Abstract 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 [34]GSE65682, [35]GSE69528, [36]GSE54514, [37]GSE63042, and [38]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. Keywords: Sepsis, mRNA-seq, Lactylation, Single-cell RNA sequencing technology Subject terms: Diagnostic markers, Predictive markers, Prognostic markers, High-throughput screening Preface Annually, sepsis impacts close to 49 million individuals worldwide, leading to around 11 million fatalities, representing a fifth of all global deaths^[39]1. After years of research, the pathogenesis, diagnosis, and treatment of sepsis remain challenging^[40]2. The altered energy metabolism and stable hemodynamics of septic patients are the primary focus of researchers. Studies on the gene expression and chemical processes in patients with sepsis have shown that the transition from using oxygen for energy production to relying on glycolysis is an important part of the early stage^[41]3. Lactic acid, a byproduct of sugar fermentation, is a crucial marker for the severity of sepsis and the mortality rate^[42]4. In patients with sepsis, lactate accumulation can occur due to increased lactate production caused by hypoxia, increased aerobic glycolysis, and metabolic abnormalities resulting from impaired mitochondrial function. Additionally, decreased lactate clearance may contribute to this accumulation. As a result, one of the key objectives of intensified therapy for sepsis is to reduce serum lactate levels at an early stage^[43]5. Nevertheless, lowering serum lactate levels in the advanced phases of sepsis does not significantly enhance the results^[44]6. Recent research has shown that the perception of lactic acid as a byproduct of energy metabolism, occurring only under hypoxic conditions, is changing. It is now believed that lactic acid is fundamental in energy conversion through blood circulation and is not merely a temporary metabolite. Lactic acid can carry out various tasks, including anti-inflammatory, immune regulation, and tissue repair, by moving between cells and within cells^[45]7. The specific way lactate influences the immune imbalance in sepsis is still not understood. Using high-performance liquid chromatography (HPLC) - tandem mass spectrometry (MS/MS), the core histones of Michigan Cancer Foundation-7 were examined by Professor Yingming Zhao’s research group. During the analysis, they detected the same post-translational modification (m = 72.021 Da) in the three protein hydrolysates^[46]8. The procedure included attaching an acetyl group to the ε-amino group of lysine residues. They used four different methods to show that histone acetylation modifications were present. Additionally, they created a pan-acetyllysine antibody and confirmed the existence of histone acetylation modifications through protein imprinting. This showed that l-lactic acid from an external source led to a dose-dependent rise in histone acetylation levels. Experiments using stable isotope l-lactate sodium^[47]13C3) showed that lysine acylation can come from lactate. In summary, the findings indicate that histone lactylation is a protein alteration caused by lactate. The finding links glycolysis to epigenetics, offering new avenues for sepsis research^[48]9. This study analyzed mRNA sequencing data from sepsis patients and normal individuals’ peripheral blood to discover genes with differential expression between the two groups. Lactylation genes related to sepsis were obtained by crossing the differential genes with lactylation genes. Core genes were screened using PPI. Core gene validation was conducted using a dataset obtained from the GEO database. Finally, we screened lactylation genes closely related to sepsis as potential reference indicators for sepsis diagnosis, prognosis judgment, and guidance (Fig. [49]1). Fig. 1. [50]Fig. 1 [51]Open in a new tab Illustrates the research workflow. Here, Sepsis denotes the sepsis group, and NC refers to the normal control volunteer group. PCA stands for Principal Component Analysis, GO represents Gene Ontology, and KEGG refers to the Kyoto Encyclopedia of Genes and Genomes. PPI signifies the Protein-Protein Interaction network, while scRNA-seq represents single-cell RNA sequencing technology. Methods Research design and collection of blood samples Peripheral blood samples for mRNA sequencing were obtained from 20 sepsis patients in the Southwest Medical University Affiliated Hospital ICU, and 10 healthy individuals sequencing results were used to screen for differential genes^[52]10. This study’s dataset, which was generated and analyzed, is available in the China National Gene Bank DataBase (CNGBdb) repository at [53]https://db.cngb.org/ with the accession CNP0002611. Here are the revised inclusion criteria for sepsis cases:1) The patient must meet the diagnostic criteria for sepsis.2) The patient must have no acute or chronic liver disease history.3) The patient must have no history of severe infectious diseases or immunodeficiency.4) The patient must be a first-time diagnosed case.5) The patient must have no history of metabolic diseases. Patients