LncRNA TMPO-AS1 Promotes Triple-Negative Breast Cancer by Sponging miR-383-5p to Trigger the LDHA Axis

Background: Understanding the heterogeneous nature of breast cancer, including the role of LDHA expression regulation via non-coding RNAs in prognosis, is still unknown, highlighting the need for more research into its molecular roles and diagnostic approaches. Methods: The study utilized various computer tools to analyze the differences between LDHA in tissues and cancer cells. It used data from TIMER 2.0, UALCAN


Introduction
Breast cancer is a global disease with 1.7 million new cases and 500,000 deaths, mostly resulting from metastasis that hinders further treatment [1].Triple-negative breast cancer (TNBC) patients have worse outcomes due to a higher rate of metastasis compared to non-TNBC patients [2].Standard cytotoxic chemotherapy and radiotherapy are the only treatments for TNBC patients, but these treatments are ineffective, and a significant (35%-40%) relapse occurs within 5 years after diagnosis [3].Many drugs targeting angiogenesis, DNA repair, growth, and epigenetic modification have been evaluated, but clinical results are promising.Breast cancer is a complex disease with diverse histological and molecular expression profiles, risk factors, and clinical courses.Advances in molecular gene expression profiling have led to the emergence of different molecular subclassification systems with different clinical outcomes [4].DNA microarray analysis classified breast cancer into Luminal A, Luminal B, HER2-positive, basal-like, and normal-like subtypes

RESEARCH ARTICLE
LncRNA TMPO-AS1 Promotes Triple-Negative Breast Cancer by Sponging miR-383-5p to Trigger the LDHA Axis [3].A new molecular hunter is needed for treatment decisions, especially in advanced patients where treatment is limited and palliative.[5].Lactate dehydrogenase (LDH) is an enzyme that plays an important role in anaerobic glycolysis and glycolysis.It influences cancer patients [6].The human genome contains four LDH genes: LDHA, LDHB, LDHC, and LDHD [7].Metabolic changes are important features of many cancers and can predict their onset and progression.Cancer cells prefer anaerobic glycolysis over mitochondrial oxidative phosphorylation and produce glycolytic intermediates and lactate as another fuel [7].High levels of LDH in cancer cells indicate that cells are More resistant to chemotherapy and radiation, leading to poor outcomes [8].LDHA is associated with various types of cancer in humans, including pancreatic cancer [9], laryngeal squamous cell carcinoma [10], cancer of the head and neck [11], kidney cancer [12], stomach cancer, prostate cancer [13], breast cancer [14], hepatocellular carcinoma [15], oral squamous cell carcinoma (OSCC) [16], and hematopoietic cancer [17].Inhibiting LDHA can reduce tumor proliferation, migration, invasion, angiogenesis, and cancer metastasis and increase the sensitivity of cancer cells to chemotherapy and radiotherapy [8].Tamoxifen resistance is associated with decreased ATP production and increased tumor glycolysis, resulting in the induction of autophagy [18].Epithelial mesenchymal transition (EMT) development and metastasis in prostate cancer are associated with overexpression of LDHA and immunosuppression of CPTII, highlighting the importance of pharmacological inhibition in reducing EMT development [19].LDHA expression is important in hypoxic states and pancreatic ductal adenocarcinoma [20].Despite the recognition of LDHA's significance, current inhibitors available, such as FX-11, Gossyplo, Oxamata, and Galloflavin, are mostly chemical or synthetic-based [21].Researchers are exploring natural product-based compounds like withanolides, derived from Withania somnifera, as a substitute for chemical-based inhibitors due to their potential to have fewer side effects and greater bioavailability as they address limitations such as toxicity and drug resistance [22].Withanolides have various pharmacologic properties, including adaptogenic, diuretic, anti-inflammatory, sedative/anxiolytic, cytotoxic, antitussive, and immunomodulatory effects [23].Understanding the relationship between LDHA reporting status and prognosis is crucial for cancer patient survival and recurrence.Long-non-coding RNAs (lncRNAs) play a significant role in cancer metastasis, regulating processes like the epithelial-mesenchymal transition.Thousands of lncRNAs have abnormal expression or mutations in various cancer types, acting as competitive-endogenous RNAs (ceRNAs) by engaging with microRNAs.Keeping all these problems in mind, the study aims to investigate the expression levels of TMPO-AS1, miR-383-5p, and the LDHA-mediated hypoxia signaling pathway in BRCA samples in order to create non-invasive molecular markers for improved breast cancer surveillance using publicly available datasets.This work also aims to find a selective molecule, such as withanolide (withanolide D + NADH, withaferin A + NADH, withanolide O + NADH, withanolide E + NADH, withanolide G + NADH, and withasomnine +NADH), that targets breast cancer via nanoliposome encapsulation and LDHA overexpression.The efficacy of these withanolide compounds was compared with a well-known inhibitor of LDHA, i.e., LDH-IN-1, which was employed in a study done by Liu et al., in which they suppressed the expression of LDHA using LDH-IN-1 in colorectal cancer [24].

