Lactylation-Related Gene Signature Effectively Predicts Prognosis and Treatment Responsiveness in Hepatocellular Carcinoma

Characterization of lactylation-based phenotypes and molecular biomarkers in sepsis-associated acute respiratory distress syndrome

Sepsis-associated acute respiratory distress syndrome (ARDS) is a heterogeneous disease with high morbidity and mortality. Lactylation plays a crucial role in sepsis and sepsis-induced lung injury. This study aimed to identify distinct lactylation-based phenotypes in patients with sepsis-associated ARDS and determine relevant molecular biomarkers. We analyzed blood transcriptome and clinical data from patients with sepsis-associated ARDS and calculated the lactylation activity. KEGG pathway analysis, drug sensitivity prediction, and immune cell infiltration analysis were performed. Candidate molecular biomarkers were identified by intersecting the feature genes extracted from four machine learning models. Lactylation activity showed significant heterogeneity among patients with sepsis-associated ARDS, which enabled the classification into low- and high-lactylation activity phenotypes. Patients with high-lactylation experienced longer hospital stays and higher mortality rates, as well as distinct signaling pathways, drug responses, and circulating immune cell abundances. Six key markers (ALDOB, CCT5, EP300, PFKP, PPIA, and SIRT1) were identified to differentiate the two lactylation activity phenotypes, all significantly correlated with circulating immune cell populations. This study revealed significant heterogeneity in lactylation activity phenotypes among patients with sepsis-associated ARDS and identified potential biomarkers to facilitate the application of these phenotypes in clinical practice.

Lactylation-related molecular subtyping reveals the immune heterogeneity and clinical characteristics in ulcerative colitis

BACKGROUND

Ulcerative colitis (UC) is a chronic inflammatory disease linked to early-onset colorectal cancer and metabolic abnormalities. While intestinal lactate disturbances are observed in UC, the role of lactate and lactylation in its pathogenesis remains unclear. The lack of specific biomarkers reflecting these processes limits understanding of their biological significance.

METHODS

UC subtypes were classified using ConsensusClusterPlus and NMF based on LRGs. Immune infiltration was assessed with ssGSEA, xCell, and CIBERSORT. WGCNA identified subtype-specific gene modules, and Lasso regression pinpointed hub genes. Single-cell analysis determined cellular localization, while WB and IHC validated findings in clinical, mouse, and cell models. Prognostic machine learning models evaluated the clinical significance of these results.

RESULTS

LRGs distinguished UC patients from controls and stratified them into high and low immune infiltration groups. MSN and MAPRE1, strongly linked to UC, showed elevated expression in vitro and in vivo. They aid in diagnosing UC and UC-associated colorectal cancer and serve as predictors of UC severity and response to immunosuppressants.

CONCLUSION

Using high-throughput transcriptomic data, we identified hub LRGs and highlighted the role of lactate-mediated lactylation in UC. MSN and MAPRE1 were confirmed to be upregulated in an inflammatory environment, underscoring their potential for personalized UC diagnosis and treatment.

Role of lactylation and immune infiltration in atherosclerosis: novel insights from bioinformatics analyses

Introduction

The existing evidence indicates that atherosclerosis (AS) plays a pivotal role in the progression and exacerbation of cardiovascular diseases and their associated complications. Current diagnostic and therapeutic strategies for atherosclerosis are limited in their ability to facilitate early detection and personalized treatment. This study employs a systems biology approach to investigate the role of lactylation-related genes (LRGs) in the pathogenesis of atherosclerosis, while considering the well-established correlation between inflammatory responses and atherosclerosis development.

Methods

In this study, we utilized datasets obtained from the Gene Expression Omnibus (GEO) as well as data from previous studies on lactylation-related genes (LRGs). Following this, we identified 17 lactylation related genes associate with atherosclerosis (AS-LRGs) from the GSE100927 dataset. Subsequently, we employed the validation dataset (GSE43292) to assess these 17 AS-LRGs, resulting in the identification of 12 more reliable candidate genes. These genes were further analyzed for functional enrichment through Gene Ontology (GO) annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and gene set enrichment analysis (GSEA). To elucidate the potential utility of AS-LRGs in diagnosing high-risk plaques, we assessed their expression in both early and late stages of atherosclerosis, as well as in high- and low-risk plaques. We then constructed interaction networks to elucidate the potential regulatory relationships among LRGs, miRNAs, transcription factors, and drugs. Finally, we utilized the single sample Gene Set Enrichment Analysis (ssGSEA) method to investigate immune infiltration in AS and evaluate the levels of immune cell infiltration.

Results

We identified 12 lactylation-related genes that are more reliably associated with atherosclerosis: five upregulated genes (LSP1, IKZF1, MNDA, RCC2, and WAS) and seven downregulated genes (CSRP2, PPP1CB, CSRP1, HEXIM1, CALD1, PDLIM1, and RANBP2).

Discussion

This study elucidates the pivotal role of lactylation in atherosclerosis (AS) and establishes a robust foundation for future research into targeted therapies and clinical applications of the identified biomarkers.

BRD4 as the key lactylation related gene in heart failure identified through bioinformatics analysis

Lactylation modification is postulated to influence the progression of heart failure (HF) through diverse pathways, albeit the underlying mechanisms remain elusive. Methods In this study, bioinformatics approaches were employed to analyze the HF dataset (GSE5406) retrieved from the Gene Expression Omnibus, with the objective of identifying lactylation-related genes (LRGs). Key LRGs implicated in HF were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) and Weighted Gene Co-Expression Network Analysis (WGCNA). The diagnostic efficacy and biological significance of these genes were evaluated through receiver operating characteristic (ROC) curve analysis, Gene Set Enrichment Analysis, and immune cell infiltration analysis. Furthermore, the findings were validated using single-cell sequencing datasets (GSE161470) and in vitro cell models to ascertain the expression patterns and functional roles of the identified key LRGs. A total of 276 LRGs were identified from the HF dataset. Initial screening utilizing two bioinformatics analysis methods pinpointed BRD4 as a potential pivotal LRG influencing HF progression. ROC analysis revealed a high diagnostic accuracy for BRD4, with an Area Under the Curve score of 0.877. Immune cell infiltration and single-cell data analyses indicated that BRD4 exhibits a strong association with immune cells, including mast cells, T cells, and macrophages, and demonstrates significantly elevated expression in these immune cells as well as in cardiomyocytes. Both BRD4 mRNA and protein levels were found to be upregulated compared to control groups. This study represents the first to utilize multiple bioinformatics analysis methods to identify BRD4 as a key LRG in HF, thereby establishing a foundation for future investigations into acylation-related mechanisms in HF.

Establishing a Prognostic Model Correlates to Inflammatory Response Pathways for Prostate Cancer via Multiomic Analysis of Lactylation-Related Genes

Prostate cancer (PCa) continues to pose substantial clinical challenges, with molecular heterogeneity significantly impacting therapeutic decision-making and disease trajectories. Emerging evidence implicates protein lactylation-a novel epigenetic regulatory mechanism-in oncogenic processes, though its prognostic relevance in PCa remains underexplored. Through integrative bioinformatics interrogation of lactylation-associated molecular signatures, we established prognostic correlations using multivariable feature selection methodologies. Initial screening via differential expression analysis (limma package) coupled with Cox proportional hazards modeling revealed 11 survival-favorable regulators and 16 hazard-associated elements significantly linked to biochemical recurrence. To enhance predictive precision, ensemble machine learning frameworks were implemented, culminating in a 10-gene lactylation signature demonstrating robust discriminative capacity (concordance index = 0.738) across both primary (TCGA-PRAD) and external validation cohorts (DKFZ). Multivariable regression confirmed the lactylation score's prognostic independence, exhibiting prominent associations with clinicopathological parameters including tumor staging and metastatic potential. The developed clinical-molecular nomogram achieved superior predictive accuracy (C - index > 0.7) through the synergistic integration of biological and clinical covariates. Tumor microenvironment deconvolution uncovered distinct immunological landscapes, with high-risk stratification correlating with enriched stromal infiltration and immunosuppressive phenotypes. Pathway enrichment analyses implicated chromatin remodeling processes and cytokine-mediated inflammatory cascades as potential mechanistic drivers of prognostic divergence. Therapeutic vulnerability profiling demonstrated differential response patterns: low-risk patients exhibited enhanced immune checkpoint inhibitor responsiveness, whereas high-risk subgroups showed selective chemosensitivity to docetaxel and mitoxantrone. Functional validation in PC-3 models revealed AK5 silencing induced proapoptotic effects, suppressed metastatic potential of migration and invasion, and modulated immune checkpoint regulation through CD276 coexpression. These multimodal findings position lactylation dynamics, particularly AK5-mediated pathways, as promising therapeutic targets and stratification biomarkers in PCa management.

Identification of lactylation-associated fibroblast subclusters predicting prognosis and cancer immunotherapy response in colon cancer

BACKGROUND

Lactylation plays an important role in tumor progression. This study aimed to clarify the impact of lactylation on cancer-associated fibroblasts(CAFs).

METHODS

Single-cell and bulk RNA sequence data, along with survival information, were obtained from TCGA and GEO datasets. Significant lactylation-associated genes were acquired by differential analysis and used to construct a prognostic model via Cox and LASSO regression analyses. Next, single-cell analysis, enrichment and pathway analysis, pseudotemporal trajectory and survival analysis were used to identify significant lactylation-associated fibroblast subclusters in colon cancer. IMvigor210 and PRJEB23709 cohorts were applied to assess the response to immunotherapy. In vitro experiments were conducted to explore how lactylation affect fibroblasts.

RESULTS

We established a lactylation-associated prognostic model with 17 risk genes in TCGA and further validated it in GEO datasets. Single-cell analysis revealed the lactylation level of fibroblasts in colon cancer was greater than that in normal tissues. Moreover, five lactylation-associated fibroblast subclusters were identified via the NMF algorithm. Patients with lower scores of FB_2_CALD1, FB_3_TPM4 and FB_4_AHNAK subclusters had better clinical prognosis in colon cancer and were more likely to benefit from immunotherapy. Further experiments demonstrated that lactylation could enhance the proliferation, migration and invasion ability of fibroblasts and up-regulate the expression of COL1A1, which was similar to the effect of colon cancer cells.