will be excluded if they have major organ dysfunction, concurrent malignancies, or have been treated with immunosuppressive therapy within the last 3 months—patients with severe congenital diseases or deformities. Every individual involved in the study provided their signature on the informed consent documents, and the research plan underwent evaluation and endorsement from the Ethics Committee at the Affiliated Hospital of Southwest Medical University (Ethics number ky2018029). The clinical trial registration number was ChiCTR1900021261. Research involving human participants was conducted by the Helsinki Declaration^[54]11. Based on previous studies, 332 lactylation genes have been collected^[55]12. RNA sequencing Total RNA was extracted from blood samples using the TRIzol method (Invitrogen, Carlsbad, CA, USA). The RNA was subsequently measured with an Agilent 2100 instrument from Thermo Fisher Scientific in Massachusetts, USA. Initially, oligonucleotides were designed for a specific target, and RNase H agents were employed to eliminate ribosomal RNA (rRNA). After purifying with SPRI beads, the RNA was fragmented by high temperatures with divalent cations. Afterward, the RNA fragments were transformed into initial-strand cDNA with the help of reverse transcriptase and random primers. The second strand of cDNA was created by utilizing DNA polymerase I and RNase H. The library’s quality and quantity were evaluated through two techniques: analyzing fragment size distribution with an Agilent 2100 bioanalyzer and quantifying the library using real-time quantitative PCR (QPCR) with a TaqMan Probe. The certified libraries were subjected to paired-end sequencing with the BGISEQ-500/MGISEQ-2000 platform from BGI-Shenzhen, China. Afterward, the mRNA sequencing data underwent filtration with SOAP nuke (HTTPS//github.com/BGI-flexlab/SOAPnuke), and the clean reads obtained were stored in FASTQ format^[56]13. Selection of differential genes We utilized Principal Component Analysis (PCA) to examine inter-group separation trends and identify outliers in the experimental model while assessing the original data’s variability between and within groups. To distinguish genes that are different between the sepsis group and the normal group, the criteria for selection were established as an absolute fold change (FC) value of at least 2 and a false discovery rate (FDR) below 0.05. Differential analysis results were obtained using the ggplot2 package in R and were visualized as volcano plots to identify DEGs. DEGs between the normal and sepsis groups were identified using the online tool iDEP 2.1 at ([57]http://bioinformatics.sdstate.edu/idep/)^[58]13. Finally, we crossed the differentially expressed genes with lactylation genes to obtain lactylation genes associated with sepsis. GO and KEGG analysis Growing evidence suggests that a single gene does not typically control the onset of a biological process; instead, the process arises from the coordinated expression of multiple genes and the combined effect of a specific group of proteins. Gene Ontology (GO) is a globally recognized system for classifying gene functions, offering a constantly updated vocabulary to fully depict the characteristics of genes and gene products in living organisms. GO consists of three ontologies: Molecular Function (MF), Cellular Component (CC), and Biological Process (BP), which detail the functions, locations, and processes of genes, respectively^[59]14. We used the GO database to assess the involvement of our target genes in particular functions across cellular components, molecular functions, and biological processes. The R package ‘cluster Profiler’ was used for GO analysis on overlapping genes, with statistical significance defined as P < 0.05. Pathway enrichment analysis, similar to GO functional enrichment analysis, uses the KEGG Pathway as a unit and applies the hypergeometric test to detect Pathways significantly enriched in genes differentially expressed compared to all quantified genes in the species. This analysis helps pinpoint the primary biochemical metabolic and signaling pathways involved by the differentially expressed genes^[60]15. The analysis of shared genes for enrichment was conducted using the ‘cluster Profiler’ tool in R version 4.2.1, with a significance level set at P < 0.05. Identification of lactylation-related hub genes in sepsis patient To identify core genes, we analyzed the overlapping genes in the STRING database (HTTPS//cn.string-db.org/)^[61]16. The closer a gene is to the center; its functional role and external connectivity are more significant. Survival analysis To investigate the critical functions of potential core genes selected using the PPI method in determining patient prognosis in sepsis, we downloaded the public dataset [62]GSE65682 on the relationship between genes and prognosis. [63]GSE65682 contains gene expression values and clinical outcome data for 478 patients with sepsis and 365 survivors^[64]17. We conducted survival analysis using GraphPad Prism 8 software, with a log-rank test where P < 0.05 was statistically