Functional Heterogeneity and Transcriptional Factor Analysis
CancerSEA is a unique database that examines different functions of cancer cells at the cellular level (http://biocc.hrbmu.edu.cn/CancerSEA/home)[36].It describes the functional state map of an individual cancer, which includes 14 functional states (such as stem cells, invasion, metastasis, proliferation, EMT, angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, hypoxia, inflammation, and more).CancerSEA was used to determine the relationship of LDHA gene expression to different biological processes.Further, GEPIA2, TIMER, and OncoDB databases were used to find the correlation between LDHA and the transcriptional factor.

Co-expressed Functional Enrichment Analysis
The Enrichr database (https://maayanlab.cloud/Enrichr/) [37] was used to identify LDHA co-expressed genes, and the GSCALite database (http://bioinfo.life.hust.edu.cn/web/GSCALite/) was further utilized to validate the co-expressed genes in breast cancer [38].Finally, the LDHA gene ontology was analyzed using the TISIDB database.Furthermore, we used TNMplot to assess the co-expressed genes for metastatic function, and then we used TIMER 2.0 to investigate the relationship of the LDHA gene with the co-expressed genes.

Non-coding-Regulatory Network Analysis
We used the GSE55807, GSE41245, and GSE75367 ctcRbase datasets to identify miRNAs targeting the LDHA gene.UALCAN and GSCA were used for validation.Additionally, UALCAN and KM plotter were used to score is greater than the known inhibitor was selected for further validation.The protein-ligand complex was visualized using UCSF Chimera and LigPlot+ [48].

Statistical Analysis
Differences in LDHA gene expression between tumor and normal tissues were analyzed by t test.The relationship between LDHA gene expression and prognosis was analyzed with an online model.Survival, LDHA performance heterogeneity and gene enrichment between the two groups were compared using Log-Rank tests.Statistical significance is P<0.05.Validity data were analyzed through statistical analysis based on online databases.

LDHA Expression in Human Breast Cancer: A Pan-Cancer Approach
First, we analyzed LDHA expression in all TCGA cancers, including breast cancer, using UALCAN, TIMER, TCGA-Portal and ONCOMX databases to assess cancer differentiation.The results showed that the amount of LDHA expressed in tissues of malignant tumors was generally higher than in normal tissues (Figure 1A-C and Supplementary Table 1).Importantly, as shown in Figure 2A, TCGA analysis of UALCAN expression data showed that LDHA gene expression was increased in tumors from cancer patients, with an average ninefold upregulation compared to normal tissues (p = < 1e-12).Using TCGAportal (P<0.05),ENCORI (P = 2.9e-12), GEPIA2 (P<0.05), and OncoDB databases (P = 2.8e-19), similar patterns of overexpression were revealed in tumor patients, as shown in Figure 2B-E.Additionally, the prognostic role of the LDHA gene in cancer was examined using the KM Plotter database, comparing the survival outcomes of patients with high and low LDHA expression levels, and reviewing its relationship with survival rates, and we found that the LDHA expression level was high.LDHA expression levels are associated with poor prognosis in breast cancer patients.Side effects for cancer patients were Relapse-free survival (RFS) (HR = 1.48, 95% CI: 1.34-1.65,P = 5.3e-14) and overall survival evaluate the significance of LDHA-related miRNAs with pathological conditions.Non-Coding RNA Cancer Atlas (TANRIC, http://bioinformatics.mdanderson.org/main/TANRIC: Overview) [39] ,UALCAN and ENCORI libraries were used to control lncRNA analysis.