CONCLUSION

This study identified key fibroblast subclusters with prognostic value and implied that lactylation might help transform fibroblasts into CAFs in colon cancer for the first time, which provides new paths for understanding the evolution of CAFs and cancer therapeutic strategies.

Integration of scRNA-seq and bulk RNA-seq to reveal the association and potential molecular mechanisms of metabolic reprogramming regulated by lactylation and chemotherapy resistance in ovarian cancer

Objective

Ovarian cancer (OC) ranks among the foremost causes of mortality in gynecological malignancies, with chemoresistance being the primary factor contributing to unfavorable prognosis. This work seeks to clarify the mechanisms of resistance-related lactylation in OC, intending to offer novel theoretical foundations and therapy strategies for addressing chemoresistance.

Methods

Through the combined analysis of bulk RNA-seq and single-cell RNA-seq data, we initially found lactylation genes linked to chemoresistance. Subsequently, we employed differential expression analysis, survival analysis, enrichment analysis, and other methodologies to further investigate the roles and molecular mechanisms of these genes in tumor resistance. Ultimately, we investigated the differential expression of these genes in resistant and non-resistant tissues and cells via experimentation.

Results

We found two candidate genes associated with lactylation chemoresistance, ALDH1A1 and S100A4. Analysis of single-cell data indicated that tumor cells represent the primary cell subpopulation relevant to resistance studies. Subpopulation analysis indicated that several tumor cell subtypes were markedly linked to resistance, with elevated expression levels of ALDH1A1 and S100A4 in the resistant subpopulation, notably correlating with various immunological and metabolic pathways. Analysis of metabolic pathways indicated that oxidative phosphorylation and glycolysis activity was elevated in the resistant subpopulation, and lactic acid buildup was associated with chemoresistance. The investigation of the marker gene protein-protein interaction network in the resistant subgroup elucidated the intricate interactions among these genes. The expression levels of ALDH1A1 and S100A4 in the OC tissues of the platinum-resistant cohort were markedly elevated compared to the sensitive cohort, with a considerable rise in S100A4 expression observed in resistant OC cells, demonstrating co-localization with lactylation.

Conclusion

This work elucidates the significant function of lactylation in OC chemoresistance and identifies ALDH1A1 and S100A4 as possible genes associated with drug resistance. These findings enhance our comprehension of the mechanisms behind chemoresistance in OC and offer critical insights for the formulation of novel therapeutic options.

Decoding Osteosarcoma's Lactylation Gene Expression: Insights Into Prognosis, Immune Dynamics, and Treatment

Osteosarcoma (OS), characterized by a complex tumor microenvironment, poses challenges in treatment, metastasis, and therapy resistance. This study examined the impact of lactylation, a posttranslational modification, on gene expression and tumor behavior in OS, particularly its influence on prognosis, immune cell infiltration, and chemotherapy response. Utilizing data from the Gene Expression Omnibus series accession number 21257 (GSE21257) and the Therapeutically Applicable Research to Generate Effective Treatments on Osteosarcoma (TARGET-OS) datasets, the investigation focused on analyzing the expression profiles of 267 lactylation modifier genes, which were selected from a total of 336 lactylation-related genes compiled from various studies in the literature. The methods included unsupervised clustering using "ConsensusClusterPlus" heatmap generation with "pheatmap" pathway analysis from several databases, and immune cell infiltration assessment using the "single-sample Gene Set Enrichment Analysis (ssGSEA)" function. The research revealed 36 significant lactylation-related genes in OS, categorizing them into two clusters with distinct survival and biological characteristics. One cluster demonstrated poor prognosis due to increased tumor cell proliferation and specific immune cell variations, also showcasing genes that enhance tumor growth and metastasis, thus indicating its aggressive nature and adverse outcomes for patients. These insights are crucial for understanding the molecular mechanisms of OS and identifying therapeutic targets. Therefore, the study elucidates the role of lactylation-related genes in the prognosis, pathogenesis, and treatment response of OS, laying the groundwork for further exploration into potential therapeutic targets and the underlying mechanisms within OS.

Post-translational modifications in hepatocellular carcinoma: unlocking new frontiers in immunotherapy

Liver cancer, particularly hepatocellular carcinoma (HCC), is one of the most common and aggressive malignancies worldwide. Immunotherapy has shown promising results in treating HCC, but its efficacy is often limited by complex mechanisms of immune evasion. Post-translational modifications (PTMs) of proteins play a critical role in regulating the immune responses within the tumor microenvironment (TME). These modifications influence protein function, stability, and interactions, which either promote or inhibit immune cell activity in cancer. In this mini-review, we explore the diverse PTMs that impact immune evasion in liver cancer, including glycosylation, phosphorylation, acetylation, and ubiquitination. We focus on how these PTMs regulate key immune checkpoint molecules such as PD-L1, CTLA-4, and the TCR complex. Furthermore, we discuss the potential of targeting PTMs in combination with existing immunotherapies to enhance the effectiveness of treatment in HCC. Understanding the role of PTMs in immune regulation may lead to the development of novel therapeutic strategies to overcome resistance to immunotherapy in liver cancer.

Regulatory role and therapeutic prospect of lactate modification in cancer

Post-translational modifications (PTMs) of proteins refer to the process of adding chemical groups, sugars, or other molecules to specific residues of target proteins following their biosynthesis by ribosomes. PTMs play a crucial role in processes such as signal transduction, epigenetics, and disease development. Lactylation is a newly discovered PTM that, due to its close association with lactate-the end product of glycolytic metabolism-provides a new perspective on the connection between cellular metabolic reprogramming and epigenetic regulation. Studies have demonstrated that lactylation plays a significant role in tumor progression and is associated with poor clinical prognosis. Abnormal histone lactylation can influence gene expression in both tumor cells and immune cells, thereby regulating tumor progression and immunosuppression. Lactylation of non-histone proteins can also modulate processes such as tumor proliferation and drug resistance. This review summarizes the latest research progress in the field of lactylation, highlighting its roles and mechanisms in tumorigenesis, tumor development, the tumor microenvironment, and immunosuppression. It also explores the potential application value of lactylation in tumor-targeted therapy and combined immunotherapy.

Establishment of a Lactylation-Related Gene Signature for Hepatocellular Carcinoma Applying Bulk and Single-Cell RNA Sequencing Analysis

Background: Lactylation is closely involved in cancer progression, but its role in hepatocellular carcinoma (HCC) is unclear. The present work set out to develop a lactylation-related gene (LRG) signature for HCC. Methods: The lactylation score of tumor and normal groups was calculated using the gene set variation analysis (GSVA) package. The single-cell RNA sequencing (scRNA-seq) analysis of HCC was performed in the "Seurat" package. Prognostic LRGs were selected by performing univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses to develop and validate a Riskscore model. Functional enrichment analysis was conducted by gene set enrichment analysis (GSEA) using the "clusterProfiler" package. Genomic characteristics between different risk groups were compared, and tumor mutational burden (TMB) was calculated by the "Maftools" package. Immune cell infiltration was assessed by algorithms of cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT), microenvironment cell populations-counter (MCP-counter), estimating the proportions of immune and cancer cells (EPIC), tumor immune estimation resource (TIMER), and single-sample gene set enrichment analysis (ssGSEA). Immunotherapy response was predicted by the tumor immune dysfunction and exclusion (TIDE) algorithm. Drug sensitivity was analyzed using the "pRRophetic" package. A nomogram was established using the "rms" package. The expressions of the prognostic LRGs in HCC cells were verified by in vitro test, and cell counting kit-8 (CCK-8), wound healing, and transwell assays were carried out to measure the viability, migration, and invasion of HCC cells. Results: The lactylation score, which was higher in the tumor group than in the normal group, has been confirmed as an independent factor for the prognostic evaluation in HCC. Six prognostic LRGs, including two protective genes (FTCD and APCS) and four risk genes (LGALS3, C1orf43, TALDO1, and CCT5), were identified to develop a Riskscore model with a strong prognostic prediction performance in HCC. The scRNA-seq analysis revealed that LGALS3 was largely expressed in myeloid cells, while APCS, FTCD, TALDO1, CCT5, and C1orf43 were mainly expressed in hepatocytes. The high-risk group was primarily enriched in the pathways involved in tumor occurrence and development, with higher T cell infiltration. Moreover, the high-risk group was found to be less responsive to immunotherapy but was more sensitive to chemotherapeutic drugs. By integrating Riskscore and clinical features, a nomogram with a high predictive accuracy was developed. Additionally, C1orf43, CCT5, TALDO1, and LGALS3 were highly expressed in HCC cells. Silencing CCT5 inhibited the viability, migration, and invasion of HCC cells. Conclusion: The present work developed a novel LRG gene signature that could be considered a promising therapeutic target and biomarker for HCC.

Integrative bioinformatics and experimental analyses identify U2SURP as a novel lactylation-related prognostic signature in esophageal carcinoma

The lactylation modification has been implicated in several cancer types; however, the role of lactylation modification-related genes in esophageal carcinoma (EC) remains underexplored. Utilizing a set of 16 lactylation modification-related genes, cohorts of patients with EC were stratified into two distinct clusters, characterized by significant disparities in both survival outcomes and the immune microenvironment. An extensive bioinformatics analysis unveiled 382 differentially expressed genes (DEGs) between these two clusters. A subsequent univariate Cox regression analysis identified 24 DEGs specifically associated with lactylation, forming the basis of a constructed lactylation-related score. The resultant lactylation-related score exhibited notable predictive efficacy for survival and other clinicopathological traits, which was validated through calibration curves, Kaplan-Meier survival curves and the Wilcoxon test. Moreover, the lactylation-related score displayed a close correlation with immune cell infiltration in EC. Notable differential expressions of immune checkpoints and regulators were observed between groups stratified by low and high lactylation scores, with the latter exhibiting a more favorable response to anti-PD-1/PD-L1 therapy. Furthermore, the expression profile of U2 snRNP associated SURP domain containing (U2SURP), a constituent of the lactylation-related score, underwent both ex vivo and in vitro validation. The expression of U2SURP was significantly associated with lactylation levels, histological grade and tumor stage. Notably, knockdown of U2SURP expression inhibited the lactylation levels, immune genes IL-1A and IL-1B, proliferation, migration and invasion of EC cells. In conclusion, the lactylation-related score developed in the present study showed promise in predicting the prognosis and immunotherapeutic responses among patients with EC. Moreover, the identification of U2SUPR as a novel oncogene in EC suggests its potential as a prospective therapeutic target for EC treatment.

