A Novel Lactate Metabolism-Related Gene Signature for Predicting Clinical Outcome and Tumor Microenvironment in Hepatocellular Carcinoma

Identification and validation of a lenvatinib resistance-related prognostic signature in HCC, in which PFKFB4 contributes to tumor progression and lenvatinib resistance

BACKGROUND

Drug resistance reflects the evolution of tumors and represents the leading factor behind recurrence and death. Lenvatinib is the first-line therapy for hepatocellular carcinoma (HCC), but its effectiveness is limited by rapid development of resistance. Therefore, we aimed to identify lenvatinib resistance-related genes and assess their influence on prognosis and treatment response in HCC.

METHODS

The GSE186191 dataset served as the discovery cohort to identify lenvatinib resistance-related genes. A Venn diagram analysis delineated the intersection between lenvatinib resistance-related genes and prognostic-associated genes derived from The Cancer Genome Atlas (TCGA) database. The LASSO Cox regression model was implemented to construct a multigene signature in the TCGA cohort. A nomogram was built by integrating the TNM stage and our prognostic model. The gene signature and nomogram were further validated using HCC patients from the International Cancer Genome Consortium (ICGC) cohort. Mutation signatures, therapeutic response, functional enrichment, and immune profile analyses were performed in the two groups. Two lenvatinib-resistant (LR) HCC cells were established using a concentration gradient increment method. PFKFB4 expression was detected via qRT-PCR and western blot assay. The CCK-8 assay and flow cytometry were utilized to evaluate the proliferation and apoptosis of LR cells under different interventions.

RESULTS

We developed a lenvatinib resistance-related gene signature (ALPK3, SLC2A2, CTSV, and PFKFB4), and demonstrated that's a precise, independent, and specific prognostic model for HCC patients. High-risk patients were characterized by a predisposition to TP53 mutations, aggressive tumor features, and treatment resistance. The risk score was significantly associated with immune cell infiltration, immune checkpoint expression, angiogenesis, and tumor stemness. PFKFB4 was overexpressed in LR cells, and its knockdown significantly enhances the antiproliferative and pro-apoptotic effects of lenvatinib on resistant cells.

CONCLUSIONS

The lenvatinib resistance-related prognostic signature exhibits strong predictive power for prognosis in HCC patients and may serve as an effective tool for guiding treatment decisions. PFKFB4 promotes tumor progression and lenvatinib resistance, highlighting its potential as a novel therapeutic target for HCC.

CLINICAL TRIAL NUMBER

Not applicable.

Effects of Exercise Intensity and Time on Efficacy of Paclitaxel and Doxorubicin and Immune Microenvironment in the 4T1 Breast Cancer Model

Background: Chemotherapy is the mainstay treatment for metastatic triple negative breast cancer (TNBC). However, many patients still die of metastasis after chemotherapy, making it worthwhile to develop strategies to increase chemotherapy efficacy when treating metastatic TNBC. Previous studies have shown that exercise has the potential to inhibit breast cancer metastasis and enhance the effect of chemotherapy, and the level of exercise had a significant effect on tumor metastasis. However, the effect of different doses of exercise-referring to the combination of intensity and duration-on tumor metastasis during breast cancer chemotherapy remains unclear. Methods: 4T1 TNBC subcutaneous tumors were treated with paclitaxel (PTX) and doxorubicin hydrochloride (DOX), as well as different intensities and duration of exercise. Tumor growth, survival, metastatic burden, and the frequencies of some important immune cells were measured to determine the effects, and underlying mechanism, of different exercise doses on the anti-cancer efficacy of PTX and DOX. Results: The combination of high-dose exercise with PTX and DOX promoted metastasis formation, shortened mouse survival, and up-regulated the neutrophil/T lymphocyte ratio in the lungs. In contrast, low-dose exercise synergized with PTX and DOX to suppress metastasis, prolonged the survival of mice, decreased the neutrophil/T lymphocyte ratio, and up-regulated the percentages of NK cells within the metastatic microenvironment. The combination of different exercise dose with PTX and DOX did not affect primary tumor growth. Conclusions: The intensity and time of exercise might affect efficacy of PTX and DOX; however, TNBC patients should be careful concerning the intensity and time of exercises while undergoing chemotherapy. Breast cancer patients might be best served by participating in low levels of exercise, and avoiding excessive exercise, during PTX and DOX therapy.

Identifying cancer prognosis genes through causal learning

Accurate identification of causal genes for cancer prognosis is critical for estimating disease progression and guiding treatment interventions. In this study, we propose CPCG (Cancer Prognosis's Causal Gene), a two-stage framework identifying gene sets causally associated with patient prognosis across diverse cancer types using transcriptomic data. Initially, an ensemble approach models gene expression's impact on survival with parametric and semiparametric hazard models. Subsequently, an iterative conditional independence test combined with graph pruning is utilized to infer the causal skeleton, thereby pinpointing prognosis-related genes. Experiments on transcriptomic data from 18 cancer types sourced from The Cancer Genome Atlas Project demonstrate CPCG's effectiveness in predicting prognosis under four evaluation metrics. Validations on 24 additional datasets covering 12 cancer types from the Gene Expression Omnibus and the Chinese Glioma Genome Atlas Project further demonstrate CPCG's robustness and generalizability. CPCG identifies a concise but reliable set of genes, obviating the need for gene combination enumeration for survival time estimation. These genes are also proved closely linked to crucial biological processes in cancer. Moreover, CPCG constructs a stable causal skeleton and exhibits insensitivity to the order of data shuffling. Overall, CPCG is a powerful tool for extracting cancer prognostic biomarkers, offering interpretability, generalizability, and robustness. CPCG holds promise for facilitating targeted interventions in clinical treatment strategies.

A novel hypoxia- and lactate metabolism-related prognostic signature to characterize the immune landscape and predict immunotherapy response in osteosarcoma

Background

Immunotherapy has shown considerable promise in cancer treatment, yet only a minority of osteosarcoma patients derive benefits from this approach. Hypoxia and lactate metabolism are two predominant characteristics of the tumor microenvironment. These features are crucial for molding the immune landscape and thus have the potential to act as predictive indicators for immunotherapy response.