Protein and ligand data acquisition
The three-dimensional crystal structures of Lactate Dehydrogenase A(LDHA) from Homo sapiens (PDB ID: 5W8L) was retrieved from the Protein Data Bank (https://www.rcsb.org)[42].The structure was prepared for molecular docking by removing water molecules and three chains by using pymol software [43] that results into only chain A was left for molecular docking studies.Ligand binding site was retrieved by analysing the various Lactate Dehydrogenase A (LDHA) and inhibitor complexes available in PDB.The chemical structures of ligand were retrieved from PubChem database (http:// PubChem.ncbi.nlm.nih.gov)[44] and all six ligands were: Withanolide D, Withaferine A, Withanolide O, Withanolide E, Withanolide G, Withasomnine.The structure minimization was carried out by UCSF Chimera [45] with the 1000 steepest descent steps, followed by addition of Gasteiger charges and polar hydrogen atoms.

Protein-ligand Molecular Docking
To comprehend the binding interactions of ligands with LDHA protein, a molecular docking analysis was conducted using the Auto Dock Tools 1.5.6 (ADT) [46].Gasteiger partial charges were assigned to the ligand atoms after including the electron density contributed by non-polar hydrogen bonds and ligand torsions.Other than that, the Kollman charges were used for the protein incorporating the influence of polar hydrogen atoms.The calculation of both charges and solvation parameters was performed using AutoDock Tools module.The protein binding sites were explored by Auto Dock 4.2 [47], Lamarckian Genetic Algorithm (LGA).A grid of 70×70×70 points along x, y, z direction with a 0.375 Å grid spacing centred with the dimensions along x, y, and z as -17.9, 39.24, and -40 Å.Ten docked conformations were generated for each ligand as the number of genetic algorithm runs was 10 and ligands were ranked according to their docking scores.The top two hits whose docking

Correlation of LDHA expression with ER, PR, and HER2
The study then evaluated breast cancer cells, including ER+/ER-, PR+/PR-, and HER2+/HER2, to determine the effect of LDHA in relation to specific subtypes.The expression of LDHA in metastases was compared with the bc-GenExMiner 5.0 database.Subsequently, hormone receptor-negative tumor subtypes expressed more LDHA than hormone receptor-positive subtypes, as shown in Figure 3A-D.LDHA, was found to be associated with ER-(p = 0.0001), PR-(p = 0.0001), ER-/PR-(p = 0.0001), and HER2+ (p = 0.0001).In addition, we also compared the effectiveness of LDHA between non-basal-like and basallike and non-TNBC and TNBC.As shown in Figure 3E-F, significantly greater (p = 0.0001) LDHA expression was found in basal-like and TNBC subtypes compared to nonbasal-like and non-TNBC subtypes.We also investigated the expression of the LDHA gene in breast cancer cells (Luminal A, Luminal B, Basal, and Her2) using TISIDB, TCGAportal, and bc-GenExMiner databases, as shown in the Figure 3G-I, and found that it is highly expressed in breast cancer subtypes.Additionally, we analyzed the above data using UALCAN and found that LDHA activity was more closely related to the aggressiveness of breast cancer: Normal vs. luminal (P = <1e-12), normal vs. HER2+ (P = 5.3e-04), and P = 9.1e-12 for normal versus TNBC, as shown in Figure 3J.

Metastasis and circulating tumor cells are associated with LDHA Expression
To determine LDHA's role in metastasis, we analyzed gene expression in tumors from breast cancer patients by using TNMplot database (Gene Chip), which showed significantly higher LDHA expression than normalmetastatic tissue (P = 1.38e-12) as shown in Figure 4A, Further the ctcRbase database confirmed the LDHA gene's involvement in metastasis as shown Figure 4B, also, Figure 4C illustrates that LDHA expression is highly associated with circulating tumor cells (CTCs).