Histone Lactylation-Driven Ubiquitin-Specific Protease 34 Promotes Cisplatin Resistance in Hepatocellular Carcinoma

Background

Ubiquitin-specific protease 34 (USP34) is a deubiquitinase that has been shown to play a critical role in the process of tumor drug-resistance. The objective of this study was to investigate the role of USP34 in cisplatin resistance in hepatocellular carcinoma (HCC).

Methods

Firstly, we analyzed the USP34 levels in cisplatin-sensitive and -resistant patients using The Cancer Genomic Atlas (TCGA) data from Gene Expression Profiling Interactive Analysis (GEPIA2). The cell viability and half-maximal inhibitory concentration (IC50) were measured by Cell Counting Kit-8 (CCK-8) assay. The cell apoptosis of HepG2 and HepG2/DDP cells was detected by annexin V-fluorescein isothiocyanate/propidium iodide (FITC/PI) double staining. The expression levels of USP34, multidrug resistance-associated protein 1 (MRP1), p-glycoprotein (p-gp), pan-lysine lactylation (Pan-Kla), histone H3 lysine 18 lactylation (H3K18la), lactate dehydrogenase A (LDHA) and lactate dehydrogenase B (LDHB) were measured by Western blot. HCC samples from the GEPIA2 database were used to determine the correlation between USP34 with LDHA and LDHB expression.

Results

USP34 was significantly upregulated in cisplatin-resistant HCC tissues and cells. Functional studies found that knockdown of USP34 inhibited HepG2 and HepG2/DDP cell proliferation and survival. Importantly, knockdown of USP34 enhanced cisplatin sensitivity in HepG2 and HepG2/DDP cells. Mechanistically, lactylation of histones promoted the expression level of USP34 in HepG2/DDP cells.

Conclusion

USP34 promotes the progression of HCC by regulating histone lactylation levels and cisplatin resistance in HCC.

Multiomic machine learning on lactylation for molecular typing and prognosis of lung adenocarcinoma

To integrate machine learning and multiomic data on lactylation-related genes (LRGs) for molecular typing and prognosis prediction in lung adenocarcinoma (LUAD). LRG mRNA and long non-coding RNA transcriptomes, epigenetic methylation data, and somatic mutation data from The Cancer Genome Atlas LUAD cohort were analyzed to identify lactylation cancer subtypes (CSs) using 10 multiomics ensemble clustering techniques. The findings were then validated using the GSE31210 and GSE13213 LUAD cohorts. A prognosis model for LUAD was developed using the identified hub LRGs to divide patients into high- and low-risk groups. The effectiveness of this model was validated. We identified two lactylation CSs, which were validated in the GSE31210 and GSE13213 LUAD cohorts. Nine hub LRGs, namely HNRNPC, PPIA, BZW1, GAPDH, H2AFZ, RAN, KIF2C, RACGAP1, and WBP11, were used to construct the prognosis model. In the subsequent prognosis validation, the high-risk group included more patients with stage T3 + 4, N1 + 2 + 3, M1, and III + IV cancer; higher recurrence/metastasis rates; and lower 1, 3, and 5 year overall survival rates. In the oncogenic pathway analysis, most of the oncogenic mutations were detected in the high-risk group. The tumor microenvironment analysis illustrated that immune activity was notably elevated in low-risk patients, indicating they might more strongly respond to immunotherapy than high-risk patients. Further, oncoPredict analysis revealed that low-risk patients have increased sensitivity to chemotherapeutics. Overall, we developed a model that combines multiomic analysis and machine learning for LUAD prognosis. Our findings represent a valuable reference for further understanding the important function of lactylation modification pathways in LUAD progression.

Non-histone lactylation: unveiling its functional significance

Lactylation, a newly discovered protein posttranslational modification (PTM) in 2019, primarily occurs on lysine residues. Lactylation of histones was initially identified, and subsequent studies have increasingly demonstrated its widespread presence on non-histone proteins. Recently, high-throughput proteomics studies have identified a large number of lactylated proteins and sites, revealing their global regulatory role in disease development. Notably, this modification is catalyzed by lactyltransferase and reversed by delactylase, with numerous new enzymes, such as AARS1/2, reported to be involved. Specifically, these studies have revealed how lactylation exerts its influence through alterations in protein spatial conformation, molecular interactions, enzyme activity and subcellular localization. Indeed, lactylation is implicated in various physiological and pathological processes, including tumor development, cardiovascular and cerebrovascular diseases, immune cell activation and psychiatric disorders. This review provides the latest advancements in research on the regulatory roles of non-histone protein lactylation, highlighting its crucial scientific importance for future studies.

Genetic association of tertiary lymphoid structure-related gene signatures with HCC based on Mendelian randomization and machine learning and construction of prognosis model

BACKGROUND

Tertiary lymphoid structures (TLS) are formed in numerous cancer types. However, their value and significance in hepatocellular carcinoma (HCC) is unclear.

METHODS

We performed differential genes expression analysis of TLS-related Genes (TLSG) based on The Cancer Genome Atlas (TCGA) database, and performed Mendelian randomization (MR) analysis using expression quantitative trait loci, and then took their intersecting genes. A TLSG prognostic signature (TLSGPS)-based risk score was constructed using Least Absolute Shrinkage and Selection Operator (LASSO), univariate and multivariate COX regression analysis, and survival analysis was then performed. We used the International Cancer Genome Consortium for outside validation. We also performed biological function, tumor mutational burden, immune infiltration, single-cell analysis, CeRNA and drug sensitivity analysis based on TLSGPS.

RESULTS

Three TLSGs (HM13, CSTB, CDCA7L) were identified to construct the TLSGPS, which showed good predictive ability and outperformed most prognostic signatures. MR suggested that HM13 (OR = 0.9997, 95 %CI: 0.9994-0.9999, P = 0.014) and CSTB (OR = 0.9997, 95 %CI: 0.9995-0.9999, P = 0.048) were negatively correlated with the risk of HCC onset, while CDCA7L (OR = 1.0004, 1.0001-1.0007, P = 0.0161) was the opposite. The differences in biological functions between the TLSGPS-based high-risk group (HRG) and low-risk group (LRG) involved cell proliferation, differentiation, and drug metabolism. HRG plus high mutations exhibited extremely poor survival. HRG had higher abundance of immune cell-oncogenic phenotypes, higher immune escape ability, and greater sensitivity to Afatinib, Dasatinib, and Gefitinib.

CONCLUSION

3 TLSGs identified by machine learning and MR can predict the onset, prognosis and clinical treatment of HCC patients, and had significant genetic association with HCC.

Lactate-induced protein lactylation in cancer: functions, biomarkers and immunotherapy strategies

Lactate, long viewed as a byproduct of glycolysis and metabolic waste. Initially identified within the context of yogurt fermentation, lactate's role extends beyond culinary applications to its significance in biochemical processes. Contemporary research reveals that lactate functions not merely as the terminal product of glycolysis but also as a nexus for initiating physiological and pathological responses within the body. Lysine lactylation (Kla), a novel post-translational modification (PTM) of proteins, has emerged as a pivotal mechanism by which lactate exerts its regulatory influence. This epigenetic modification has the potential to alter gene expression patterns, thereby impacting physiological and pathological processes. Increasing evidence indicates a correlation between lactylation and adverse prognosis in various malignancies. Consequently, this review article aims to encapsulate the proteins that interact with lactate, elucidate the role of lactylation in tumorigenesis and progression, and explore the potential therapeutic targets afforded by the modulation of lactylation. The objective of this review is to clarify the oncogenic significance of lactylation and to provide a strategic framework for future research directions in this burgeoning field.

Multiomic analysis of lactylation and mitochondria-related genes in hepatocellular carcinoma identified MRPL3 as a new prognostic biomarker

Background

Recent research has highlighted lactate's crucial role in epigenetic regulation, particularly by influencing histone modifications that drive the initiation and progression of hepatocellular carcinoma (HCC). While mitochondria are known to regulate tumor behavior, the interaction between lactate metabolism and mitochondrial function in cancer tissues remains underexplored. Understanding this relationship may provide deeper insights into tumor metabolic reprogramming and reveal novel therapeutic targets for HCC and other malignancies.

Methods

We conducted a comprehensive screening of lactylation- and mitochondria-associated genes (LMRGs) in HCC patients, followed by clustering based on these genes. Prognostic outcomes and pathway enrichment were analyzed across the identified clusters. Additionally, we developed a prognostic model based on LMRGs, examining its implications for survival, immune response, and drug sensitivity. In vitro experiments were performed to validate the expression patterns and functional role of MRPL3 in HCC.

Results

We developed a prognostic model, named the LMRG model, incorporating three key genes: ACACA, MRPL3, and MRPS23. This model revealed significant differences in survival outcomes, immune responses, and drug sensitivity between patients with high and low LMRG scores. MRPL3 was found to be overexpressed in HCC, playing a critical role in tumor growth and metastasis. These results were further validated through in vitro experiments, confirming MRPL3's role in HCC cell proliferation and invasion.

Conclusion

We created a predictive model, LMRG, and identified MRPL3 as a key biomarker. Our findings suggest that MRPL3 has significant potential as a reliable predictive biomarker for clinical applications in HCC diagnosis and treatment.