Methods

Prognostic modeled genes were identified through univariate and multivariate Cox regression as well as LASSO regression analyses. The tumor microenvironment was evaluated using ESTIMATE, CIBERSORT, and ImmuCellAI analyses. Tide prediction and expression of immune checkpoints, MHC molecules, chemokines, interleukins, interferons, receptors, and other cytokines were utilized to estimate immunotherapy efficacy. Single-cell analysis was performed to demonstrate the expression of modeled genes among various immune cell types. Experimental validation was carried out to verify the expression and functions of SFXN4 and SQOR.

Results

A potent signature was constructed with 8 genes related to hypoxia and lactate metabolism, including MAFF, COL5A2, FAM162A, SQOR, UQCRB, SFXN4, PFKFB2 and COX6A2. A nomogram incorporating risk scores and other clinical features demonstrated excellent predictive capacity. Osteosarcoma patients with high-risk scores exhibited poor prognosis and more "cold" tumor characteristics. According to the ESTIMATE algorithm, these patients displayed lower immune, stromal, and ESTIMATE scores, partially attributed to inadequate infiltration of key immunocytes. The Ciborsort analysis similarly indicated that high-risk individuals had diminished infiltration of critical anti-tumor immune cells such as Cytotoxic T cells, CD4+ T cells, and NK cells. The low expression levels of certain immune checkpoints, MHC molecules, chemokines, interleukins, interferons, receptors, and other cytokines in high-risk cases suggested their unsatisfactory responses to immune treatment. Tide prediction further demonstrated that fewer individuals classified as high risk may exhibit sensitivity to immune checkpoint inhibitor therapy. Notably, SFXN4 was found to be highly expressed in osteosarcoma tissues and cells; it promoted the growth, migration, and invasion of osteosarcoma cells, while SQOR had the opposite effect.

Conclusion

Our research has developed a robust hypoxia- and lactate metabolism-related gene signature, providing a solid theoretical foundation for prognosis prediction, classification of "cold" and "hot" tumors, accessing immunotherapy response, and directing personalized treatment for osteosarcoma.

FOXM1 Upregulates O-GlcNAcylation Level Via The Hexosamine Biosynthesis Pathway to Promote Angiogenesis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) presents significant challenges in treatment and prognosis because of its aggressive nature and high metastatic potential. This study aims to investigate the role of the hexosamine biosynthesis pathway (HBP) and its association with HCC progression and prognosis. We identified SPP1 and FOXM1 as hub genes within the HBP pathway, showing their correlation with poor prognosis and late-stage progression. In addition, the analysis uncovered the complex participation of the HBP pathway in nutrients and oxygen reactions, PI3K-AKT signaling, AMPK activation, and angiogenesis regulation. The disruption of these pathways is pivotal in influencing the growth and progression of HCC. Targeting the HBP presents a promising therapeutic approach to modulate the tumor microenvironment, thereby enhancing the efficacy of immunotherapy. In addition, FOXM1 was identified as the HBP pathway regulator, influencing cellular O-GlcNAcylation level and VEGF secretion, thereby promoting angiogenesis in HCC. Inhibition of O-GlcNAcylation significantly hindered angiogenesis, which is suggested as a potential avenue for therapeutic intervention. Our research demonstrates the practicality of using the HBP-related gene as a prognostic marker in liver cancer patients and suggests targeting FOXM1 as a novel avenue for personalized therapy.

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.

Harnessing CD8+ T cell dynamics in hepatitis B virus-associated liver diseases: Insights, therapies and future directions

Hepatitis B virus (HBV) infection playsa significant role in the etiology and progression of liver-relatedpathologies, encompassing chronic hepatitis, fibrosis, cirrhosis, and eventual hepatocellularcarcinoma (HCC). Notably, HBV infection stands as the primary etiologicalfactor driving the development of HCC. Given the significant contribution ofHBV infection to liver diseases, a comprehensive understanding of immunedynamics in the liver microenvironment, spanning chronic HBV infection,fibrosis, cirrhosis, and HCC, is essential. In this review, we focused on thefunctional alterations of CD8+ T cells within the pathogenic livermicroenvironment from HBV infection to HCC. We thoroughly reviewed the roles ofhypoxia, acidic pH, metabolic reprogramming, amino acid deficiency, inhibitory checkpointmolecules, immunosuppressive cytokines, and the gut-liver communication in shapingthe dysfunction of CD8+ T cells in the liver microenvironment. Thesefactors significantly impact the clinical prognosis. Furthermore, we comprehensivelyreviewed CD8+ T cell-based therapy strategies for liver diseases,encompassing HBV infection, fibrosis, cirrhosis, and HCC. Strategies includeimmune checkpoint blockades, metabolic T-cell targeting therapy, therapeuticT-cell vaccination, and adoptive transfer of genetically engineered CD8+ T cells, along with the combined usage of programmed cell death protein-1/programmeddeath ligand-1 (PD-1/PD-L1) inhibitors with mitochondria-targeted antioxidants.Given that targeting CD8+ T cells at various stages of hepatitis Bvirus-induced hepatocellular carcinoma (HBV + HCC) shows promise, we reviewedthe ongoing need for research to elucidate the complex interplay between CD8+ T cells and the liver microenvironment in the progression of HBV infection toHCC. We also discussed personalized treatment regimens, combining therapeuticstrategies and harnessing gut microbiota modulation, which holds potential forenhanced clinical benefits. In conclusion, this review delves into the immunedynamics of CD8+ T cells, microenvironment changes, and therapeuticstrategies within the liver during chronic HBV infection, HCC progression, andrelated liver diseases.

