We applied the Wilcoxon rank sum test (also known as the Mann-Whitney U test), a nonparametric method, to assess gene expression differences between cancerous and normal tissue across multiple cancer types (44, 45). This test was chosen for its suitability with non-normally distributed data, unlike parametric alternatives that require normal distribution assumptions. Statistical significance was established at α = 0.05, indicating meaningful divergence between the median values of two independent sample groups. We analyzed de-identified gene expression data, represented as transcripts per million (TPM), from The Cancer Genome Atlas (TCGA) for tumor samples and late-stage normal tissue samples from the Genotype-Tissue Expression (GTEx) project. These datasets were accessed via the UCSC Xena database. To mitigate inter-dataset variability, the gene expression values were normalized using Z-scores, allowing for consistent dimensional comparisons. Our primary analysis centered on the glioblastoma multiforme (GBM) dataset, through which we investigated gene expression disparities between cancerous and non-cancerous tissues. This detailed comparison allowed us to observe exercise-induced changes in gene expression, providing insights into potential therapeutic implications for managing burns and sepsis within the cancer context.

This study provides an overview of methylation levels across diverse genomic regions, including the TSS1500 region (200–1500 bp upstream of the transcription start site), the proximal promoter region (the first 200 bp upstream from the TSS), the first exon, and the 5’ untranslated region (UTR) (46, 47). For each sample, median methylation values within these regions were determined to represent the sample’s overall methylation status. To investigate the correlation between methylation levels and gene expression, we applied Spearman’s rank correlation, a non-parametric test ideal for assessing relationships between variables that may not exhibit linear patterns. In this analysis, methylation levels served as the independent variables, while gene expression levels were treated as dependent variables. The strength of these associations was quantified using the Spearman rank correlation coefficient. Additionally, we employed the Wilcoxon rank sum test to compare methylation levels between tumor and normal tissue samples. This non-parametric test is appropriate for comparing two independent sample groups without assuming a specific data distribution, thereby enabling a reliable distinction between tumor and normal tissue methylation patterns across different sample sources. Through this approach, we identified significant methylation differences, enhancing our understanding of tumor biology and the potential role of exercise in modulating responses to burns and sepsis.

In this study, we employed ChIPseeker, an R package tailored for analyzing and visualizing both ChIP-seq and ATAC-seq data (48, 49). Utilizing ChIPseeker’s annotatePeak function, we conducted an in-depth analysis of transcription start sites (TSS) within promoter regions of genes. We set the parameter tssRegion = c (-3000, 3000) to cover the 3000 base pairs upstream and downstream of the TSS, enabling a comprehensive examination of genomic elements, including transcription factor binding sites and histone modifications. To visualize coverage, we utilized ChIPseeker’s covplot function, generating plots that illustrate the distribution of peaks across the genome in human ATAC-seq data. These coverage plots reveal not only the spatial distribution of peaks along chromosomes but also offer detailed insights, including gene names, tumor types, chromosomal positions, and genetic distances, conveniently displayed on the left side of the graph. This visual approach provides researchers with a holistic and precise representation of the ATAC-seq data, enhancing our understanding of exercise-induced gene regulation in the context of burns and sepsis.

To evaluate the prognostic significance of various genes, we applied a univariate Cox regression model using the survival package in R (50, 51). For each gene, we derived hazard ratios along with their 95% confidence intervals by fitting the data into a Cox proportional hazards model through the coxph() function. The results were visualized in a heatmap to facilitate comparative analysis and enhance interpretability.

To investigate the impact of exercise on genes associated with burns and sepsis across various cancer types, we analyzed copy number alterations and DNA methylation data from The Cancer Genome Atlas (TCGA) (52, 53). Patient samples were organized in a structured matrix format, with rows representing samples and columns representing genes or genomic regions. After quality control steps to exclude low-quality samples and probes, data were standardized to minimize technical variation. Using tools like GISTIC and CNAnorm, we identified and categorized genomic amplification and deletion events, quantifying their frequencies across the genome. DNA methylation levels at gene promoter regions were assessed on the UALCAN platform, comparing differences between normal and cancerous tissues to elucidate the influence of exercise on wound healing and infection responses. For methylation pattern analysis in specific cancer-associated genes, we utilized the ‘gene visualization’ module available in MethSurv. To further understand genomic impacts, Mutation Annotation Files (MAF) from TCGAbiolinks and tumor mutation burden (TMB) calculations via maftools allowed us to explore links between these genomic features and immunotherapy responsiveness. Statistical analyses, including correlation and survival analyses, were conducted on copy number alteration, DNA methylation, and TMB data to assess their associations with exercise-modulated gene expression in wound healing and infection. These analyses aimed to evaluate the potential impact of these genomic variations on tumor progression and patient outcomes.

