LDHA upregulation independently predicts poor survival in lung adenocarcinoma, but not in lung squamous cell carcinoma

Construction of a prognostic model based on memory CD4+ T cell-associated genes for lung adenocarcinoma and its applications in immunotherapy

The association between memory CD4+ T cells and cancer prognosis is increasingly recognized, but their impact on lung adenocarcinoma (LUAD) prognosis remains unclear. In this study, using the cell-type identification by estimating relative subsets of RNA transcripts algorithm, we analyzed immune cell composition and patient survival in LUAD. Weighted gene coexpression network analysis helped identify memory CD4+ T cell-associated gene modules. Combined with module genes, a five-gene LUAD prognostic risk model (HOXB7, MELTF, ABCC2, GNPNAT1, and LDHA) was constructed by regression analysis. The model was validated using the GSE31210 data set. The validation results demonstrated excellent predictive performance of the risk scoring model. Correlation analysis was conducted between the clinical information and risk scores of LUAD samples, revealing that LUAD patients with disease progression exhibited higher risk scores. Furthermore, univariate and multivariate regression analyses demonstrated the model independent prognostic capability. The constructed nomogram results demonstrated that the predictive performance of the nomogram was superior to the prognostic model and outperformed individual clinical factors. Immune landscape assessment was performed to compare different risk score groups. The results revealed a better prognosis in the low-risk group with higher immune infiltration. The low-risk group also showed potential benefits from immunotherapy. Our study proposes a memory CD4+ T cell-associated gene risk model as a reliable prognostic biomarker for personalized treatment in LUAD patients.

CENPE and LDHA were potential prognostic biomarkers of chromophobe renal cell carcinoma

BACKGROUND

Most sarcomatoid differentiated renal cell carcinoma was differentiated from Chromophobe renal cell carcinoma (KICH) and related to a bad prognosis. Thus, finding biomarkers is important for the therapy of KICH.

METHODS

The UCSC was used for determining the expression of mRNA and miRNA and clinical data in KICH and normal samples. KEGG and GO were used for predicting potential function of differently expressed genes (DEGs). Optimal prognostic markers were determined by Lasso regression. Kaplan-Meier survival, ROC, and cox regression were used for assessing prognosis value. GSEA was used for predicting potential function of markers. The relations between markers and immune cell infiltration were determined by Pearson method. The upstream miRNA of markers was predicted in TargetScan and DIANA.

RESULTS

The 6162 upregulated and 13,903 downregulated DEGs were identified in KICH. Further CENPE and LDHA were screened out as optimal prognostic risk signatures. CENPE was highly expressed while LDHA was lowly expressed in KICH samples, and the high expressions of 2 genes contributed to bad prognosis. The functions of CENPE and LDHA were mainly enriched in proliferation related pathways such as cell cycle and DNA replication. In addition, the correlation of 2 genes with immune infiltrates in KICH was also observed. Finally, we found that has-miR-577 was the common upstream of 2 genes and the binding sites can be predicted.

CONCLUSION

CENPE and LDHA were identified as the important prognostic biomarkers in KICH, and they might be involved in the proliferation of cancer cell.

Integrated analysis of single-cell and Bulk RNA sequencing reveals a malignancy-related signature in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD), the most common histotype of lung cancer, may have variable prognosis due to molecular variations. The research strived to establish a prognostic model based on malignancy-related risk score (MRRS) in LUAD.

Methods

We applied the single-cell RNA sequencing (scRNA-seq) data from Tumor Immune Single Cell Hub database to recognize malignancy-related geneset. Meanwhile, we extracted RNA-seq data from The Cancer Genome Atlas database. The GSE68465 and GSE72094 datasets from the Gene Expression Omnibus database were downloaded to validate the prognostic signature. Random survival forest analysis screened MRRS with prognostic significance. Multivariate Cox analysis was leveraged to establish the MRRS. Furthermore, the biological functions, gene mutations, and immune landscape were investigated to uncover the underlying mechanisms of the malignancy-related signature. In addition, we used qRT-PCR to explore the expression profile of MRRS-constructed genes in LUAD cells.

Results

The scRNA-seq analysis revealed the markers genes of malignant celltype. The MRRS composed of 7 malignancy-related genes was constructed for each patient, which was shown to be an independent prognostic factor. The results of the GSE68465 and GSE72094 datasets validated MRRS's prognostic value. Further analysis demonstrated that MRRS was involved in oncogenic pathways, genetic mutations, and immune functions. Moreover, the results of qRT-PCR were consistent with bioinformatics analysis.

Conclusion

Our research recognized a novel malignancy-related signature for predicting the prognosis of LUAD patients and highlighted a promising prognostic and treatment marker for LUAD patients.