Identification and functional analysis of lactic acid metabolism-related differentially expressed genes in hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality rate that seriously threatens human health. We aimed to investigate the expression, prognostic value, and immune cell infiltration of lactic acid metabolism-related genes (LAMRGs) in HCC using bioinformatics. Methods: The HCC database (The Cancer Genome Atlas-Liver Hepatocellular Carcinoma) was downloaded from the Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) between normal and tumor groups were identified. The LAMRGs were obtained from literature and GeneCards and MSigDB databases. Lactic acid metabolism-related differentially expressed genes (LAMRDEGs) in HCC were screened from the DEGs and LAMRGs. Functional enrichment analyses of the screened LAMRDEGs were further conducted using Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Set Enrichment Analysis (GSEA). The genes were used in multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses to construct a prognostic model. Then, a protein-protein interaction network was constructed using STRING and CTD databases. Furthermore, the CIBERSORTx online database was used to assess the relationship between immune cell infiltration and hub genes. Results: Twenty-eight lactic acid metabolism-related differentially expressed genes (LAMRDEGs) were identified. The GO and KEGG analyses showed that the LAMRDEGs were related to the prognosis of HCC. The GSEA indicated that the LAMRDEGs were significantly enriched in tumor related pathways. In the multivariate Cox regression analysis, 14 key genes (E2F1, SERPINE1, GYS2, SPP1, PCK1, CCNB1, CYP2C9, IGFBP3, KDM8, RCAN1, ALPL, FBP1, NQO1, and LCAT) were found to be independent prognostic factors of HCC. Finally, the LASSO and Cox regression analyses showed that six key genes (SERPINE1, SPP1, CCNB1, CYP2C9, NQO1, and LCAT) were associated with HCC prognosis. Moreover, the correlation analyses revealed that the expression of the six key genes were associated with immune infiltrates of HCC. Conclusion: The LAMRDEGs can predict the prognosis and may be associated with immune cells infiltration in patients with HCC. These genes might be the promising biomarkers for the prognosis and treatment of HCC.

Identification and validation of a lactate metabolism-related six-gene prognostic signature in intrahepatic cholangiocarcinoma

PURPOSE

Intrahepatic cholangiocarcinoma (iCCA) is a highly malignant and fatal liver tumor with increasing incidence worldwide. Lactate metabolism has been recently reported as a crucial contributor to tumor progression and immune regulation in the tumor microenvironment. However, it remains poorly identified about the biological functions of lactate metabolism in iCCA, which hinders the development of prognostic tools and therapeutic interventions.

METHODS

The univariate Cox regression analysis and Boruta algorithm were utilized to identify key lactate metabolism-related genes (LMRGs), and a prognostic signature was constructed based on LMRG scores. Genomic variations and immune cell infiltration were evaluated in the high and low LMRG score groups. Finally, the biological functions of key LMRGs were verified with in vitro and in vivo experiments.

RESULTS

Patients in the high LMRG score group exhibit a poor prognosis compared to those in the low LMRG score group, with a high frequency of TP53 and KRAS mutations. Moreover, the infiltration and function of NK cells were compromised in the high LMRG score group, consistent with the results from two independent single-cell RNA sequencing datasets and immunohistochemistry of tissue microarrays. Experimental data revealed that lactate dehydrogenase A (LDHA) knockdown inhibited proliferation and migration in iCCA cell lines and tumor growth in immunocompetent mice.

CONCLUSION

Our study revealed the biological roles of LDHA in iCCA and developed a reliable lactate metabolism-related prognostic signature for iCCA, offering promising therapeutic targets for iCCA in the clinic.

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.

A Lactic Acid Metabolism-Related Gene Signature for Predicting Clinical Outcome and Tumor Microenvironmental Status in Patients with Hepatocellular Carcinoma

This study aims to build a prognostic model based on lactic acid metabolism-related genes (LMRGs) to predict survival outcomes and tumor microenvironment status of Hepatocellular carcinoma (HCC) patients. The model was used to calculate riskscores of clinical samples. Survival analysis and Cox regression analysis were conducted to verify the independence and reliability of the riskscore to determine its clinical significance in prognosis evaluation of HCC. Additionally, we conducted a comprehensive analysis of tumor mutation burden (TMB), immune cell infiltration, and gene set molecular function in the high- and low-risk groups. We obtained 134 LMRGs mainly involved in cellular calcium homeostasis and calcium signaling pathways. The LMRGs in the risk assessment model included PFKFB4, SLC16A3, ADRA2B, SLC22A1, QRFPR, and PROK1. This study discovered much shorter overall survival and median survival time of patients with higher riskscores when compared to those with lower riskscores. It was indicated that for independent prediction of patients' prognosis, the riskscore had a significant clinical value. A remarkable difference was also found regarding TMB between the two groups. Finally, cell experiments demonstrated that the knockout of PFKFB4 and SLC16A3 genes suppressed lactate. Our research demonstrated that the riskscore, established based on LMRGs, is a promising biomarker.

Metabolic reprogramming in the tumor microenvironment of liver cancer

The liver is essential for metabolic homeostasis. The onset of liver cancer is often accompanied by dysregulated liver function, leading to metabolic rearrangements. Overwhelming evidence has illustrated that dysregulated cellular metabolism can, in turn, promote anabolic growth and tumor propagation in a hostile microenvironment. In addition to supporting continuous tumor growth and survival, disrupted metabolic process also creates obstacles for the anticancer immune response and restrains durable clinical remission following immunotherapy. In this review, we elucidate the metabolic communication between liver cancer cells and their surrounding immune cells and discuss how metabolic reprogramming of liver cancer impacts the immune microenvironment and the efficacy of anticancer immunotherapy. We also describe the crucial role of the gut-liver axis in remodeling the metabolic crosstalk of immune surveillance and escape, highlighting novel therapeutic opportunities.

Identification of lactate-related subgroups and prognostic model in triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer that exhibits elevated glycolytic capacity. Lactate, as a byproduct of glycolysis, is considered a major oncometabolite that plays an important role in oncogenesis and remodeling of the tumor microenvironment. However, the potential roles of lactate in TNBC are not yet fully understood. In this study, our goal was to identify prognosis-related lactate genes (PLGs) and construct a lactate-related prognostic model (LRPM) for TNBC.

METHODS

First, we applied lactate-related genes to classify TNBC samples using a hierarchical clustering algorithm. Then, we performed the log-rank analysis and the least absolute shrinkage and selection operator analysis to screen PLGs and construct the LRPM. The biological functions of the identified PLGs in TNBC were investigated using CCK8 assay and clone formation assay. Finally, we constructed a nomogram based on the lactate-risk score and tumor clinical stage. We used the operating characteristic curve and decision curve analysis to evaluate the predictive capability of the nomogram.

RESULTS

Our results showed that the TNBC samples could be classified into two subgroups with different survival probabilities. Three genes (NDUFAF3, CARS2 and FH), which can suppress TNBC cell proliferation, were identified as PLGs. Moreover, the LRPM and nomogram exhibited excellent predictive performance for TNBC patient prognosis.

