Pan-cancer analysis of post-translational modifications reveals shared patterns of protein regulation

Integrated transcriptome analysis and combinatorial machine learning to construct a homeostatic model of acetylation for ccRCC and validate the key gene GCNT4

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is one of the most common malignant tumors of the urinary system. Protein acetylation plays a key role in regulating cellular processes and cancer signaling pathways. This study explores the potential biological mechanisms of ccRCC from the perspective of acetylation.

METHODS

This study obtained RNA-seq data and clinical information of ccRCC from TCGA and ICGC, and single-cell RNA sequencing datasets from the GEO database. Ten machine learning algorithms and their 101 combinations were used to analyze the prognostic significance of acetylation-related differentially expressed genes (DEGs) and to construct a prognostic risk model. GSEA was used to analyze the enrichment of different signaling pathways in high-risk and low-risk groups, and the correlation between immune infiltration and risk scores was assessed. Finally, the function of the key gene GCNT4 was verified through cell experiments.

RESULTS

This study identified 84 acetylation-regulated key genes with significant expression differences between tumor and normal tissues, closely linked to patient prognosis. The LASSO + RSF combination model performed best, and the model could accurately predict patient prognosis. The survival of patients in the high-risk group was significantly worse than that in the low-risk group. High expression of GCNT4 was associated with better survival prognosis and was expressed at higher levels in normal tissues than tumor tissues. Overexpression of GCNT4 significantly inhibited the proliferation, invasion, and migration of renal cancer cells and may affect acetylation by regulating the levels of O-GlcNAc modification in cells.

CONCLUSION

This study constructed a ccRCC acetylation homeostasis model via transcriptome analysis and machine learning, validating GCNT4 as a key gene. High expression of GCNT4 is associated with better survival prognosis and affects acetylation by regulating O-GlcNAc modification levels, inhibiting the proliferation and migration of renal cancer cells, providing a new potential target for the treatment of ccRCC.

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.

Tumor-Intrinsic SIRPA Drives Pyroptosis Evasion in Head and Neck Cancer

Pyroptosis, a gasdermin-mediated immunogenic cell death, has been shown to elicit adaptive antitumor immune responses, thereby augmenting the response to cancer immunotherapy when pyroptosis is therapeutically activated. However, despite increased gasdermin E (GSDME) expression, significant pyroptosis remains elusive in certain tumor types, and the underlying regulatory mechanisms are poorly understood. In this study, we observed high signal regulatory protein α1 (SIRPA) expression in head and neck squamous cell carcinoma (HNSCC) cells, a target in cancer immunotherapy. Intriguingly, SIRPA inhibition markedly augmented pyroptosis activity in tumor tissues and modulated tumor growth in a HNSCC mouse model. Subsequent investigations revealed that SIRPA knockout upregulated GSDME expression and potentiated cisplatin-induced pyroptosis in cancer cells. Integrative transcriptomics and metabolomics analysis suggested that the SIRPA knockout profoundly altered protein ubiquitination and augmented argininosuccinic acid levels in cancer cells. Specifically, we demonstrated that ubiquitin-specific peptidase 18 (USP18), a deubiquitinating enzyme, targets GSDME for deubiquitination and that USP18 knockdown suppressed cisplatin-induced pyroptosis. Notably, we found that succinylation of GSDME, which is mediated by succinyl-CoA, promotes GSDME cleavage without affecting caspase-3 activation. Further experiments indicated that SIRPA expression in tumor cells can decrease the antitumor efficacy of chemotherapy and immunotherapy in HNSCC mouse models. In summary, our findings reveal a novel mechanism of pyroptosis evasion in HNSCC, whereby tumor-intrinsic SIRPA enhances GSDME ubiquitylation and inhibits its succinylation. These insights suggest that inhibiting SIRPA expression may improve the efficacy of immunotherapy for HNSCC by inducing pyroptosis.

TPX2 lactylation is required for the cell cycle regulation and hepatocellular carcinoma progression

Targeting protein for Xklp2 (TPX2) is critical for mitosis and spindle assembly because of its control of Aurora kinase A (AURKA). However, the regulation of TPX2 activity and its subsequent effects on mitosis and cancer progression remain unclear. Here, we show that TPX2 is lactylated at K249 in hepatocellular carcinoma (HCC) tumour tissues and that this process is regulated by the lactylase CBP and the delactylase HDAC1. Lactate reduction via either shRNAs targeting lactate dehydrogenase A or the lactate dehydrogenase A inhibitor GSK2837808A decreases the level of TPX2 lactylation. Importantly, TPX2 lactylation is required for the cell cycle regulation and tumour growth. Mechanistically, TPX2 lactylation disrupts protein phosphatase 1 (PP1) binding to AURKA, enhances AURKA T288 phosphorylation, and facilitates the cell cycle progression. Overall, our study reveals a previously unappreciated role of TPX2 lactylation in regulating cell cycle progression and HCC tumorigenesis, exposing an important correlation between metabolic reprogramming and cell cycle regulation in HCC.

Targeting EP300 in diffuse large b-cell lymphoma: efficacy of A485 and synergistic effects with XPO1 inhibition

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is an aggressive hematopoietic malignancy, necessitating the exploration of innovative therapeutic approaches. Targeting epigenetic mechanisms has emerged as a promising avenue for cancer treatment. EP300 belongs to the KAT3 family of histone/non-histone lysine acetyltransferases, regulating gene expression by acetylating H3K27. However, the role of EP300 and its potential as a targeted therapy in DLBCL remains unknown.

METHODS

Public datasets were collected to evaluate the expression and clinical significance of epigenetic modification-related genes in patients with DLBCL. Flow cytometry, colony formation, and western blotting were conducted to investigate the function of EP300. CCK8, proliferation, cell cycle, and apoptosis assays, as well as experiments in tumor-bearing mouse models were conducted to determine the therapeutic effect of the EP300 inhibitor A485 alone or in combination with the XPO1 inhibitor KPT8602. RNA-seq was used to investigate the molecular mechanisms underlying the inhibition of DLBCL development by A485.

RESULTS

EP300 is frequently overexpressed in DLBCL and is associated with poor prognosis, highlighting its potential role in lymphoma progression. In this study, we found that A485, a novel small-molecule inhibitor targeting the conserved histone acetyltransferase (HAT) domain of EP300, significantly reduced H3K27Ac levels and demonstrated potent antitumor effects in DLBCL cells, both in vitro and in vivo. Furthermore, we showed that A485 attenuated DLBCL progression by inhibiting the MYC and E2F1 pathways. Notably, the combination of A485 with the XPO1 inhibitor KPT8602 produced synergistic anti-lymphoma in vitro and in vivo effects in DLBCL cell lines. This combination therapy resulted in enhanced tumor suppression in a DLBCL xenograft model with minimal toxicity. These findings suggested that targeting EP300, particularly in conjunction with XPO1 inhibition, could represent a promising therapeutic strategy for DLBCL treatment.

CONCLUSIONS

Our study elucidated that EP300 inhibition, especially in combination with XPO1 blockade, could serve as a promising therapeutic strategy for the treatment of DLBCL.