20-Hydroxyecdysone Confers Antioxidant and Antineoplastic Properties in Human Non-Small Cell Lung Cancer Cells

20-Hydroxyecdysone (20E) is an arthropod hormone which is synthesized by some plants as part of their defense mechanism. In humans, 20E has no hormonal activity but possesses a number of beneficial pharmacological properties including anabolic, adaptogenic, hypoglycemic, and antioxidant properties, as well as cardio-, hepato-, and neuroprotective features. Recent studies have shown that 20E may also possess antineoplastic activity. In the present study, we reveal the anticancer properties of 20E in Non-Small Cell Lung Cancer (NSCLC) cell lines. 20E displayed significant antioxidant capacities and induced the expression of antioxidative stress response genes. The RNA-seq analysis of 20E-treated lung cancer cells revealed the attenuation of genes involved in different metabolic processes. Indeed, 20E suppressed several enzymes of glycolysis and one-carbon metabolism, as well as their key transcriptional regulators-c-Myc and ATF4, respectively. Accordingly, using the SeaHorse energy profiling approach, we observed the inhibition of glycolysis and respiration mediated by 20E treatment. Furthermore, 20E sensibilized lung cancer cells to metabolic inhibitors and markedly suppressed the expression of Cancer Stem Cells (CSCs) markers. Thus, in addition to the known beneficial pharmacological activities of 20E, our data uncovered novel antineoplastic properties of 20E in NSCLC cells.

Guidelines on lung adenocarcinoma prognosis based on immuno-glycolysis-related genes

OBJECTIVES

This study developed a new model for risk assessment of immuno-glycolysis-related genes for lung adenocarcinoma (LUAD) patients to predict prognosis and immunotherapy efficacy.

METHODS

LUAD samples and data obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases are used as training and test columns, respectively. Twenty-two (22) immuno-glycolysis-related genes were screened, the patients diagnosed with LUAD were divided into two molecular subtypes by consensus clustering of these genes. The initial prognosis model was developed using the multiple regression analysis method and Receiver Operating characteristic (ROC) analysis was used to verify its predictive potential. Gene set enrichment analysis (GSEA) showed the immune activities and pathways in different risk populations, we calculated immune checkpoints, immune escape, immune phenomena (IPS), and tumor mutation burden (TMB) based on TCGA datasets. Finally, the relationship between the model and drug sensitivity was analyzed.

RESULTS

Fifteen (15) key differentially expressed genes (DEGs) with prognostic value were screened and a new prognostic model was constructed. Four hundred and forty-three (443) samples were grouped into two different risk cohorts based on median model risk values. It was observed that survival rates in high-risk groups were significantly low. ROC curves were used to evaluate the model's accuracy in determining the survival time and clinical outcome of LUAD patients. Cox analysis of various clinical factors proved that the risk score has great potential as an independent prognostic factor. The results of immunological analysis can reveal the immune infiltration and the activity of related functions in different pathways in the two risk groups, and immunotherapy was more effective in low-risk patients. Most chemotherapeutic agents are more sensitive to low-risk patients, making them more likely to benefit.

CONCLUSION

A novel prognostic model for LUAD patients was established based on IGRG, which could more accurately predict the prognosis and an effective immunotherapy approach for patients.

Discovery of the First Lactate Dehydrogenase Proteolysis Targeting Chimera Degrader for the Treatment of Pancreatic Cancer

Lactate dehydrogenase (LDH) is a key glycolytic enzyme and biomarker of aggressive cancers. LDHA and LDHB are two main LDH subunits, and both are frequently overexpressed in tumors and essential for tumor growth. A number of LDHA/B small-molecule inhibitors have been developed. Here, we report the discovery of the first LDH proteolysis targeting chimera (PROTAC) degrader, compound 22 (MS6105). 22 potently degraded LDHA in a time- and ubiquitin-proteasome system-dependent manner. Using an unbiased global proteomic study, we confirmed that 22 degraded both LDHA and LDHB significantly. 22 was significantly more potent than the parent LDH inhibitor in suppressing the growth of both quasi-mesenchymal state and epithelial state pancreatic cancer cell lines. Furthermore, 22 was bioavailable in mice through intraperitoneal injection. Overall, 22 could be a valuable chemical tool for the research community to explore pathophysiological functions of LDH in vitro and in vivo.

Expression of HIF-1α and Genes Involved in Glucose Metabolism Is Increased in Cervical Cancer and HPV-16-Positive Cell Lines

Cervical cancer (CC) is the most common cancer in women in the lower genital tract. The main risk factor for developing CC is persistent infection with HPV 16. The E6 and E7 oncoproteins of HPV 16 have been related to metabolic reprogramming in cancer through the regulation of the expression and stability of HIF-1α and consequently of the expression of its target genes, such as HIF1A (HIF-1α), SLC2A1 (GLUT1), LDHA, CA9 (CAIX), SLC16A3 (MCT4), and BSG (Basigin or CD147), which are involved in glucose metabolism. This work aimed to evaluate the expression of HIF-1α, GLUT1, LDHA, CAIX, MCT4, and Basigin in patient samples and CC cell lines. To evaluate the expression level of HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG genes in tissue from patients with CC and normal tissue, the TCGA dataset was used. To evaluate the expression level of these genes by RT-qPCR in CC cell lines, HPV-negative (C-33A) and HPV-16-positive (SiHa and Ca Ski) cell lines were used. Increased expression of HIF1A, SLC2A1, LDHA, SLC16A3, and BSG was found in Ca Ski and CA9 in SiHa compared to C-33A. Similar results were observed in CC tissues compared to normal tissue obtained by bioinformatics analysis. In conclusion, the expression of HIF-1α, GLUT1, LDHA, CAIX, MCT4, and BSG genes is increased in CC and HPV-16-positive cell lines.

