetVis lowers barriers for researchers in rapidly generating high-quality visualizations to gain biological insights from their phosphoproteomics datasets. It is available at: https://gumuslab.github.io/PhosNetVis/.

Comprehensive analysis of PDE2A: a novel biomarker for prognostic value and immunotherapeutic potential in human cancers

Phosphodiesterase 2A (PDE2A) plays a pivotal role in modulating cyclic nucleotide metabolism. Recent studies have shown that PDE2A is associated with some tumors, but its expression profiles, prognostic significance, and immunological roles in diverse cancer types remain unclear. Utilizing advanced bioinformatics tools, we performed a comprehensive analysis of PDE2A gene expression in multiple human cancers. Our study revealed that PDE2A expression was significantly reduced in the majority of cancer types and clinicopathological stages (I to IV) compared to normal tissues. Additionally, PDE2A expression was closely related to the prognosis of cancers such as stomach adenocarcinoma (STAD), ovarian serous cystadenocarcinoma (OV), and liver hepatocellular carcinoma (LIHC). Cox regression analyses indicated that PDE2A can act as an independent prognostic factor for these cancers. The level of PDE2A DNA methylation was significantly decreased in most cancers. Genetic alterations in PDE2A predominantly manifest in the form of amplifications. Moreover, infiltrating cells and immune checkpoint genes, including PDCD1, exhibited notable correlations with PDE2A expression. Significant associations were observed between PDE2A expression and tumor mutation burden as well as microsatellite instability. Single cell sequencing revealed PDE2A's crucial role in regulating differentiation and angiogenesis of cancer cells. Functional enrichment analysis emphasized the important role of PDE2A in synaptic transmission and tumor development. Aberrant expression of PDE2A influenced the sensitivity of various antitumor and chemotherapy drugs. This research provided a comprehensive analysis of PDE2A in human cancers, highlighting its potential as both a prognostic marker and an immunotherapy target for future research.

Exploring the Complexity of Pan-Cancer: Gene Convergences and in silico Analyses

Cancer is a complex and multifaceted group of diseases characterized by highly intricate mechanisms of tumorigenesis and tumor progression, which complicates diagnosis, prognosis, and treatment. In recent years, targeted therapies have gained prominence by focusing on specific mutations and molecular features unique to each tumor type, offering more effective and personalized treatment options. However, it is equally critical to explore the genetic commonalities across different types of cancer, which has led to the rise of pan-cancer studies. These approaches help identify shared therapeutic targets across various tumor types, enabling the development of broader and potentially more widely applicable treatment strategies. This review aims to provide a comprehensive overview of key concepts related to tumors, including tumorigenesis processes, the tumor microenvironment, and the role of extracellular vesicles in tumor biology. Additionally, we explore the molecular interactions and mechanisms driving tumor progression, with a particular focus on the pan-cancer perspective. To achieve this, we conducted an in silico analysis using publicly available datasets, which facilitated the identification of both common and divergent genetic and molecular patterns across different tumor types. By integrating these diverse areas, this review offers a clearer and deeper understanding of the factors influencing tumorigenesis and highlights potential therapeutic targets.

Artificial intelligence and omics in malignant gliomas

Glioblastoma multiforme (GBM) is one of the most common and aggressive type of malignant glioma with an average survival time of 12-18 mo. Despite the utilization of extensive surgical resections using cutting-edge neuroimaging, and advanced chemotherapy and radiotherapy, the prognosis remains unfavorable. The heterogeneity of GBM and the presence of the blood-brain barrier further complicate the therapeutic process. It is crucial to adopt a multifaceted approach in GBM research to understand its biology and advance toward effective treatments. In particular, omics research, which primarily includes genomics, transcriptomics, proteomics, and epigenomics, helps us understand how GBM develops, finds biomarkers, and discovers new therapeutic targets. The availability of large-scale multiomics data requires the development of computational models to infer valuable biological insights for the implementation of precision medicine. Artificial intelligence (AI) refers to a host of computational algorithms that is becoming a major tool capable of integrating large omics databases. Although the application of AI tools in GBM-omics is currently in its early stages, a thorough exploration of AI utilization to uncover different aspects of GBM (subtype classification, prognosis, and survival) would have a significant impact on both researchers and clinicians. Here, we aim to review and provide database resources of different AI-based techniques that have been used to study GBM pathogenesis using multiomics data over the past decade. We summarize different types of GBM-related omics resources that can be used to develop AI models. Furthermore, we explore various AI tools that have been developed using either individual or integrated multiomics data, highlighting their applications and limitations in the context of advancing GBM research and treatment.