Harnessing technologies to unravel gastric cancer heterogeneity

Gastric cancer arises from complex carcinogenic factor interactions, with limited treatment options due to the lack of targetable driver gene mutations and significant tumor heterogeneity. Recent studies have provided promising novel approaches to improve our understanding of gastric cancer heterogeneity through integrated characterization, combining genomics with emerging technologies. Delineating the molecular changes and targeting specific molecular subtypes will enhance the efficacy of gastric cancer treatment and improve clinical outcomes. This review provides a comprehensive overview of current technologies used in gastric cancer research, highlighting key discoveries and treatment strategies driven by these innovations. Finally, we discuss the emerging technology-guided directions and potential breakthroughs that could enhance the understanding of gastric cancer tumor heterogeneity, ultimately improving clinical outcomes.

DNAJC12 downregulation induces neuroblastoma progression via increased histone H4K5 lactylation

Neuroblastoma (NB) is the most common extracranial solid tumor in children. Despite treatment advances, the survival rates of high-risk NB patients remain low. This highlights the urgent need for a deeper understanding of the molecular mechanisms driving NB progression to support the development of new therapeutic strategies. In this study, we demonstrated that the reduced levels of DNAJC12, a protein involved in metabolic regulation, are associated with poor prognosis in NB patients. Our data indicate that low DNAJC12 expression activates glycolysis in NB cells, leading to increased lactic acid production and histone H4 lysine 5 lactylation (H4K5la). Elevated H4K5la upregulates the transcription of COL1A1, a gene implicated in cell metastasis. Immunohistochemistry staining of NB patient samples confirmed that high H4K5la levels correlate with poor clinical outcomes. Furthermore, we showed that inhibiting glycolysis, reducing H4K5la, or targeting COL1A1 can mitigate the invasive behavior of NB cells. These findings reveal a critical link between metabolic reprogramming and epigenetic modifications in the context of NB progression, suggesting that H4K5la could serve as a novel diagnostic and prognostic marker, and shed light on identifying new therapeutic targets within metabolic pathways for the treatment of this aggressive pediatric cancer.

Synergistic regulation of DACH1 stability by acetylation and deubiquitination promotes colorectal cancer progression

Colorectal cancer (CRC) remains a significant challenge in oncology, with limited therapeutic options for aggressive subtypes. This study elucidates the critical roles of USP7 and DACH1 in CRC, revealing their involvement in tumor progression and potential as therapeutic targets. USP7 was identified as a deubiquitinase that stabilizes DACH1, enhancing its tumor-promoting activities. Mechanistically, USP7 directly interacts with DACH1, protecting DACH1 from UHRF1-induced ubiquitination and degradation by removing ubiquitin chains, particularly K48-linked types, which are crucial for protein degradation. Additionally, acetylation at K680 on DACH1, mediated by acetyltransferase GCN5, enhances its interaction with USP7, further influencing DACH1 stability and function. Clinically, high levels of USP7 and DACH1 correlate with poor prognosis in CRC patients, underscoring their significance in disease progression. These findings suggest that targeting the USP7-DACH1 axis could offer a novel therapeutic strategy for managing CRC, particularly in forms characterized by aggressive and metastatic behaviors.

Leveraging AI to explore structural contexts of post-translational modifications in drug binding

Post-translational modifications (PTMs) play a crucial role in allowing cells to expand the functionality of their proteins and adaptively regulate their signaling pathways. Defects in PTMs have been linked to numerous developmental disorders and human diseases, including cancer, diabetes, heart, neurodegenerative and metabolic diseases. PTMs are important targets in drug discovery, as they can significantly influence various aspects of drug interactions including binding affinity. The structural consequences of PTMs, such as phosphorylation-induced conformational changes or their effects on ligand binding affinity, have historically been challenging to study on a large scale, primarily due to reliance on experimental methods. Recent advancements in computational power and artificial intelligence, particularly in deep learning algorithms and protein structure prediction tools like AlphaFold3, have opened new possibilities for exploring the structural context of interactions between PTMs and drugs. These AI-driven methods enable accurate modeling of protein structures including prediction of PTM-modified regions and simulation of ligand-binding dynamics on a large scale. In this work, we identified small molecule binding-associated PTMs that can influence drug binding across all human proteins listed as small molecule targets in the DrugDomain database, which we developed recently. 6,131 identified PTMs were mapped to structural domains from Evolutionary Classification of Protein Domains (ECOD) database.Scientific contribution: Using recent AI-based approaches for protein structure prediction (AlphaFold3, RoseTTAFold All-Atom, Chai-1), we generated 14,178 models of PTM-modified human proteins with docked ligands. Our results demonstrate that these methods can predict PTM effects on small molecule binding, but precise evaluation of their accuracy requires a much larger benchmarking set. We also found that phosphorylation of NADPH-Cytochrome P450 Reductase, observed in cervical and lung cancer, causes significant structural disruption in the binding pocket, potentially impairing protein function. All data and generated models are available from DrugDomain database v1.1 ( http://prodata.swmed.edu/DrugDomain/ ) and GitHub ( https://github.com/kirmedvedev/DrugDomain ). This resource is the first to our knowledge in offering structural context for small molecule binding-associated PTMs on a large scale.

GIMAP8 could serve as a potential prognostic factor for lung adenocarcinoma and is closely related to immunity

GTPase IMAP family member 8 (GIMAP8) plays a key role in pathophysiology of several malignancies. The objective of this current research endeavor was to investigate the prognosis value of GIMAP8 in lung adenocarcinoma and examine how it relates to immunity. Expression profiles associated with GIMAP8 and related clinical details were acquired from The Cancer Genome Atlas database, and we conducted survival analysis, enrichment analysis and immune infiltration studies. Additionally, we evaluated the effect of GIMAP8 on radiation resistance of tumor by in vivo and in vitro experiments. Our results showed that lung adenocarcinoma tumor tissues exhibited lower GIMAP8 levels compared to nearby normal tissues. Furthermore, decreased GIMAP8 expression strongly correlated with poorer OS. The expression of GIMAP8 is closely related to the formation of radiation resistance in tumor cells. GSEA identified multiple signaling pathways linked to GIMAP8, including immune-related, chemokine, cell adhesion molecule, and NF-κB signaling pathways. GIMAP8 expression strongly correlated with the expression of immune checkpoint molecules, tumor mutational burden, tumor neoantigen burden, immune cells, and tumor immune microenvironment. GIMAP8 was found to have an inhibitory effect on lung adenocarcinoma and was closely related to the immune response. Moreover, GIMAP8 may also influence radiation resistance in tumors.

Histone modifications and metabolic reprogramming in tumor-associated macrophages: a potential target of tumor immunotherapy

Histone modifications, including methylation, acetylation, lactylation, phosphorylation, ubiquitination, SUMOylation, ADP-ribosylation, and crotonylation, critically regulate tumor-associated macrophages (TAMs) polarization by modulating gene expression and functional states. Reprogramming TAMs from M2 to M1 phenotypes through epigenetic targeting has emerged as a promising strategy to enhance anti-tumor immunity and improve the efficacy of cancer immunotherapy. This review explores the role of histone modifications in TAM biology, their interplay with metabolic reprogramming, and the opportunities and challenges in developing epigenetic-based therapies to advance cancer immunotherapy.

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.