Establishing a Novel Gene Signature Related to Histone Modifications for Predicting Prognosis in Lung Adenocarcinoma

Background

Epigenetic modifications have been revealed to play an important role in tumorigenesis and tumor development. This study aims to analyze the role of histone modifications and the prognostic values of histone modifications in lung adenocarcinoma (LUAD). The promoters and enhancers of protein encoding genes (PCGs) were the regions of enriched histone modifications.

Methods

Expression profiles and clinical information of LUAD samples were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Histone modification data of LUAD cell lines were downloaded from Encyclopedia of DNA Elements (ENCODE) database. Limma R package was used to identify differentially expressed PCGs. To identify molecular subtypes, consensus clustering was conducted based on the expression of dysregulated PCGs with abnormal histone modifications. Univariate Cox regression analysis and stepwise Akaike information criterion (stepAIC) were utilized to establish a prognostic model.

Results

We identified a total of 699 epigenetic dysregulated genes with 122 of them significantly correlating with LUAD prognosis. We constructed three molecular subtypes (C1, C2, and C3) based on the 122 prognostic genes. C2 had the longest overall survival while C1 had the worst prognosis. In addition, three subtypes had differential immune infiltration and the response to immune checkpoint inhibitors. Moreover, we identified a risk model containing 5 epi-PCGs that had favorable performance to predict prognosis in different datasets.

Conclusions

This study further supported the critical histone modifications in LUAD development. Three subtypes may provide guidance for the immunotherapy of LUAD patients. Importantly, the prognostic model had great potential to predict LUAD prognosis.

Integrative metabolomics and transcriptomics analysis reveals novel therapeutic vulnerabilities in lung cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) comprises the majority (~85%) of all lung tumors, with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) being the most frequently diagnosed histological subtypes. Multi-modal omics profiling has been carried out in NSCLC, but no studies have yet reported a unique metabolite-related gene signature and altered metabolic pathways associated with LUAD and LUSC.

METHODS

We integrated transcriptomics and metabolomics to analyze 30 human lung tumors and adjacent noncancerous tissues. Differential co-expression was used to identify modules of metabolites that were altered between normal and tumor.

RESULTS

We identified unique metabolite-related gene signatures specific for LUAD and LUSC and key pathways aberrantly regulated at both transcriptional and metabolic levels. Differential co-expression analysis revealed that loss of coherence between metabolites in tumors is a major characteristic in both LUAD and LUSC. We identified one metabolic onco-module gained in LUAD, characterized by nine metabolites and 57 metabolic genes. Multi-omics integrative analysis revealed a 28 metabolic gene signature associated with poor survival in LUAD, with six metabolite-related genes as individual prognostic markers.

CONCLUSIONS

We demonstrated the clinical utility of this integrated metabolic gene signature in LUAD by using it to guide repurposing of AZD-6482, a PI3Kβ inhibitor which significantly inhibited three genes from the 28-gene signature. Overall, we have integrated metabolomics and transcriptomics analyses to show that LUAD and LUSC have distinct profiles, inferred gene signatures with prognostic value for patient survival, and identified therapeutic targets and repurposed drugs for potential use in NSCLC treatment.

Salvianolic Acid B Suppresses Non-Small-Cell Lung Cancer Metastasis through PKM2-Independent Metabolic Reprogramming

Objective

Salvianolic acid B (Sal B) has been demonstrated to be a potential chemoprevention agent for several cancers. Herein, we investigated the pharmacological function of Sal B on non-small-cell lung cancer (NSCLC) metastasis.

Methods

Two NSCLC cell lines (NCI-H2030 and NCI-H1650) were disposed of by 200 μM Sal B or 10 μM PKM2 agonist TEPP-46. Wound healing and transwell experiments were implemented for analyzing migratory and invasive capacities. Epithelial-to-mesenchymal transition (EMT) markers β-catenin and E-cadherin were measured via western blotting. Cellular bioenergetics were evaluated with glucose uptake, lactate production, enolase activity, cellular ATP levels, as well as seahorse-based oxygen consumption rate (OCR), extracellular acidification rate (ECAR) analysis. Metabolic reprogramming markers PKM2, LDHA, and GLUT1 were detected via western blotting and immunofluorescence.

