LDHA as a predictive biomarker and its association with the infiltration of immune cells in pancreatic adenocarcinoma

Introduction

Pancreatic adenocarcinoma (PAAD), a lethal subtype of pancreatic cancer, is a significant global cause of cancer mortality, demanding urgent research for improved diagnosis and treatment strategies to address its aggressive nature (1,2). Despite advancements in surgical procedures and chemotherapy, PAAD remains a leading cause of cancer death in developed countries and across the globe (2). Currently, the most common treatments for PAAD include surgical resection, radiotherapy, and chemotherapy, yet patients may exhibit varying progression-free survival (PFS) and overall survival (OS) outcomes despite receiving the same treatment, likely due to individual responses to tumor immune infiltrates (3). Prognostic biomarkers are essential for identifying patients at high risk of recurrence following radical surgery, as these can enhance personalized treatment strategies for those with PAAD (4-6).

Under physiological oxygen conditions, pancreatic cancer cells predominantly rely on glycolysis for energy production, producing large amounts of lactic acid and small amounts of adenosine triphosphate, which is accompanied by glucose degradation, a phenomenon known as the Warburg effect (7). A study indicates tumor cells’ metabolic reprogramming is key contributor to the resistance of tumor cells to chemoradiotherapy (8). Reversing the abnormal metabolism of tumor cells can significantly improve the sensitivity of patients to treatment, which would significantly prolong the survival of patients and improve their quality of life (9). In line with this, accumulating evidence suggests that the tumor microenvironment, reprogrammed metabolism glucose, amino acid and lipid metabolism, and metabolic crosstalk contribute to unrestrained pancreatic tumor progression (10). The regulation of the entire glycolysis process is primarily governed by the key enzymes HK1, GAPDH, pyruvate kinase isozyme type M2 (PKM2), and lactate dehydrogenase A (LDHA) (11-14).

LDHA is an important enzyme, as it is involved in the conversion of pyruvate to lactate and is a hallmark of aggressive cancers. Dysregulated expression of LDHA induces tumor cells to adopt lactate metabolism pathway, which is closely linked to tumor progression and treatment resistance. A hypoxic and acidic tumor microenvironment promote tumor immune escape while limiting the efficacy of immunotherapy (15). One study showed that overexpression LDHA is associated with poor prognosis in hepatocellular carcinoma (16), breast cancer (17), cholangiocarcinoma (18), papillary thyroid carcinoma (19), and pancreatic cancer and is correlated with a poor outcome. However, the pathological and clinical effects of LDHA expression and its immunological characteristics in PAAD still remain to be observed and investigated. Therefore, this study analyzed LDHA expression in PAAD using data from The Cancer Genome Atlas (TCGA) and investigated whether there is a correlation between LDHA expression and the progression of PAAD (20). Currently, immunotherapy provides limited efficacy for those with PAAD, and the underlying molecular mechanisms related its poor performance are unclear. Understanding the effects of LDHA in tumor energy metabolism and the immune microenvironment may improve the effectiveness of cancer immunotherapy (21-24). We present this article in accordance with the REMARK reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-24-560/rc).

Methods

Analysis of the Oncomine and TCGA datasets

Clinical information and gene expression data were downloaded from TCGA (https://cancergenome.nih.gov/) and International Cancer Genome Consortium (ICGC; https://docs.icgc-argo.org/docs/data-access/daco/applying.) databases. The Oncomine database (http://www.oncomine.org) contains 264 independent datasets that include 35 cancer types and supports various methods of analysis. The expression of LDHA PPAD was investigated using the Human Protein Atlas (HPA) database (https://www.proteinatlas.org/). To avoid bias, another three independent patient populations from the ICGC portal database (http://bioinfo.henu.edu.cn/PAAD/PAADList.jsp) and EMTAB6314 were validated for LDHA expression and prognosis (19-28). Our study used open-source data that are freely available for research and publication and have no associated ethical concerns or other limitations. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Analysis of clinicopathological characteristics

RNA sequencing data from samples of 178 patients with PAAD and from adjacent tissues used as a normal control were obtained from TCGA. Corresponding clinicopathological data, including age, gender, pathological stage, tumor clinical stage, and OS, were also obtained from the database. Due to the missing of clinical data in some patients, the overall number of patients in each group was not completely consistent.

Survival analysis of LDHA expression in patients with PAAD

By analyzing the survival data of PAAD from TCGA database, we established a Cox proportional hazards model that considered all variables and their pairwise interactions. This model retained variables and interactions that were clinically significant and statistically significant (P<0.05) during the simplification process, allowing us to account for the complex interactions in the survival analysis of patients with PAAD. This model could potentially facilitate the precise treatment of individual patients and identify the dynamic factors affecting survival using unique tumor characteristics.