An analysis of the heterogeneity and gene enrichment of LDHA expression and transcription factors
According to CancerSEA data, there is a strong association between LDHA gene expression and hypoxia (r=0.60), as shown in Figure 5A, illustrating hypoxiarelated mechanisms.It is well known that hypoxiainduced transcription factors can affect tumor growth.The GEPIA2 database was used to explore if the LDHA gene is connected with genes that govern hypoxia.A substantial correlation was observed between the two genes hypoxia inducing factor 1-α (HIF1A) and LDHA, with a correlation value of 0.41 and a p value of <0, as shown in Figure 5B.Interestingly, the study used TIMER to identify the relationship between the HIF1A and LDHA genes, which showed a significant correlation of 0.36 and a p value of 2.5 e-36 in Figure 5C, and a similar data was found out using OncoDB which showed the correlation value to be 0.34 and P value = 2.01e-33 as shown in Figure 5D.This study also examined the HIF1A gene's prognostic significance in breast cancer using the KM Plotter database.Results showed that breast cancer patients with high HIF1A expression improved significantly, including relapse-free survival (RFS) (HR = 1.39, 95% CI: 1.26-1.54,P = 1.6e-10) and overall survival (OS) showed that it had a poor prognosis (HR = 1.58, 95% CI: 1.31-1.92,P = 1.9e-06), distant metastasis-free survival (DMFS) (HR = 1.21, 95% CI: 1.04-1.41,P = 0.016), and post-progression survival (PPS) (HR = 1.84, 95% CI: 1.45-2.33,P = 2.8e-07), as shown in Figure 5E-H.These data suggest that HIF1A is a negative marker of cancer.The Enrichr database was used to identify associated genes to understand the molecular mechanisms underlying the association between the LDHA gene and cancer.Ten genes were found to be associated with LDHA, specifically EIF2S1, ENO1, VDAC1, VDAC2, PGK1, PPIA, PSMD14, RAN, TPI1, and TUBA1C.These gene were further validated using the TIMER 2.0 database, and the data showed a strong association between LDHA and the genes, indicating a specific association in cancer, as shown in Figure 6A.
The study used the GSCA database to determine gene expression patterns in cancer patients and showed major log fold changes for each gene participant in case of BRCA as mentioned in Table 1.This study also examined the combined gene expression i.e. gene variant analysis (GSVA) score of the co-expressed genes in tumor vs normal types and found a comparatively high expression in BRCA with a P value of 5.09e-46, further, expression level in patterns of breast cancer pathological subtypes Basal, Her2, LumA, LumB, and Normal, showed their association with aggressiveness with a p value of 3.12e-42 as shown in Figure 6B-C.Additionally, UALCAN data showed that only PGK1, PPIA, PSMD14, RAN TPI1, TUBA1C, and VDAC1 were overexpressed in breast cancer patients compared with normal individuals, as shown in Supplementary Figure 1A-G.Additionally, the KM plotter database revealed that all seven genes, including LDHA and HIF1A transcripts, were associated with poor survival in cancer patients.Data showed a significant association between these genes and breast cancer in RFS (HR = 1.87, 95% CI: 1.6-2.18,P = 7e-16) and OS (HR = 1.76, 95% CI: 1.34-2.31),P = 4.1e-5) and DMFS (HR = 1.68, 95% CI: 1.29-2.2,P = 0.00012), respectively, as shown in Supplementary Figure 1H-J.

LDHA Correlates to Tumor Infiltration of Immune Cells (TIICs)
The TISIDB database plays an important role in determining the effectiveness of cancer treatment and patient outcomes.In the study, the relationship between LDHA performance and TIIC was analyzed using GSCA and TISDB.Created a heat map including various TIICs, including M1 macrophages, M2 macrophages, M0 macrophages, T follicular helper cells, resting memory CD4 T cells, gamma T cells, CD8 T cells, regulatory T cells, Naïve CD4 T brain, resting NK cells, activated mast cells, memory B cells, monocytes, neutrophils, eosinophils, and plasma cells.The heat map of TISIDB data shows that the LDHA gene is closely related to the penetration of dendritic cells, especially in breast cancer, as shown in Supplementary Figure 4A-B.Further analysis of GSCA data confirmed this relationship, finding a correlation between LDHA expression and Act_DC infiltration in the breast (coefficient R = 0.44, P = 1.9e-55) as shown in Supplementary Figure 4C.Additionally, LDHA and its synergistic gene (GSVA) also showed infiltration with Act_DC with the correlation value, R = 0.48 and P = 8.2e-127 (Supplementary Figure 4D).