Results

The results showed that Sal B disposal weakened the migration and invasion of NCI-H2030 and NCI-H1650 cells and inactivated the EMT process according to downregulation of β-catenin and upregulation of E-cadherin. Sal B-treated NSCLC cells displayed decreased glucose uptake, lactate production, enolase activity, cellular ATP levels, OCR, and ECAR, indicating a reduction in metabolic reprogramming. Additionally, Sal B downregulated the expression of PKM2, LDHA, and GLUT1. TEPP-46 may reverse the inhibitory effect of Sal B on metastasis as well as metabolic reprogramming.

Conclusion

Our findings provide evidence that Sal B enables to weaken NSCLC metastasis through PKM2-independent metabolic reprogramming, which sheds light on the promising therapeutic usage of Sal B in treating NSCLC.

A novel mTOR-associated gene signature for predicting prognosis and evaluating tumor immune microenvironment in lung adenocarcinoma

BACKGROUND

The mechanistic target of rapamycin (mTOR) was proven to have great impact on apoptosis, cell proliferation, autophagy, and many other fundamental cellular processes; moreover, it closely correlates with tumor occurrence and development. However, few studies have constructed signatures based on mTOR-associated genes to assess multiple indicators of prognosis in lung adenocarcinoma (LUAD) patients.

METHODS

mTOR-associated gene sets, whole mRNA expression matrices, and clinical information of LUAD patients in training and validation cohorts were obtained from multiple public databases. Multiple methods were used to screen candidate genes, construct signatures, validate internally and externally, and conduct further studies: differentially expressed gene analysis, LASSO Cox regression analysis, Cox regression analysis, risk factor analysis, nomogram analysis, functional enrichment analysis, analyses in tumor immune microenvironment, and therapy.

RESULTS

A prognostic signature containing 8 genes (LDHA, SLA, WNT7A, PLK1, CCT6A, BTG2, TXNRD1, and DDIT4) was constructed. It performed well in both internal and external validation. Subsequent analysis found that the prognostic signature was of great significance in evaluating the tumor immune microenvironment and could guide the treatment of patients with LUAD to a certain extent.

CONCLUSION

The constructed mTOR-associated gene signature accurately predicted the prognostic pattern of patients with LUAD and is expected to be extremely useful in guiding LUAD therapy.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on B cell marker genes to predict prognosis and immunotherapy response in lung adenocarcinoma

As an essential component of the tumor microenvironment, B cells exist in all stages of tumor and exert important roles in anti-tumor immunity and shaping tumor development. We aimed to explore the expression profile of B cell marker genes and construct a prognostic signature based on these genes in Lung adenocarcinoma (LUAD). A total of 1268 LUAD patients from different cohorts were enrolled in this study. We performed an analysis of single-cell RNA-sequencing (scRNA-seq) data from Gene expression omnibus (GEO) database to identify B cell marker genes in LUAD. TCGA database was used to construct signature, and six cohorts from GEO database were used for validation. We also investigated the association between this signature and immunotherapy response. Based on 258 B cell marker genes identified by scRNA-seq analysis, a nine-gene signature was constructed for prognostic prediction in TCGA dataset, which classified patients into high-risk and low-risk groups according to overall survival. The multivariate analysis demonstrated that the signature was an independent prognostic factor. The signature's predictive power was verified in other six independent cohorts and different clinical subgroups. Analysis of immune profiles showed that high-risk groups presented discriminative immune-cell infiltrations and immune-suppressive states. More importantly, risk scores of the signature were closely correlated with PD-L1, tumor mutation burden, neoantigens, and tumor immune dysfunction and exclusion score. Our study proposed a novel prognostic signature based on B cell marker genes for LUAD patients. The signature could effectively indicate LUAD patients' survival and serve as a predictor for immunotherapy.

BUB1B (BUB1 Mitotic Checkpoint Serine/Threonine Kinase B) Promotes Lung Adenocarcinoma by Interacting with Zinc Finger Protein ZNF143 and Regulating Glycolysis

Lung adenocarcinoma (LUAD) is one of the most common causes of cancer death in men. BUB1B (BUB1 mitotic checkpoint serine/threonine kinase B) has been reported to contribute to the initiation and development of several cancers. Here, we aimed to explore the potential role of BUB1B in LUAD. We found BUB1B was upregulated in LUAD, suggesting its potential role as a biomarker for LUAD diagnosis. Significantly, LUAD patients with high BUB1B expression had a shorter survival time than those with low BUB1B expression. Knocking-out BUB1B resulted in suppression of cell proliferation, migration, and invasion in vitro, and inhibition of tumor growth in the xenograft experiment. Further analysis revealed that BUB1B regulates glycolysis in LUAD and interacting with ZNF143 in LUAD cells. The interaction was demonstrated by silencing ZNF143, which led to a decrease in proliferation, migration, and invasion in LUAD cells, whereas overexpressing BUB1B had the opposite effects. Our study suggested that the ZNF143/BUB1B axis plays a pivotal role in LUAD progression, which might be a potential target for LUAD management.