This study utilized data from The Cancer Genome Atlas (TCGA) repository, incorporating tumor and normal tissue samples from a specific cancer type (54, 55). The data underwent stringent quality control processes, during which invalid samples and probes were excluded to ensure robustness. Following quality filtering, normalization was applied to mitigate technical variability inherent in the dataset, including adjustments for background noise, often present in raw files. We employed the “limma” package within the R environment, which facilitates background correction, normalization, and statistical analyses for identifying genes with significant differential expression. The criteria for selection involved both log2 fold change (log₂FC) and p-values. Log₂FC quantified the relative changes in gene expression, while the p-value assessed statistical significance. For gene set enrichment analysis (GSEA), we used the “clusterProfiler” package in R to annotate and visualize enriched pathways associated with differentially expressed genes in public databases such as KEGG, GO, and Reactome. We relied on the Enrichment Score (ES), which ranges from 0 to 1, as a metric to determine the relevance of pathway alterations in relation to gene expression changes. Finally, to visualize the findings, we utilized R packages, including “ggplot2,” to create various graphical representations. This included simple bar charts, scatter plots, and heatmaps, allowing a clear interpretation of the results. This methodological framework provides a structured approach to interpreting the molecular implications of gene expression variations in cancer.

Human umbilical vein endothelial cells (HUVECs) were seeded in culture flasks and maintained in Dulbecco’s modified Eagle medium (DMEM, low glucose), supplemented with 10% fetal bovine serum (FBS), 1% endothelial cell growth supplement, and 1% penicillin-streptomycin solution. The cells were incubated at 37°C in a humidified chamber with 5% CO₂ under normoxic conditions. To establish an in vitro model, HUVECs were subsequently exposed to 1 μg/mL of lipopolysaccharide (LPS). Eight hours before the experiments, cells were cultured in serum-free DMEM. For differentiation, THP-1 cells were treated with 100 ng/mL phorbol 12-myristate 13-acetate (PMA) for five days to induce macrophage-like characteristics. RAW 264.7 mouse macrophages (ATCC, Rockville, MD, USA) were cultured in DMEM containing 10% heat-inactivated FBS, 100 U/mL penicillin, and 100 μg/mL streptomycin, and incubated at 37°C in a 5% CO₂ atmosphere.

HUVECs, seeded at a density of 5 × 10³ cells per well, were transfected with MLIP knockdown, overexpression, or control plasmids and plated in 96-well plates to assess proliferation. After an initial 16-hour incubation, the cells were either stimulated with LPS or left untreated for 0, 6, and 24-hour intervals. Cell proliferation was measured using the Cell Counting Kit-8 (CCK-8; Beyotime, Beijing, China, Cat#C0038), in accordance with the manufacturer’s protocol. Absorbance was recorded at 450 nm using a BioTek microplate reader (BioTek, U.S.A.).

Total RNA was isolated from the three experimental groups using Trizol reagent (Invitrogen, USA). The isolated RNA was reverse-transcribed into complementary DNA (cDNA) using the PrimeScript II 1st Strand cDNA Synthesis Kit (Takara, Shiga, Japan). Real-time RT-PCR was performed with the SYBR Premix Ex Taq II (Takara, Shiga, Japan) on a StepOnePlus Real-Time PCR system (Applied Biosystems, CA, USA). The relative mRNA expression levels were quantified using the 2^−ΔΔCt method, with GAPDH as the internal control.