Analysis of protein-ligand interaction between LDHA inhibitors and Withanolide
To be considered a candidate for an inhibitor, it must bind a specific active site cavity with significant binding affinity.For this, the binding mechanisms of the docked molecules were assessed using LDHA-ligand complexes derived from post-docking analyses.All six ligands were docked both with and without NADH.3. Binding mode shows that the ligands bind in the active site cavity and follow a similar pattern to that of the reference inhibitor (PubChem CID 131955127), as shown in Supplementary Figure 5A.Based on binding affinity and binding pose, Withaferine A and Withanolide D were used for further analysis.Ligand Withaferine A, with a binding affinity of -9.3 kcal/mol, binds in the active site cavity of LDHA, forming three H-bonds between the ligand and the amino acids Arg105, Ser195, and His192, as shown in Supplementary Figure 5B.In addition, it also forms five hydrophobic interactions with Pro138, Ile141, Tyr238, Ile241, and Ile325.Withanolide D binds to LDHA with a binding energy of -10 kcal/mol.It formed four H-bonds with LDHA, two H-bonds formed between Withanolide D and residue Arg105 and Ser195, His192 individually making two H-bonds with ligand, because of the additional H-bond it has a greater binding affinity for LDHA as shown in Supplementary Figure 5C.By following a similar interaction pattern as Withaferine A, it forms five hydrophobic interactions with Pro138, Ile141, Tyr238, Ile241, and Ile325.