A promising prognostic signature for lung adenocarcinoma (LUAD) patients basing on 6 hypoxia-related genes

BACKGROUND

Hypoxia signaling plays a critical role in the development of lung adenocarcinoma (LUAD). We herein aimed to explore the prognostic value of hypoxia-related genes and construct the hypoxia-related prognostic signature for LUAD patients.

METHODS

A total of 26 hypoxia-related genes were collected. Five hundred thirteen and 246 LUAD samples were obtained from the Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. Univariate Cox regression and LASSO Cox regression analyses were conducted to screen the hypoxia-related genes associated with the prognosis of LUAD patients, which would be used for constructing prognosis predictive model for LUAD patients. Multivariate Cox regression analysis was done to determine the independent prognostic factors. The Nomogram model was constructed to predict the prognosis of LUAD patients.

RESULTS

Based on 26 hypoxia-related genes, LUAD samples could be divided into 4 clusters with different prognoses. Among which, 6 genes were included to construct the Risk Score and the LUAD patients with higher Risk Score had worse prognosis. Besides, the Nomogram based on all the independent risk factors could relatively reliably predict the survival probability. And 9 types of immune cells' infiltration was significantly differential between high and low risk LUAD patients.

CONCLUSION

The Risk Score model based on the 6 crucial hypoxia-related genes could relatively reliably predict the prognosis of LUAD patients.

A Novel Prognostic and Predictive Signature for Lung Adenocarcinoma Derived from Combined Hypoxia and Infiltrating Immune Cell-Related Genes in TCGA Patients

Background

The hypoxia and immune status of the lung adenocarcinoma (LUAD) microenvironment appear to have combined impacts on prognosis. Therefore, deriving a prognostic signature by integrating hypoxia- and immune infiltrating cell-related genes (H&IICRGs) may add value over prognostic indices derived from genes driving either process alone.

Methods

Differentially expressed H&IICRGs (DE-H&IICRGs) were identified in The Cancer Genome Atlas transcriptomic data using limma, CIBERSORT, weighted gene co-expression network analysis, and intersection analysis. A stepwise Cox regression model was constructed to identify prognostic genes and to produce a gene signature based on DE-H&IICRGs. The potential biological functions associated with the gene signature were explored using functional enrichment analysis. The prognostic signature was externally validated in a separate cohort from the Gene Expression Omnibus database.

Results

Five prognostic genes associated with overall survival in LUAD were used in the DE-H&IICRG-based prognostic signature. Patients in the high-risk group had an inferior prognosis, which was validated in an independent external cohort, and had lower expression of most immune checkpoint genes. In multivariate analysis, only risk score and T stage were independent prognostic factors. Differentially expressed genes (DEGs) associated with the risk score were enriched for pathways related to cell cycle, hypoxia regulation, and immune response. TIDE analyses showed that low-risk LUAD patients might also respond better to immunotherapy.

Conclusion

This study establishes and validates a prognostic profile for LUAD patients that combines hypoxia and immune infiltrating cell-related genes. This signature may have clinical application both for prognostication and guiding individualized immunotherapy.

Loss of DSTYK activates Wnt/β-catenin signaling and glycolysis in lung adenocarcinoma

Aberrant activation of Wnt/β-catenin signaling and dysregulation of metabolism have been frequently observed in lung cancer. However, the molecular mechanism by which Wnt/β-catenin signaling is regulated and the link between Wnt/β-catenin signaling and cancer metabolism are not fully understood. In this study, we showed that the loss of dual serine/threonine tyrosine protein kinase (DSTYK) led to the activation of Wnt/β-catenin signaling and upregulation of its target gene, lactate dehydrogenase (LDHA), and thus the elevation of lactate. DSTYK phosphorylated the N-terminal domain of β-catenin and inhibited Wnt/β-catenin signaling, which led to the inhibition of cell growth, colony formation and tumorigenesis in a lung adenocarcinoma mouse model. DSTYK was downregulated in lung cancer tissues, and its expression was positively correlated with the survival of patients with lung adenocarcinoma. Taken together, these results demonstrate that the loss of DSTYK activates Wnt/β-catenin/LDHA signaling to promote the tumorigenesis of lung cancer and that DSTYK may be a therapeutic target.

Therapeutic resistance in pancreatic ductal adenocarcinoma: Current challenges and future opportunities

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in the United States. Although chemotherapeutic regimens such as gemcitabine+ nab-paclitaxel and FOLFIRINOX (FOLinic acid, 5-Fluroruracil, IRINotecan, and Oxaliplatin) significantly improve patient survival, the prevalence of therapy resistance remains a major roadblock in the success of these agents. This review discusses the molecular mechanisms that play a crucial role in PDAC therapy resistance and how a better understanding of these mechanisms has shaped clinical trials for pancreatic cancer chemotherapy. Specifically, we have discussed the metabolic alterations and DNA repair mechanisms observed in PDAC and current approaches in targeting these mechanisms. Our discussion also includes the lessons learned following the failure of immunotherapy in PDAC and current approaches underway to improve tumor's immunological response.