Cytotoxicity was assessed by quantifying lactate dehydrogenase (LDH) release using a commercial assay kit, following the manufacturer’s instructions. Briefly, 50 μL of supernatant from each well was collected and incubated with reduced nicotinamide adenine dinucleotide (NADH) and pyruvate for 15 minutes at 37°C. The reaction was terminated with the addition of 0.4 mol/L NaOH. LDH activity was measured by recording the absorbance at 440 nm on a SpectraMax M2 spectrophotometer (Molecular Devices, Sunnyvale, CA, USA) and expressed as U/g protein. Additionally, oxidative stress markers, including superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activities, along with reactive oxygen species (ROS) and malondialdehyde (MDA) levels, were measured using commercially available kits, following the manufacturer’s protocols.

Apoptosis and cell polarization were evaluated in both treated and untreated cells using flow cytometry. Following treatment, cells were harvested and stained with FITC-Annexin V and propidium iodide (PI) for 10 minutes at room temperature, following the protocol provided by the Annexin V-EGFP Apoptosis Detection Kit (KeyGEN BioTECH). For polarization assessment, cells were additionally stained with antibodies targeting M1 polarization markers when required. Data acquisition and analysis were conducted on a Fluorescence-Activated Cell Sorting (FACS) Calibur flow cytometer (Becton-Dickinson, Sparks, MD, USA) with associated software. For the colony formation assay, cells were plated in 6-well plates and treated with designated drugs for 6 hours according to the experimental groups for reactive oxygen species (ROS) detection. A fluorescent probe, DCFH-DA (10 µM; Solarbio, China), was diluted in a serum-free medium, and cells were incubated with it for 30 minutes at 37°C. FITC signals were subsequently detected via flow cytometry.

Cells were plated in 6-well culture plates and incubated overnight to ensure adherence. The cells were then fixed with 3.7% paraformaldehyde at room temperature for 15 minutes and subsequently permeabilized in cold methanol at -20°C for 15 minutes. Blocking was performed using a buffer containing 5% normal goat serum and 0.5% Triton X-100 in PBS for 1 hour at room temperature. Primary antibodies were added, and cells were incubated overnight at 4°C. After washing three times with PBS for 10 minutes each, cells were incubated at room temperature with Alexa Fluor 488-conjugated goat anti-rabbit secondary antibody (Cat# A-11034) and Alexa Fluor 594-conjugated goat anti-mouse secondary antibody (Cat# A-11004; Thermo Fisher), both diluted 1:500 in blocking buffer. Prior to imaging, nuclei were stained with DAPI (Cat# D9542; Sigma) for 30 minutes at room temperature. Images were obtained using a Nikon Eclipse E800 fluorescence microscope.

Data are presented as mean ± standard deviation (SD). Statistical analyses were conducted using GraphPad Prism 8 software. Group differences were assessed using either Student’s t-test or analysis of variance (ANOVA), depending on the experimental design. A p-value of less than 0.05 (P < 0.05) was considered statistically significant.

In our study, we investigated gene expression differences and pathway enrichment between two sample groups using a variety of analytical approaches. The UMAP plot ( Figure 1A ) revealed distinct clustering between the groups, indicating significant differences in gene expression profiles. This clustering suggests that the gene expression profiles of the groups are sufficiently divergent to merit further investigation into the underlying molecular mechanisms. The volcano plot ( Figure 1B ) highlighted differentially expressed genes. This visualization not only underscores the extent of differential expression but also aids in the identification of key genes that may be crucial in the context of burn-induced sepsis and its potential association with cancer. GSEA identified significantly enriched pathways ( Figure 1C ), including those related to immune response activation, metabolic pathways, and tumor suppressor inhibition. The top four enriched pathways ( Figure 1D ) were further explored, with the enrichment scores and the specific positions of genes within these pathways clearly displayed. A heatmap ( Figure 1E ) illustrates the Z-scores of gene expression for genes within these pathways, providing a clear visualization of expression patterns across all samples. This comprehensive analysis highlights significant differences in gene expression and pathway enrichment between the two groups, emphasizing key pathways involved in disease mechanisms and pinpointing potential therapeutic targets. These findings lay the groundwork for identifying therapeutic targets and biomarker candidates, which could play a crucial role in the development of more effective treatment strategies for burn-induced sepsis and its potential link to cancer.