Discussion
Breast cancer survival has improved with targeted drugs and hormonal therapy, but high mortality rates persist.New therapeutic biomarkers like CERBB-2 and RTK are being explored for improved patient outcomes [49,50].Advances in genomics have led to the discovery of genetic targets in cancer research, with LDHA expression being a crucial oncogene for tumor growth and metastasis, as highlighted by various studies [51].Identifying patients with high LDHA expression before treatment is important for individualized treatment [52].Endocrine therapy to eliminate estrogen-dependent cell proliferation has been shown to reduce recurrence and death in most patients with early breast cancer [53].Liu, Cui, Feng, and Ho have all contributed to the understanding of the role of LDHA in breast cancer.LDHA is crucial in glucose metabolism and regulates the production of acetyl-CoA, which is essential for EMT-related gene transcription [54][55][56][57].When LDHA levels decrease, it inhibits EMT genes and activates autophagy via AMPK signaling.High LDHA expression is associated with worse cancer outcomes [56].Combination chemotherapy is being investigated to improve the therapeutic index of anti-cancer drugs.Targeting cellular metabolism is an interesting method for obtaining specific antibodies without affecting the normal body.LDHA, an enzyme crucial in the diagnosis of neoplastic diseases like Hodgkin lymphoma and multiple myeloma [58], was overexpressed in breast cancer cells, metastatic tissues, and brain tumors, leading to shorter survival (RFS) and overall survival compared to OS and distant metastasis-free survival (DMFS).Feng, Yang, and Yadav have all contributed to the understanding of LDHA's role in cancer patient survival, with its increased expression in breast cancer cells, metastatic tissues, and brain tumors [55,59,60].
In addition to using the Human Protein Atlas database, we also found that LDHA was overexpressed in breast cancer patients compared to normal subjects, as shown in Supplementary Figure 3 Based on gene Gene Ontology using TISIDB database we also found that LDHA and its molecular biology associated with Hypoxia as mentioned in Supplementary Table 2. Extensive research has shown that cancer is affected by dysregulation of microRNAs, small non-coding RNA molecules that control gene expression.Dysregulation of miRNAs can occur through various mechanisms, such as DNA amplification, deletions, mutations, epigenetic silencing, or inhibition of miRNA activity.Some known cancer-associated miRNAs include let-7, miR-200 family, miR-10b, miR-21, miR-335, miR-301, miR-155, miR-34a, and miR-205 [64,65].In our study, we found that the decrease of miRNA miR-383-5p is important in the initiation and development of cancer and increases the risk of cancer.Its overexpression inhibits cell proliferation, migration, and invasion by targeting LDHA.TCGA RNAseq data showed that miR-383-5p expression was reduced in breast cancer compared to normal controls.Patients with low miR-383-5p expression have poor differentiation, good tumor metastasis, and high-grade TNM.Using CancerMIRNome data, we also examined the pan-cancer perspective of miR-383-5p and found that miR-383-5p expression was downregulated in all cancer types, including breast cancer, based on ROC analysis (Supplementary Figure 6).Greater molecular heterogeneity is an important problem in breast cancer that affects treatment response and patient prognosis.This study aims to identify genes with high levels of expression (HHE) and their association with prognosis.In this study, the Enrichr repository identified 10 coexpressed genes related to LDHA that correlated well with those in the TIMER 2.0 repository.The GSCA and UALCAN databases were used to identify gene expression in breast cancer patients.This shows that there are large changes for each gene.The KM plotter database showed that all seven genes, including the LDHA data mean, were associated with poor survival in cancer patients.The Cancer Genome Atlas repository shows that thymopoietin antisense transcript 1 (TMPO-AS1) is a functional lncRNA associated with growth biomarkers in breast and lung cancer.TMPO-AS1 positivity is associated with a poor prognosis in breast cancer patients and is estrogen dependent.TMPO-AS1 appears to be upregulated in MCF-7 cells resistant to endocrine therapy and can be induced by estrogen.It supports the growth and survival of estrogen receptor-positive breast cancer cells in vitro and in vivo.TMPO-AS1 exerts oncogenic functions in various cancers, such as pancreatic cancer, uterine cancer, non-small cell lung cancer, breast cancer, breast cancer, cancer, and lung adenocarcinoma.It suppresses cancer by targeting the AKT/rapamycin kinase (mTOR) signaling mechanism and suppresses the malignant phenotype of retinoblastoma cells by inhibiting miR-199a-5p, which targets HIF-1α -LncRNAs and plays an important role in HCC development, such as promoting angiogenic mimicry and increasing cancer cells in HCC.In this study, we also analyzed the expression of TMPO-AS1 in breast cancer and demonstrated its important role in tumor growth.Overexpressed LDHA promotes TMPO-AS1 transcription and has been shown to recruit miR-383-5p.This study reveals for the first time the biological role and molecular mechanism of LDHA/TMPO-AS1 in TNBC and is expected to identify it as a new therapeutic target.
The tumor microenvironment plays a crucial role in tumor progression to malignant phenotypes, with gene expression in tumor cells and stromal cells influencing their interaction.This study used GSCA and TISIDB data to analyze the relationship between LDHA performance and TIICs.Using various TIICs, the LDHA gene is closely associated with the entry of Act_DC Cells especially in BRCA.LDHA, a health marker, has a significant impact on cancer development, making it a potential therapeutic target in human cancer treatment.Analysis of pathways associated with LDHA in breast cancer development reveals potential pathways and genes that can be used as checkpoints to prevent or reduce cancer cells, making them useful biomarkers for effective treatment.Drug Bank was used to measure LDHA, and results showed that many chemicals show affinity with LDHA, including stiripentol, copper, artenimol, etheno-NAD, nicotinamide, oxamic acid, and etheno-NAD.Stipentol is recommended for its pharmacological and inhibitory activities as mentioned in Supplementary Table 3.In this study we have proposed a natural based alternative i.e. use of withanolides, which has proven to be more affective as compared to chemical based or synthetic based products.In a study conducted by Lacombe et al., they suggested that withanolide D could be a promising radiosensitizer for cancer cells by inhibiting NHEJ pathway and promoting mitotic catastrophe [66].Very interestingly, in an another study conducted by Chien et al., they showed that withaferine A possess anti-cancer effects by inducing antiproliferation, apoptosis, and DNA damage in an oxidative stress-dependent manner in case of bladder cancer [67].Also, our study shows that withanolide analogs could be attractive molecules to test LDHA inhibition.In silico docking studies predict tight binding of Withaferine A (-9.3kcal/mol) and Withanolide D (-10kcal/mol) by forming hydrogen bonds with LDHA active site residues Arg105, Ser195, and His192.These interactions may lead to decrease LDHA expression levels in cancer cells.
In conclusion, the study reveals a strong association between LDHA expression and HIF-1α in breast cancer, with LDHA expression being inversely associated with miR-383-5p and positively correlated with TMPO-AS1.This suggests that HIF-1α influences LDHA gene expression regulation, potentially promoting breast cancer progression.Overexpression of LDHA and HIF-1α correlates with OS, RFS, DMFS, and PPS in breast cancer patients, suggesting LDHA could be a prognostic factor and therapeutic target.LDHA mediates the conversion of pyruvate and lactate, and high expression of LDHA, HIF-1α, TMPO-AS1, along with low expression of miR-383-5p is a hallmark of many cancers.It was seen that the sponge formation between TMPO-AS1 and hsa-let-7b-5p may have a significant affect on LDHA expression regulation.Due to the sponging effect, the normal functioning of hsa-let-7b-5p gets disrupted, which in turn affects its ability to effectively regulate LDHA mRNA levels, in breast cancer.LDHA is also closely associated with Act-DC cells, influencing the tumor microenvironment with hypoxia conditions and downregulating miR-383-5p.Withanolide-LDHA complexes, esp.withaferine A and withanolide D, exhibit strong binding interactions with LDHA and could be prominent scaffolds to develop potential small-molecule inhibitors to decrease the LDHA expression level for the development of highly effective therapeutics against breast cancer.