Multicancer analyses of short tandem repeat variations reveal shared gene regulatory mechanisms

Short tandem repeats (STRs) have been reported to influence gene expression across various human tissues. While STR variations are enriched in colorectal, stomach, and endometrial cancers, particularly in microsatellite instable tumors, their functional effects and regulatory mechanisms on gene expression remain poorly understood across these cancer types. Here, we leverage whole-exome sequencing and gene expression data to identify STRs for which repeat lengths are associated with the expression of nearby genes (eSTRs) in colorectal, stomach, and endometrial tumors. While most eSTRs are cancer-specific, shared eSTRs across multiple cancers exhibit consistent effects on gene expression. Notably, coding-region eSTRs identified in all three cancer types show positive correlations with nearby gene expression. We further validate the functional effects of eSTRs by demonstrating associations between somatic eSTR mutations and gene expression changes during the transition from normal to tumor tissues, suggesting their potential roles in tumorigenesis. Combined with DNA methylation data, we perform the first quantitative analysis of the interplay between STR variations and DNA methylation in tumors. We identify eSTRs where repeat lengths are associated with methylation levels of nearby CpG sites (meSTRs) and show that >70% of eSTRs are significantly linked to local DNA methylation. Importantly, the effects of meSTRs on DNA methylation remain consistent across cancer types. Overall, our findings enhance the understanding of how functional STR variations influence gene expression and DNA methylation. Our study highlights shared regulatory mechanisms of STRs across multiple cancers, offering a foundation for future research into their broader implications in tumor biology.

MARVELD1 inhibits Neuro2a cell migration and tumorigenesis via regulating the transcriptional coactivators and protein methylation

Neuronal cell proliferation and migration play an important regulatory role in the development and tumorigenesis of the nervous system. MARVEL domain-containing protein 1 (MARVELD1) is a potential tumor suppressor gene whose function in nervous system diseases is unclear. This study aimed to analyze the function and molecular mechanism of MARVELD1 gene in Neuro2a cell migration and tumor growth. We found that MARVELD1 could inhibit the tumor development with low-expression of Ki67, high-expression of cleaved-caspase3 and strong signal TUNEL staining. Moreover, MARVELD1 suppressed migration and epithelial to mesenchymal transition process in Neuro2a cells. Mass spectrometry analysis indicated that MARVELD1 could bind to PPP1CB, PPP1CC and NRAS. The RNA-sequencing analysis showed that MARVELD1 regulated transcriptional coactivators and protein methylation. These genes ultimately affected the pathways related to cell migration. These data highlight the potential usefulness of MARVELD1 as a potential target for preventing nervous system tumor genesis and metastasis.

Mass Spectrometry-Based Proteomics for Next-Generation Precision Oncology

Cancer is the leading cause of death worldwide characterized by patient heterogeneity and complex tumor microenvironment. While the genomics-based testing has transformed modern medicine, the challenge of diverse clinical outcomes highlights unmet needs for precision oncology. As functional molecules regulating cellular processes, proteins hold great promise as biomarkers and drug targets. Mass spectrometry (MS)-based clinical proteomics has illuminated the molecular features of cancers and facilitated discovery of biomarkers or therapeutic targets, paving the way for innovative strategies that enhance the precision of personalized treatment. In this article, we introduced the tools and current achievements of MS-based proteomics, choice of discovery and targeted MS from discovery to validation phases, profiling sensitivity from bulk samples to single-cell level and tissue to liquid biopsy specimens, current regulatory landscape of MS-based protein laboratory-developed tests (LDTs). The challenges, success and future perspectives in translating research MS assay into clinical applications are also discussed. With well-designed validation studies to demonstrate clinical benefits and meet the regulatory requirements for both analytical and clinical performance, the future of MS-based assays is promising with numerous opportunities to improve cancer diagnosis, treatment, and monitoring.

RNF128 promotes gastric cancer progression by inhibiting autophagy-dependent ferroptosis through Beclin1 ubiquitination

As an important protein post-translational modification process, ubiquitination plays an indispensable role in the regulation of gastric cancer (GC) occurrence and development. And recent studies have demonstrated that this modification is closely related to regulated cell death. This suggests that our therapeutic approach to inhibit the malignant progression of GC by regulating the intracellular death mode through ubiquitination modification becomes possible. Although ubiquitination modification has been well described in some tumorigenesis, its potential role and specific mechanisms are still unknown. In the present study, we identified RNF128, an E3 ubiquitin ligase with a RING structural domain, whose expression was significantly increased in GC. In-depth studies showed that knockdown of RNF128 significantly inhibited GC cell proliferation and increased intracellular autophagic flux and lipid peroxidation production, and we hypothesized that autophagy-dependent ferroptosis might be the main mode of death mediated by RNF128. Mechanistically, RNF128 directly binds and ubiquitinates degradation of Beclin1 through its PA structural domain and significantly inhibits the Beclin1/solute transport family 7 member 11(SLC7A11)/glutathione peroxidase 4(GPX4) axis. Taken together, our study reports for the first time that RNF128 acts as a tumor promoter to inhibit autophagy-dependent ferroptosis in GCs by targeting Beclin1. These data provide new insights into the activation of intracellular ferroptosis to inhibit malignant tumor progression and are expected to provide a new strategy for molecular therapy in clinical GC patients.

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.

Post-translational acylation of proteins in cardiac hypertrophy

Acylations are post-translational modifications in which functional groups are attached to amino acids on proteins. Most acylations (acetylation, butyrylation, crotonylation, lactylation, malonylation, propionylation and succinylation) involve lysine but cysteine (palmitoylation) and glycine (myristoylation) residues can also be altered. Acylations have important roles in physiological and pathophysiological processes, including cardiac hypertrophy and related cardiovascular diseases. These post-translational modifications influence chromatin architecture, transcriptional regulation and metabolic pathways, thereby affecting cardiomyocyte function and pathology. The dynamic interaction between these acylations and their regulatory enzymes, such as histone acetyltransferases, histone deacetylases and sirtuins, underscores the complexity of cellular homeostasis and pathological processes. Emerging evidence highlights the therapeutic potential of targeting acylations to modulate enzyme activity and metabolite levels, offering promising avenues for novel treatments. In this Review, we explore the diverse mechanisms through which acylations contribute to cardiac hypertrophy, highlighting the complexity and potential therapeutic targets in this regulatory network.

Exploring the impact of RPN1 on tumorigenesis and immune response in cancer

The ribophorin family, including RPN1, has been associated with tumor progression, but its specific role in pan-cancer dynamics remains unclear. Using data from TCGA, GTEx, and Ualcan databases, we investigated the relationship of RPN1 with prognosis, genomic alterations, and epigenetic modifications across various cancers. Differential analysis revealed elevated RPN1 expression in multiple cancer types, indicating a potential prognostic value. Amplification was the predominant mutation type of RPN1 in pan-cancer, with notable correlations with DNA methylation and copy number variation. Gene set variation analysis identified RPN1's involvement in cancer development, immunity, and metabolism. Additionally, RPN1 expression correlated with the tumor microenvironment, immune response factors, and response to anti-tumor therapies. Functional validation in triple-negative breast cancer, glioblastoma, and bladder cancer cell lines demonstrated the role of RPN1 in tumor cell proliferation and migration. Our findings highlight RPN1 as a potential biomarker for cancer diagnosis and treatment response in pan-cancer therapy.