Development of a gene signature associated with iron metabolism in lung adenocarcinoma

There are few studies on the role of iron metabolism genes in predicting the prognosis of lung adenocarcinoma (LUAD). Therefore, our research aims to screen key genes and to establish a prognostic signature that can predict the overall survival rate of lung adenocarcinoma patients. RNA-Seq data and corresponding clinical materials of 594 adenocarcinoma patients from The Cancer Genome Atlas(TCGA) were downloaded. GSE42127 of Gene Expression Omnibus (GEO) database was further verified. The multi-gene prognostic signature was constructed by the Cox regression model of the Least Absolute Shrinkage and Selection Operator (LASSO). We constructed a prediction signature with 12 genes (HAVCR1, SPN, GAPDH, ANGPTL4, PRSS3, KRT8, LDHA, HMMR, SLC2A1, CYP24A1, LOXL2, TIMP1), and patients were split into high and low-risk groups. The survival graph results revealed that the survival prognosis between the high and low-risk groups was significantly different (TCGA: P < 0.001, GEO: P = 0.001). Univariate and multivariate Cox regression analysis confirmed that the risk value is a predictor of patient OS (P < 0.001). The area under the time-dependent ROC curve (AUC) indicated that our signature had a relatively high true positive rate when predicting the 1-year, 3-year, and 5-year OS of the TCGA cohort, which was 0.735, 0.711, and 0.601, respectively. In addition, immune-related pathways were highlighted in the functional enrichment analysis. In conclusion, we developed and verified a 12-gene prognostic signature, which may be help predict the prognosis of lung adenocarcinoma and offer a variety of targeted options for the precise treatment of lung cancer.

A pan-cancer transcriptomic study showing tumor specific alterations in central metabolism

Recently, there has been a resurgence of interest in metabolic rewiring of tumors to identify clinically relevant genes. However, most of these studies have had either focused on individual tumors, or are too general, providing a broad outlook on overall changes. In this study, we have first curated an extensive list of genes encoding metabolic enzymes and metabolite transporters relevant to carbohydrate, fatty acid and amino acid oxidation and biosynthesis. Next, we have used publicly available transcriptomic data for 20 different tumor types from The Cancer Genome Atlas Network (TCGA) and focused on differential expression of these genes between tumor and adjacent normal tissue. Our study revealed major transcriptional alterations in genes that are involved in central metabolism. Most tumors exhibit upregulation in carbohydrate and amino acid transporters, increased glycolysis and pentose phosphate pathway, and decreased fatty acid and amino acid oxidation. On the other hand, the expression of genes of the tricarboxylic acid cycle, anaplerotic reactions and electron transport chain differed between tumors. Although most transcriptomic alterations were conserved across many tumor types suggesting the initiation of common regulatory programs, expression changes unique to specific tumors were also identified, which can provide gene expression fingerprints as potential biomarkers or drug targets. Our study also emphasizes the value of transcriptomic data in the deeper understanding of metabolic changes in diseases.

Improvement of Diagnostic Accuracy for Pancreatic Cancer with Serum Lactate Dehydrogenase

Purpose

Due to the lack of early-stage detection, pancreatic cancer (PC) remains a devastating disease worldwide. Lactate dehydrogenase (LDH) is associated with tumorigenesis and cancer progression. This study aims to analyze the diagnostic improvements in serum LDH levels combined with other common tumor biomarkers, including carbohydrate antigen 19–9 (CA19–9) and carcinoembryonic antigen (CEA), for monitoring PC.

Patients and Methods

A retrospective analysis was performed on 73 patients with newly diagnosed PC, 90 patients with pancreatic benign diseases (PBD), and 92 people with healthy physical examination (HPE) at Zhongda Hospital, Southeast University from July 2013 to July 2020. The diagnostic efficiencies of serum levels of LDH, CA19–9, and CEA were analyzed through receiver operating characteristic (ROC) curves for PC. The sensitivity and specificity were evaluated at an optimal cutoff. The prognostic impacts of LDH on PC patients were also assessed.

Results

The LDH level was elevated in 21 (28.77%) patients with PC, 3 (3.33%) PBD patients, and no HPE individuals (P<0.05). The sensitivities of LDH, CA19–9, and CEA for the diagnosis of PC were 63.0%, 78.1%, and 72.6%, respectively, but the combination of these three markers increased predictive sensitivity significantly to 87.6%. The specificities of LDH, CA19–9, and CEA for the diagnosis of PC were 93.4%, 84.1%, and 73.1%, respectively. The combined specificity reached up to 96.7%. The medium survival time of PC patients with low-level LDH was 21 ± 5.1 months, whereas that of patients with high-level LDH was only 7 ± 0.92 months (P<0.05).