Figure 1 .
Figure 1.Expression Pattern of LDHA in Pan-Cancer (A) Expression profile of LDHA was determined by the UALCAN database for tumor versus normal samples; Red bars = Tumors, blue bars corresponding normal tissue.(B) expression of LDHA in pan-cancer by TIMER 2.0 meta-analysis; tumors compared with matched normal samples, Red bar-dot plot = Tumors, blue bar-dot plot corresponding normal tissue.Error bars represent SD. ***p < 0.001.(C) expression of LDHA in different types of cancers by TISIDB database.

Figure 4 .
Figure 4. LDHA Expression in Tumors from Breast Cancer Patients with Metastasis.(A) Boxplot of LDHA expression between normal, tumor, and metastasis in breast cancer patients using the Gene Chip-TNMplot database; (B) Boxplot of LDHA expression in normal tissue, circulatory tumor cells (CTC); and secondary metastasis sites from breast cancer patients using ctcRbase database (C).
. Additionally, UALCAN data was also used to analyze miR-383-5p expression in patients with different tumors, lymph nodes, and histological types.As shown in the Figure 7E-G, it presents a lower expression in the final stage of each pathological stage.

Figure 6 .
Figure 6.(A) Expression correlation between LDHA and top 10 co-expressed genes by using TIMER 2.0.Expression Pattern of all co-expressed genes in breast cancer (B) Tumor vs Normal (C) Subtypes of BRCA using GSCA.
The binding affinity of all ligands was enhanced in the presence of NADH.The binding affinity of all ligands is summarized Asian Pacific Journal of Cancer Prevention, Vol 25 2941 DOI:10.31557/APJCP.2024.25.8.2929 TMPO-AS1 Promotes Triple-Negative Breast Cancer by Sponging miR-383-5p to Trigger the LDHA in Table

Table 1 .
LDHA and its Co-Expressed Genes
. LDHA, a key protein in tumor growth and cell cycle, is linked to various clinicopathological features, including hypoxia and tumor prognosis.Studies by Dong et al. (2023) and Koukourakis (2003) have highlighted the role of LDHA in hypoxia, a microenvironmental factor in malignant tumors [61,62].Chiche et al. (2010) found that LDHA catalyzes the last step of glycolysis in hypoxia [63].HIF-1 and HIF-2, transcriptional regulators, modulate cellular and systemic adaptive responses, affecting genes crucial for tumor growth and cell cycle.This study demonstrates the relationship between LDHA and HIF-1α expression in cancer, and high HIF-1α expression predicts poor outcomes (RFS, OS, DMFS, and PPS) in cancer patients.