Pan-cancer analysis shapes the understanding of cancer biology and medicine

Advances in multi-omics datasets and analytical methods have revolutionized cancer research, offering a comprehensive, pan-cancer perspective. Pan-cancer studies identify shared mechanisms and unique traits across different cancer types, which are reshaping diagnostic and treatment strategies. However, continued innovation is required to refine these approaches and deepen our understanding of cancer biology and medicine. This review summarized key findings from pan-cancer research and  explored their potential to drive future advancements in oncology.

Leveraging AI to Explore Structural Contexts of Post-Translational Modifications in Drug Binding

Post-translational modifications (PTMs) play a crucial role in allowing cells to expand the functionality of their proteins and adaptively regulate their signaling pathways. Defects in PTMs have been linked to numerous developmental disorders and human diseases, including cancer, diabetes, heart, neurodegenerative and metabolic diseases. PTMs are important targets in drug discovery, as they can significantly influence various aspects of drug interactions including binding affinity. The structural consequences of PTMs, such as phosphorylation-induced conformational changes or their effects on ligand binding affinity, have historically been challenging to study on a large scale, primarily due to reliance on experimental methods. Recent advancements in computational power and artificial intelligence, particularly in deep learning algorithms and protein structure prediction tools like AlphaFold3, have opened new possibilities for exploring the structural context of interactions between PTMs and drugs. These AI-driven methods enable accurate modeling of protein structures including prediction of PTM-modified regions and simulation of ligand-binding dynamics on a large scale. In this work, we identified small molecule binding-associated PTMs that can influence drug binding across all human proteins listed as small molecule targets in the DrugDomain database, which we developed recently. 6,131 identified PTMs were mapped to structural domains from Evolutionary Classification of Protein Domains (ECOD) database. Scientific contribution. Using recent AI-based approaches for protein structure prediction (AlphaFold3, RoseTTAFold All-Atom, Chai-1), we generated 14,178 models of PTM-modified human proteins with docked ligands. Our results demonstrate that these methods can predict PTM effects on small molecule binding, but precise evaluation of their accuracy requires a much larger benchmarking set. We also found that phosphorylation of NADPH-Cytochrome P450 Reductase, observed in cervical and lung cancer, causes significant structural disruption in the binding pocket, potentially impairing protein function. All data and generated models are available from DrugDomain database v1.1 (http://prodata.swmed.edu/DrugDomain/) and GitHub (https://github.com/kirmedvedev/DrugDomain). This resource is the first to our knowledge in offering structural context for small molecule binding-associated PTMs on a large scale.

FBXO45 restricts HIV-1 replication by inducing SQSTM1/p62-mediated autophagic degradation of Tat

As a key regulator of human immunodeficiency virus type 1 (HIV-1) transcription, Tat plays an essential role in viral replication and latency, making it a promising target for designing viral control strategies. Identifying host factors that modulate Tat and exploring the underlying mechanisms will benefit our understanding of HIV-1 transcriptional regulation and provide valuable insights into Tat-based therapeutic strategies. Here, by employing the TurboID approach, we discovered high-affinity binding between FBXO45 and Tat. Our findings demonstrate that FBXO45 negatively regulates Tat by promoting Tat ubiquitination and directing it to autophagic degradation. Autophagic degradation of Tat has been reported, but the specific underlying mechanisms remain unidentified. We elucidated this issue by providing evidence that FBXO45-mediated Tat polyubiquitination is an essential prerequisite for this process. Silencing of FBXO45 leads to a deficiency of autophagy receptor SQSTM1/p62 to bind and facilitate the autophagic degradation of Tat. Our results further underscore the crosstalk between post-translational modifications of Tat by demonstrating that the phosphorylation site of the Tat S62 residue is required for ubiquitination induced by FBXO45. Furthermore, in the context of the regulation of HIV-1, FBXO45 inhibits viral replication and maintains the latency of HIV-1 by suppressing viral transcription. Importantly, FBXO45 overexpression significantly attenuated viral rebound after antiretroviral therapy withdrawal. In summary, our findings suggest a novel role for FBXO45 in regulating HIV-1 replication by inducing the ubiquitination and SQSTM1/p62-dependent autophagic degradation of Tat. Considering the indispensable role of Tat in the regulation of HIV-1 replication and reactivation, FBXO45 may be a potential target for therapeutic intervention against HIV-1.IMPORTANCEHIV-1 Tat plays an indispensable role in regulating viral transcription and is a promising target for achieving a functional cure for AIDS. Identifying the host factors that modulate Tat expression could benefit the development of anti-HIV-1 strategies targeting Tat. Using TurboID assay, we identified a significant interaction between FBXO45 and Tat. Functionally, FBXO45 ubiquitinates and directs Tat for SQSTM1/p62-mediated autophagic degradation, thereby effectively restricting HIV-1 replication and maintaining HIV-1 latency by suppressing Tat-dependent viral transcription. These findings uncover a novel role for FBXO45 in regulating Tat and broaden our understanding of the host mechanisms involved in Tat processing.

A novel lactylation-related gene signature to predict prognosis and treatment response in lung adenocarcinoma

Background

Lactylation, a novel post-translational modification, has emerged as a critical regulatory mechanism in various biological processes, including tumor progression. However, its role and associated gene signatures in lung adenocarcinoma (LUAD) remain unclear.

Methods

RNA sequencing data of LUAD patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Unsupervised clustering was used to identify lactylation-related genes. A risk prognostic model was constructed using least absolute shrinkage and selection operator regression analysis and subsequently validated. A nomogram was then employed to optimize the clinical applicability of the risk score. Additionally, various algorithms were used to explore the relationship between the risk score and immune infiltration levels, with model genes analyzed based on single-cell sequencing. The effects of RCCD1 knockdown on LUAD cell proliferation and migration were evaluated through CCK8 and transwell assays.

Results

Higher risk scores were associated with poorer overall survival prognosis. Immune analysis revealed that the risk score may play a role in regulating the tumor microenvironment. Additionally, these risk scores were found to be associated with chemotherapy drug sensitivity. A series of experiments further demonstrated that RCCD1 promotes LUAD cell proliferation and migration in vitro.

Conclusion

This study highlights the critical role of lactylation-related gene signatures in LUAD and their association with immune cell infiltration, providing insights into potential therapeutic targets and biomarkers for clinical application.

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.

Tumor Metastasis: Mechanistic Insights and Therapeutic Intervention

Metastasis remains a leading cause of cancer‐related deaths, defined by a complex, multi‐step process in which tumor cells spread and form secondary growths in distant tissues. Despite substantial progress in understanding metastasis, the molecular mechanisms driving this process and the development of effective therapies remain incompletely understood. Elucidating the molecular pathways governing metastasis is essential for the discovery of innovative therapeutic targets. The rapid advancements in sequencing technologies and the expansion of biological databases have significantly deepened our understanding of the molecular drivers of metastasis and associated drug resistance. This review focuses on the molecular drivers of metastasis, particularly the roles of genetic mutations, epigenetic changes, and post‐translational modifications in metastasis progression. We also examine how the tumor microenvironment influences metastatic behavior and explore emerging therapeutic strategies, including targeted therapies and immunotherapies. Finally, we discuss future research directions, stressing the importance of novel treatment approaches and personalized strategies to overcome metastasis and improve patient outcomes. By integrating contemporary insights into the molecular basis of metastasis and therapeutic innovation, this review provides a comprehensive framework to guide future research and clinical advancements in metastatic cancer.