Functional enrichment analysis

The application of Gene Ontology (GO) frameworks allows researchers to systematically describe the functions of gene products across all forms of life. This approach provides a comprehensive understanding of the characteristic biological traits and transcript data derived from high-throughput genomic studies. In this analysis, we used the “ggplot2” package (version 3.3.3) and “cluster Profiler” package (version 3.14.3) in R (The R Foundation to Statistical Computing) to conduct a detailed examination of biological processes (BPs) and molecular functions (MFs).

Furthermore, we employed Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to identify specific pathways that were differentially expressed between gene sets, providing higher-order functional insights that are essential for the functional interpretation and practical application of genomic information.

To clarify the potential BPs underlying the expression of LDHA, we conducted gene set enrichment analysis (GSEA). This analysis aimed to highlight the differences in BPs between groups with high and low LDHA expression levels, using a P value cutoff of <0.05 and a false discovery rate (FDR) of <0.25. To ensure the robustness of our findings, we set the number of permutations to 1,000, allowing for a reliable assessment of the significance of the observed differences.

Tumor immune microenvironment analysis

Single-sample GSEA via R package was used to calculate the proportional amounts of 22 invading immune cell types in each tumor sample (25). The ESTIMATE algorithm was used to deduce the immune scores for each sample. The relation of LDHA gene expression with immune infiltration was also analyzed with the Tumor Immune Estimation Resource 2.0 (TIMER2.0; http://timer.cistrome.org/). TIMER2.0 is a comprehensive resource for the systematic analysis of immune infiltrates across diverse cancer types.

Statistical analysis

All statistical analyses were conducted using GraphPad Prism version 13.0 (GraphPad Software, Inc., La Jolla, CA, USA) and R software (http://www.r-project.org/). Association coefficients were determined via the Pearson correlation method. Statistical comparisons among groups were conducted using the Wilcoxon rank-sum test or the Mann-Whitney test. Data are presented as the mean ± standard deviation. The prognostic significance of LDHA in pancreatic cancer was evaluated via receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC). The relationship between LDHA expression and patient clinicopathological characteristics was assessed via the Chi-squared test. Both univariate and multivariate survival analyses were performed employing the Cox proportional hazards regression model. Variables identified as significant in the univariate analysis were subsequently included in the multivariate survival analysis. Statistical significance was determined at a P value less than 0.05.

Results

Overexpression of LDHA in various malignancies

The details of the LDHA expression analyses are summarized in Figure 1 and include the ROC curves for the ability of LDHA expression to identify tumor and normal tissue (Figure 2A). Additionally, the protein expression information from the HPA database indicated that LDHA was highly expressed in PAAD specimens at the protein level as compared to its expression in normal controls (https://www.proteinatlas.org/ENSG00000134333-LDHA/pathology/pancreatic+cancer#img). Unfortunately, the protein expression data for LDHA in the adjacent normal tissues were not available in this database. The ROC curve also showed that LDHA was highly expressed in tumor tissues, while the expression was relatively low in adjacent normal tissues [AUC =0.95; 95% confidence interval (CI): 0.92–0.97] (Figure 2A).

Figure 1 LDHA expression levels in different types of human cancers. (A) Increased or decreased LDHA in the datasets of various cancers compared with normal tissues in the Oncomine database. (B) Human LDHA expression levels in different tumor types from TCGA database were determined via TIMER. *, P<0.05; **, P<0.01; ***, P<0.001, ns: no statistical difference between two sets of data when compared. LDHA, lactate dehydrogenase A; CNS, central nervous system; TPM, transcripts per million; ns, not significant.

Figure 2 LDHA expression was high in tumor tissue and was associated with clinical prognosis. (A) ROC curve of LDHA high expression in tumor tissue specifically (AUC =0.95; 95% CI: 0.92–0.97; P<0.001). (B-E) Kaplan-Meier survival curves comparing the high and low expression of LDHA in OSpaad databases. (F-H) Survival curves of DFI in three independent PAAD cohorts. TPR, true positive rate; FPR, false positive rate; LDHA, lactate dehydrogenase A; AUC, area under the curve; HR, hazard ratio; CI, confidence interval; OS, overall survival; PAAD, pancreatic adenocarcinoma; DFI, disease-free interval; TCGA, The Cancer Genome Atlas; ICGC, International Cancer Genome Consortium; ROC, receiver operating characteristic; IHC, immunohistochemistry.