significant. Receiver operating characteristic (ROC) curve The ROC curve (Receiver Operating Characteristic) was utilized in the dataset [65]GSE69528 from the GEO database^[66]18. The dataset was submitted in June 2015 and consisted of a sepsis group (n = 83) and a normal control group (n = 55), from which blood transcriptome data were obtained^[67]19. The diagnostic accuracy of the lactylation gene for sepsis was evaluated by analyzing the ROC curve using MedCalc software. Meta-analysis validation To further evaluate the expression of the lactylation genes associated with sepsis across different populations, we validated our findings using publicly available data. We retrieved the sepsis datasets [68]GSE54514^[69]20, [70]GSE63042^[71]21, and [72]GSE95233^[73]22 from the GEO database. The patients were divided into SEPSIS Survivors (SEPSIS survivors) and non-survivors (SEPSIS non-survivors). A forest plot was created after a meta-analysis of the core genes’ expression levels. Single-cell sequencing We collected five peripheral blood samples (two from healthy individuals, one from the SIRS group, and two from sepsis patients) and isolated PBMCs using density gradient centrifugation for 10× Genomics analysis^[74]10. The 10×Genomics system uses microfluidic technology to trap cells in droplets with Cell Barcode-carrying beads, retrieve droplets with cells, and then break cells in droplets to enable mRNA from cells to attach to the Cell Barcode on the bead, creating single-cell GEMs^[75]23. cDNA libraries were generated in droplets through reverse transcription reactions, with sample index sequences on the reads to distinguish the target sequences’ origin. The raw data generated by high-throughput sequencing (raw reads) were in fastq-format sequences. The Cell Ranger by 10×Genomics software was used to control the initial data quality. Following this stage, the Seurat program was utilized to conduct additional analysis and manipulation of the information. Principal component analysis (PCA) was performed to reduce the dimensions of gene expression levels linearly. The outcomes of the PCA analysis were displayed graphically utilizing a nonlinear method for reducing dimensionality known as t-distributed stochastic neighbor embedding (tSNE). Results Screening of lactylation genes associated with sepsis A total of 20 patients with sepsis and 10 healthy individuals were included in this study, and RNA sequencing was performed on peripheral blood samples. Quality control analysis of the sequenced mRNA revealed that all the samples could be clearly distinguished into two groups (Fig. [76]2A). A comparison was made between the two mRNA datasets generated from the sequencing data. A combined 4890 DEmRNAs were detected using the standards of |FC| ≥ 2 and FDR < 0.05, consisting of 2498 mRNAs that were increased and 2392 mRNAs that were decreased (Fig. [77]2B-C). Based on previous research, we collected 332 lactylation genes. By intersecting 4890 differential genes with 332 lactylation genes, we obtained 55 sepsis-related lactylation genes (Fig. [78]2D). Fig. 2. [79]Fig. 2 [80]Open in a new tab Screening for lactylation genes related to sepsis. (A)PCA is also known as Principal Component Analysis. Principal Component 1 is on the x-axis, while Principal Component 2 is on the y-axis. Each point represents a sample. (B)Volcano plot displaying genes with differential expression. Fold change is represented on the x-axis, while the negative logarithm of the P value is represented on the y-axis. The color green indicates genes that are upregulated and differentially expressed (n = 2498), while red indicates downregulated and differentially expressed (n = 2392). (C)Distribution plot showing differentially expressed genes, with green indicating upregulated genes and red indicating downregulated genes. (D)In the Venn diagram, blue represents lactylation genes, and red represents sepsis differentially expressed genes, overlapping 55 genes. GO and KEGG Functional enrichment analysis of GO revealed that these cross genes were mainly associated with cellular components such as cell and cell junctions. In contrast, the molecular functions enriched in cross-genes were mainly related to binding and catalytic activity. Furthermore, the genetic processes related to the hybrid genes primarily included cellular adhesion and regulatory mechanisms (Fig. [81]3A). KEGG analysis identified 101 noteworthy items, including 9 Cellular Processes, 7 Environmental Information Processing, 10 Genetic Information Processing, 50 Human Diseases, 8 metabolism, and 17 Organismal Systems. In the most significant pathways in terms of cross-gene significance, amino acid metabolism and immune diseases were observed (Fig. [82]3B). Fig. 3. [83]Fig. 3 [84]Open in a new tab Cross-genetic analysis of GO and KEGG. (A) GO enrichment analysis. Three enrichment results are shown on the horizontal axis, with the number of enriched genes on the right vertical axis and the percentage of enriched genes on the left horizontal axis. (B) KEGG analysis. The percentage of enriched genes is shown on the horizontal