Causal Inference and Annotation of Phosphoproteomics Data in Multiomics Cancer Studies

Protein phosphorylation plays a crucial role in regulating diverse biological processes. Perturbations in protein phosphorylation are closely associated with downstream pathway dysfunctions, whereas alterations in protein expression could serve as sensitive indicators of pathological status. However, there are currently few methods that can accurately identify the regulatory links between protein phosphorylation and expression, given issues like reverse causation and confounders. Here, we present Phoslink, a causal inference model to infer causal effects between protein phosphorylation and expression, integrating prior evidence and multiomics data. We demonstrated the feasibility and advantages of our method under various simulation scenarios. Phoslink exhibited more robust estimates and lower false discovery rate than commonly used Pearson and Spearman correlations, with better performance than canonical instrumental variable selection methods for Mendelian randomization. Applying this approach, we identified 345 causal links involving 109 phosphosites and 310 proteins in 79 lung adenocarcinoma (LUAD) samples. Based on these links, we constructed a causal regulatory network and identified 26 key regulatory phosphosites as regulators strongly associated with LUAD. Notably, 16 of these regulators were exclusively identified through phosphosite-protein causal regulatory relationships, highlighting the significance of causal inference. We explored potentially druggable phosphoproteins and provided critical clues for drug repurposing in LUAD. We also identified significant mediation between protein phosphorylation and LUAD through protein expression. In summary, our study introduces a new approach for causal inference in phosphoproteomics studies. Phoslink demonstrates its utility in potential drug target identification, thereby accelerating the clinical translation of cancer proteomics and phosphoproteomic data.

Targeting FDFT1 Reduces Cholesterol and Bile Acid Production and Delays Hepatocellular Carcinoma Progression Through the HNF4A/ALDOB/AKT1 Axis

Targeting cholesterol metabolism is a novel direction for tumor therapy. Unfortunately, the current use of statins for hepatocellular carcinoma (HCC) is controversial. Herein, farnesyl-diphosphate farnesyltransferase 1 (FDFT1) is identified as a novel target for treating HCC and a potential alternative to statins. Twenty-three key genes in cholesterol biosynthesis are screened, and FDFT1 is identified via public databases (The Cancer Genome Atlas, International Cancer Genome Consortium and Gene Expression Omnibus). Clinical samples reveal that FDFT1 is highly expressed in HCC tissues, and this phenotype is strongly associated with a poor prognosis. Functionally, FDFT1 knockdown inhibits the proliferation and metastasis of HCC cells and suppresses hepatocarcinogenesis in vitro and in vivo, whereas FDFT1 overexpression promotes HCC cell proliferation and metastasis. Mechanistically, FDFT1 downregulation decreases cholesterol and bile acid levels and then increases hepatocyte nuclear factor 4 alpha (HNF4A) transcriptional activity. Experiments indicate that HNF4A combines with the promoter of aldolase B (ALDOB) and promotes the ALDOB transcription and that ALDOB combines with AKT serine/threonine kinase 1 (AKT1) and inhibits AKT1 phosphorylation. Moreover, FDFT1 knockdown combined with AKT inhibitor (AZD5363) treatment shows remarkable therapeutic potential. FDFT1 inhibition reduces cholesterol and bile acid levels to delay HCC progression through the HNF4A/ALDOB/AKT1 axis. Thus, targeting FDFT1 may be a novel potential strategy for treating HCC.

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.

Comparative sequencing study of mismatch repair and homology-directed repair genes in endometrial cancer and breast cancer patients from Kazakhstan

Endometrial cancer has been associated with pathogenic variants in mismatch repair (MMR) genes, especially in the context of the hereditary Lynch Syndrome. More recently, pathogenic variants in genes of homology-directed repair (HDR) have also been suggested to contribute to a subset of endometrial cancers. In the present hospital-based study, we investigated the relative distribution of pathogenic MMR or HDR gene variants in a series of 342 endometrial cancer patients from the Oncology Clinic in Almaty, Kazakhstan. In comparison, we also sequenced 178 breast cancer patients from the same population with the same gene panel. Identified variants were classified according to ClinVar, ESM1b, and AlphaMissense prediction tools. We found 10 endometrial cancer patients (2.9%) carrying pathogenic or likely pathogenic variants in MMR genes (7 MSH6, 1 MSH2, 2 MUTYH), while 14 endometrial cancer patients (4.1%) carried pathogenic variants in HDR genes (4 BRCA2, 3 BRCA1, 3 FANCM, 2 SLX4, 1 BARD1, 1 BRIP1). In the breast cancer series, we found 8 carriers (4.5%) of pathogenic or likely pathogenic variants in MMR genes (2 MSH2, 2 MSH6, 4 MUTYH) while 12 patients (6.7%) harbored pathogenic or likely pathogenic HDR gene variants (5 BRCA1, 3 BRCA2, 1 BRIP1, 1 ERRC4, 1 FANCM, 1 SLX4). One patient who developed breast cancer first and endometrial cancer later carried a novel frameshift variant in MSH6. Our results indicate that MMR and HDR gene variants with predicted pathogenicity occur at substantial frequencies in both breast and endometrial cancer patients from the Kazakh population.

Identifying Sex Differences in Lung Adenocarcinoma Using Multi-Omics Integrative Protein Signaling Networks

Lung adenocarcinoma (LUAD) exhibits differences between the sexes in incidence, prognosis, and therapy, suggesting underexplored molecular mechanisms. We conducted an integrative multi-omics analysis using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) datasets to contrast transcriptomes and proteomes between sexes. We used TIGER to analyze TCGA-LUAD expression data and found sex-biased activity of transcription factors (TFs); we used PTM-SEA with CPTAC-LUAD proteomics data and found sex-biased kinase activity. We combined these to construct a kinase-TF signaling network and discovered druggable pathways linked to cancer-related processes. We also found significant sex biases in clinically relevant TFs and kinases, including NR3C1, AR, and AURKA. Using the PRISM drug screening database, we identified potential sex-specific drugs, such as glucocorticoid receptor agonists and aurora kinase inhibitors. Our findings emphasize the importance of considering sex and using multi-omics network methods to discover personalized cancer therapies.

Implication of protein post translational modifications in gastric cancer

Gastric cancer (GC) is one of the most common and highly lethal malignant tumors worldwide, and its occurrence and development are regulated by multiple molecular mechanisms. Post-translational modifications (PTM) common forms include ubiquitylation, phosphorylation, acetylation and methylation. Emerging research has highlighted lactylation and glycosylation. The diverse realm of PTM and PTM crosstalk is linked to many critical signaling events involved in neoplastic transformation, carcinogenesis and metastasis. This review provides a comprehensive overview of the impact of PTM on the occurrence and progression of GC. Specifically, aberrant PTM have been shown to alter the proliferation, migration, and invasion capabilities of GC cells. Moreover, PTM are closely associated with resistance to chemotherapeutic agents in GC. Notably, this review also discusses the phenomenon of PTM crosstalk, highlighting the interactions among PTM and their roles in regulating signaling pathways and protein functions. Therefore, in-depth investigation into the mechanisms of PTM and the development of targeted therapeutic strategies hold promise for advancing early diagnosis, treatment, and prognostic evaluation of GC, offering novel insights and future research directions.