Conclusion

The serum LDH level was higher in PC patients than in PBD patients and HPE individuals and was associated with a poor prognosis. The combined assessment of LDH, CEA, and CA19–9 showed higher sensitivity and specificity for the diagnosis of PC.

LDHA induces EMT gene transcription and regulates autophagy to promote the metastasis and tumorigenesis of papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is one of the most common kinds of endocrine-related cancer and has a heterogeneous prognosis. Metabolic reprogramming is one of the hallmarks of cancers. Aberrant glucose metabolism is associated with malignant biological behavior. However, the functions and mechanisms of glucose metabolism genes in PTC are not fully understood. Thus, data from The Cancer Genome Atlas database were analyzed, and lactate dehydrogenase A (LDHA) was determined to be a potential novel diagnostic and therapeutic target for PTCs. The research objective was to investigate the expression of LDHA in PTCs and to explore the main functions and relative mechanisms of LDHA in PTCs. Higher expression levels of LDHA were found in PTC tissues than in normal thyroid tissues at both the mRNA and protein levels. Higher expression levels of LDHA were correlated with aggressive clinicopathological features and poor prognosis. Moreover, we found that LDHA not only promoted PTC migration and invasion but also enhanced tumor growth both in vitro and in vivo. In addition, we revealed that the metabolic products of LDHA catalyzed induced the epithelial-mesenchymal transition process by increasing the relative gene H3K27 acetylation. Moreover, LDHA knockdown activated the AMPK pathway and induced protective autophagy. An autophagy inhibitor significantly enhanced the antitumor effect of FX11. These results suggested that LDHA enhanced the cell metastasis and proliferation of PTCs and may therefore become a potential therapeutic target for PTCs.

Comprehensive Profiling of Genomic and Transcriptomic Differences between Risk Groups of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Lung cancer is the second most frequently diagnosed cancer type and responsible for the highest number of cancer deaths worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are subtypes of non-small-cell lung cancer which has the highest frequency of lung cancer cases. We aimed to analyze genomic and transcriptomic variations including simple nucleotide variations (SNVs), copy number variations (CNVs) and differential expressed genes (DEGs) in order to find key genes and pathways for diagnostic and prognostic prediction for lung adenocarcinoma and lung squamous cell carcinoma. We performed a univariate Cox model and then lasso-regularized Cox model with leave-one-out cross-validation using The Cancer Genome Atlas (TCGA) gene expression data in tumor samples. We generated 35- and 33-gene signatures for prognostic risk prediction based on the overall survival time of the patients with LUAD and LUSC, respectively. When we clustered patients into high- and low-risk groups, the survival analysis showed highly significant results with high prediction power for both training and test datasets. Then, we characterized the differences including significant SNVs, CNVs, DEGs, active subnetworks, and the pathways. We described the results for the risk groups and cancer subtypes separately to identify specific genomic alterations between both high-risk groups and cancer subtypes. Both LUAD and LUSC high-risk groups have more downregulated immune pathways and upregulated metabolic pathways. On the other hand, low-risk groups have both up- and downregulated genes on cancer-related pathways. Both LUAD and LUSC have important gene alterations such as CDKN2A and CDKN2B deletions with different frequencies. SOX2 amplification occurs in LUSC and PSMD4 amplification in LUAD. EGFR and KRAS mutations are mutually exclusive in LUAD samples. EGFR, MGA, SMARCA4, ATM, RBM10, and KDM5C genes are mutated only in LUAD but not in LUSC. CDKN2A, PTEN, and HRAS genes are mutated only in LUSC samples. The low-risk groups of both LUAD and LUSC tend to have a higher number of SNVs, CNVs, and DEGs. The signature genes and altered genes have the potential to be used as diagnostic and prognostic biomarkers for personalized oncology.

Model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomes

BACKGROUND

Although the tumour immune microenvironment is known to significantly influence immunotherapy outcomes, its association with changes in gene expression patterns in hepatocellular carcinoma (HCC) during immunotherapy and its effect on prognosis have not been clarified.

METHODS

A total of 365 HCC samples from The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA-LIHC) dataset were stratified into training datasets and verification datasets. In the training datasets, immune-related genes were analysed through univariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO)-Cox analyses to build a prognostic model. The TCGA-LIHC, GSE14520, and Imvigor210 cohorts were subjected to time-dependent receiver operating characteristic (ROC) and Kaplan-Meier survival curve analyses to verify the reliability of the developed model. Finally, single-sample gene set enrichment analysis (ssGSEA) was used to study the underlying molecular mechanisms.

RESULTS

Five immune-related genes (LDHA, PPAT, BFSP1, NR0B1, and PFKFB4) were identified and used to establish the prognostic model for patient response to HCC treatment. ROC curve analysis of the TCGA (training and validation sets) and GSE14520 cohorts confirmed the predictive ability of the five-gene-based model (AUC > 0.6). In addition, ROC and Kaplan-Meier analyses indicated that the model could stratify patients into a low-risk and a high-risk group, wherein the high-risk group exhibited worse prognosis and was less sensitive to immunotherapy than the low-risk group. Functional enrichment analysis predicted potential associations of the five genes with several metabolic processes and oncological signatures.