CONCLUSION

We have developed a novel LRPM that enables risk stratification and identification of poor molecular subtypes in TNBC patients, showing great potential in clinical practice.

Characterization and application of a lactate and branched chain amino acid metabolism related gene signature in a prognosis risk model for multiple myeloma

BACKGROUND

About 10% of hematologic malignancies are multiple myeloma (MM), an untreatable cancer. Although lactate and branched-chain amino acids (BCAA) are involved in supporting various tumor growth, it is unknown whether they have any bearing on MM prognosis.

METHODS

MM-related datasets (GSE4581, GSE136337, and TCGA-MM) were acquired from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Lactate and BCAA metabolism-related subtypes were acquired separately via the R package "ConsensusClusterPlus" in the GSE4281 dataset. The R package "limma" and Venn diagram were both employed to identify lactate-BCAA metabolism-related genes. Subsequently, a lactate-BCAA metabolism-related prognostic risk model for MM patients was constructed by univariate Cox, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses. The gene set enrichment analysis (GSEA) and R package "clusterProfiler"were applied to explore the biological variations between two groups. Moreover, single-sample gene set enrichment analysis (ssGSEA), Microenvironment Cell Populations-counter (MCPcounte), and xCell techniques were applied to assess tumor microenvironment (TME) scores in MM. Finally, the drug's IC50 for treating MM was calculated using the "oncoPredict" package, and further drug identification was performed by molecular docking.

RESULTS

Cluster 1 demonstrated a worse prognosis than cluster 2 in both lactate metabolism-related subtypes and BCAA metabolism-related subtypes. 244 genes were determined to be involved in lactate-BCAA metabolism in MM. The prognostic risk model was constructed by CKS2 and LYZ selected from this group of genes for MM, then the prognostic risk model was also stable in external datasets. For the high-risk group, a total of 13 entries were enriched. 16 entries were enriched to the low-risk group. Immune scores, stromal scores, immune infiltrating cells (except Type 17 T helper cells in ssGSEA algorithm), and 168 drugs'IC50 were statistically different between two groups. Alkylating potentially serves as a new agent for MM treatment.

CONCLUSIONS

CKS2 and LYZ were identified as lactate-BCAA metabolism-related genes in MM, then a novel prognostic risk model was built by using them. In summary, this research may uncover novel characteristic genes signature for the treatment and prognostic of MM.

MiR-494 induces metabolic changes through G6pc targeting and modulates sorafenib response in hepatocellular carcinoma

BACKGROUND

Metabolic reprogramming is a well-known marker of cancer, and it represents an early event during hepatocellular carcinoma (HCC) development. The recent approval of several molecular targeted agents has revolutionized the management of advanced HCC patients. Nevertheless, the lack of circulating biomarkers still affects patient stratification to tailored treatments. In this context, there is an urgent need for biomarkers to aid treatment choice and for novel and more effective therapeutic combinations to avoid the development of drug-resistant phenotypes. This study aims to prove the involvement of miR-494 in metabolic reprogramming of HCC, to identify novel miRNA-based therapeutic combinations and to evaluate miR-494 potential as a circulating biomarker.

METHODS

Bioinformatics analysis identified miR-494 metabolic targets. QPCR analysis of glucose 6-phosphatase catalytic subunit (G6pc) was performed in HCC patients and preclinical models. Functional analysis and metabolic assays assessed G6pc targeting and miR-494 involvement in metabolic changes, mitochondrial dysfunction, and ROS production in HCC cells. Live-imaging analysis evaluated the effects of miR-494/G6pc axis in cell growth of HCC cells under stressful conditions. Circulating miR-494 levels were assayed in sorafenib-treated HCC patients and DEN-HCC rats.

RESULTS

MiR-494 induced the metabolic shift of HCC cells toward a glycolytic phenotype through G6pc targeting and HIF-1A pathway activation. MiR-494/G6pc axis played an active role in metabolic plasticity of cancer cells, leading to glycogen and lipid droplets accumulation that favored cell survival under harsh environmental conditions. High miR-494 serum levels associated with sorafenib resistance in preclinical models and in a preliminary cohort of HCC patients. An enhanced anticancer effect was observed for treatment combinations between antagomiR-494 and sorafenib or 2-deoxy-glucose in HCC cells.

CONCLUSIONS

MiR-494/G6pc axis is critical for the metabolic rewiring of cancer cells and associates with poor prognosis. MiR-494 deserves attention as a candidate biomarker of likelihood of response to sorafenib to be tested in future validation studies. MiR-494 represents a promising therapeutic target for combination strategies with sorafenib or metabolic interference molecules for the treatment of HCC patients who are ineligible for immunotherapy.

Establishment and validation of a prognostic signature for pancreatic ductal adenocarcinoma based on lactate metabolism-related genes

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal malignancy with poor prognosis. To improve patient outcomes, it is necessary to gain a better understanding of the oncogenesis and progression of this disease. Metabolic reprogramming, particularly the regulation of lactate metabolism, is known to have a significant impact on tumor microenvironment and could provide valuable insights for the management of PDAC patients. In this study, we aimed to investigate the prognostic potential of lactate metabolism-related genes (LMRGs). Methods: Transcriptomic data of patients with PDAC along with the clinical outcomes were retrieved from The Cancer Genome Atlas database, and the expression data in normal pancreas from Genotype-Tissue Expression dataset were adopted as the normal control. By using Cox and LASSO regression models, we identified key genes that are differentially expressed in cancerous tissues and related to prognosis. To determine the prognostic value of LMRGs in PDAC, we evaluated their clinical significance and model performance using both the area under the receiver operator characteristic curve (AUC) and calibration curves. In addition, we evaluated the drug sensitivity prediction and immune infiltration by using oncoPredict algorithm, single sample gene set enrichment analysis and Tumor Immune Estimation Resource. Results: A total of 123 LMRGs were identified through differential gene screening analysis, among which 7 LMRGs were identified to comprise a LMRGs signature that independently predict overall survival of these PDAC patient. The AUC values for the LMRGs signature were 0.786, 0.820, 0.837, and 0.816 for predicting 1-, 2-, 3- and 5-year overall survival respectively. Furthermore, this prognostic signature was used to stratify patients into high-risk and low-risk groups, with the former having worse clinical outcomes. This observation was further validated through analysis of the International Cancer Genome Consortium database. In addition, lower sensitivity to gemcitabine and infiltration of immune effector cells were observed in the cancer tissue of patients in the high-risk group. Conclusion: In conclusion, our data suggests that a genomic signature comprised of these LMRGs may be a novel predictor of overall clinical outcomes and present therapeutic potential for PDAC patients.