Lactylation modulation identifies key biomarkers and therapeutic targets in KMT2A-rearranged AML

Acute Myeloid Leukemia (AML) with KMT2A rearrangements (KMT2Ar), found on chromosome 11q23, is often called KMT2A-rearranged AML (KMT2Ar-AML). This variant is highly aggressive, characterized by rapid disease progression and poor outcomes. Growing knowledge of epigenetic changes, especially lactylation, has opened new avenues for investigation and management of this subtype. Lactylation plays a significant role in cancer, inflammation, and tissue regeneration, but the underlying mechanisms are not yet fully understood. This research examined the influence of lactylation on gene expression within KMT2Ar-AML, initially identifying twelve notable lactylation-dependent differentially expressed genes (DEGs). Using advanced machine learning techniques, six key lactylation-associated genes (PFN1, S100A6, CBR1, LDHB, LGALS1, PRDX1) were identified as essential for prognostic evaluation and linked to relevant disease pathways. The study also suggested PI3K inhibitors and Pevonedistat as possible therapeutic options to modulate immune cell infiltration. Our findings confirm the critical role of lactylation in KMT2Ar-AML and identify six key genes that may serve as biomarkers for diagnosis and treatment. In addition to highlighting the need for further validation in clinical settings, these findings contribute to our understanding of KMT2Ar-AML's molecular mechanisms.

Lactylation in health and disease: physiological or pathological?

Lactate is an indispensable substance in various cellular physiological functions and plays regulatory roles in different aspects of energy metabolism and signal transduction. Lactylation (Kla), a key pathway through which lactate exerts its functions, has been identified as a novel posttranslational modification (PTM). Research indicates that Kla is an essential balancing mechanism in a variety of organisms and is involved in many key cellular biological processes through different pathways. Kla is closely related to disease development and represents a potential and important new drug target. In line with existing reports, we searched for newly discovered Kla sites on histone and nonhistone proteins; reviewed the regulatory mechanisms of Kla (particularly focusing on the enzymes directly involved in the reversible regulation of Kla, including "writers" (modifying enzymes), "readers" (modification-binding enzymes), and "erasers" (demodifying enzymes); and summarized the crosstalk between different PTMs to help researchers better understand the widespread distribution of Kla and its diverse functions. Furthermore, considering the "double-edged sword" role of Kla in both physiological and pathological contexts, this review highlights the "beneficial" biological functions of Kla in physiological states (energy metabolism, inflammatory responses, cell fate determination, development, etc.) and its "detrimental" pathogenic or inducive effects on pathological processes, particularly malignant tumors and complex nontumor diseases. We also clarify the molecular mechanisms of Kla in health and disease, and discuss its feasibility as a therapeutic target. Finally, we describe the detection technologies for Kla and their potential applications in diagnosis and clinical settings, aiming to provide new insights for the treatment of various diseases and to accelerate translation from laboratory research to clinical practice.

Emerging Roles of High-mobility Group Box-1 in Liver Disease

High-mobility group box-1 (HMGB1) is an architectural chromosomal protein with various roles depending on its cellular localization. Extracellular HMGB1 functions as a prototypical damage-associated molecular pattern that triggers inflammation and adaptive immune responses, mediated by specific cell surface receptors, including receptors for advanced glycation end products and toll-like receptors. Post-translational modifications of HMGB1 significantly impact various cellular processes that contribute to the pathogenesis of liver diseases. Recent studies have highlighted the close relationship between HMGB1 and the pathogenesis of acute liver injuries, including acetaminophen-induced liver injury, hepatic ischemia-reperfusion injury, and acute liver failure. In chronic liver diseases, HMGB1 plays a role in nonalcoholic fatty liver disease, alcohol-associated liver disease, liver fibrosis, and hepatocellular carcinoma. Targeting HMGB1 as a therapeutic approach, either by inhibiting its release or blocking its extracellular function, is a promising strategy for treating liver diseases. This review aimed to summarize the available evidence on HMGB1's role in liver disease, focusing on its multifaceted signaling pathways, impact on disease progression, and the translation of these findings into clinical interventions.

Comprehensive analysis identifies a lactylation-related signature for predicting prognosis and guiding therapies in colon adenocarcinoma

PURPOSE

This study aimed to identify a lactylation-related gene signature for predicting prognosis and guiding therapies in colon adenocarcinoma (COAD). We seek to address the challenges in COAD prognostication due to tumor heterogeneity and variable treatment responses.

METHODS

The study employed integrative bioinformatics analyses on multi-omics data from public databases, including gene expression profiles, clinical data, and lactylation-related genes (LRGs). The least absolute shrinkage and selection operator (LASSO) regression analysis and Cox risk model were applied to develop a prognostic signature. The predictive capabilities of the signature were assessed in four independent COAD cohorts (GSE39582, GSE71187, GSE75500, and GSE17536). Functional enrichment, immune infiltrations, and scRNA-seq analysis were performed to investigate biological processes and the tumor microenvironment (TME). Additionally, functional assays were performed to assess the impact of gene knockdown on COAD cell behavior.

RESULTS

A 3-gene signature (SUSD5, FABP4, CALB2) was identified, demonstrating robust predictive performance for clinical outcomes in COAD patients across multiple cohorts. The signature revealed involvement in critical cancer-related biological processes and showed potential in guiding therapeutic decisions. The bulk RNA-seq and scRNA-seq analysis suggested that LRGs modulates the TME, particularly immune cell populations like mast cells. Knockdown of CALB2 significantly suppressed COAD cell proliferation, invasion, and migration.

CONCLUSION

This comprehensive analysis identified a lactylation-related signature with significant prognostic and therapeutic implications for COAD. The findings highlight the importance of lactylation in COAD biology and offer novel insights for developing personalized treatment strategies, potentially improving patient outcomes in this prevalent malignancy.

Comprehensive Analysis of the SUMO-related Signature: Implication for Diagnosis, Prognosis, and Immune Therapeutic Approaches in Cervical Cancer

SUMOylation, an important post-translational protein modification, plays a critical role in cancer development and immune processes. This study aimed to construct diagnostic and prognostic models for cervical cancer (CC) using SUMOylation-related genes (SRGs) and explore their implications for novel clinical therapies. We analyzed the expression profiles of SRGs in CC patients and identified 15 SRGs associated with CC occurrence. After the subsequent qPCR verification of 20 cases of cancer and adjacent tissues, 13 of the 15 SRGs were differentially expressed in cancer tissues. Additionally, we identified molecular markers associated with the prognosis and recurrence of CC patients, based on SRGs. Next, a SUMOScore, based on SRG expression patterns, was generated to stratify patients into different subgroups. The SUMOScore showed significant associations with the tumor microenvironment, immune function features, immune checkpoint expression, and immune evasion score in CC patients, highlighting the strong connection between SUMOylation factors and immune processes. In terms of immune therapy, our analysis identified specific chemotherapy drugs with higher sensitivity in the subgroups characterized by high and low SUMOScore, indicating potential treatment options. Furthermore, we conducted drug sensitivity analysis to evaluate the response of different patient subgroups to conventional chemotherapy drugs. Our findings revealed enrichment of immune-related pathways in the low-risk subgroup identified by the prognostic model. In conclusion, this study presents diagnostic and prognostic models based on SRGs, accompanied by a comprehensive index derived from SRGs expression patterns. These findings offer valuable insights for CC diagnosis, prognosis, treatment, and immune-related analysis.

Lactylation-related gene signature accurately predicts prognosis and immunotherapy response in gastric cancer

Background

Gastric cancer (GC) is a malignant tumor associated with significant rates of morbidity and mortality. Hence, developing efficient predictive models and directing clinical interventions in GC is crucial. Lactylation of proteins is detected in gastric cancer tumors and is linked to the advancement of gastric cancer.

Methods

The The Cancer Genome Atlas (TCGA) was utilized to analyze the gene expression levels associated with lactylation. A genetic pattern linked to lactylation was created using Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. The predictive ability of the model was evaluated and confirmed in the Gene Expression Omnibus (GEO) cohort, where patients were divided into two risk groups based on their scores. The study examined the relationship between gene expression and the presence of immune cells in the context of immunotherapy treatment. In vitro cytotoxicity assays, ELISA and PD-1 and PD-L1interaction assays were used to assess the expression of PD-L1 while knocking down SLC16A7.

Results

29 predictive lactylation-related genes with differential expression were discovered. A signature consisting of three genes was developed and confirmed. Patients who had higher risk scores experienced worse clinical results. The group with lower risk showed increased Tumor Immune Dysfunction and Exclusion (TIDE) score and greater responsiveness to immunotherapy. The tumor tissues secrete more lactate acid than normal tissues and express more PD-L1 than normal tissues, that is, lactate acid promotes the immune evasion of tumor cells. In GC, the lactylation-related signature showed strong predictive accuracy. Utilizing both anti-lactylation and anti-PD-L1 may prove to be an effective approach for treating GC in clinical settings. We further proved that one of the lactate metabolism related genes, SCL16A7 could promote the expression of PD-L1 in GC cells.

Conclusion

The risk model not only provides a basis for better prognosis in GC patients, but also is a potential prognostic indicator to distinguish the molecular and immune characteristics, and the response from Immune checkpoint inhibitors (ICI) therapy and chemotherapy in GC.

The Research Progress of Metformin Regulation of Metabolic Reprogramming in Malignant Tumors

BACKGROUND

Metabolism reprogramming is a crucial hallmark of malignant tumors. Tumor cells demonstrate enhanced metabolic efficiency, converting nutrient inputs into glucose, amino acids, and lipids essential for their malignant proliferation and progression. Metformin, a commonly prescribed medication for type 2 diabetes mellitus, has garnered attention for its potential anticancer effects beyond its established hypoglycemic benefits.

METHODS

This review adopts a comprehensive approach to delineate the mechanisms underlying metabolite abnormalities within the primary metabolic processes of malignant tumors.

RESULTS

This review examines the abnormal activation of G protein-coupled receptors (GPCRs) in these metabolic pathways, encompassing aerobic glycolysis with increased lactate production in glucose metabolism, heightened lipid synthesis and cholesterol accumulation in lipid metabolism, and glutamine activation alongside abnormal protein post-translational modifications in amino acid and protein metabolism. Furthermore, the intricate metabolic pathways and molecular mechanisms through which metformin exerts its anticancer effects are synthesized and analyzed, particularly its impacts on AMP-activated protein kinase activation and the mTOR pathway. The analysis reveals a multifaceted understanding of how metformin can modulate tumor metabolism, targeting key nodes in metabolic reprogramming essential for tumor growth and progression. The review compiles evidence that supports metformin's potential as an adjuvant therapy for malignant tumors, highlighting its capacity to interfere with critical metabolic pathways.