Magnesium-dependent-protein phosphatase 1B regulates the protein arginine methyltransferase 5 through the modulation of myosin phosphatase

Dysregulation of the expression levels and the activity of kinases/phosphatases is an intrinsic hallmark of tumor transformation and progression, as either as a primary cause or consequence. The myosin phosphatase (MP)/protein arginine methyltransferase 5 (PRMT5)/histone (H4) pathway is an oncogenic signaling pathway downregulating the gene expression of tumor suppressors. However, the upstream regulators of the pathway are unknown. We show that the Mg2+-dependent protein phosphatase 1 B (PP2Cb or PPM1B) interacts and regulates MP through the MYPT1 regulatory subunit, and this interplay results in the inactivation of the tumorigenic pathway driven by PRMT5. The phospho-Thr696 inhibitory residues of the MYPT1 regulatory subunit of MP was dephosphorylated by PPM1B. The inhibition of PPM1B by sanguinarine resulted in the deactivation of MP and the increased activity of PRMT5 leading to increased symmetric dimethylation of histone H4 in HeLa cells. The overexpression of the PPM1B had the opposite action. The overexpression of PPM1B decreased the colonization activity of HeLa cells through modulation of MP. Finally, human cervical carcinoma biopsies showed almost complete elimination of PPM1B compared to their healthy control counterparts. The phosphorylation of the inhibitory MYPT1pT696 and the regulatory PRMT5pT80 residues and the symmetric dimethylation of H4 were elevated in the cancer biopsies and it resulted in a decrease in retinoblastoma protein expression. The results indicate a tumor suppressor role of the PPM1B/MP axis via inhibition of PRMT5, thereby regulating gene expression through H4 arginine dimethylation. Collectively, PPM1B is a tumor suppressor and a possible tumor marker for cervical carcinoma.

Unlocking the multifaceted molecular functions and diverse disease implications of lactylation

In recent years, a significant breakthrough has emerged in biology, the identification of lactylation, a novel post-translational process. This intriguing modification is not limited to a specific class of proteins but occurs across a diverse range, including histones, signalling molecules, enzymes, and substrates. It can exert a broad regulatory role in various diseases, ranging from developmental anomalies and neurodegenerative disorders to inflammation and cancer. Thus, it presents exciting opportunities for exploring innovative treatment approaches. As a result, there has been a recent surge of research interest, leading to a deeper understanding of the molecular mechanisms and regulatory functions underlying lactylation within physiological and pathological processes. Here, we review the detection and molecular mechanisms of lactylation, from biological functions to disease effects, providing a systematic overview of the mechanisms and functions of this post-translational modification.

Integrative multi-omics and machine learning identify a robust signature for discriminating prognosis and therapeutic targets in bladder cancer

Background: Bladder cancer (BLCA) is a common malignant tumor whose pathogenesis has not yet been fully elucidated. This study analyzed prognostic genes in BLCA by integrating transcriptomics and proteomics data, and established prognostic models, aiming to offer novel insights for BLCA therapy. Methods: Transcriptomic, proteomic, and protein acetylation sequencing were conducted on six BLCA tumor tissues and six paraneoplastic tissue samples. Furthermore, data from TCGA-BLCA, GSE13507, and single-cell RNA sequencing (scRNA-seq) datasets were integrated. Initially, differential expression analysis identified candidate genes regulated by acetylation. These genes were further refined by intersecting with scRNA-DEG obtained from the scRNA-seq dataset, resulting in the identification of key genes. Subsequently, consistency clustering analysis was performed based on these key genes. Prognostic models were then developed utilizing Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression. Independent prognostic factors were determined through independent prognostic analysis, followed by the establishment of a nomogram model. Additionally, gene set enrichment analysis (GSEA), immune cell infiltration analysis, mutation analysis, and drug sensitivity analysis were conducted between the two risk groups to elucidate underlying mechanisms. Results: A total of 15 key genes were obtained by crossing 284 candidate genes with 510 scRNA-DEGs. Patients in the TCGA-BLCA dataset were categorized into two subtypes based on the 15 key genes. Next, a risk model was developed using five prognostic genes (CTSE, XAGE2, MAP1A, CASQ2, and FXYD6), and a nomogram model was developed using age, pathologic T, pathologic N, and risk score. A total of 1089 GO entries and 49 KEGG pathways, including cytokine-cytokine receptor interactions, ECM receptor interactions, etc., were involved in all genes in both risk groups. The immunization score, matrix score, and ESTIMATE score were significantly higher in the low-risk group than in the high-risk group. Conclusion: CTSE, XAGE2, MAP1A, CASQ2 and FXYD6 were selected as prognostic genes in BLCA, risk model and nomogram model predicting the prognosis of BLCA patients were constructed. These were helpful for prognostic assessment of BLCA.

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.

Exploring the landscape of post-translational modification in drug discovery

Post-translational modifications (PTMs) play a crucial role in regulating protein function, and their dysregulation is frequently associated with various diseases. The emergence of epigenetic drugs targeting factors such as histone deacetylases (HDACs) and histone methyltransferase enhancers of zeste homolog 2 (EZH2) has led to a significant shift towards precision medicine, offering new possibilities to overcome the limitations of traditional therapeutics. In this review, we aim to systematically explore how small molecules modulate PTMs. We discuss the direct targeting of enzymes involved in PTM pathways, the modulation of substrate proteins, and the disruption of protein-enzyme interactions that govern PTM processes. Additionally, we delve into the emerging strategy of employing multifunctional molecules to precisely regulate the modification levels of proteins of interest (POIs). Furthermore, we examine the specific characteristics of these molecules, evaluating their therapeutic benefits and potential drawbacks. The goal of this review is to provide a comprehensive understanding of PTM-targeting strategies and their potential for personalized medicine, offering a forward-looking perspective on the evolution of precision therapeutics.

The present and future of the Cancer Dependency Map

Despite tremendous progress in the past decade, the complex and heterogeneous nature of cancer complicates efforts to identify new therapies and therapeutic combinations that achieve durable responses in most patients. Further advances in cancer therapy will rely, in part, on the development of targeted therapeutics matched with the genetic and molecular characteristics of cancer. The Cancer Dependency Map (DepMap) is a large-scale data repository and research platform, aiming to systematically reveal the landscape of cancer vulnerabilities in thousands of genetically and molecularly annotated cancer models. DepMap is used routinely by cancer researchers and translational scientists and has facilitated the identification of several novel and selective therapeutic strategies for multiple cancer types that are being tested in the clinic. However, it is also clear that the current version of DepMap is not yet comprehensive. In this Perspective, we review (1) the impact and current uses of DepMap, (2) the opportunities to enhance DepMap to overcome its current limitations, and (3) the ongoing efforts to further improve and expand DepMap.

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.