Tumor Mutational Burden for Predicting Prognosis and Therapy Outcome of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), the primary hepatic malignancy, represents the second-highest cause of cancer-related death worldwide. Many efforts have been devoted to finding novel biomarkers for predicting both patients' survival and the outcome of pharmacological treatments, with a particular focus on immunotherapy. In this regard, recent studies have focused on unravelling the role of tumor mutational burden (TMB), i.e., the total number of mutations per coding area of a tumor genome, to ascertain whether it can be considered a reliable biomarker to be used either for the stratification of HCC patients in subgroups with different responsiveness to immunotherapy, or for the prediction of disease progression, particularly in relation to the different HCC etiologies. In this review, we summarize the recent advances on the study of TMB and TMB-related biomarkers in the HCC landscape, focusing on their feasibility as guides for therapy decisions and/or predictors of clinical outcome.

Partners in crime: The feedback loop between metabolic reprogramming and immune checkpoints in the tumor microenvironment

The tumor microenvironment (TME) is a complex and constantly changing cellular system composed of heterogeneous populations of tumor cells and non-transformed stromal cells, such as stem cells, fibroblasts, endothelial cells, pericytes, adipocytes, and innate and adaptive immune cells. Tumor, stromal, and immune cells consume available nutrients to sustain their proliferation and effector functions and, as a result of their metabolism, produce a wide array of by-products that gradually alter the composition of the milieu. The resulting depletion of essential nutrients and enrichment of by-products work together with other features of the hostile TME to inhibit the antitumor functions of immune cells and skew their phenotype to promote tumor progression. This review briefly describes the participation of the innate and adaptive immune cells in recognizing and eliminating tumor cells and how the gradual metabolic changes in the TME alter their antitumor functions. In addition, we discuss the overexpression of the immune checkpoints and their ligands as a result of nutrient deprivation and by-products accumulation, as well as the amplification of the metabolic alterations induced by the immune checkpoints, which creates an immunosuppressive feedback loop in the TME. Finally, the combination of metabolic and immune checkpoint inhibitors as a potential strategy to treat cancer and enhance the outcome of patients is highlighted.

Genes Modulating Butyrate Metabolism for Assessing Clinical Prognosis and Responses to Systematic Therapies in Hepatocellular Carcinoma

Butyrate, one of the major products of the gut microbiota, has played notable roles in diverse therapies for multiple tumors. Our study aimed to determine the roles of genes that modulate butyrate metabolism (BM) in predicting the clinical prognosis and responses to systemic therapies in hepatocellular carcinoma (HCC). The genes modulating BM were available from the GeneCard database, and gene expression and clinical information were obtained from TCGA-LIHC, GEO, ICGC-JP, and CCLE databases. Candidate genes from these genes that regulate BM were then identified by univariate Cox analysis. According to candidate genes, the patients in TCGA were grouped into distinct subtypes. Moreover, BM- related gene signature (BMGs) was created via the LASSO Cox algorithm. The roles of BMGs in identifying high-risk patients of HCC, assessing the prognoses, and predicting systematic therapies were determined in various datasets. The statistical analyses were fulfilled with R 4.1.3, GraphPad Prism 8.0 and Perl 5.30.0.1 software. In the TCGA cohort, most butyrate-related genes were over-expressed in the B cluster, and patients in the B cluster showed worse prognoses. BMGs constructed by LASSO were composed of eight genes. BMGs exhibited a strong performance in evaluating the prognoses of HCC patients in various datasets, which may be superior to 33 published biomarkers. Furthermore, BMGs may contribute to the early surveillance of HCC, and BMGs could play active roles in assessing the effectiveness of immunotherapy, TACE, ablation therapy, and chemotherapeutic drugs for HCC. BMGs may be served as novel promising biomarkers for early identifying high-risk groups of HCC, as well as assessing prognoses, drug sensitivity, and the responses to immunotherapy, TACE, and ablation therapy in patients with HCC.

Comprehensive Analysis of Cellular Senescence-Related Genes in Prognosis, Molecular Characterization and Immunotherapy of Hepatocellular Carcinoma

BACKGROUND

Cellular senescence is a tumor suppressive response in which the cell cycle is in a state of permanent arrest and can inhibit tumor cell proliferation. In recent years, induction of cellular senescence has been shown to be important for antitumor therapy, and the link between cellular senescence and clinical prognosis and immunotherapy of hepatocellular carcinoma is still unknown.

METHODS

We performed enrichment analysis of genes in three cellular senescence gene sets, screened for gene sets significantly enriched in hepatocellular carcinoma and extracted genes from them. Signature were constructed using senescence-related genes, and their expression was verified at the protein and RNA levels. Survival, clinical staging and grading, immune infiltration, immunotherapy, and drug sensitivity were also analyzed between risk groups.

RESULTS

The q-PCR and immunohistochemistry results revealed significant differences in the expression of the signature genes between normal and tumor tissues. Significant differences in clinicopathological features, prognosis and immune infiltration were observed between risk groups. In the low-risk group, better OS and lower TMB scores were demonstrated, while the high-risk group had higher immune checkpoint expression, as well as lower risk of immune escape. In addition, we found that the High-risk group was more sensitive to sorafenib.

CONCLUSION

In summary, the signature constructed using aging-related genes can reliably predict patient prognosis and immunotherapy efficacy, providing a new idea for immune system therapy of hepatocellular carcinoma.