The relationship between LDHA expression and clinicopathological characteristics in PAAD was analyzed with TCGA database (Table 1). There were no significant relationships between LDHA expression and lymph node or distance metastasis. However, the high expression of LDHA correlated significantly with age and T stage (P<0.05).

High LDHA expression was associated with poor prognosis

To investigate the relationship between LDHA expression and prognosis in patients with PADD, we collected four independent databases (EMTAB6134, TCGA, ICGC-sequence and ICGC-array group). According to the median expression level of LDHA, patients were stratified into two groups: low and high expression. Kaplan-Meier survival analysis revealed that patients with higher LDHA expression levels experienced a reduced PFS rate [hazard ratio (HR) =1.92, 95% confidence interval (CI): 1.23–3.0; P=0.004; Figure 2B] and poorer OS (HR =2.02, 95% CI: 1.34–3.03; P<0.001; Figure 2C). Univariate Cox regression analysis demonstrated a significant correlation between the upregulation of LDHA and adverse PFS and OS (P<0.001; Tables 2,3) (26). Other clinical features, including N stage, T stage, histologic grade, primary therapy outcome, and residual tumor were also found to be associated with poor PFS and OS (P<0.05). Moreover, multivariate Cox proportional hazard regression analysis demonstrated that high LDHA expression (P<0.001 for PFS and OS) and primary therapy outcome and residual tumor (P<0.05 for OS and PFS) were independent risk factors for poor prognosis. Radiation therapy and lower histological grade served as an independent predictor of better OS (P<0.05); however, they were not associated with PFS. To avoid patient population bias, three additional independent samples (EMTAB6134, ICGC_Seq, and ICGC_array) were included and analyzed with the OSpaad program (27), which confirmed the high expression of LDHA to be significantly associated with OS (ICGC_Seq: HR =1.48, 95% CI: 1.12–1.95, P=0.005; ICGC_array: HR =2.18, 95% CI: 1.58–3.02, P<0.001; EMTAB6134: HR =1.50, 95% CI: 1.12–2.01, P=0.006) (Figure 2D-2F), but non-significantly associated with PFS (HR =1.37, 95% CI: 0.97–1.93, P=0.07; HR =1.32, 95% CI: 0.89–1.97, P=0.16) (Figure 2G,2H). Collectively, these results show that LDHA expression may be a prognostic biomarker of PAAD.

GO and KEGG enrichment analyses

The GO enrichment analysis indicated that high LDHA expression was linked with the hypoxia inducible factor-1 (HIF-1) signaling pathway, glycolysis, coenzyme binding, and the pyruvate and nicotinamide nucleotide and pyridine nucleotide metabolic processes (Figure 3A). Cell composition enrichment analysis indicated LDHA expression was closely related with cell-cell junction, cell-substrate junction, cell-substrate adherent junction, collagen-containing extracellular matrix, and focal adhesion (Figure 3B). These results suggest that the LDHA gene is involved in anaerobic fermentation and tumor energy and material metabolism. A high expression of LDHA can remodel the connection between cells and extracellular matrix, which may lead to tumor invasion and distant metastasis.

Figure 3 Functional enrichment analysis of DEGs in TCGA-PAAD patients. (A,B) GO enrichment analysis of the target module genes, biological process analysis, and cellular component analysis. The y-axis represents the enriched KEGG terms. The x-axis represents the fold of enrichment. The size of the dot represents the number of genes under a specific term, and the color of the dots represents the adjusted P value. (C-E) HIF-1, TGF-β, and K-Ras gene sets with statistically significant differences in GSEA analysis. HIF-1, hypoxia inducible factor-1; NES, normalized enrichment score; P.adj, adjusted P value; FDR, false discovery rate; DN, down-regulation; DEGs, differentially expressed genes; TCGA, The Cancer Genome Atlas; PAAD, pancreatic adenocarcinoma; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, gene set enrichment analysis; K-Ras, Kirsten rat sarcoma viral oncogene homolog; TGF-β, transforming growth factor-β.

LDHA expression was correlated with activation of the glycolysis, HIF-1, and Kirsten rat sarcoma viral oncogene homolog (K-Ras) pathways

GSEA revealed that the LDHA gene was significantly enriched in glycolysis [normalized enrichment score (NES) =1.84; adjusted P value (P.adjust) =0; FDR =0.002; Figure 3C], HIF-1 (NES =1.97; P.adjust <0.001; FDR =0; Figure 3D), and K-Ras pathway (NES =−1.442; P.adjust = 0.04; FDR =0.018; Figure 3E). Indeed, we found that there was a positive correlation between LDHA and transforming growth factor-β (TGF-β) pathway activation (Figure 4A,4B) and the K-Ras gene mutation upregulated its own expression (Figure 4C), and the expression level of LDHA was positively correlated with K-Ras gene expression and mutation status (Figure 4D). Similarly, these findings indicate that LDHA plays a crucial role in tumor cell metabolic reprogramming.