Construction of a Cancer Stem Cell related Histone Acetylation Regulatory Genes Prognostic Model for Hepatocellular Carcinoma via Bioinformatics Analysis: Implications for Tumor Chemotherapy and Immunity

BACKGROUND

Cancer stem cells (CSC) play an important role in the development of Liver Hepatocellular Carcinoma (LIHC). However, the regulatory mechanisms between acetylation- associated genes (HAGs) and liver cancer stem cells remain unclear.

OBJECTIVE

To identify a set of histone acetylation genes (HAGs) with close associations to liver cancer stem cells (LCSCs), and to construct a prognostic model that facilitates more accurate prognosis assessments for LIHC patients.

METHODS

LIHC expression data were downloaded from the public databases. Using mRNA expression- based stemness indices (mRNAsi) inferred by One-Class Logistic Regression (OCLR), Differentially Expressed Genes (DEGs) (mRNAsi-High VS. mRNAsi-Low groups) were intersected with DEGs (LIHC VS. normal samples), as well as histone acetylation-associated genes (HAGs), to obtain mRNAsi-HAGs. A risk model was constructed employing the prognostic genes, which were acquired through univariate Cox and Least Shrinkage and Selection Operator (LASSO) regression analyses. Subsequently, independent prognostic factors were identified via univariate and multivariate Cox regression analyses and then a nomogram for prediction of LIHC survival was developed. Additionally, immune infiltration and drug sensitivity analysis were performed to explore the relationships between prognostic genes and immune cells. Finally, the expressions of selected mRNAsi-HAGs were validated in the LIHC tumor sphere by quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) assay and western blot analysis.

RESULTS

Among 13 identified mRNAsi-HAGs, 3 prognostic genes (HDAC1, HDAC11, and HAT1) were selected to construct a risk model (mRNAsi-HAGs risk score = 0.02 * HDAC1 + 0.09 * HAT1 + 0.05 * HDAC11). T-stage, mRNAsi, and mRNAsi-HAGs risk scores were identified as independent prognostic factors to construct the nomogram, which was proved to predict the survival probability of LIHC patients effectively. We subsequently observed strongly positive correlations between mRNAsi-HAGs risk score and tumor-infiltrating T cells, B cells and macrophages/monocytes. Moreover, we found 8 drugs (Mitomycin C, IPA 3, FTI 277, Bleomycin, Tipifarnib, GSK 650394, AICAR and EHT 1864) had significant correlations with mRNAsi-HAGs risk scores. The expression of HDAC1 and HDAC11 was higher in CSC-like cells in the tumor sphere.

CONCLUSION

This study constructed a mRNAsi and HAGs-related prognostic model, which has implications for potential immunotherapy and drug treatment of LIHC.

Diverse perspectives on proteomic posttranslational modifications to address EGFR-TKI resistance in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the main histological subtype of lung cancer. For locally advanced and advanced NSCLC, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI)-targeted therapy has been the first choice for NSCLC patients with EGFR mutations. TKIs, as targeted drugs, inhibit kinase activity and autophosphorylation by competitively binding to the ATP binding site of the EGFR tyrosine kinase domain, which blocks the signal transduction mediated by EGFR and thus inhibits the proliferation of tumor cells. However, drug resistance to TKIs is inevitable. EGFR is also a highly glycosylated receptor tyrosine kinase, and a wide range of crosstalk occurs between phosphorylation and glycosylation. Therefore, can the phosphorylation state be altered by glycosylation to improve drug resistance? In this review, we summarize phosphorylation, glycosylation and the crosstalk between these processes as well as the current research status and methods. We also summarize the autophosphorylation and glycosylation sites of the EGFR protein and their crosstalk. By exploring the relationship between EGFR glycosylation and autophosphorylation in targeted TKI therapy, we find that research on EGFR glycosylation is crucial for targeted NSCLC treatment and will become a research direction for identifying potential targets related to regulating TKI drug sensitivity.

The evolution of S-nitrosylation detection methodology and the role of protein S-nitrosylation in various cancers

S-nitrosylation (SNO) modification, a nitric oxide (NO)-mediated post-translational modification (PTM) of proteins, plays an important role in protein microstructure, degradation, activity, and stability. Due to the presence of reducing agents, the SNO modification process mediated by NO derivatives is often reversible and unstable. This reversible transformation between SNO modification and denitrification often influences the structure, activity, and function of proteins. The reversibility of SNO modifications also poses a challenge when verifying changes in the biological functions of proteins. Moreover, SNO modification of key signaling pathway proteins, such as caspase-3, NF-κB, and Bcl-2, can affect tumor proliferation, invasion, and apoptosis. The SNO-modified proteins play important roles in both promoting and inhibiting cancer, which indirectly confirms the duality and complexity of SNO modification functions. This article reviews the biological significance of various SNO-modified proteins in different cancers, providing a theoretical basis for determining whether the related changes of SNO-modified proteins are universal in cancers. Additionally, this review presents a comprehensive and detailed summary of the evolution of detection methods for SNO-modified proteins, providing a possible methodological basis for future research on SNO-modified proteins.

Chemical perturbations impacting histone acetylation govern colorectal cancer differentiation

Dysregulated epigenetic programs that restrict differentiation, reactivate fetal genes, and confer phenotypic plasticity are critical to colorectal cancer (CRC) development. By screening a small molecule library targeting epigenetic regulators using our dual reporter system, we found that inhibiting histone deacetylase (HDAC) 1/2 promotes CRC differentiation and anti-tumor activity. Comprehensive biochemical, chemical, and genetic experiments revealed that on-target blockade of the HDAC1/2 catalytic domain mediated the differentiated phenotype. Unbiased profiling of histone posttranslational modifications induced by HDAC1/2 inhibition nominated acetylation of specific histone lysine residues as potential regulators of differentiation. Genome-wide assessment of implicated marks indicated that H3K27ac gains at HDAC1/2-bound regions associated with open chromatin and upregulation of differentiation genes upon HDAC1/2 inhibition. Disrupting H3K27ac by degrading acetyltransferase EP300 rescued HDAC1/2 inhibitor-mediated differentiation of a patient-derived CRC model using single cell RNA-sequencing. Genetic screens revealed that DAPK3 contributes to CRC differentiation induced by HDAC1/2 inhibition. These results highlight the importance of specific chemically targetable histone modifications in governing cancer cell states and epigenetic reprogramming as a therapeutic strategy in CRC.

Interpretable deep cross networks unveiled common signatures of dysregulated epitranscriptomes across 12 cancer types

Cancer is a complex and multifaceted group of diseases characterized by uncontrolled cell growth that leads to the formation of malignant tumors. Recent studies suggest that N6-methyladenosine (m6A) RNA methylation plays pivotal roles in cancer pathology by influencing various cellular processes. However, the degree to which these mechanisms are shared across different cancer types remains unclear. In this study, we analyze an expansive array of 167 m6A epitranscriptome profiles covering 12 distinct cancer types and their originating normal tissues. We trained 12 distinct, cancer type-specific interpretable deep cross network models, which successfully distinguish between specific pairs of normal and cancer m6A contexts using integrated information from both the sequences and curated genomic knowledge. Interestingly, cross-cancer type testing indicated the existence of shared genomic patterns across various cancers at the epitranscriptome level. A pan-cancer model was subsequently developed to identify these shared patterns that could not be observed in a single cancer type. Our analysis uncovered, for the first time, a common epitranscriptome signature shared across multiple cancer types, particularly associated with RNA hybridization process and aberrant splicing. This highlights the importance of a comprehensive understanding of the pan-cancer epitranscriptome and holding potential implications in the development of RNA methylation-based therapeutics for various cancers.