Dual Roles of Lactate in EGFR-TKI-Resistant Lung Cancer by Targeting GPR81 and MCT1

Lactate is critical in modeling tumor microenvironment causing chemotherapy resistance; however, the role of lactate in tyrosine kinase inhibitor (TKI) resistance has not been fully known. The aim of this study was to evaluate whether lactate could mediate TKI resistance through GPR81 and MCT1 in non-small-cell lung cancer (NSCLC). Here, we showed that lactate enhanced the cell viability and restrained erlotinib-induced apoptosis in PC9 and HCC827 cells. GPR81 and AKT expression were significantly increased with the addition of lactate, and siGPR81 reduced AKT expression resulting in a raised apoptosis rate with erlotinib treatment. Furthermore, we found that lactate also promoted MCT1 exposure, and inhibiting MCT1 with AZD3965 markedly impaired the glycolytic capacity. A significant increase of GPR81 and MCT1 expression was observed in insensitive tissues compared with sensitive ones by immunostaining in NSCLC patients. Our results indicate that lactate adopts dual strategies to promote TKI resistance in NSCLC, not only activating AKT signaling by GPR81, but also giving energy supply through MCT1-mediated input. Targeting GPR81 and MCT1 may provide new therapeutic modalities for TKI resistance in NSCLC.

Involvement of abnormal dystroglycan expression and matriglycan levels in cancer pathogenesis

Dystroglycan (DG) is a glycoprotein composed of two subunits that remain non-covalently bound at the plasma membrane: α-DG, which is extracellular and heavily O-mannosyl glycosylated, and β-DG, an integral transmembrane polypeptide. α-DG is involved in the maintenance of tissue integrity and function in the adult, providing an O-glycosylation-dependent link for cells to their extracellular matrix. β-DG in turn contacts the cytoskeleton via dystrophin and participates in a variety of pathways transmitting extracellular signals to the nucleus. Increasing evidence exists of a pivotal role of DG in the modulation of normal cellular proliferation. In this context, deficiencies in DG glycosylation levels, in particular those affecting the so-called matriglycan structure, have been found in an ample variety of human tumors and cancer-derived cell lines. This occurs together with an underexpression of the DAG1 mRNA and/or its α-DG (core) polypeptide product or, more frequently, with a downregulation of β-DG protein levels. These changes are in general accompanied in tumor cells by a low expression of genes involved in the last steps of the α-DG O-mannosyl glycosylation pathway, namely POMT1/2, POMGNT2, CRPPA, B4GAT1 and LARGE1/2. On the other hand, a series of other genes acting earlier in this pathway are overexpressed in tumor cells, namely DOLK, DPM1/2/3, POMGNT1, B3GALNT2, POMK and FKTN, hence exerting instead a pro-oncogenic role. Finally, downregulation of β-DG, altered β-DG processing and/or impaired β-DG nuclear levels are increasingly found in human tumors and cell lines. It follows that DG itself, particular genes/proteins involved in its glycosylation and/or their interactors in the cell could be useful as biomarkers of certain types of human cancer, and/or as molecular targets of new therapies addressing these neoplasms.

Integrative multiomics evaluation reveals the importance of pseudouridine synthases in hepatocellular carcinoma

Background: The pseudouridine synthases (PUSs) have been reported to be associated with cancers. However, their involvement in hepatocellular carcinoma (HCC) has not been well documented. Here, we assess the roles of PUSs in HCC. Methods: RNA sequencing data of TCGA-LIHC and LIRI-JP were downloaded from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), respectively. GSE36376 gene expression microarray was downloaded from the Gene Expression Omnibus (GEO). Proteomics data for an HBV-related HCC cohort was obtained from the CPTAC Data Portal. The RT-qPCR assay was performed to measure the relative mRNA expression of genes in clinical tissues and cell lines. Diagnostic efficiency was evaluated by the ROC curve. Prognostic value was assessed using the Kaplan-Meier curve, Cox regression model, and time-dependent ROC curve. Copy number variation (CNV) was analyzed using the GSCA database. Functional analysis was carried out with GSEA, GSVA, and clusterProfiler package. The tumor microenvironment (TME) related analysis was performed using ssGSEA and the ESTIMATE algorithm. Results: We identified 7 PUSs that were significantly upregulated in HCC, and 5 of them (DKC1, PUS1, PUS7, PUSL1, and RPUSD3) were independent risk factors for patients' OS. Meanwhile, the protein expression of DKC1, PUS1, and PUS7 was also upregulated and related to poor survival. Both mRNA and protein of these PUSs were highly diagnostic of HCC. Moreover, the CNV of PUS1, PUS7, PUS7L, and RPUSD2 was also associated with prognosis. Further functional analysis revealed that PUSs were mainly involved in pathways such as genetic information processing, substance metabolism, cell cycle, and immune regulation. Conclusion: PUSs may play crucial roles in HCC and could be used as potential biomarkers for the diagnosis and prognosis of patients.

Identification of cell senescence molecular subtypes in prediction of the prognosis and immunotherapy of hepatitis B virus-related hepatocellular carcinoma

Hepatitis B virus (HBV)-infected hepatocellular carcinoma (HCC) has a high incidence and fatality rate worldwide, being among the most prevalent cancers. The growing body of data indicating cellular senescence (CS) to be a critical factor in hepatocarcinogenesis. The predictive value of CS in HBV-related HCC and its role in the immune microenvironment are unknown. To determine the cellular senescence profile of HBV-related HCC and its role in shaping the immune microenvironment, this study employed a rigorous evaluation of multiple datasets encompassing 793 HBV-related HCC samples. Two novel distinct CS subtypes were first identified by nonnegative matrix factorization, and we found that the senescence-activated subgroup had the worst prognosis and correlated with cancer progression. C1 and C2 were identified as the senescence-suppressed and senescence-activated subgroups. The immune microenvironment indicated that C2 exhibited a relatively low immune status, higher tumor purity, and lower immune scores and estimated scores, while the C1 subgroup possessed a better prognosis. The CS score signature based on five genes (CENPA, EZH2, G6PD, HDAC1, and PRPF19) was established using univariate Cox regression and the lasso method. ICGC-LIRI and GSE14520 cohorts were used to validate the reliability of the CS scoring system. In addition, we examined the association between the risk score and hallmark pathways through gene set variation analysis and gene set enrichment analysis. The results revealed a high CS score to be associated with the activation of cell senescence-related pathways. The CS score and other clinical features were combined to generate a CS dynamic nomogram with a better predictive capacity for OS at 1, 2, and 3 years than other clinical parameters. Our study demonstrated that cellular senescence patterns play a non-negligible role in shaping the characteristics of the immune microenvironment and profoundly affecting tumor prognosis. The results of this study will help predict patient prognosis more accurately and may assist in development of personalized immunotherapy for HBV-related HCC patients.