CONCLUSION

In conclusion, this review offers a comprehensive overview of the plausible mechanisms mediating metformin's influence on tumor metabolism, fostering a deeper comprehension of its anticancer mechanisms. By expanding the clinical horizons of metformin and providing insight into metabolism-targeted tumor therapies, this review lays the groundwork for future research endeavors aimed at refining and advancing metabolic intervention strategies for cancer treatment.

Predicting lymphoma prognosis using machine learning-based genes associated with lactylation

BACKGROUND

Lactylation, a newly discovered PTM involving lactic acid, is linked to solid tumor proliferation and metastasis. Lymphoma patients exhibit high lactic acid levels, yet lactylation's role in lymphoma is underexplored. This study aimed to identify lactylation-related genes in lymphoma using tumor databases and assess their predictive value in patient prognosis through cell experiments and clinical specimens.

METHODS

Using TCGA and GEO datasets, we analyzed the expression levels of lactylation-related genes in diffuse large B-cell lymphoma patients. We also evaluated the prognostic significance of lactylation gene risk scores, exploring their impact on drug sensitivity and tumor immune function. Key lactylation-affecting genes were identified and functionally validated through cell experiments and mouse in vivo experiments. Additionally, the relationship between lactylation and lymphoma prognosis was examined in clinical specimens.

RESULTS

We identified 70 genes linked to diffuse large B-cell lymphoma prognosis from the lactylation-related gene set. Using clinical data and a COX regression algorithm, we developed an optimized lactylation Riskscore model. This model significantly correlated with prognosis and showed differences in immune cell infiltration, particularly macrophages. High-risk patients showed resistance to chemotherapy drugs but responded well to immunotherapy. HNRNPH1, a lactylation-related gene, influenced patient prognosis, apoptosis, cell cycle distribution in lymphoma cells, and tumor volume in mice. In lymphoma specimens, lactylation levels correlated with Bcl-2, C-myc, and P53 levels.

CONCLUSIONS

Lactylation impacts diffuse large B-cell lymphoma prognosis, tumor immune function, and drug resistance. Our lactylation-based Riskscore model aids in patient stratification and treatment selection. HNRNPH1 regulates lactylation, thereby affecting patient prognosis.

Lactylation in cancer: Mechanisms in tumour biology and therapeutic potentials

Lactylation, a recently identified form of protein post-translational modification (PTM), has emerged as a key player in cancer biology. The Warburg effect, a hallmark of tumour metabolism, underscores the significance of lactylation in cancer progression. By regulating gene transcription and protein function, lactylation facilitates metabolic reprogramming, enabling tumours to adapt to nutrient limitations and sustain rapid growth. Over the past decade, extensive research has revealed the intricate regulatory network underlying lactylation in tumours. Large-scale sequencing and machine learning have confirmed the widespread occurrence of lactylation sites across the tumour proteome. Targeting lactylation enzymes or metabolic pathways has demonstrated promising anti-tumour effects, highlighting the therapeutic potential of this modification. This review comprehensively explores the mechanisms of lactylation in cancer cells and the tumour microenvironment. We expound on the application of advanced omics technologies for target identification and data modelling within the lactylation field. Additionally, we summarise existing anti-lactylation drugs and discuss their clinical implications. By providing a comprehensive overview of recent advancements, this review aims to stimulate innovative research and accelerate the translation of lactylation-based therapies into clinical practice. KEY POINTS: Lactylation significantly influences tumour metabolism and gene regulation, contributing to cancer progression. Advanced sequencing and machine learning reveal widespread lactylation sites in tumours. Targeting lactylation enzymes shows promise in enhancing anti-tumour drug efficacy and overcoming chemotherapy resistance. This review outlines the clinical implications and future research directions of lactylation in oncology.

Construction of an endoplasmic reticulum stress and cuproptosis -related lncRNAs signature in chemosensitivity in hepatocellular carcinoma by comprehensive bioinformatics analysis

Endoplasmic reticulum stress (ERS) and cuproptosis have remarkable effects on hepatocellular carcinoma (HCC) leading to a poor prognosis. The current study aimed to explore credible signature for predicting the prognosis of HCC based on ERS and cuproptosis-related lncRNAs. In our study, clinical and transcriptomic profiles of HCC patients were obtained from the Cancer Genome Atlas (TCGA) database. An ERS and cuproptosis-related lncRNA prognostic signature, including NRAV, SNHG3, LINC00839 and AC004687.1, was determined by correlation tests, Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) methods. Survival and predictive value were evaluated using Kaplan-Meier and receiver operating characteristic (ROC) curves, while calibration and nomograms curves were developed. Besides the enrichment analyses for ERS and cuproptosis-related lncRNAs, mutational status and immune status were assessed with TMB and ESTIMATE. Additionally, consensus cluster analysis was employed to compare cancer subtype differences, while drug sensitivity and immunologic efficacy were evaluated for further exploration. qRT-PCR and CCK-8 were utilized to verify the alteration of the prognostic lncRNAs expression and proliferation in vitro. High-risk groups exhibited poorer prognosis. The signature exhibited robust predictive value as an independent prognostic indicator and showed significant correlation with clinicopathological features. In the enriched analysis, biological membrane pathways were enriched. Low-risk patients had lower TMB and higher immune status. A cluster analysis revealed that cluster 2 had the best clinical immunological efficacy and most active immune function. In brief, our constructed signature with ERS and cuproptosis-related lncRNAs was associated survival outcomes of HCC, and can be used to predict the clinical classification and curative effect.

Exploring the prognostic and diagnostic value of lactylation-related genes in sepsis

The discovery of Lactylation may pave the way for novel approaches to investigating sepsis. This study focused on the prognostic and diagnostic significance of lactylated genes in sepsis. RNA sequencing was performed on blood samples from 20 sepsis patients and 10 healthy individuals at Southwest Medical University in Luzhou, Sichuan, China. Genes associated with sepsis were identified through analysis of RNA sequencing data. Afterward, the genes that were expressed differently were compared with the lactylation genes, resulting in the identification of 55 lactylation genes linked to sepsis. The overlapping genes underwent analysis using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-Protein Network Interactions were used to screen for the core genes. The datasets GSE65682, GSE69528, GSE54514, GSE63042, and GSE95233 were obtained from the GEO database to validate core genes. Survival analysis evaluated the predictive significance of central genes in sepsis, while Receiver Operating Characteristic (ROC) Curve analysis was employed to establish the diagnostic value of genes. Additionally, a meta-analysis was conducted to confirm the precision of RNA-seq data. We obtained five peripheral blood samples, including two from healthy individuals, one from a patient with systemic inflammatory response syndrome (SIRS), and two from patients with sepsis. These samples were used to identify the specific location of core genes using 10×single-cell sequencing. High-throughput sequencing and bioinformatics techniques identified two lactylation-related genes (S100A11 and CCNA2) associated with sepsis. Survival analysis indicated that septic patients with reduced levels of S100A11 had a decreased 28-day survival rate compared to those with elevated levels. Conversely, individuals exhibiting decreased CCNA2 levels demonstrated a greater likelihood of surviving for 28 days than those in the high expression category, indicating a favorable association with survival rates among sepsis patients (P < 0.05). Both genes showed high sensitivity and specificity based on the ROC curve, with AUC values of 0.961 for S100A11 and 0.890 for CCNA2. The meta-analysis revealed that S100A11 exhibited high levels of expression in the sepsis survivors, whereas it displayed low levels of expression in the non-survivors; on the other hand, CCNA2 demonstrated low expression in the sepsis survivors and high expression in the non-survivors (P < 0.05). Single-cell RNA sequencing ultimately showed that monocyte macrophages, T cells, and B cells exhibited high expression levels of the crucial genes associated with sepsis-induced lactylation. In conclusion, the lactylation genes S100A11 and CCNA2 are strongly linked to sepsis and could be valuable markers for diagnosing, predicting outcomes, and providing guidance for sepsis.

The role of nonhistone lactylation in disease

In 2019, a novel post-translational modification termed lactylation was identified, which established a connection among lactate, transcriptional regulation and epigenetics. Lactate, which is traditionally viewed as a metabolic byproduct, is now recognized for its significant functional role, including modulating the tumor microenvironment, engaging in signaling and interfering in immune regulation. While research on lactylation (KLA) is advancing, the focus has primarily been on histone lactylation. This paper aims to explore the less-studied area of nonhistone lactylation, highlighting its involvement in certain diseases and physiological processes. Additionally, the clinical relevance and potential implications of nonhistone lactylation will be discussed.

Identification of lysine lactylation (kla)-related lncRNA signatures using XGBoost to predict prognosis and immune microenvironment in breast cancer patients

Breast cancer (BC) stands as a predominant global malignancy, significantly contributing to female mortality. Recently uncovered, histone lysine lactylation (kla) has assumed a crucial role in cancer progression. However, the correlation with lncRNAs remains ambiguous. Scrutinizing lncRNAs associated with Kla not only improves clinical breast cancer management but also establishes a groundwork for antitumor drug development. We procured breast tissue samples, encompassing both normal and cancerous specimens, from The Cancer Genome Atlas (TCGA) database. Utilizing Cox regression and XGBoost methods, we developed a prognostic model using identified kla-related lncRNAs. The model's predictive efficacy underwent validation across training, testing, and the overall cohort. Functional analysis concerning kla-related lncRNAs ensued. We identified and screened 8 kla-related lncRNAs to formulate the risk model. Pathway analysis disclosed the connection between immune-related pathways and the risk model of kla-related lncRNAs. Significantly, the risk scores exhibited a correlation with both immune cell infiltration and immune function, indicating a clear association. Noteworthy is the observation that patients with elevated risk scores demonstrated an increased tumor mutation burden (TMB) and decreased tumor immune dysfunction and exclusion (TIDE) scores, suggesting heightened responses to immune checkpoint blockade. Our study uncovers a potential link between Kla-related lncRNAs and BC, providing innovative therapeutic guidelines for BC management.

Single-cell RNA sequencing integrated with bulk RNA sequencing analysis reveals the protective effects of lactate-mediated lactylation of microglia-related proteins on spinal cord injury

BACKGROUND AND OBJECTIVES

Spinal cord injury (SCI) results in significant neurological deficits, and microglia play the critical role in regulating the immune microenvironment and neurological recovery. Protein lactylation has been found to modulate the function of immune cells. Therefore, this study aimed to elucidate the effects of glycolysis-derived lactate on microglial function and its potential neuroprotective mechanisms via lactylation after SCI.