Our study aimed to identify differentially expressed genes (DEGs) and significantly enriched pathways between tumor and normal tissue samples. To achieve this, we utilized a combination of statistical and computational tools, with the results presented in various figures to provide clarity and a comprehensive understanding of the findings. Figure 2A presents a volcano plot that illustrates the distribution of DEGs between tumor and normal groups. The dashed lines represent thresholds for statistical significance, enabling the clear identification of the most relevant genes affected by the conditions under investigation. GSEA was conducted to identify significantly enriched pathways, with Figure 2B depicting the statistical significance of these pathways. This figure reveals the biological processes most influenced by the differential gene expression patterns observed between tumor and normal tissues. The pathways selected based on their statistical significance offer valuable insights into the biological processes that are most affected by the differentially expressed genes. Further GSEA results highlighted the top four enriched pathways, as shown in Figure 2C . Additionally, the gene distribution within these pathways is shown, offering a clearer understanding of how specific genes contribute to pathway enrichment and their potential role in tumor development. Figure 2D displays a heatmap that visualizes gene expression levels across all samples, specifically focusing on genes related to the identified pathways. Each row in the heatmap corresponds to a unique gene, while each column represents an individual sample. Collectively, these analyses provide a detailed view of the molecular alterations occurring in tumor and normal tissues. The identification of significantly enriched pathways and differentially expressed genes contributes to a deeper understanding of the biological functions and pathways involved in tumorigenesis.

In this study, we analyzed the expression landscape of exercise-influenced genes associated with burns and sepsis. The datasets used included GSE193428, GSE61477, and a dataset of exercise-related genes, collectively providing a comprehensive profile of gene expression. By integrating data from these diverse sources, we aimed to investigate the shared and unique gene expression patterns influenced by exercise in the context of burns, sepsis, and cancer. As shown in Figure 3A , a Venn diagram illustrates the overlap and unique characteristics of DEGs across the datasets. Specifically, 626 genes were unique to GSE193428, 911 were specific to GSE61477, and 194 were exclusive to the exercise-related genes. Additionally, 53 genes overlapped between GSE193428 and GSE61477, while 10 genes were common between GSE61477 and the exercise-related gene dataset. Key genes identified among these, such as FARSA, SVIL, LAMP2, and COL12A1, were found to be influenced by exercise in both burns and sepsis, suggesting their potential roles in exercise-mediated recovery processes. These genes are involved in critical biological functions such as protein synthesis, cell signaling, and tissue repair, all of which are vital for enhancing the body’s response to burns, sepsis, and potentially cancer development. Figures 3B, C present the differential expression analysis of these genes across various cancer types. Figure 3B illustrates the expression levels using non-paired samples, capturing the variability in gene expression due to exercise, burns, and sepsis. In contrast, Figure 3C employs paired samples to compare tumor and adjacent normal tissues, providing a more refined analysis that controls for individual variability and highlights consistent exercise-influenced expression patterns. Expanding on these analyses, Figure 3D integrates data from both the TCGA and GTEx projects, enabling a comprehensive comparison of gene expression profiles in malignant and healthy tissues. This figure offers further insights into how physical activity may modulate expression changes associated with burns and sepsis. At the top of Figure 3D , the sample sizes for each cancer type are displayed. Below, a heatmap uses color coding to represent upregulated and downregulated genes, highlighting the expression patterns across cancer types. These findings underscore the significant influence of exercise on transcriptional patterns associated with burns and sepsis in the context of cancer. The consistent expression changes observed in exercise-influenced genes across various datasets suggest that exercise may be a critical modulator of recovery from both sepsis and burns, with potential implications for cancer prevention and survival.