axis, with the primary categories of enrichment displayed on the right side and the secondary categories within the primary categories shown on the left vertical axis. Identification of lactylation-related hub genes in sepsis patient In the present study, we constructed a cross-genes PPI network. GAPDH, S100A11, H2BC14, PARP1, TP53, CCNA2, NCL, S100A4, and H2BC13 were located at the core of the network (Fig. [85]4). These lactylation genes may be core genes potentially associated with sepsis, which we will validate in future studies. Fig. 4. [86]Fig. 4 [87]Open in a new tab PPI network diagram. The sepsis differential gene and the lactylation cross-gene were analyzed for PPI. The results show that GAPDH, S100A11, H2BC14, PARP1, TP53, CCNA2, NCL, S100A4, and H2BC13 are in the core of the network. Identification of Lactylation-related genes for Prognostic and Diagnostic Value in Sepsis A significant relationship was identified between S100A11, CCNA2, and sepsis in survival analysis, influencing the 28-day survival rate of sepsis. Patients exhibiting reduced levels of S100A11 had a decreased 28-day survival rate compared to those with elevated levels, indicating an inverse relationship with the survival rate of sepsis patients. Conversely, individuals exhibiting reduced levels of CCNA2 had a greater likelihood of surviving for 28 days than those with elevated levels, indicating a favorable association with the survival rate among individuals with sepsis. Statistically significant variations were observed (P < 0.05) (Fig. [88]5A-B). The finding suggested a robust connection between the pair of lactylation genes and the prognosis of individuals suffering from sepsis. Their expression levels could serve as a new area of focus for sepsis research. The ROC curve analysis revealed that S100A11 and CCNA2 exhibited relatively elevated levels of sensitivity and specificity, achieving AUC values of 0.961 and 0.890, respectively (Fig. [89]5C-D). The results offer important information for creating treatments focused on lactylation for sepsis. Table [90]1 provides detailed information on the GEO dataset. Fig. 5. [91]Fig. 5 [92]Open in a new tab Core gene survival analysis and ROC curve. (A-B) Based on the GEO database [93]GSE65682, survival curves were plotted. The red line indicates the high-expression group, whereas the green line represents the low-expression group. The survival rate is depicted on the vertical axis, while the horizontal axis shows 28 days for survival. Patients with low expression of S100A11 had a lower 28-day survival rate compared to those with high expression, indicating a negative association with the survival rate of septic patients (P < 0.05). Patients with low CCNA2 expression had a higher 28-day survival rate than those with high expression, indicating a positive association with septic patient survival (P < 0.05). (C-D) ROC curves based on the GEO database [94]GSE69528. Specificity is represented on the horizontal axis, while sensitivity is on the vertical axis. The findings suggest that S100A11 and CCNA2 show high levels of sensitivity and specificity, achieving AUC values of 0.961 and 0.890, respectively. Table 1. The [95]GSE65682 dataset from the GEO database was used for survival analysis, and the [96]GSE54514, [97]GSE63042, and [98]GSE95233 datasets were employed for meta-analysis. Analysis of the ROC curve was conducted with the dataset [99]GSE69528. GSE datasets Organism Platform Number of samples [100]GSE65682 Whole Blood of Human [101]GPL13667 802 [102]GSE69528 Whole Blood of Human [103]GPL10558 138 [104]GSE54514 Whole Blood of Human [105]GPL6947 163 [106]GSE63042 Whole Blood of Human [107]GPL9115 106 [108]GSE95233 Whole Blood of Human [109]GPL570 124 [110]Open in a new tab Meta-analysis validation Using meta-analysis, we analyzed the transcription levels of S100A11 and CCNA2 in sepsis datasets ([111]GSE54514, [112]GSE63042, and [113]GSE95233) from the GEO public database. Analysis indicated that S100A11 exhibited high expression levels in the sepsis survivors while showing low expression levels in the non-survivors. Conversely, CCNA2 exhibited decreased levels of expression in the sepsis survivors’ group while displaying elevated levels of expression in the sepsis non-survivors group. Statistically significant variations were observed (P < 0.05) (Fig. [114]6A-B). Fig. 6. [115]Fig. 6 [116]Open in a new tab According to the meta-analysis of the GEO databases [117]GSE54514, [118]GSE63042, and [119]GSE95233, S100A11 exhibited high expression levels in the sepsis survival group. Still, it was low in the non-survival group, whereas CCNA2 displayed low expression in the sepsis survival group but high in the non-survival group. The disparities showed statistical significance with a P value less than 0.05. Single-cell RNA sequencing analysis This report was based on the analysis of five single-cell transcriptome sequencing samples. The count of eligible cells varied between 6,000 and 12,000 after removing duplicate cells, numerous cells, and cells undergoing apoptosis. Hierarchical clustering was used to categorize the