METHODS

Single-cell RNA sequencing (scRNA-seq) data were obtained from figshare to analyze cellular and molecular alterations within the spinal cord post-SCI, further focusing on the expression of microglia-related genes for cell sub-clustering, trajectory analysis, and glycolysis function analysis. We also evaluated the expression of lactylation-related genes in microglia between day 7 after SCI and sham group. Additionally, we established the mice SCI model and performed the bulk RNA sequencing in a time-dependent manner. The expression of glycolysis- and lactylation-related genes was evaluated, as well as the immune infiltration analysis based on the lactylation-related genes. Then, we investigated the bio-effects of lactate on the inflammation and polarization phenotype of microglia. Finally, adult male C57BL/6 mice were subjected to exercise first to increase lactate level, before SCI surgery, aiming to evaluate the protective effects of lactate-mediated lactylation of microglia-related proteins on SCI.

RESULTS

scRNA-seq identified a subcluster of microglia, recombinant chemokine C-X3-C-motif receptor 1+ (CX3CR1+) microglia, which is featured by M1-like phenotype and increased after SCI. KEGG analysis revealed the dysfunctional glycolysis in microglia after SCI surgery, and AUCell analysis suggested that the decreased glycolysis an increased oxidative phosphorylation in CX3CR1+ microglia. Differential gene analysis suggested that several lactylation-related genes (Fabp5, Lgals1, Vim, and Nefl) were downregulated in CX3CR1+ microglia at day 7 after SCI, further validated by the results from bulk RNA sequencing. Immunofluorescence staining indicated the expression of lactate dehydrogenase A (LDHA) in CX3CR1+ microglia also decreased at day 7 after SCI. Cellular experiments demonstrated that the administration of lactate could increase the lactylation level and inhibit the pro-inflammatory phenotype in microglia. Functionally, exercise-mediated lactate production resulted in improved locomotor recovery and decreased inflammatory markers in SCI mice compared to SCI alone.

CONCLUSIONS

In the subacute phase of SCI, metabolic remodeling in microglia may be key therapeutic targets to promote nerve regeneration, and lactate contributed to neuroprotection after SCI by influencing microglial lactylation and inflammatory phenotype, which offered a novel approach for therapeutic intervention.

The Role of Pyruvate Kinase M2 Posttranslational Modification in the Occurrence and Development of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the deadly malignant tumors that directly leads to the death of nearly one million people worldwide every year, causing a serious burden on society. In the presence of sufficient oxygen, HCC cells rapidly generate energy through aerobic glycolysis, which promotes tumor cell proliferation, immune evasion, metastasis, angiogenesis, and drug resistance. Pyruvate kinase M2 (PKM2) is a key rate-limiting enzyme in glycolysis. In recent years, studies have found that PKM2 not only exerts pyruvate kinase activity in the process of glucose metabolism, but also exerts protein kinase activity in non-metabolic pathways to affect tumor cell processes, and its activity is flexibly regulated by various posttranslational modifications such as acetylation, phosphorylation, lactylation, ubiquitination, SUMOylation, and so forth. This review summarizes the role of posttranslational modifications of PKM2-related sites in the development of HCC.

Identification of a lactylation-related gene signature to characterize subtypes of hepatocellular carcinoma using bulk sequencing data

BACKGROUND

Prior studies indicate that lactylation regulates various biological mechanisms within cancer. However, lactylation-related genes (LRGs) have been found to have limited value in predicting the prognosis of hepatocellular carcinoma (HCC). The aim of this study was to review HCC LRGs using data from The Cancer Genome Atlas (TCGA).

METHODS

The RNA sequencing data and related clinical information of patients with HCC patients were collected from the TCGA database. A total of 20 LRGs were selected and bioinformatics analysis was performed. A consistency cluster analysis was conducted to classify the HCC tumors. Using a lactylation-related model of HCC, prognosis, immune cell infiltration, and immunotherapy was evaluated.

RESULTS

A total of 4,378 genes were associated with prognosis. Twenty LRGs (i.e., ACIN1, RAN, PPP1CB, ALDOB, SUMO2, THOC2, HDAC1, SF3A1, SF3B1, HNRNPM, PPP1CC, SRRM1, PRPF6, HDAC2, H2AFV, ALYREF, H2AFZ, H2AFX, HNRNPK, and MAGOH) were identified. The 20 LRGs were used to divide TCGA-HCC patients into low-risk (G1) and high-risk (G2) categories. The upregulated genes in the G1 group primarily participate in the p53 signaling pathway, focal adhesion, extracellular matrix (ECM)-receptor interaction, and cell cycle, while the downregulated genes primarily participate in the glycolysis/gluconeogenesis, carbon metabolism, and biosynthesis of amino acids. The box plots showed a significant difference in the immune cell populations, with a higher abundance of B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and myeloid dendritic cells in the G1 than the G2 HCC samples. Further, the box plots showed higher expression levels of seven of the eight immune checkpoint inhibitor (ICI)-related genes in the G1 HCC samples than the G2 samples. There was a significant disparity in the cancer stem cell (CSC) scores between the G1 and G2 TCGA-HCC patients. Additionally, the G1 TCGA-HCC patients had higher tumor immune dysfunction and exclusion (TIDE) scores than the G2 TCGA-HCC patients. The prognosis of the HCC patients was also predicted using a six-LRG model, comprising HDAC2, SRRM1, SF3B1, HDAC1, THOC2, and PPP1CB.

CONCLUSIONS

Strong correlation between LRGs and tumor classification as well as immunity in patients with HCC was identified. LRG signatures serve as reliable prognostic markers for HCC.

Lactylation signature identifies liver fibrosis phenotypes and traces fibrotic progression to hepatocellular carcinoma

Introduction

Precise staging and classification of liver fibrosis are crucial for the hierarchy management of patients. The roles of lactylation are newly found in the progression of liver fibrosis. This study is committed to investigating the signature genes with histone lactylation and their connection with immune infiltration among liver fibrosis with different phenotypes.

Methods

Firstly, a total of 629 upregulated and 261 downregulated genes were screened out of 3 datasets of patients with liver fibrosis from the GEO database and functional analysis confirmed that these differentially expressed genes (DEGs) participated profoundly in fibrosis-related processes. After intersecting with previously reported lactylation-related genes, 12 DEGs related to histone lactylation were found and narrowed down to 6 core genes using R algorithms, namely S100A6, HMGN4, IFI16, LDHB, S100A4, and VIM. The core DEGs were incorporated into the Least absolute shrinkage and selection operator (LASSO) model to test their power to distinguish the fibrotic stage.

Results

Advanced fibrosis presented a pattern of immune infiltration different from mild fibrosis, and the core DEGs were significantly correlated with immunocytes. Gene set and enrichment analysis (GSEA) results revealed that core DEGs were closely linked to immune response and chemokine signaling. Samples were classified into 3 clusters using the LASSO model, followed by gene set variation analysis (GSVA), which indicated that liver fibrosis can be divided into status featuring lipid metabolism reprogramming, immunity immersing, and intermediate of both. The regulatory networks of the core genes shared several transcription factors, and certain core DEGs also presented dysregulation in other liver fibrosis and idiopathic pulmonary fibrosis (IPF) cohorts, indicating that lactylation may exert comparable functions in various fibrotic pathology. Lastly, core DEGs also exhibited upregulation in HCC.

Discussion

Lactylation extensively participates in the pathological progression and immune infiltration of fibrosis. Lactylation and related immune infiltration could be a worthy focus for the investigation of HCC developed from liver fibrosis.

The relationship and clinical significance of lactylation modification in digestive system tumors

Lactylation, an emerging post-translational modification, plays a pivotal role in the initiation and progression of digestive system tumors. This study presents a comprehensive review of lactylation in digestive system tumors, underscoring its critical involvement in tumor development and progression. By focusing on metabolic reprogramming, modulation of the tumor microenvironment, and the molecular mechanisms regulating tumor progression, the potential of targeting lactylation as a therapeutic strategy is highlighted. The research reveals that lactylation participates in gene expression regulation and cell signaling by affecting the post-translational states of histones and non-histone proteins, thereby influencing metabolic pathways and immune evasion mechanisms in tumor cells. Furthermore, this study assesses the feasibility of lactylation as a therapeutic target, providing insights for clinical treatment of gastrointestinal cancers. Future research should concentrate on elucidating the mechanisms of lactylation, developing efficient lactylation inhibitors, and validating their therapeutic efficacy in clinical trials, which could transform current cancer treatment and immunotherapy approaches. In summary, this review emphasizes the crucial role of lactylation in tumorigenesis and progression through a detailed analysis of its molecular mechanisms and clinical significance.

Identification of a novel lactylation-related gene signature predicts the prognosis of multiple myeloma and experiment verification

Multiple myeloma (MM) is an incurable hematological malignancy with poor survival. Accumulating evidence reveals that lactylation modification plays a vital role in tumorigenesis. However, research on lactylation-related genes (LRGs) in predicting the prognosis of MM remains limited. Differentially expressed LRGs (DELRGs) between MM and normal samples were investigated from the Gene Expression Omnibus database. Univariate Cox regression and LASSO Cox regression analysis were applied to construct gene signature associated with overall survival. The signature was validated in two external datasets. A nomogram was further constructed and evaluated. Additionally, Enrichment analysis, immune analysis, and drug chemosensitivity analysis between the two groups were investigated. qPCR and immunofluorescence staining were performed to validate the expression and localization of PFN1. CCK-8 and flow cytometry were performed to validate biological function. A total of 9 LRGs (TRIM28, PPIA, SOD1, RRP1B, IARS2, RB1, PFN1, PRCC, and FABP5) were selected to establish the prognostic signature. Kaplan-Meier survival curves showed that high-risk group patients had a remarkably worse prognosis in the training and validation cohorts. A nomogram was constructed based on LRGs signature and clinical characteristics, and showed excellent predictive power by calibration curve and C-index. Moreover, biological pathways, immunologic status, as well as sensitivity to chemotherapy drugs were different between high- and low-risk groups. Additionally, the hub gene PFN1 is highly expressed in MM, knocking down PFN1 induces cell cycle arrest, suppresses cell proliferation and promotes cell apoptosis. In conclusion, our study revealed that LRGs signature is a promising biomarker for MM that can effectively early distinguish high-risk patients and predict prognosis.