Figure 4 provides an in-depth analysis of promoter methylation levels and their impact on mRNA expression in burn- and sepsis-related genes affected by physical activity. Figure 4A presents a heatmap that displays variations in promoter methylation levels for genes such as ACADM, ALDH4A1, COL12A1, FARS2, G6PC, HSP90A, KCNJ2, KCNQ1, PER1, PHKB, PUS1, RYR1, SVIL, TK2, and VAPA. In this heatmap, red represents hypermethylation, blue indicates hypomethylation, and white denotes no significant change. The analysis compares methylation patterns across patient samples, distinguishing those who underwent exercise intervention from those who did not, revealing notable differences that suggest the influence of exercise on the epigenetic regulation of these genes. Figure 4B illustrates the relationship between promoter methylation levels and corresponding mRNA expression levels, employing the same color scheme: red for a negative correlation, blue for a positive correlation, and white for no significant correlation. This heatmap demonstrates how changes in promoter methylation can impact gene expression, with hypermethylation generally associated with reduced mRNA expression and hypomethylation linked to increased expression levels. The analysis was conducted with rigorous statistical evaluations, examining parameters such as distribution, mean, median, standard deviation, and variance to ensure objectivity and precision in data interpretation. These findings highlight the potential regulatory role of exercise in gene expression related to burn and sepsis recovery, emphasizing the significance of considering epigenetic modifications in therapeutic strategies and suggesting the potential benefits of exercise interventions in clinical settings. Future research should aim to further elucidate the mechanisms behind these epigenetic modifications and their implications for recovery in burn and sepsis. Investigating how specific exercise interventions might influence gene methylation pathways and understanding their functional consequences on gene expression will provide deeper insights into the potential therapeutic benefits of exercise. This analysis provides valuable insights into the influence of exercise on promoter methylation and gene expression, making a substantial contribution to the growing field of exercise genomics and its role in medical treatments.

Promoter methylation analysis revealed varying methylation patterns across burn and sepsis-related genes influenced by exercise. ACADM showed mainly unmethylated or low methylation, with variable CpG site methylation ( Supplementary Figure 1A ). ALDH4A1 had predominantly unmethylated sites ( Supplementary Figure 1B ), while COL12A1 exhibited low to medium methylation hotspots ( Supplementary Figure 1C ). FARSA had a balanced methylation pattern with some sites highly methylated ( Supplementary Figure 1D ). G0S2 showed low methylation levels with specific CpG site variation ( Supplementary Figure 1E ). HSPA8 and LAMP2 were primarily lowly methylated ( Supplementary Figures 1F, I ). KCNJ2 and KCNQ1 exhibited medium to high methylation ( Supplementary Figures 1G, H ). MGME1 and SVIL had balanced methylation with significant site-specific methylation ( Supplementary Figures 1J, P ). MLIP showed varied methylation, notably high levels ( Supplementary Figure 1K ), while PER1 and PHKB had low to medium methylation ( Supplementary Figures 1L, M ). PUS1 had medium to high methylation ( Supplementary Figure 1N ), and RYR1 was largely unmethylated ( Supplementary Figure 1O ). TK2 mainly exhibited low methylation ( Supplementary Figure 1Q ), and VAPA was mostly unmethylated or lowly methylated ( Supplementary Figure 1R ). These results highlight exercise’s potential role in regulating the epigenetic modification of these genes, providing insight into the underlying molecular mechanisms and potential therapeutic applications.

Our study examined the correlation between the expression of burn- and sepsis-related genes and tumor prognosis, specifically focusing on Disease-Free Interval (DFI), Disease-Specific Survival (DSS), Overall Survival (OS), and Progression-Free Interval (PFI) ( Figure 5 ). In the Disease-Free Interval (DFI) panel ( Figure 5A ), a heatmap displays the relationship between gene expression and DFI in tumor patients. Here, red squares denote genes associated with increased risk, while blue squares indicate protective genes, with significant correlations marked (p < 0.05). Similarly, the Disease-Specific Survival (DSS) panel ( Figure 5B ) presents a heatmap that illustrates the correlation between gene expression and DSS, maintaining the same color scheme and significance threshold. The Overall Survival (OS) panel ( Figure 5C ) shows correlations between gene expression and OS, with red indicating risky genes and blue representing protective genes. Only statistically significant correlations (p < 0.05) are displayed to emphasize meaningful relationships. Lastly, the Progression-Free Interval (PFI) panel ( Figure 5D ) provides a heatmap showing the correlation between gene expression and PFI, using consistent color coding and significance criteria. These findings highlight the significant influence of specific gene expressions on various tumor prognosis metrics, offering valuable insights that could guide the development of potential therapeutic targets. The study emphasizes the importance of considering the molecular landscape shaped by burn and sepsis-related genes, which may provide novel opportunities for personalized treatments in cancer patients, particularly those recovering from burns or sepsis.