Figure 4 LDHA expression was positively correlated with the activation of the TGF-β and K-Ras mutation pathways. (A,B) Pearson correlation of LDHA and TGF-β and K-Ras expression. Both genes had a significant correlation (P<0.05). The x-axis represents the expression level of LDHA, and the y-axis represents the expression level of TGF-β and K-Ras in TCGA-PAAD patients. (C) Mutation of the K-Ras gene upregulated its own mRNA expression level (P<0.001). (D) Patients with K-Ras mutation showed higher LDHA mRNA expression (P<0.001). TGF-β, transforming growth factor-β; LDHA, lactate dehydrogenase A; FPKM, fragments per kilobase of transcript per million fragments mapped; TPM, transcripts per million; WT, wild type; TCGA, The Cancer Genome Atlas; PAAD, pancreatic adenocarcinoma.

Relationship between LDHA expression and the tumor immune microenvironment

The immune status and cell infiltration associated with LDHA expression was analyzed (Figure 5A). After adjustments were made according to purity, the T helper 2 (Th2) cell subpopulation was found to be positively correlated with LDHA expression, while most immune cells, including dendritic cells (DCs), interstitial DCs (iDCs), CD8⁺ T cells, B cells, T cells, and plasmacytoid DCs (pDCs) were negatively correlated with gene expression. The immune score of tumors was negatively correlated with LDHA expression (P<0.05; Figure 5B). After the correlation was adjusted according to purity, the results revealed the LDHA expression level had significant positive correlations with infiltrating levels of CD4⁺ Th2 cells (r=0.282; P=1.87e−04), M1 macrophages (r=0.401; P=5.53e−08), and myeloid-derived suppressor cells (MDSCs) (r=0.568; P=5.25e−16). Meanwhile, LDHA expression was negatively correlated with CD4⁺ memory T cells (r=−0.153; P=4.53e−02), CD8⁺ effector memory T cells (r=−0.185; P=1.55e−02, class-switched memory B cells (r =−0.19; P=1.27e−02), M2 macrophages (r=−0.283; P=1.78e−04), and natural killer (NK) T cells (r=−0.155; P=4.34e−2). Finally, LDHA expression showed no significant correlations with tumor purity or infiltration levels of CD4⁺ Th1 (r=−0.004; P=9.61e−01) (Figure 5C). These results strongly suggest that LDHA plays a certain role in immune infiltration in PAAD and may contribute to the limited curative effect of immunotherapy and poor prognosis.

Figure 5 Correlation of LDHA expression with immune depression status in patients with PAAD. (A) LDHA expression demonstrated a significantly positive correlation with the infiltration levels of Th2 T cells and CD56 bright NK cells and negatively correlations with NK cells, cytotoxic T cells, CD8⁺ T cells, B cells T cells, Th17 cells, and pDCs (n=179). (B) LDHA expression demonstrated a significantly negative correlation with tumor ESTIMATE score and immune score. (C) LDHA expression demonstrated a significantly positive correlation with CD4⁺ Th2 cells, M1 macrophages and MDSC, and a significantly negative correlation with the infiltration levels of CD4⁺ memory T cells, CD8⁺ effector memory T cells, class-switched memory B cells, M2 macrophages, and NK T cells. Th, T helper; NK, natural killer; aDC, activated dendritic cell; TReg, regulatory T cell; Tcm, central memory T cell; DC, dendritic cell; Tgd, gamma delta T cell; Tem, effector memory T cell; iDC, immature dendritic cell; TFH, follicular helper T cell; pDC, plasmacytoid dendritic cell; TPM, transcripts per million; LDHA, lactate dehydrogenase A; PAAD, pancreatic adenocarcinoma; MDSC, myeloid-derived suppressor cell.

Discussion

The current treatments for pancreatic cancer are not satisfactory, with the efficacy of immunotherapy being particularly poor. Cancer metabolism is heavily reliant on the dysregulation of enzymatic pathways, with oxidative glycolysis remaining a prominent characteristic (28,29). In this study, we observed an increased expression of LDHA in multiple types of cancerous tumors, encompassing PAAD. The ROC curve analysis indicated that LDHA may have strong diagnostic ability in differentiating healthy individuals from those with PAAD, thus highlighting the pivotal function of LDHA in the process of cancerous transformation (15,30) and its potential as a target for treatment.