Role of lactate and lactate metabolism in liver diseases (Review)

Lactate is a byproduct of glycolysis, and before the Warburg effect was revealed (in which glucose can be fermented in the presence of oxygen to produce lactate) it was considered a metabolic waste product. At present, lactate is not only recognized as a metabolic substrate that provides energy, but also as a signaling molecule that regulates cellular functions under pathophysiological conditions. Lactylation, a post‑translational modification, is involved in the development of various diseases, including inflammation and tumors. Liver disease is a major health challenge worldwide. In normal liver, there is a net lactate uptake caused by gluconeogenesis, exhibiting a higher net lactate clearance rate compared with any other organ. Therefore, abnormalities of lactate and lactate metabolism lead to the development of liver disease, and lactate and lactate metabolism‑related genes can be used for predicting the prognosis of liver disease. Targeting lactate production, regulating lactate transport and modulating lactylation may be potential treatment approaches for liver disease. However, currently there is not a systematic review that summarizes the role of lactate and lactate metabolism in liver diseases. In the present review, the role of lactate and lactate metabolism in liver diseases including liver fibrosis, non‑alcoholic fatty liver disease, acute liver failure and hepatocellular carcinoma was summarized with the aim to provide insights for future research.

[Role and Mechanism of Lactylation in Cancer]

Post translational modifications (PTMs) can change the properties of a protein by covalent addition of functional groups to one or more amino acids, and influence almost all aspects of normal cell biology and pathogenesis. Lactylation is a novel identified PTM, and has been found in both histone and non-histone proteins. Since associated with the end product of glycolysis-- lactate, lactylation modification could provide a new perspective for understanding the relationship between metabolic reprogramming and epigenetic modifications. Accumulated evidences suggest that lactylation play important roles in tumor progression and links to poor prognosis in clinical studies. Histone lactylation can affect gene expression in tumor cells and immunological cells, further promoting tumor progression and immune suppression. Lactylation on non-histone proteins can also regulate tumor progression and drug resistance. In this review, we aimed to summarize the roles of lactylation in cancer progression, microenvironment interactions and immune suppression, try to identify new molecular targets for cancer therapy and provide a new direction for combined targeted therapy and immunotherapy.
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A novel lactylation-related gene signature for effectively distinguishing and predicting the prognosis of ovarian cancer

Background

Lactylation has been found to regulate several types of biological processes in cancer. However, there is limited research on lactylation-related genes in predicting the prognosis of ovarian cancer (OC). This study aimed to explore the functional roles of lactylation-related genes in OC.

Methods

Based on TCGA database, we obtained RNA sequencing data and clinical characteristics of patients with OC. Fourteen lactylation-related genes were screened for bioinformatic analysis in OC. Tumor classification of OC was constructed via a consistency cluster analysis. We examined the prognosis, immune-cell infiltration, and immunotherapy in relation to a lactylation-related model for OC.

Results

A total of 707 prognostic genes and 14 key lactylation-related genes (SNRPA1, MPHOSPH6, POLDIP3, RB1, AHNAK, MAGOHB, CALM1, EP300, HDAC1, HDAC2, HDAC3, SIRT1, SIRT2, and SIRT3) were identified in TCGA-OC patients. Based on 14 genes involved in lactylation, TCGA-OC patients were split into low-risk (G1) and high-risk (G2) groups. Downregulated differentially expressed genes (DEGs) in the low-risk G1 group were associated with thermogenesis, oxidative phosphorylation, neutrophil extracellular trap formation, and interleukin 17 (IL-17) signaling pathway, whereas upregulated DEGs were associated with proteoglycans in cancer, focal adhesion, Wnt signaling pathway, extracellular matrix (ECM)-receptor interaction, and the adherens junction. The immune activity of the low-risk G1 group was lower than that of the high-risk G2 group. Gemcitabine, bleomycin, and doxorubicin had lower half-maximal inhibitory concentration (IC50) values in the high-risk G2 patients with OC, while cisplatin and paclitaxel had higher IC50 values compared to the low-risk G1 patients. The prognosis of patients with OC was also predicted with the help of an eight-lactylation-related gene prognostic model, comprising SNRPA1, MPHOSPH6, POLDIP3, RB1, HDAC1, CALM1, HDAC2, and SIRT2.

Conclusions

The lactylation-related genes are closely related to tumor classification and immunity in patients with OC. There was good prognostic predictive performance for OC based on a lactylation-related signature. Our findings may offer new insights into the diagnosis and treatment of OC.

Immunotherapy and drug sensitivity predictive roles of a novel prognostic model in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most significant causes of cancer-related deaths in the worldwide. Currently, predicting the survival of patients with HCC and developing treatment drugs still remain a significant challenge. In this study, we employed prognosis-related genes to develop and externally validate a predictive risk model. Furthermore, the correlation between signaling pathways, immune cell infiltration, immunotherapy response, drug sensitivity, and risk score was investigated using different algorithm platforms in HCC. Our results showed that 11 differentially expressed genes including UBE2C, PTTG1, TOP2A, SPP1, FCN3, SLC22A1, ADH4, CYP2C8, SLC10A1, F9, and FBP1 were identified as being related to prognosis, which were integrated to construct a prediction model. Our model could accurately predict patients' overall survival using both internal and external datasets. Moreover, a strong correlation was revealed between the signaling pathway, immune cell infiltration, immunotherapy response, and risk score. Importantly, a novel potential drug candidate for HCC treatment was discovered based on the risk score and also validated through ex vivo experiments. Our finds offer a novel perspective on prognosis prediction and drug exploration for cancer patients.

A hypoxia-glycolysis-lactate-related gene signature for prognosis prediction in hepatocellular carcinoma

BACKGROUND

Liver cancer ranks sixth in incidence and third in mortality globally and hepatocellular carcinoma (HCC) accounts for 90% of it. Hypoxia, glycolysis, and lactate metabolism have been found to regulate the progression of HCC separately. However, there is a lack of studies linking the above three to predict the prognosis of HCC. The present study aimed to identify a hypoxia-glycolysis-lactate-related gene signature for assessing the prognosis of HCC.

METHODS

This study collected 510 hypoxia-glycolysis-lactate genes from Molecular Signatures Database (MSigDB) and then classified HCC patients from TCGA-LIHC by analyzing their hypoxia-glycolysis-lactate genes expression. Differentially expressed genes (DEGs) were screened out to construct a gene signature by LASSO-Cox analysis. Univariate and multivariate regression analyses were used to evaluate the independent prognostic value of the gene signature. Analyses of immune infiltration, somatic cell mutations, and correlation heatmap were conducted by "GSVA" R package. Single-cell analysis conducted by "SingleR", "celldex", "Seurat", and "CellCha" R packages revealed how signature genes participated in hypoxia/glycolysis/lactate metabolism and PPI network identified hub genes.

RESULTS

We classified HCC patients from TCGA-LIHC into two clusters and screened out DEGs. An 18-genes prognostic signature including CDCA8, CBX2, PDE6A, MED8, DYNC1LI1, PSMD1, EIF5B, GNL2, SEPHS1, CCNJL, SOCS2, LDHA, G6PD, YBX1, RTN3, ADAMTS5, CLEC3B, and UCK2 was built to stratify the risk of HCC. The risk score of the hypoxia-glycolysis-lactate gene signature was further identified as a valuable independent factor for estimating the prognosis of HCC. Then we found that the features of clinical characteristics, immune infiltration, somatic cell mutations, and correlation analysis differed between the high-risk and low-risk groups. Furthermore, single-cell analysis indicated that the signature genes could interact with the ligand-receptors of hepatocytes/fibroblasts/plasma cells to participate in hypoxia/glycolysis/lactate metabolism and PPI network identified potential hub genes in this process: CDCA8, LDHA, YBX1.

CONCLUSION

The hypoxia-glycolysis-lactate-related gene signature we built could provide prognostic value for HCC and suggest several hub genes for future HCC studies.

Constructing lactylation-related genes prognostic model to effectively predict the disease-free survival and treatment responsiveness in prostate cancer based on machine learning

Background: Prostate cancer (PCa) is one of the most common malignancies in men with a poor prognosis. It is therefore of great clinical importance to find reliable prognostic indicators for PCa. Many studies have revealed the pivotal role of protein lactylation in tumor development and progression. This research aims to analyze the effect of lactylation-related genes on PCa prognosis. Methods: By downloading mRNA-Seq data of TCGA PCa, we obtained the differential genes related to lactylation in PCa. Five machine learning algorithms were used to screen for lactylation-related key genes for PCa, then the five overlapping key genes were used to construct a survival prognostic model by lasso cox regression analysis. Furthermore, the relationships between the model and related pathways, tumor mutation and immune cell subpopulations, and drug sensitivity were explored. Moreover, two risk groups were established according to the risk score calculated by the five lactylation-related genes (LRGs). Subsequently, a nomogram scoring system was established to predict disease-free survival (DFS) of patients by combining clinicopathological features and lactylation-related risk scores. In addition, the mRNA expression levels of five genes were verified in PCa cell lines by qPCR. Results: We identified 5 key LRGs (ALDOA, DDX39A, H2AX, KIF2C, RACGAP1) and constructed the LRGs prognostic model. The AUC values for 1 -, 3 -, and 5-year DFS in the TCGA dataset were 0.762, 0.745, and 0.709, respectively. The risk score was found a better predictor of DFS than traditional clinicopathological features in PCa. A nomogram that combined the risk score with clinical variables accurately predicted the outcome of the patients. The PCa patients in the high-risk group have a higher proportion of regulatory T cells and M2 macrophage, a higher tumor mutation burden, and a worse prognosis than those in the low-risk group. The high-risk group had a lower IC50 for certain chemotherapeutic drugs, such as Docetaxel, and Paclitaxel than the low-risk group. Furthermore, five key LRGs were found to be highly expressed in castration-resistant PCa cells. Conclusion: The lactylation-related genes prognostic model can effectively predict the DFS and therapeutic responses in patients with PCa.