KDM5D histone demethylase mediates p38α inactivation via its enzymatic activity to inhibit cancer progression

The p38 MAP kinase (MAPK) signaling pathway plays pivotal roles in various cellular processes. Phosphorylation serves as a canonical way to regulate p38α activation through a phosphorylation cascade. Thus, understanding the mechanism governing p38α phosphorylation is important. The present study demonstrated that p38α undergoes methylation at K165, which promote its phosphorylation in tumor cells. Inhibition of p38α methylation impairs p38α phosphorylation, repressing tumor progression in vitro and in vivo. Mechanistically, KDM5D is a demethylase that interacts with p38α, mediating demethylation at K165 and inhibiting p38α phosphorylation. Moreover, KDM5D is expressed at low levels in non-small cell lung cancer (NSCLC), and high KDM5D expression is positively correlated with cancer survival. KDM5D markedly inhibits cell proliferation and migration via inactivating p38α, thereby slowing cancer progression in xenograft models. In summary, these findings highlight KDM5D as a demethylase of p38α at K165, elucidating a unique role for lysine demethylation in integrating cytoplasmic kinase-signaling cascades. The present results revealed the critical role of KDM5D in suppressing tumor progression, suggesting that KDM5D can serve as a potential drug target for combating hyperactive p38α-driven lung cancer.

Strategies to boost antibody selectivity in oncology

Antibodies in oncology are being equipped with toxic cargoes and effector functions that can kill cells at very low concentrations. A key challenge is that most targets on cancer cells are also present on at least some healthy cells. Shared targets can result in off-tumor binding and compromise the safety and potential of therapeutic candidates. In this review, we survey strategies that can help direct biologics to cancer sites more selectively. These strategies are becoming increasingly feasible thanks to advances in molecular design and engineering. The objective is to create therapeutics that exploit changes in cancer and leverage the human body infrastructure, enabling therapeutics that discriminate not just self from non-self but diseased from healthy tissue.

Comprehensive review on leucine-rich pentatricopeptide repeat-containing protein (LRPPRC, PPR protein): A burgeoning target for cancer therapy

Leucine-rich pentatricopeptide repeat-containing (LRPPRC), known as the gene mutations that cause Leigh Syndrome French Canadian, encodes a high molecular weight PPR protein (157,905 Da), LRPPRC. LRPPRC binds to DNA, RNA, and proteins to regulate transcription and translation, leading to changes in cell fate. Increasing evidence indicates that LRPPRC plays a pivotal role in various human diseases, particularly cancer in recent years. Here, we review the structure, function, molecular mechanism, as well as inhibitors of LRPPRC. LRPPRC expression elevates in most cancer types and high expression of LRPPRC predicts the poor prognosis of cancer patients. Targeting LRPPRC suppresses tumor progression by affecting several cancer hallmarks, including signal transduction, cancer metabolism, and immune regulation. LRPPRC is a promising target in cancer research, serving as both a biomarker and therapeutic target. Further studies are required to extend the understanding of LRPPRC function and molecular mechanism, as well as to refine novel therapeutic strategies targeting LRPPRC in cancer therapy.

PhosCancer: A comprehensive database for investigating protein phosphorylation in human cancer

Protein phosphorylation is a crucial post-translational modification implicated in cancer pathogenesis, offering potential diagnostic and therapeutic targets. Here, we developed PhosCancer, a user-friendly database for extracting biologically and clinically relevant insights from phosphoproteomics data. Leveraging data from the CNHPP and CPTAC, PhosCancer encompasses 174,587 phosphosites from 14 datasets spanning 12 cancer types. Through extensive statistical analyses and integration of annotations from external resources, PhosCancer serves as a convenient one-stop platform facilitating the exploration of phosphorylation profiles across different cancer types. Not only does PhosCancer encompass basic information, 3D structure, functional domains, and upstream kinases, but also provides quantitative associations with nine clinical features, and the relevance with hallmarks in both cancer-specific and pan-cancer views. PhosCancer is a valuable resource for cancer researchers and clinicians, promoting the identification of clinically actionable biomarkers and further facilitating the clinical applications of phosphoproteomic data.

Artificial intelligence with mass spectrometry-based multimodal molecular profiling methods for advancing therapeutic discovery of infectious diseases

Infectious diseases, driven by a diverse array of pathogens, can swiftly undermine public health systems. Accurate diagnosis and treatment of infectious diseases-centered around the identification of biomarkers and the elucidation of disease mechanisms-are in dire need of more versatile and practical analytical approaches. Mass spectrometry (MS)-based molecular profiling methods can deliver a wealth of information on a range of functional molecules, including nucleic acids, proteins, and metabolites. While MS-driven omics analyses can yield vast datasets, the sheer complexity and multi-dimensionality of MS data can significantly hinder the identification and characterization of functional molecules within specific biological processes and events. Artificial intelligence (AI) emerges as a potent complementary tool that can substantially enhance the processing and interpretation of MS data. AI applications in this context lead to the reduction of spurious signals, the improvement of precision, the creation of standardized analytical frameworks, and the increase of data integration efficiency. This critical review emphasizes the pivotal roles of MS based omics strategies in the discovery of biomarkers and the clarification of infectious diseases. Additionally, the review underscores the transformative ability of AI techniques to enhance the utility of MS-based molecular profiling in the field of infectious diseases by refining the quality and practicality of data produced from omics analyses. In conclusion, we advocate for a forward-looking strategy that integrates AI with MS-based molecular profiling. This integration aims to transform the analytical landscape and the performance of biological molecule characterization, potentially down to the single-cell level. Such advancements are anticipated to propel the development of AI-driven predictive models, thus improving the monitoring of diagnostics and therapeutic discovery for the ongoing challenge related to infectious diseases.

Crosstalk between O-GlcNAcylation and ubiquitination: a novel strategy for overcoming cancer therapeutic resistance

Developing resistance to cancer treatments is a major challenge, often leading to disease recurrence and metastasis. Understanding the underlying mechanisms of therapeutic resistance is critical for developing effective strategies. O-GlcNAcylation, a post-translational modification that adds GlcNAc from the donor UDP-GlcNAc to serine and threonine residues of proteins, plays a crucial role in regulating protein function and cellular signaling, which are frequently dysregulated in cancer. Similarly, ubiquitination, which involves the attachment of ubiquitin to to proteins, is crucial for protein degradation, cell cycle control, and DNA repair. The interplay between O-GlcNAcylation and ubiquitination is associated with cancer progression and resistance to treatment. This review discusses recent discoveries regarding the roles of O-GlcNAcylation and ubiquitination in cancer resistance, their interactions, and potential mechanisms. It also explores how targeting these pathways may provide new opportunities to overcome cancer treatment resistance in cancer, offering fresh insights and directions for research and therapeutic development.