CONCLUSIONS

We established a novel five-gene-based prognostic model based on the tumour immune microenvironment that can predict immunotherapy efficacy in HCC patients.

Identification of a 5-Gene Metabolic Signature for Predicting Prognosis Based on an Integrated Analysis of Tumor Microenvironment in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is a common subtype of lung cancer with a depressing survival rate. The reprogramming of tumor metabolism was identified as a new hallmark of cancer in tumor microenvironment (TME), and we made a comprehensive exploration to reveal the prognostic role of the metabolic-related genes. Transcriptome profiling data of LUAD were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Based on the extracted metabolic-related genes, a novel 5-gene metabolic prognostic signature (including GNPNAT1, LPGAT1, TYMS, LDHA, and PTGES) was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. This signature confirmed its robustness and accuracy by external validation in multiple databases. It could be an independent risk factor for LUAD, and the nomograms possessed moderately accurate performance with the C-index of 0.755 (95% confidence interval: 0.706–0.804) and 0.691 (95% confidence interval: 0.636–0.746) in training set and testing set. This signature could reveal the metabolic features according to the results of gene set enrichment analysis (GSEA) and meanwhile monitor the status of TME through ESTIMATE scores and the infiltration levels of immune cells. In conclusion, this gene signature is a cost-effective tool which could indicate the status of TME to provide more clues in the exploration of new diagnostic and therapeutic strategy.

Structural and functional regulation of lactate dehydrogenase-A in cancer

Dysregulated metabolism is one of the hallmarks of cancer. Under normal physiological conditions, ATP is primarily generated by oxidative phosphorylation. Cancers commonly undergo a dramatic shift toward glycolysis, despite the presence of oxygen. This phenomenon is known as the Warburg effect, and requires the activity of LDHA. LDHA converts pyruvate to lactate in the final step of glycolysis and is often upregulated in cancer. LDHA inhibitors present a promising therapeutic option, as LDHA blockade leads to apoptosis in cancer cells. Despite this, existing LDHA inhibitors have shown limited clinical efficacy. Here, we review recent progress in LDHA structure, function and regulation as well as strategies to target this critical enzyme.

Prognostic Value of Lactate Dehydrogenase Expression in Different Cancers: A Meta-Analysis

BACKGROUND

This meta-analysis was performed to elucidate the association between the expression of lactate dehydrogenase (LDH) and the prognosis of various malignant neoplasms.

MATERIALS AND METHODS

Qualified studies were systematically identified from relevant databases, including PubMed, Cochrane Library, Embase, WanFang, and HowNet. A total of 17 eligible studies with 4,176 patients that complied with the inclusion criteria were enrolled in this meta-analysis. The 17 articles were published from 2011 to 2018. The hazard ratio (HR) and 95% confidence interval (95%CI) were obtained from the selected studies.

RESULTS

The analysis that included all LDH-related studies showed a significant association with the overall survival (OS) outcome (HR = 1.74, P = 0.001) but exhibited an insignificant association with the disease-free survival (DFS) or recurrence-free survival (RFS) outcome (HR = 1.40, P = 0.072). High lactate dehydrogenase A (LDHA) expression was significantly relevant to inferior OS (HR = 1.88, 95%CI: 1.37-2.59) and DFS or RFS (HR = 1.56, 95%CI: 1.29-1.89).

CONCLUSIONS

High LDH expression and the prognostic outcome of various cancer patients are significantly correlated. High LDHA expression is a promising biomarker for forecasting the survival of patients and the recurrence of different cancers in these patients. Further associative studies are required due to the complex role of LDH genes.

High KRT8 Expression Independently Predicts Poor Prognosis for Lung Adenocarcinoma Patients

Keratin 8 (KRT8), a type II basic intermediate filament (IF) protein, is essential for the development and metastasis of various cancers. In this study, by analyzing RNA-seq data from the Cancer Genome Atlas (TCGA)-lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), we have determined the expression profile of KRT8, and assessed its prognostic significance and the possible mechanism underlying the dysregulation. Our results showed that KRT8 mRNA expression was significantly up-regulated in both LUAD and LUSC tissues compared with normal lung tissues. The high KRT8 expression group for LUAD patients significantly reduced overall survival (OS) and recurrence-free survival (RFS). Univariate and multivariate analysis revealed that KRT8 expression was an independent prognostic indicator for poor OS and RFS in LUAD patients. However, KRT8 expression had no prognostic value in terms of OS and RFS for LUSC. By exploring DNA copy number alterations (CNAs) of the KRT8 gene in LUAD, we found that DNA low copy gain (+1 and +2) was associated with elevated KRT8 mRNA expression. From the above findings, we have deduced that KRT8 is aberrantly expressed in LUAD tissues and that its expression might independently predict poor OS and RFS for LUAD patients, but not for LUSC patients.