Multi-omics analysis to identify lung squamous carcinoma lactate metabolism-related subtypes and establish related index to predict prognosis and guide immunotherapy

Integrative machine learning model for subtype identification and prognostic prediction in lung squamous cell carcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) is a leading cause of cancer-related mortality, and tumor heterogeneity could result in diverse prognostic subtypes. Traditional prognostic factors, like tumor, node, and metastasis (TNM) staging, offer limited predictive accuracy. This study aims to identify LUSC subtypes and develop predictive models that have the potential to improve prognosis prediction accuracy and support personalized treatment.

METHODS

Expression and clinical data were collected from three datasets. One dataset (TCGA-LUSC) was used as a training set, while the others (GSE30219 and GSE73403) were independent testing sets. Unsupervised clustering was applied to the training set to identify LUSC subtypes. The relationship between survival outcomes and these identified subtypes was validated in the testing sets using binary machine learning models and survival curve analysis. The impact of chemotherapy on the prognosis for subtypes was also presented. Subsequently, four survival machine learning models were developed to predict LUSC prognosis. These models were validated in the testing sets and integrated into an online tool to assist in survival prediction.

RESULTS

Two subtypes, C1 and C2, were identified in the training set. The C1 subtype was associated with poorer survival outcomes and was enriched in cancer-associated fibroblasts and macrophages. In contrast, the C2 subtype correlated with better outcomes and was enriched in CD8 + T cells. Regarding chemotherapy, the C2 subtype with chemotherapy showed the best survival outcomes compared to other groups. A 9-gene signature was derived from the model's importance values for subtype prediction and included TGM2, AOC3, TBXA2R, RGS3, DLC1, MMP19, ACVRL1, TCF21, and TIMP3. This signature outperformed 14 published signatures and clinical variables at survival prediction with the highest time-dependent AUC (tdAUC) and concordance index (C-index). Four machine learning models were developed using this signature, achieving tdAUC values of 0.712 and 0.684 and C-index values of 0.682 and 0.625 in the independent testing sets. An online tool for predicting survival probabilities for LUSC patients up to 10 years post-treatment is available at https://hznuduan.shinyapps.io/LCSP/ .

CONCLUSION

We identified two LUSC subtypes by unsupervised clustering and developed an online tool for prognosis prediction using supervised machine learning models.

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.

A lactate metabolism-related signature predicting patient prognosis and immune microenvironment in ovarian cancer

Introduction

Ovarian cancer (OV) is a highly lethal gynecological malignancy with a poor prognosis. Lactate metabolism is crucial for tumor cell survival, proliferation, and immune evasion. Our study aims to investigate the role of lactate metabolism-related genes (LMRGs) in OV and their potential as biomarkers for prognosis, immune microenvironment, and immunotherapy response.

Methods

Ovarian samples were collected from the TCGA cohort. And 12 lactate-related pathways were identified from the MsigDB database. Differentially expressed genes within these pathways were designated as LMRGs, which undergo unsupervised clustering to identify distinct clusters based on LMRGs. Subsequently, we assessed survival outcomes, immune cell infiltration levels, Hallmaker pathway activation patterns, and chemotaxis among different subtypes. After conducting additional unsupervised clustering based on differentially expressed genes (DEGs), significant differences in the expression of LMRGs between the two clusters were observed. The differentially expressed genes were subjected to subsequent functional enrichment analysis. Furthermore, we construct a model incorporating LMRGs. Subsequently, the lactate score for each tumor sample was calculated based on this model, facilitating the classification of samples into high and low groups according to their respective lactate scores. Distinct groups examined disparities in survival prognosis, copy number variation (CNV), single nucleotide variation (SNV), and immune infiltration. The lactate score served as a quantitative measure of OV's lactate metabolism pattern and an independent prognostic factor.

Results

This study investigated the potential role of LMRGs in tumor microenvironment diversity and prognosis in OV, suggesting that LMRGs play a crucial role in OV progression and the tumor microenvironment, thus serving as novel indicators for prognosis, immune microenvironment status, and response to immunotherapy.

Dissecting the role of lactate metabolism LncRNAs in the progression and immune microenvironment of osteosarcoma

BACKGROUND

The process of lactate metabolism has been proved to play a critical role in the progression of various cancers and to influence the immune microenvironment, but its potential role in osteosarcoma remains unclear.

METHODS

We have acquired transcriptomic and clinical data from 84 osteosarcoma samples and 70 normal bone samples from the TARGET and GTEx databases. We identified differentially expressed lactate metabolism-related LncRNAs (LRLs) in osteosarcoma and performed Cox regression and LASSO regression to establish LRLs prognostic signature (LRPS). The reliability of LRPS performance was examined by separate prognostic analysis, viability curves and receiver operating characteristic (ROC) curves. Furthermore, the effects of LRPS on the immune microenvironment of osteosarcoma were investigated, and the functions of the focal genes were experimentally validated.

RESULT

A total of 856 differentially expressed LRLs were identified and 5 of them were selected to construct LRPS, which was a better prognostic predictor for osteosarcoma compared with other published prognostic signatures (AUC up to 0.947 and 0.839 in the training and test groups, respectively, with adj-p<0.05 for KM curves). We found that LRPS significantly affected the immune infiltration of osteosarcoma, while RP11-472M19.2 significantly promoted the metastasis of osteosarcoma, which was well validated experimentally. Encouragingly, a number of sensitive drugs were identified for LRPS and RP11-472M19.2 high-risk groups.

CONCLUSION

Our study shows that lactate metabolism plays a crucial role in the development of osteosarcoma and has been well validated experimentally, providing extremely important insights into the clinical treatment and in-depth research of osteosarcoma.