Identification of SLC2A1 as a predictive biomarker for survival and response to immunotherapy in lung squamous cell carcinoma

BACKGROUND

As one of the common subtypes of non-small lung cancer, lung squamous cell carcinoma (LUSC) patients with advanced stage have few choices of treatment strategies. Therefore, it is urgent to discover genes that are associated with the survival and efficacy of immunotherapies.

METHOD

Differential gene expression analyses were conducted using TCGA LUSC bulk-sequencing and single-cell RNA-sequencing data. Prognostic genes were identified from the TCGA LUSC cohort. Protein expression validation and survival analyses were performed. Experiments were conducted to explore the underlying mechanisms. In addition, the correlation between gene expression and pathological response to adjuvant immunochemotherapy was also investigated.

RESULTS

After a series of bioinformatic analyses, solute carrier family 2 member 1(SLC2A1), encoding glucose transporter-1 (GLUT1), was found to be differentially expressed between tumor and normal tissues. GLUT1 was subsequently identified as an independent prognostic factor for LUSC. GSEA analysis revealed the glycolysis metabolism pathway of KEGG enriched in SLC2A1high tumor tissues. LASSO analyses revealed that tumor tissues with high expression of SLC2A1 were associated with high levels of protein lactylation. We found that SLC2A1 was preferentially expressed by SPP1+ macrophages in the tumor microenvironment, and the expression of SLC2A1 was associated with the abundance of SPP1+ macrophages. Immunofluorescence demonstrated GLUT1 and HIF1α colocalization in tumor-infiltrating macrophages. In vitro experiments showed HIF-1α-induced macrophage polarization under hypoxia, and GLUT1 inhibition blocked this polarization. In addition, SLC2A1 was negatively associated with the common immune checkpoint molecules, such as programmed cell death 1(PD-1), T cell immunoreceptor with Ig and ITIM domains (TIGIT), cytotoxic T-lymphocyte associated protein 4 (CTLA4) and lymphocyte activating 3 (LAG3), while showed a positive association with CD44. Finally, we observed that there was a significant correlation between pre-adjuvant-treatment GLUT1 expression and the pathological response.

CONCLUSION

SLC2A1 expression was differentially upregulated in tumor tissues, and elevated GLUT1 expression was associated with worse survival and poor pathological response to adjuvant immunochemotherapy. Upregulation of GLUT1 promoted macrophage polarization into the M2 phenotype. The findings will contribute to guiding the treatment selection for LUSC patients and providing personalized immunotherapy strategies.

A multi-dimensional approach to unravel the intricacies of lactylation related signature for prognostic and therapeutic insight in colorectal cancer

BACKGROUND

Lactylation, a novel contributor to post-translational protein modifications, exhibits dysregulation across various tumors. Nevertheless, its intricate involvement in colorectal carcinoma, particularly for non-histone lactylation and its intersection with metabolism and immune evasion, remains enigmatic.

METHODS

Employing immunohistochemistry on tissue microarray with clinical information and immunofluorescence on colorectal cell lines, we investigated the presence of global lactylation and its association with development and progression in colorectal cancer as well as its functional location. Leveraging the AUCell algorithm alongside correlation analysis in single-cell RNA sequencing data, as well as cox-regression and lasso-regression analysis in TCGA dataset and confirmed in GEO dataset, we identified a 23-gene signature predicting colorectal cancer prognosis. Subsequently, we analyzed the associations between the lactylation related gene risk and clinical characteristics, mutation landscapes, biological functions, immune cell infiltration, immunotherapy responses, and drug sensitivity. Core genes were further explored for deep biological insights through bioinformatics and in vitro experiments.

RESULTS

Our study innovatively reveals a significant elevation of global lactylation in colorectal cancer, particularly in malignant tumors, confirming it as an independent prognostic factor for CRC. Through a comprehensive analysis integrating tumor tissue arrays, TCGA dataset, GEO dataset, combining in silico investigations and in vitro experiments, we identified a 23-gene Lactylation-Related Gene risk model capable of predicting the prognosis of colorectal cancer patients. Noteworthy variations were observed in clinical characteristics, biological functions, immune cell infiltration, immune checkpoint expression, immunotherapy responses and drug sensitivity among distinct risk groups.

CONCLUSIONS

The Lactylation-Related Gene risk model exhibits significant potential for improving the management of colorectal cancer patients and enhancing therapeutic outcomes, particularly at the intersection of metabolism and immune evasion. This finding underscores the clinical relevance of global lactylation in CRC and lays the groundwork for mechanism investigation and targeted therapeutic strategies given the high lactate concentration in CRC.

Lactylation-related gene signature for prognostic prediction and immune infiltration analysis in breast cancer

Background

Lactylation is implicated in various aspects of tumor biology, but its relation to breast cancer remains poorly understood. This study aimed to explore the roles of the lactylation-related genes in breast cancer and its association with the tumor microenvironment.

Methods

The expression and mutation patterns of lactylation-related genes were analyzed using the breast cancer data from The Cancer Genome Atlas (TCGA) database and GSE20685 datasets. Unsupervised clustering was used to identify two lactylation clusters. A lactylation-related gene signature was developed and validated using the training and validation cohorts. Immune cell infiltration and drug response were assessed.

Results

We analyzed the mRNA expression, copy number variations, somatic mutations, and correlation networks of 22 lactylation-related genes in breast cancer tissues. We identified two distinct lactylation clusters with different survival outcomes and immune microenvironments. We further classified the patients into two gene subtypes based on lactylation clusters and identified a 7-gene signature for breast cancer survival prognosis. The prognostic score based on this signature demonstrated prognostic value and predicted the therapeutic response.

Conclusion

Lactylation-related genes play a critical role in breast cancer by influencing tumor growth, immune microenvironment, and drug response. This lactylation-related gene signature may serve as a prognostic marker and a potential therapeutic target for breast cancer.

Identification of lactylation gene CALML5 and its correlated lncRNAs in cutaneous melanoma by machine learning

As a product of glycolysis, lactate contributes to cancer proliferation, immunosuppression, and metastasis via histone lactylation. However, the relationship between cutaneous melanoma (CM) and lactylation-associated genes and lncRNAs has remained unclear. In this study, 4 mechanism learning algorithms and integrated bioinformatic analyses were employed to identify the core lactylation-associated genes and lncRNAs. Subsequently, 2 risk signatures based on the hub lactylation-associated genes and lncRNAs were constructed for CM patients. As a result, CALML5 was identified as a core lactylation-associated gene in CM, and its expression was found to be associated with patients survival and immune infiltration, suggesting its relevance as a potential therapeutic target. Additionally, this study provided clarification on hub CALML5-associated lncRNAs in CM, offering insights into their roles in the disease. Meanwhile, 2 identified risk signatures were both strongly linked to the prognosis and cancer growth of CM, underscoring their potential as valuable prognostic indicators. Furthermore, mechanistic analyses suggested a significant association between the risk signature and the immune microenvironment in CM, highlighting potential immune-related implications in disease progression. In conclusion, we propose that lactylation-associated genes and lncRNAs hold promise as potential targets in CM. Moreover, our findings revealed a significant correlation between lactylation and the immune microenvironment, providing crucial insights for guiding individualized treatment strategies in CM.

Development of a Comprehensive Gene Signature Linking Hypoxia, Glycolysis, Lactylation, and Metabolomic Insights in Gastric Cancer through the Integration of Bulk and Single-Cell RNA-Seq Data

BACKGROUND

Hypoxia and anaerobic glycolysis are cancer hallmarks and sources of the metabolite lactate. Intriguingly, lactate-induced protein lactylation is considered a novel epigenetic mechanism that predisposes cells toward a malignant state. However, the significance of comprehensive hypoxia-glycolysis-lactylation-related genes (HGLRGs) in cancer is unclear. We aimed to construct a model centered around HGLRGs for predicting survival, metabolic features, drug responsiveness, and immune response in gastric cancer.

METHODS

The integration of bulk and single-cell RNA-Seq data was achieved using data obtained from the TCGA and GEO databases to analyze HGLRG expression patterns. A HGLRG risk-score model was developed based on univariate Cox regression and a LASSO-Cox regression model and subsequently validated. Additionally, the relationships between the identified HGLRG signature and multiple metabolites, drug sensitivity and various cell clusters were explored.

RESULTS

Thirteen genes were identified as constituting the HGLRG signature. Using this signature, we established predictive models, including HGLRG risk scores and nomogram and Cox regression models. The stratification of patients into high- and low-risk groups based on HGLRG risk scores showed a better prognosis in the latter. The high-risk group displayed increased sensitivity to cytotoxic drugs and targeted inhibitors. The expression of the HGLRG BGN displayed a strong correlation with amino acids and lipid metabolites. Notably, a significant difference in immune infiltration, such as that of M1 macrophages and CD8 T cells, was correlated with the HGLRG signature. The abundant DUSP1 within the mesenchymal components was highlighted by single-cell transcriptomics.

CONCLUSION

The innovative HGLRG signature demonstrates efficacy in predicting survival and providing a practical clinical model for gastric cancer. The HGLRG signature reflects the internal metabolism, drug responsiveness, and immune microenvironment components of gastric cancer and is expected to boost patients' response to targeted therapy and immunotherapy.

Histone lactylation regulates cancer progression by reshaping the tumor microenvironment

As a major product of glycolysis and a vital signaling molecule, many studies have reported the key role of lactate in tumor progression and cell fate determination. Lactylation is a newly discovered post-translational modification induced by lactate. On the one hand, lactylation introduced a new era of lactate metabolism in the tumor microenvironment (TME), and on the other hand, it provided a key breakthrough point for elucidation of the interaction between tumor metabolic reprogramming and epigenetic modification. Studies have shown that the lactylation of tumor cells, tumor stem cells and tumor-infiltrating immune cells in TME can participate in the development of cancer through downstream transcriptional regulation, and is a potential and promising tumor treatment target. This review summarized the discovery and effects of lactylation, as well as recent research on histone lactylation regulating cancer progression through reshaping TME. We also focused on new strategies to enhance anti-tumor effects via targeting lactylation. Finally, we discussed the limitations of existing studies and proposed new perspectives for future research in order to further explore lactylation targets. It may provide a new way and direction to improve tumor prognosis.