This study explores the impact of exercise on genes associated with burns and sepsis across various cancer types, revealing several key findings. Supplementary Figure 2A shows mutation frequencies of these genes in 20 cancer types, with color intensity representing mutation rates. A waterfall plot ( Supplementary Figure 2B ) highlights mutation variability across categories. Figures 2C, D illustrate the correlation between gene expression and Tumor Mutation Burden (TMB), with bubble sizes indicating correlation significance and colors reflecting the strength and direction. Figures 2E, F categorize samples by copy number amplifications, identifying the most common cancer types. Figure 2G presents cumulative copy number alterations, while Figure 2H compares amplifications and deletions across cancer types. Finally, Figure 2I displays a correlation matrix of gene expression in different cancers, with bubble sizes representing p-values. These findings highlight the heterogeneous genomic effects of exercise on burn- and sepsis-related genes in cancers, offering insights into potential therapeutic targets and informing personalized cancer treatment strategies.

Our analysis of gene expression following exercise across multiple cancer types revealed notable enrichments in genomic features and microbiome signatures. In Figure 6A , we observed distinctive gene expression patterns associated with burns and sepsis that correlate with CNVs. In this figure, circle sizes denote significance levels, while color gradients indicate delta values, highlighting differential expression. Similarly, Figure 6B shows the relationship between gene expression and promoter methylation, with blue representing negative correlations and red indicating positive correlations. Figures 6C, D explore the expression of these genes within different microbiomes. A heatmap in Figure 6C presents normalized expression levels, while hierarchical clustering in Figure 6D reveals co-regulated gene clusters influenced by microbial presence. Figure 6E provides a GSEA across various cancers, identifying enriched pathways. Bubble sizes correspond to the number of genes involved, and colors reflect enrichment scores, with red indicating higher scores. These findings suggest that exercise modulates gene expression in ways that affect cancer biology and microbial interactions, offering potential insights for therapeutic development. The interaction between exercise, gene expression, and microbiome modulation highlights new avenues for improving cancer treatment strategies through holistic approaches that consider genetic, epigenetic, and microbial factors.

This study performed an extensive pan-cancer analysis of burn- and sepsis-related gene sets influenced by exercise across various cancer types using four scoring methods: combined z-scores, GSVA z-scores, PLAGE z-scores, and ssGSEA z-scores. Results in Supplementary Figure 3 reveal significant differences in gene set enrichment between normal and tumor tissues. Supplementary Figure 3A presents the combined z-scores, showing notable statistical significance in cancers such as KIRC and BLCA, which suggests differential gene expression. Supplementary Figure 3B displays the GSVA z-scores, with marked variations in THCA and KICH, pointing to potential implications for tumor biology. In Supplementary Figure 3C , the PLAGE z-scores highlight significant differences in KIRP and BLCA, underscoring the influence of exercise on gene set activity. Finally, Supplementary Figure 3D shows the ssGSEA z-scores, revealing substantial changes across multiple cancers, suggesting therapeutic potential for exercise-modulated genes in cancer treatment. This analysis provides key insights into the role of exercise-related genes in cancer biology, emphasizing their potential therapeutic applications. It opens avenues for future research, particularly in identifying cancer types that could benefit from exercise-based interventions and exploring the molecular mechanisms underlying exercise-induced gene expression changes in cancer.