A novel signature of combing cuproptosis- with ferroptosis-related genes for prediction of prognosis, immunologic therapy responses and drug sensitivity in hepatocellular carcinoma

BACKGROUND

Our study aimed to construct a novel signature (CRFs) of combing cuproptosis-related genes with ferroptosis-related genes for the prediction of the prognosis, responses of immunological therapy, and drug sensitivity of hepatocellular carcinoma (HCC) patients.

METHODS

The RNA sequencing and corresponding clinical data of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), GSE76427, GSE144269, GSE140580, Cancer Cell Line Encyclopedia (CCLE), and IMvigor210 cohorts. CRFs was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm. The analyses involved in the prognosis, response to immunologic therapy, efficacy of transcatheter arterial chemoembolization (TACE) therapy, and drug sensitivity were performed. Furthermore, the molecular function, somatic mutation, and stemness analyses were further performed between the low- and high-risk groups, respectively. In this study, the statistical analyses were performed by using the diverse packages of R 4.1.3 software and Cytoscape 3.8.0.

RESULTS

CRFs included seven genes (G6PD, NRAS, RRM2, SQSTM1, SRXN1, TXNRD1, and ZFP69B). Multivariate Cox regression analyses demonstrated that CRFs were an independent risk factor for prognosis. In addition, these patients in the high-risk group presented with worse prognoses and a significant state of immunosuppression. Moreover, patients in the high-risk group might achieve greater outcomes after receiving immunologic therapy, while patients in the low-risk group are sensitive to TACE. Furthermore, we discovered that patients in the high-risk group may benefit from the administration of sunitinib. In addition, enhanced mRANsi and tumor mutation burden (TMB) yielded in the high-risk group. Additionally, the functions enriched in the low-risk group differed from those in the other group.

CONCLUSION

In summary, CRFs may be regarded not only as a novel biomarker of worse prognosis, but also as an excellent predictor of immunotherapy response, efficacy of TACE and drug sensitivity in HCC, which is worthy of clinical promotion.

Prognostic value of lactate metabolism-related gene expression signature in adult primary gliomas and its impact on the tumor immune microenvironment

Glioma is one of the most malignant intracerebral tumors, whose treatment means was limited, and prognosis was unsatisfactory. Lactate metabolism patterns have been shown to be highly heterogenous among different tumors and produce diverse impact on the tumor microenvironment. To understand the characteristics and implications of lactate metabolism gene expression, we developed a lactate metabolism-related gene expression signature of gliomas based on RNA-sequencing data of a total of 965 patient samples from TCGA, CGGA, and our own glioma cohort. Sixty-three lactate metabolism-related genes (LMGs) were differentially expressed between glioma and normal brain tissue, and consensus clustering analysis identified two clusters distinct LMG expression patterns. The consensus clusters differed in prognosis, molecular characteristics and estimated immune microenvironment landscape involving immune checkpoint proteins, T cell dysfunction and exclusion, as well as tumor purity. Univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) Cox hazard regression was applied in determining of prognosis-related lactate metabolism genes (PRLMGs), on which prognostic lactate metabolism risk score (PLMRS) was constructed. The high PLMRS group was associated with significantly poorer patient outcome. A nomogram containing PLMRS and other independent prognostic variables was established with remarkable predictive performance on patient survival. Exploration on the somatic mutations and copy number variations of the high- and low-PLMRS groups demonstrated their distinct genetic background. Together, our results indicated that the expression signature of LMG was associated with the prognosis of glioma patients and influenced the activity of immune cells in the tumor microenvironment, which may serve as a potential biomarker for predicting response of gliomas to immunotherapy.

Construction of a prognostic signature for serous ovarian cancer based on lactate metabolism-related genes

Background

The key biochemical feature of malignant tumor is the conversion of energy metabolism from oxidative phosphorylation to glycolysis, which provides sufficient capacity and raw materials for tumor cell rapid growth. Our study aims to construct a prognostic signature for ovarian cancer based on lactate metabolism-related genes (LMRGs).

Methods

Data of ovarian cancer and non-diseased ovarian data were downloaded from TCGA and the GTEx database, respectively. LMRGs were obtained from GeneCards and MSigDB databases, and the differentially expressed LMRGs were identified using limma and DESeq2 R packages. Cox regression analysis and LASSO were performed to determine the LMRGs associated with OS and develop the prognostic signature. Then, clinical significance of the prognostic signature in ovarian cancer was assessed.

Results

A total of 485 differentially expressed LMRGs in ovarian tissue were selected for subsequent analysis, of which 324 were up-regulated and 161 were down regulated. We found that 22 LMRGs were most significantly associated with OS by using the univariate regression analysis. The prognostic scoring model was consisted of 12 LMRGs (SLCO1B3, ERBB4, SLC28A1, PDSS1, BDH1, AIFM1, TSFM, PPARGC1A, HGF, FGFR1, ABCC8, TH). Kaplan-Meier survival analysis indicated that poorer overall survival (OS) in the high-risk group patients (P<0.0001). This prognostic signature could be an independent prognostic indicator after adjusting to other clinical factors. The calibration curves of nomogram for the signature at 1, 2, and 3 years and the ROC curve demonstrated good agreement between the predicted and observed survival rates of ovarian cancer patients. Furthermore, the high-risk group patients have much lower expression level of immune checkpoint-TDO2 compared with the low-risk group (P=0.024).

Conclusions

We established a prognostic signature based on LMRGs for ovarian cancer, and highlighted emerging evidence indicating that this prognostic signature is a promising approach for predicting ovarian cancer prognosis and guiding clinical therapy.

Identification of m6A modification patterns and development of m6A-hypoxia prognostic signature to characterize tumor microenvironment in triple-negative breast cancer

Background

N6-methylation (m6A) modification of RNA has been found to have essential effects on aspects of the tumor microenvironment (TME) including hypoxia status and mobilization of immune cells. However, there are no studies to explore the combined effect of m6A modification and hypoxia on molecular heterogeneity and TME of triple-negative breast cancer (TNBC).