cells into nine clusters, identifying B cells, NK cells, T cells, platelets, and monocyte-macrophages as different cell types based on their marker genes. Monocyte-macrophages were represented by clusters 3 and 5, NK cells were represented by Cluster 4, T cells were represented by clusters 1, 2, 6, and 8, B cells were represented by Cluster 7, and platelets were represented by Cluster 9(Fig. [120]7A). Single-cell RNA sequencing data analysis showed that CCNA2 was predominantly expressed in cell clusters 1, 2, 4, 6, 7, and 8, specifically in T, B, and NK cells. S100A11 was highly expressed in cell clusters 1, 2, 3, and 5, namely monocytes-macrophages and T cells (Fig. [121]7B-D). Fig. 7. [122]Fig. 7 [123]Open in a new tab Single-cell spatial map of core genes. (A)The two-dimensional t-SNE plot after PCA dimensionality reduction, where each small dot represents a cell. T cell lineages are represented by clusters 1, 2, 6, and 8; NK cell lineages are represented by cluster 4; monocyte-macrophage lineages are represented by clusters 3 and 5; B cell lineage is represented by cluster 7; and platelets are represented by cluster 9. (B)Violin plots for gene expression. The vertical axis shows the ratio of cells expressing a gene in a specific cell line. Each cell cluster’s expression status is represented on the x-axis. (C-D) The expression distributions of CCNA2 and S100A11 in human blood PBMCs. Discussion Due to progress in medical care and scientific studies, the treatment and results of sepsis have become more consistent globally. The rates of sepsis occurrence and death have shown considerable enhancement. There are no distinct indicators for diagnosis or treatment options for sepsis^[124]24. Initial research has consistently shown that elevated lactate levels indicate unfavorable results in individuals with sepsis, and it has been verified that the occurrence and fatality rates of sepsis decrease when high lactate levels are corrected during the hyperinflammatory phase^[125]25. The most recent global recommendations for treating sepsis and septic shock involve promptly monitoring and lowering serum lactate levels. It is thought that lactate contributes to immune dysfunction, especially immunoparalysis, in individuals suffering from sepsis. Certain scientists have viewed lactate as a molecule that suppresses the immune system. The discovery of lactate modification clearly explains the fundamental mechanism by which lactate regulates immune status in sepsis at the epigenetic level. The use of lactate level as an indicator of the severity or prognosis of sepsis has long been controversial. Nevertheless, lactylation is now recognized as a novel approach for determining and predicting the outcome of sepsis. Lactylation occurs in humans, animals, plants, microorganisms, and bacteria. Additionally, lactylation is controlled by lactate and works in harmony with its creation. Regulating sugar fermentation and influencing lactate production can also regulate lactylation levels. Lactylation, as a “result,” shows relative stability over time and is not affected by environmental changes, making it one of the most important clinical assessment indicators of lactylation. Most importantly, compared to lactic acid, lactylation has a dual role of “promotion” and “inhibition.” In sepsis, excessive activation of glycolysis leads to the generation and accumulation of large amounts of lactate. Metabolic enzymes in the glycolytic pathway are lactylated to suppress excessive glycolytic responses, ensuring metabolic homeostasis in the body^[126]26. Controlling lactate levels is crucial for preserving immune equilibrium within the body. Nevertheless, this feedback loop also suggests that the function of lactylation is complex. Uncovering it is just the initial phase of the investigation, with many details waiting to be explored regarding its operation. Although still in its infancy, studies on lactylation in sepsis have already demonstrated significant promise in uncovering a stronger connection between lactic acid and the immune system. Lactylation plays a significant role in oncology by affecting innate and adaptive immunity and indicating the immune status of the tumor microenvironment. This process also regulates the immune system during sepsis, and lactylation could potentially become a crucial molecule in diagnosis and treatment, replacing lactate for more effective scientific approaches. This research concentrated on examining the activity of lactylation genes in individuals with sepsis, investigating the potential prognostic and diagnostic significance of these genes in sepsis. RNA sequencing of peripheral blood from septic patients and normal individuals was conducted in a preliminary study. Differentially expressed genes were identified from the sequencing results, and the intersection between the differentially expressed genes and lactylation genes was determined to identify lactylation genes associated with sepsis. Through gene ontology analysis, it can be inferred that these cross-genes are mainly associated with cellular components, such as cells and