This study investigated the role of MLIP in regulating inflammation, oxidative stress, and macrophage polarization in LPS-induced RAW264.7 cells. Figure 7A shows that MLIP overexpression (MLIP-OE) significantly enhanced the expression of target genes, while MLIP knockdown (sh-MLIP) notably reduced expression, highlighting MLIP’s regulatory function. Figure 7B reveals that LPS treatment upregulated TNF-α, IL-6, and IL-1β mRNA levels, indicating an intensified inflammatory response. MLIP-OE attenuated these cytokine levels, suggesting its potential to suppress inflammation, while LPS + sh-MLIP further elevated inflammatory markers, indicating that MLIP inhibition exacerbates inflammation. Figure 7C demonstrates increased ROS production in the LPS group, reflecting heightened oxidative stress. MLIP-OE reduced ROS levels compared to LPS treatment alone, indicating that MLIP mitigates oxidative stress, while sh-MLIP further increased ROS levels. Figure 7D illustrates that superoxide dismutase (SOD) activity, a key antioxidant enzyme, was reduced by LPS but partially restored with MLIP-OE, underscoring the antioxidative role of MLIP. Figures 7E, F explore MLIP’s effects on macrophage polarization. MLIP-OE reduced the expression of the M1 marker CD86 and IL-1β, while increasing the expression of the M2 marker CD206, suggesting MLIP’s role in promoting a balanced macrophage polarization. IL-10 levels remained highest in the LPS group, reflecting the influence of LPS on anti-inflammatory responses. Figure 7G , through immunofluorescence staining, shows reduced IL-6 and IL-1β expression in the MLIP-OE group compared to the LPS and sh-MLIP groups, further supporting MLIP’s anti-inflammatory effects. Finally, Figure 7H presents flow cytometry data showing shifts in macrophage polarization profiles: MLIP-OE promoted a less inflammatory state, while sh-MLIP favored pro-inflammatory polarization. Overall, these results highlight the significant role of MLIP in modulating inflammation, oxidative stress, and macrophage polarization in LPS-induced RAW264.7 cells. These findings suggest that MLIP could serve as a potential therapeutic target for inflammatory diseases, particularly those involving macrophage polarization, such as autoimmune disorders, infections, and chronic inflammatory conditions.

This study explores MLIP’s role in modulating gene expression, inflammation, oxidative stress, and cell proliferation in human umbilical vein endothelial cells (HUVECs). Additionally, it investigates MLIP’s potential involvement in burn-induced sepsis and subsequent cancer progression. As illustrated in Figure 8A , MLIP overexpression markedly increased the relative mRNA levels of target genes, whereas MLIP knockdown reduced these levels, underscoring its significant function in gene regulation. Figure 8B demonstrates that MLIP overexpression enhances cell viability, as indicated by the CCK-8 assay, while knockdown decreases proliferation, suggesting MLIP’s supportive role in cell growth. Further, Figure 8C compares the expression levels of pro-inflammatory cytokines TNFα, IL-6, and IL-1β across different treatments. The LPS + MLIP-OE group displayed lower cytokine levels than the LPS-only group, underscoring MLIP’s potential anti-inflammatory effect. Conversely, increased cytokine levels in the LPS + sh-MLIP group suggest that MLIP inhibition may amplify inflammatory responses. Immunofluorescence analysis of IL-6 and IL-1β ( Figures 8D, E ) corroborates these findings, showing reduced inflammatory marker expression with MLIP overexpression and elevated levels with knockdown. Moreover, flow cytometry analysis of ROS production ( Figure 8F ) reveals that MLIP overexpression alleviates LPS-induced oxidative stress, while knockdown elevates ROS levels, highlighting MLIP’s regulatory influence on oxidative stress. Apoptosis analysis ( Figure 8G ) indicates that MLIP overexpression mitigates LPS-induced apoptosis, whereas its inhibition increases apoptosis rates, suggesting a protective role against cell death. Figure 9 provides a comprehensive view of MLIP’s involvement in burn-induced sepsis and its implications for cancer progression related to sepsis. The left panel traces the progression from burns to systemic inflammation and sepsis, showing MLIP’s role in immune modulation. Central bioinformatics analysis demonstrates differential gene expression, pathway enrichment, and pan-cancer analysis, elucidating MLIP’s influence on specific pathways and epigenetic regulation during sepsis. Prognostic analysis suggests MLIP’s potential relevance across various cancer types, while microbiome interactions hint at its immunomodulatory properties. The right panel integrates findings from in vitro studies on an LPS-induced endothelial model of sepsis, linking MLIP’s effects on cell proliferation, ROS levels, and inflammatory cytokine expression. Collectively, these findings highlight MLIP as a pivotal modulator of cellular responses under inflammatory conditions, offering a foundation for its therapeutic potential in managing sepsis and reducing cancer risk associated with inflammation. These results support the notion that MLIP may act as a central mediator of cellular responses in inflammation, oxidative stress, and immune modulation, with important implications for inflammatory diseases and cancer treatment.