Immunosuppression in cancer has become a significant challenge to the efficacy of immunotherapeutic strategies and stems from both oncogene-induced signaling within the tumor and immune cells associated with the tumor. Nevertheless, the involvement of LDHA in the onset and advancement of PAAD remains inadequately clarified. LDHA fosters the aggressive development of cancer by enhancing lactate generation, accelerating glucose absorption, and controlling various cancer-related molecules, making it a crucial enzyme in the regulation of anaerobic glycolysis (31-34). To determine the correlation between the levels of LDHA expression and the progression of PAAD, we analyzed datasets from the Oncomine database and TCGA. TCGA database was employed to examine the pathological attributes and prognosis associated with LDHA expression (22). It was found that the abnormal expression of LDHA was related to the age of patients, and a prognostic model was subsequently constructed to a provide basis for clinical evaluation of patient prognosis. Through enrichment analysis, it was found that LDHA was related to K-Ras pathway, and the expression of LDHA was positively correlated with K-Ras. K-Ras gene mutation could upregulate the expression of LDHA.

The concomitant expression of SIP1 and LDHA might indicate the presence of both epithelial-mesenchymal transition and the Warburg effect in the progression of PAAD. Additionally, we observed that the levels of LDHA expression were significantly linked to patient age. Survival analysis revealed that the higher expression of LDHA was associated with a significantly reduced OS. In our investigation, regression analyses highlighted a stronger correlation between LDHA expression and clinical outcome. Although these findings may seem counterintuitive, they are statistically valid but could be attributed to the limited sample size, and thus further validation is required. Collectively, our data suggest that LDHA expression serves as an independent prognostic factor for OS in patients with PAAD.

K-Ras is a Ras protein and plays a key role in cancer. Enabling somatic K-Ras mutations are linked to more than 15% of all human cancers, and their incidence can reach as high as 90% in certain tumor categories, including PAAD. Consequently, effectively blocking the abnormal K-Ras signaling pathway would constitute a paradigm shift in cancer treatment (35). Previous research suggests that the expression levels underlying the metabolic alterations in oncogenic K-Ras conversion can be attributed to the modifications in the activity of key enzymes in NIH-3T3 cells. These modifications, along with the metabolic shifts following oncogenic transformation—including heightened glycolysis, activation of the oxidative arm of the pentose-phosphate pathway, and reduced levels of sugar phosphates—might be correlated with the enhanced activity of glucose-6-phosphate dehydrogenase, pyruvate kinase, and lactate dehydrogenase as well as the diminished activity for transketolase (36). K-Ras gene polymorphisms are associated with susceptibility to non-Hodgkin lymphoma and gene expression and levels of LDHA (37). In our study, through enrichment analysis, it was found that LDHA was related to the K-Ras pathway, and the expression of LDHA was positively correlated with K-Ras. K-Ras mutation may upregulate the expression of LDHA and play an important role in PAAD carcinogenesis and prognosis. Thus, the relationship of LDHA with the K-Ras pathway warrants further investigation.

Immune cells are critically involved in cancer progression and treatment, and the tumor immune microenvironment exerts significant immunologically relevant changes by inducing severe immune-mediated toxicities (38). Overexpression of LDHA is closely associated with aberrant activation of the K-Ras gene pathway, glycolysis, and HIF-1, indicating that tumor cells undergo metabolic reprogramming (39). This results in a negative immune microenvironment and tumor immunosuppression, ultimately leading to poor prognosis for patients. Most glycolytic inhibitors not only inhibit the glycolysis of cancer but also that of immune cells (40).

Serganova et al. reported that reduction in LDHA inhibits the development of metastases and extends the survival of mice by altering the tumor microenvironment, which in turn regulates the immune response (41). In our study, DCs, iDCs, CD8⁺ T cells, B cells, T cells, and pDCs were negatively correlated with LDHA expression, which is consistent with and can be explained by previous studies (42-44). Although our study suggested no significant correlation between LDHA expression and total neutrophil count (as shown in Figure 5), Wang et al. (10) demonstrated a positive correlation with cancer-promoting neutrophil subtypes through single gene sequencing. The discrepancy in findings may stem from variations in research methodologies, which could be further elucidated through single cell sequencing in subsequent studies. The overexpression of LDHA in PAAD may interact with immune cell mechanisms in the following ways (45,46): firstly, it can promote glycolytic metabolism and generate lactic acid to fuel tumor energy needs, thereby influencing immune cells through changes in the microenvironment. Secondly, it can contribute to an immunosuppressive microenvironment by directly inhibiting or inducing immunosuppressive cells and weakening anti-tumor responses. Lastly, metabolites such as lactic acid may impact the metabolism and function of immune cells through specific pathways, leading to inhibition of activation and proliferation. The optimal therapeutic agents are capable of destroying tumor cells and remodeling the tumor immune microenvironment, conferring both anticancer and immunostimulatory effects (47-49).