Tumor-associated antigen prediction using a single-sample gene expression state inference algorithm

We developed a Bayesian-based algorithm to infer gene expression states in individual samples and incorporated it into a workflow to identify tumor-associated antigens (TAAs) across 33 cancer types using RNA sequencing (RNA-seq) data from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA). Our analysis identified 212 candidate TAAs, with 78 validated in independent RNA-seq datasets spanning seven cancer types. Eighteen of these TAAs were further corroborated by proteomics data, including 10 linked to liver cancer. We predicted that 38 peptides derived from these 10 TAAs would bind strongly to HLA-A02, the most common HLA allele. Experimental validation confirmed significant binding affinity and immunogenicity for 21 of these peptides. Notably, approximately 64% of liver tumors expressed one or more TAAs associated with these 21 peptides, positioning them as promising candidates for liver cancer therapies, such as peptide vaccines or T cell receptor (TCR)-T cell treatments. This study highlights the power of integrating computational and experimental approaches to discover TAAs for immunotherapy.

Multi-omics analysis reveals CMTR1 upregulation in cancer and roles in ribosomal protein gene expression and tumor growth

The mRNA cap methyltransferase CMTR1 plays a crucial role in RNA metabolism and gene expression regulation, yet its significance in cancer remains largely unexplored. Here, we present a comprehensive multi-omics analysis of CMTR1 across various human cancers, revealing its widespread upregulation and potential as a therapeutic target. Integrating transcriptomic and proteomic data from a large set of cancer samples, we demonstrate that CMTR1 is upregulated at the mRNA, protein, and phosphoprotein levels across multiple cancer types. Functional studies using CRISPR-mediated knockout and siRNA knockdown in breast cancer models show that CMTR1 depletion significantly inhibits tumor growth both in vitro and in vivo . Transcriptomic analysis reveals that CMTR1 primarily regulates ribosomal protein genes and other transcripts containing 5' Terminal Oligopyrimidine (TOP) motifs. Additionally, CMTR1 affects the expression of snoRNA host genes and snoRNAs, suggesting a broader role in RNA metabolism. Mechanistically, we propose that CMTR1's target specificity is partly determined by mRNA structure, particularly the presence of 5'TOP motifs. Furthermore, we identify a novel CMTR1 inhibitor, N97911, through in silico screening and biochemical assays, which demonstrates significant anti-tumor activity in vitro . Our findings establish CMTR1 as a key player in cancer biology, regulating critical aspects of RNA metabolism and ribosome biogenesis, and highlight its potential as a therapeutic target across multiple cancer types.

BAP1-mediated MAFF deubiquitylation regulates tumor growth and is associated with adverse outcomes in colorectal cancer

BACKGROUND

Despite improvements in colorectal cancer (CRC) treatment, the prognosis for advanced CRC patients remains poor. Disruption of protein stability is one of the important factors in cancer development and progression. In this study, we aim to identify and analyze novel dysregulated proteins in CRC, assessing their significance and the mechanisms.

METHODS

Using quantitative proteomics, expression pattern analysis, and gain-of-function/loss-of-function experiments, we identify novel functional protein dysregulated by ubiquitin-proteasome axis in CRC. Prognostic significance was evaluated in a training cohort of 546 patients and externally validated in 794 patients. Mechanistic insights are gained through molecular biology experiments, deubiquitinating enzymes (DUBs) expression library screening, and RNA sequencing.