Methods

We collected The Cancer Genome Atlas (TCGA-TNBC, N=139), the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC-TNBC, N=297), the GSE103091, GSE21653, and GSE135565 series from the Gene Expression Omnibus (GEO-TNBC, N=247), and FUSCCTNBC (N=245) for our study. The non-negative matrix factorization algorithm was used to cluster TNBC samples. Immune cell infiltration was analyzed by the CIBERSORT algorithm. The enrichment scores were calculated by single-sample gene set enrichment analysis(ssGSEA) to characterize TME in TNBC samples. Immunohistochemistry (IHC) and qRT-PCR were performed to detect the gene expression.

Results

Based on the expression of m6A-related genes, we identified three distinct m6A clusters (denoted A, B, and C) in TNBC samples. Comparing the TME characteristics among the three clusters, we observed that cluster C was strongly related to hypoxia status and immune suppression, whereas clusters A and B displayed more immune cell infiltration. Therefore, we combine m6A and hypoxia related genes to classify two m6A-hypoxia clusters of TNBC and screened six prognostic genes by LASSO-Cox regression to construct a m6A-hypoxia signature(MHPS), which divided TNBC samples into high- and low-risk groups. We identified different TME features, immune cell infiltration between the two groups, and a better immunotherapy response was observed in the low-risk group. A nomogram was constructed with tumor size, lymph node, and risk score to improve clinical application of MHPS.

Conclusion

We identified distinct TME characteristics of TNBC based on three different m6A modification patterns. Then, we constructed a specific m6A-hypoxia signature for TNBC to evaluate risk and predict immunotherapy response of patients, to enable more accurate treatment in the future.

Expression of lactate-related signatures correlates with immunosuppressive microenvironment and prognostic prediction in ewing sarcoma

Objectives: Ewing sarcoma (EWS) is an aggressive tumor of bone and soft tissue. Growing evidence indicated lactate as a pivotal mediator of crosstalk between tumor energy metabolism and microenvironmental regulation. However, the contribution of lactate-related genes (LRGs) in EWS is still unclear.

Methods: We obtained the transcriptional data of EWS patients from the GEO database and identified differentially expressed-LRGs (DE-LRGs) between EWS patient samples and normal tissues. Unsupervised cluster analysis was utilized to recognize lactate modulation patterns based on the expression profile of DE-LRGs. Functional enrichment including GSEA and GSVA analysis was conducted to identify molecular signaling enriched in different subtypes. ESTIMATE, MCP and CIBERSORT algorithm was used to explore tumor immune microenvironment (TIME) between subtypes with different lactate modulation patterns. Then, lactate prognostic risk signature was built via univariate, LASSO and multivariate Cox analysis. Finally, we performed qPCR analysis to validate candidate gene expression.

Result: A total of 35 DE-LRGs were identified and functional enrichment analysis indicated that these LRGs were involved in mitochondrial function. Unsupervised cluster analysis divided EWS patients into two lactate modulation patterns and we revealed that patients with Cluster 1 pattern were linked to poor prognosis and high lactate secretion status. Moreover, TIME analysis indicated that the abundance of multiple immune infiltrating cells were dramatically elevated in Cluster 1 to Cluster 2, including CAFs, endothelial cells, Macrophages M2, etc., which might contribute to immunosuppressive microenvironment. We also noticed that expression of several immune checkpoint proteins were clearly increased in Cluster 1 to Cluster 2. Subsequently, seven genes were screened to construct LRGs prognostic signature and the performance of the resulting signature was validated in the validation cohort. Furthermore, a nomogram integrating LRGs signature and clinical characteristics was developed to predict effectively the 4, 6, and 8-year prognosis of EWS patients.

Conclusion: Our study revealed the role of LRGs in immunosuppressive microenvironment and predicting prognosis in EWS and provided a robust tool to predict the prognosis of EWS patients.

A Novel Risk Score Model of Lactate Metabolism for Predicting over Survival and Immune Signature in Lung Adenocarcinoma

Simple Summary

Since the discovery of the WarBurg effect, the veil of the tumorigenic role of lactic acid has been gradually revealed. Recently, it was proposed that lactic acid that is produced by tumor cells was secreted into the extracellular space to create immunosuppressive tumor microenvironment (TME) in a variety of ways. However, the intersection genes and the association with immunotherapy are unclear. At present, we identified six lactate-metabolism-associated genes, which were thought to enable tumor progression, that were related to LUAD immunotherapy and we constructed an LAR-score risk model.

Abstract

Background: The role of lactate acid in tumor progression was well proved. Recently, it was found that lactate acid accumulation induced an immunosuppressive microenvironment. However, these results were based on a single gene and it was unclear that lactate acid genes were associated with immunotherapy and able to predict overall survival. Methods: Genes and survival data were acquired from TCGA, GEO and GENECARDS. PCA and TSNE were used to distinguish sample types according to lactate metabolism-associated gene expression. A Wilcox-test examined the expression differences between normal and tumor samples. The distribution in chromatin and mutant levels were displayed by Circo and MAfTools. The lactate metabolism-associated gene were divided into categories by consistent clustering and visualized by Cytoscape. Immune cell infiltration was evaluated by CIBERSORT and LM22 matrix. Enrichment analysis was performed by GSVA. We used the ConsensusClusterPlus package for consistent cluster analysis. A prognostic model was constructed by Univariate Cox regression and Lasso regression analysis. Clinical specimens were detected their expression of genes in model by IHC. Results: Most lactate metabolism-associated gene were significantly differently expressed between normal and tumor samples. There was a strong correlation between the expression of lactate metabolism-associated gene and the abundance of immune cells. We divided them into two clusters (lactate.cluster A,B) with significantly different survival. The two clusters showed a difference in signal, immune cells, immune signatures, chemokines, and clinical features. We identified 162 differential genes from the two clusters, by which the samples were divided into three categories (gene.cluster A,B,C). They also showed a difference in OS and immune infiltration. Finally, a risk score model that was composed of six genes was constructed. There was significant difference in the survival between the high and low risk groups. ROC curves of 1, 3, 5, and 10 years verified the model had good predictive efficiency. Gene expression were correlated with ORR and PFS in patients who received anti-PD-1/L1. Conclusion: The lactate metabolism-associated genes in LUAD were significantly associated with OS and immune signatures. The risk scoring model that was constructed by us was able to well identify and predict OS and were related with anti-PD-1/L1 therapy outcome.