There are certain limitations related to this study which should be noted. First, due to the retrospective nature of the analyses, a certain degree of selection bias was unavoidable. Second, all the laboratory metrics were confined to preoperative assessments, and additional data from prospective trials are needed to validate these findings. Third, it should be noted that this study was purely observational in design, and thus it is necessary to devise animal experiments to further examine the effect of LDHA intervention on tumor metabolism and tumor immune microenvironment remodeling in vivo.

Conclusions

In summary, LDHA expression was increased in PAAD tissue, which was related to patient prognosis and immune infiltration. Furthermore, elevated expression of LDHA was linked to activation of the K-Ras signaling pathway and the presence of immune cell populations in PAAD. Consequently, PDCD1 could function as a predictive biomarker for PAAD. Our findings may lead to the development of an immunologically targeted antitumor approach that encompasses metabolic rewiring of either the tumor cells themselves or the immune cells infiltrating the tumor microenvironment.

Acknowledgments

Funding: This study was supported by the Zhejiang Province Medical Science and Technology Project (No. 2022KY664), the Wenling Social Development Science and Technology Project (Nos. 2022S00103, 2022S00041, and 2022S00043), the Taizhou Social Development Science and Technology Project (Nos. 22ywb133 and 22ywb127), the Natural Science Foundation of Guangdong Province (No. 2024A1515012233), the Guangdong Municipal Department of Science and Technology—Municipal Schools (Institutes) Jointly Funded Project (No. 2023A03J0386), the Guangzhou Medical University for the First-class Professional Construction Project in 2022-Enhancement of Undergraduates’ Scientific Research and Innovation Ability Project (No. 02-408-2203-2059), the Plan On Enhancing Scientific Research in GMU (No. 2024SRP119), and the Zhejiang Chinese Medicine University Graduate Education and Teaching Reform Project (No. YJSJD2023001).