RESULTS

MAFF protein emerged as the top novel candidate substrate regulated by ubiquitin-proteasome in CRC. MAFF protein was preferentially downregulated in CRC compared to adjacent normal tissues. More importantly, multicenter cohort study identified reduced MAFF protein expression as an independent predictor of overall and disease-free survival in CRC patients. The in vitro and vivo assays showed that MAFF overexpression inhibited CRC growth, while its knockdown had the opposite effect. Intriguingly, we found the abnormal expression of MAFF protein was predominantly regulated via ubiquitination of MAFF, with K48-ubiquitin being dominant. BAP1 as a nuclear deubiquitinating enzyme (DUB), bound to and deubiquitinated MAFF, thereby stabilizing it. Such stabilization upregulated DUSP5 expression, resulting in the inhibition of ERK phosphorylation.

CONCLUSIONS

This study describes a novel BAP1-MAFF signaling axis which is crucial for CRC growth, potentially serving as a therapeutic target and a promising prognostic biomarker for CRC.

NCI's Proteomic Data Commons: A Cloud-Based Proteomics Repository Empowering Comprehensive Cancer Analysis through Cross-Referencing with Genomic and Imaging Data

Proteomics has emerged as a powerful tool for studying cancer biology, developing diagnostics, and therapies. With the continuous improvement and widespread availability of high-throughput proteomic technologies, the generation of large-scale proteomic data has become more common in cancer research, and there is a growing need for resources that support the sharing and integration of multi-omics datasets. Such datasets require extensive metadata including clinical, biospecimen, and experimental and workflow annotations that are crucial for data interpretation and reanalysis. The need to integrate, analyze, and share these data has led to the development of NCI's Proteomic Data Commons (PDC), accessible at https://pdc.cancer.gov. As a specialized repository within the NCI Cancer Research Data Commons (CRDC), PDC enables researchers to locate and analyze proteomic data from various cancer types and connect with genomic and imaging data available for the same samples in other CRDC nodes. Presently, PDC houses annotated data from more than 160 datasets across 19 cancer types, generated by several large-scale cancer research programs with cohort sizes exceeding 100 samples (tumor and associated normal when available). In this article, we review the current state of PDC in cancer research, discuss the opportunities and challenges associated with data sharing in proteomics, and propose future directions for the resource.

SIGNIFICANCE

The Proteomic Data Commons (PDC) plays a crucial role in advancing cancer research by providing a centralized repository of high-quality cancer proteomic data, enriched with extensive clinical annotations. By integrating and cross-referencing with complementary genomic and imaging data, the PDC facilitates multi-omics analyses, driving comprehensive insights, and accelerating discoveries across various cancer types.

DCS, a novel classifier system based on disulfidptosis reveals tumor microenvironment heterogeneity and guides frontline therapy for clear cell renal carcinoma

Background

Emerging evidence suggests that cell deaths are involved in tumorigenesis and progression, which may be treated as a novel direction of cancers. Recently, a novel type of programmed cell death, disulfidptosis, was discovered. However, the detailed biological and clinical impact of disulfidptosis and related regulators remains largely unknown.

Methods

In this work, we first enrolled pancancer datasets and performed multi-omics analysis, including gene expression, DNA methylation, copy number variation and single nucleic variation profiles. Then we deciphered the biological implication of disulfidptosis in clear cell renal cell carcinoma (ccRCC) by machine learning. Finally, a novel agent targeting at disulfidptosis in ccRCC was identified and verified.

Results

We found that disulfidptosis regulators were dysregulated among cancers, which could be explained by aberrant DNA methylation and genomic mutation events. Disulfidptosis scores were depressed among cancers and negatively correlated with epithelial mesenchymal transition. Disulfidptosis regulators could satisfactorily stratify risk subgroups in ccRCC, and a novel subtype, DCS3, owning with disulfidptosis depression, insensitivity to immune therapy and aberrant genome instability were identified and verified. Moreover, treating DCS3 with NU1025 could significantly inhibit ccRCC malignancy.

Conclusion

This work provided a better understanding of disulfidptosis in cancers and new insights into individual management based on disulfidptosis.