cell junctions, and the molecular functions enriched in cross-genes are predominantly related to binding and catalytic activity. Biological adhesion and regulation are the primary biological processes linked to cross-genes. According to the KEGG analysis, amino acid metabolism and immune diseases were the most significant pathways involving multiple genes. Subsequently, PPI was used to screen for core genes. We verified the gene expression levels by downloading sepsis-related datasets from GEO. It was found that lactylation genes (S100A11, CCNA2) have significant potential for the diagnosis, prognosis assessment, and guidance of sepsis. S100A11, also known as S100 calcium-binding protein A11, is a gene that encodes a protein. The gene contains a protein from the S100 group with two EF-hand calcium-binding motifs^[127]27. The S100 gene encodes proteins in the cytoplasm and nucleus of different types of cells. They are essential for controlling various cellular functions, such as advancing through the cell cycle and specializing. There are at least 13 members in the S100 gene family, all clustered-on chromosome 1q21. This gene’s protein product could play a role in movement, penetration, and the formation of tubulin polymers. Changes in the expression of this gene and chromosomal rearrangements have been linked to the spread of tumors to other parts of the body. Although S100A11 has been implicated in tumor development in previous studies^[128]28, recent research has found that S100A11 is upregulated in patients with sepsis^[129]29. Upon analysis of the relationship with immune infiltrating cells, it was noted that variations in expression were primarily linked to elevated levels of macrophage and neutrophil infiltration and reduced levels of T cell (CD4, CD8) infiltration. Ultimately, it was discovered that S100A11 can independently affect the prognosis of patients^[130]29. Our findings align with this outcome. In this study, we identified the core gene S100A11 by intersecting the differential genes of sepsis with the lactylation genes. The expression of the lactylation gene S100A11 was verified at the gene expression level by downloading datasets related to sepsis from the GEO database. The analysis of our survival data revealed that patients with low levels of S100A11 had a lower 28-day survival rate compared to those with high levels, indicating a negative association with sepsis patient survival rates. This information was obtained from [131]GSE65682. Analysis of the ROC curve using the [132]GSE69528 dataset from the GEO database indicated that S100A11 exhibits elevated levels of sensitivity and specificity, achieving an AUC of 0.961. An analysis combining various datasets found that S100A11 expression was significantly higher in sepsis survivors than non-survivors, with a P-value below 0.05. Finally, single-cell sequencing analysis revealed that S100A11 was highly expressed in monocyte macrophages. Our research began with lactylation as the starting point and ultimately identified the lactylation gene (S100A11) as having significant potential in the diagnosis, prognosis, and guidance for sepsis. CCNA2 (Cyclin A2) is a protein-coding gene. The cyclin family, which regulates the cell cycle, is activated by this gene protein to promote the G1/S and G2/M transition via cyclin-dependent kinase 2^[133]30. Diseases associated with CCNA2 include Retinoblastoma^[134]31and Adenocarcinoma^[135]32. Researchers discovered that the level of CCNA2 expression in sepsis is closely linked to the SOFA score and mortality rate, as revealed by Gene expression profiling. Our research identified the core gene CCNA2 by sorting the lactylation genes and differential genes associated with sepsis using bioinformatics methods. The analysis of our survival data showed that patients with low levels of CCNA2 had a higher 28-day survival rate compared to those with high levels, which was associated with better survival outcomes in sepsis patients, and this difference was statistically significant (P < 0.05). Based on the ROC curve results of the [136]GSE69528 dataset in the GEO database, CCNA2 exhibited high sensitivity and specificity. Additionally, a meta-analysis confirmed that CCNA2 is decreased in the sepsis survival group but increased in the non-survival group. Single-cell sequencing ultimately showed that CCNA2 is mainly found in immune cells, specifically NK cells, T cells, and B cells. Our study found that it can provide a deeper connection between lactylation and sepsis immunology. Conclusion and limitations This study investigated sepsis differential genes with lactylation genes to identify lactylation genes related to sepsis. Several datasets were obtained from the GEO repository to validate the core genes. Our study identified lactylation genes closely associated with sepsis, which may serve as reference indicators for sepsis diagnosis, prognosis assessment, and guidance. Nevertheless, our research is constrained by a limited sample size, restricted to gene-level speculation, and lacks further investigation into underlying mechanisms. Acknowledgements