Footnote

Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-24-560/rc

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-24-560/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-24-560/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019;69:7-34. [Crossref] [PubMed]
2. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49. [Crossref] [PubMed]
3. Blair TC, Alice AF, Zebertavage L, et al. The Dynamic Entropy of Tumor Immune Infiltrates: The Impact of Recirculation, Antigen-Specific Interactions, and Retention on T Cells in Tumors. Front Oncol 2021;11:653625. [Crossref] [PubMed]
4. Tan Z, Liu J, Xu J, et al. LDHA is a prognostic biomarker on the immune response in pancreatic adenocarcinoma and associated with m6A modification. J Cancer Res Clin Oncol 2023;149:4853-65. [Crossref] [PubMed]
5. Wu HF, Liu H, Zhang ZW, et al. CENPE and LDHA were potential prognostic biomarkers of chromophobe renal cell carcinoma. Eur J Med Res 2023;28:481. [Crossref] [PubMed]
6. Fang Z, Xu X, Tao L, et al. MMP11 as a prognostic biomarker correlated with immune infiltrates in pancreatic adenocarcinoma. J Gastrointest Oncol 2024;15:1224-44. [Crossref] [PubMed]
7. Pascale RM, Calvisi DF, Simile MM, et al. The Warburg Effect 97 Years after Its Discovery. Cancers (Basel) 2020;12:2819. [Crossref] [PubMed]
8. Qin C, Yang G, Yang J, et al. Metabolism of pancreatic cancer: paving the way to better anticancer strategies. Mol Cancer 2020;19:50. [Crossref] [PubMed]
9. Oresta B, Pozzi C, Braga D, et al. Mitochondrial metabolic reprogramming controls the induction of immunogenic cell death and efficacy of chemotherapy in bladder cancer. Sci Transl Med 2021;13:eaba6110. [Crossref] [PubMed]
10. Wang L, Liu Y, Dai Y, et al. Single-cell RNA-seq analysis reveals BHLHE40-driven pro-tumour neutrophils with hyperactivated glycolysis in pancreatic tumour microenvironment. Gut 2023;72:958-71. [Crossref] [PubMed]
11. Dong G, Mao Q, Xia W, et al. PKM2 and cancer: The function of PKM2 beyond glycolysis. Oncol Lett 2016;11:1980-6. [Crossref] [PubMed]
12. Jin GZ, Zhang Y, Cong WM, et al. Phosphoglucomutase 1 inhibits hepatocellular carcinoma progression by regulating glucose trafficking. PLoS Biol 2018;16:e2006483. [Crossref] [PubMed]
13. Moon JS, Hisata S, Park MA, et al. mTORC1-Induced HK1-Dependent Glycolysis Regulates NLRP3 Inflammasome Activation. Cell Rep 2015;12:102-15. [Crossref] [PubMed]
14. Jin L, Chun J, Pan C, et al. Phosphorylation-mediated activation of LDHA promotes cancer cell invasion and tumour metastasis. Oncogene 2017;36:3797-806. [Crossref] [PubMed]
15. Huang H, Lu W, Zhang X, et al. Fibroblast subtypes in pancreatic cancer and pancreatitis: from mechanisms to therapeutic strategies. Cell Oncol (Dordr) 2024;47:383-96. [Crossref] [PubMed]
16. Guo Y, Li X, Sun X, et al. Combined Aberrant Expression of NDRG2 and LDHA Predicts Hepatocellular Carcinoma Prognosis and Mediates the Anti-tumor Effect of Gemcitabine. Int J Biol Sci 2019;15:1771-86. [Crossref] [PubMed]
17. Huang X, Li X, Xie X, et al. High expressions of LDHA and AMPK as prognostic biomarkers for breast cancer. Breast 2016;30:39-46. [Crossref] [PubMed]
18. Thonsri U, Seubwai W, Waraasawapati S, et al. Overexpression of lactate dehydrogenase A in cholangiocarcinoma is correlated with poor prognosis. Histol Histopathol 2017;32:503-10. [PubMed]
19. Hou X, Shi X, Zhang W, et al. LDHA induces EMT gene transcription and regulates autophagy to promote the metastasis and tumorigenesis of papillary thyroid carcinoma. Cell Death Dis 2021;12:347. [Crossref] [PubMed]
20. Shi L, Duan R, Sun Z, et al. LncRNA GLTC targets LDHA for succinylation and enzymatic activity to promote progression and radioiodine resistance in papillary thyroid cancer. Cell Death Differ 2023;30:1517-32. [Crossref] [PubMed]
21. Tay C, Tanaka A, Sakaguchi S. Tumor-infiltrating regulatory T cells as targets of cancer immunotherapy. Cancer Cell 2023;41:450-65. [Crossref] [PubMed]
22. Xu PL, Cheng CS, Wang T, et al. Immune landscape and prognostic index for pancreatic cancer based on TCGA database and in vivo validation. BMC Cancer 2023;23:139. [Crossref] [PubMed]
23. Yang L, Chu Z, Liu M, et al. Amino acid metabolism in immune cells: essential regulators of the effector functions, and promising opportunities to enhance cancer immunotherapy. J Hematol Oncol 2023;16:59. [Crossref] [PubMed]
24. Wu Y, Kou Q, Sun L, et al. Effects of anoxic prognostic model on immune microenvironment in pancreatic cancer. Sci Rep 2023;13:9104. [Crossref] [PubMed]
25. Zou X, Lin X, Cheng H, et al. Characterization of intratumoral tertiary lymphoid structures in pancreatic ductal adenocarcinoma: cellular properties and prognostic significance. J Immunother Cancer 2023;11:e006698. [Crossref] [PubMed]
26. Chen S, Sun Z, Zhao W, et al. Oligoadenylate synthetases-like is a prognostic biomarker and therapeutic target in pancreatic ductal adenocarcinoma. Ann Transl Med 2022;10:138. [Crossref] [PubMed]
27. Zhang G, Wang Q, Yang M, et al. OSpaad: An online tool to perform survival analysis by integrating gene expression profiling and long-term follow-up data of 1319 pancreatic carcinoma patients. Mol Carcinog 2020;59:304-10. [Crossref] [PubMed]
28. Feng Y, Xiong Y, Qiao T, et al. Lactate dehydrogenase A: A key player in carcinogenesis and potential target in cancer therapy. Cancer Med 2018;7:6124-36. [Crossref] [PubMed]
29. Jiang B. Aerobic glycolysis and high level of lactate in cancer metabolism and microenvironment. Genes Dis 2017;4:25-7. [Crossref] [PubMed]
30. Kwon OK, Bang IH, Choi SY, et al. LDHA Desuccinylase Sirtuin 5 as A Novel Cancer Metastatic Stimulator in Aggressive Prostate Cancer. Genomics Proteomics Bioinformatics 2023;21:177-89. [Crossref] [PubMed]
31. Cui J, Shi M, Xie D, et al. FOXM1 promotes the warburg effect and pancreatic cancer progression via transactivation of LDHA expression. Clin Cancer Res 2014;20:2595-606. [Crossref] [PubMed]
32. He Y, Chen X, Yu Y, et al. LDHA is a direct target of miR-30d-5p and contributes to aggressive progression of gallbladder carcinoma. Mol Carcinog 2018;57:772-83. [Crossref] [PubMed]
33. Zhai X, Yang Y, Wan J, et al. Inhibition of LDH-A by oxamate induces G2/M arrest, apoptosis and increases radiosensitivity in nasopharyngeal carcinoma cells. Oncol Rep 2013;30:2983-91. [Crossref] [PubMed]
34. Pathria G, Scott DA, Feng Y, et al. Targeting the Warburg effect via LDHA inhibition engages ATF4 signaling for cancer cell survival. EMBO J 2018;37:e99735. [Crossref] [PubMed]
35. Gorfe AA, Cho KJ. Approaches to inhibiting oncogenic K-Ras. Small GTPases 2021;12:96-105. [Crossref] [PubMed]
36. de Atauri P, Benito A, Vizán P, et al. Carbon metabolism and the sign of control coefficients in metabolic adaptations underlying K-ras transformation. Biochim Biophys Acta 2011;1807:746-54. [Crossref] [PubMed]
37. Liu QH, Li XD, Song MY, et al. Association between C-myc and K-ras gene polymorphisms and non-Hodgkin lymphoma. Eur Rev Med Pharmacol Sci 2020;24:4396-403. [PubMed]
38. Michelakos T, Cai L, Villani V, et al. Tumor Microenvironment Immune Response in Pancreatic Ductal Adenocarcinoma Patients Treated With Neoadjuvant Therapy. J Natl Cancer Inst 2021;113:182-91. [Crossref] [PubMed]
39. Cheng CS, Tan HY, Wang N, et al. Functional inhibition of lactate dehydrogenase suppresses pancreatic adenocarcinoma progression. Clin Transl Med 2021;11:e467. [Crossref] [PubMed]
40. Kolb D, Kolishetti N, Surnar B, et al. Metabolic Modulation of the Tumor Microenvironment Leads to Multiple Checkpoint Inhibition and Immune Cell Infiltration. ACS Nano 2020;14:11055-66. [Crossref] [PubMed]
41. Serganova I, Cohen IJ, Vemuri K, et al. LDH-A regulates the tumor microenvironment via HIF-signaling and modulates the immune response. PLoS One 2018;13:e0203965. [Crossref] [PubMed]
42. Ren Y, Kumar A, Das JK, et al. Tumorous expression of NAC1 restrains antitumor immunity through the LDHA-mediated immune evasion. J Immunother Cancer 2022;10:e004856. [Crossref] [PubMed]
43. Jia C, Wu Y, Gao F, et al. The opposite role of lactate dehydrogenase a (LDHA) in cervical cancer under energy stress conditions. Free Radic Biol Med 2024;214:2-18. [Crossref] [PubMed]
44. Hou X, Ouyang J, Tang L, et al. KCNK1 promotes proliferation and metastasis of breast cancer cells by activating lactate dehydrogenase A (LDHA) and up-regulating H3K18 lactylation. PLoS Biol 2024;22:e3002666. [Crossref] [PubMed]
45. Singer K, Gottfried E, Kreutz M, et al. Suppression of T-cell responses by tumor metabolites. Cancer Immunol Immunother 2011;60:425-31. [Crossref] [PubMed]
46. Wang WQ, Liu L, Xu HX, et al. Infiltrating immune cells and gene mutations in pancreatic ductal adenocarcinoma. Br J Surg 2016;103:1189-99. [Crossref] [PubMed]
47. Chen M, Cen K, Song Y, et al. NUSAP1-LDHA-Glycolysis-Lactate feedforward loop promotes Warburg effect and metastasis in pancreatic ductal adenocarcinoma. Cancer Lett 2023;567:216285. [Crossref] [PubMed]
48. Dai M, Wang L, Yang J, et al. LDHA as a regulator of T cell fate and its mechanisms in disease. Biomed Pharmacother 2023;158:114164. [Crossref] [PubMed]
49. Aney KJ, Jeong WJ, Vallejo AF, et al. Novel Approach for Pancreas Transcriptomics Reveals the Cellular Landscape in Homeostasis and Acute Pancreatitis. Gastroenterology 2024;166:1100-13. [Crossref] [PubMed]