Distinct Prognostic Values of Alcohol Dehydrogenase Family Members for Non-Small Cell Lung Cancer

Integrating network pharmacology and computational biology to propose Yiqi Sanjie formula's mechanisms in treating NSCLC: molecular docking, ADMET, and molecular dynamics simulation

Background

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related deaths globally. Current treatments often do not fully meet efficacy and quality of life expectations. Traditional Chinese medicine (TCM), particularly the Yiqi Sanjie formula, shows promise but lacks clear mechanistic understanding. This study addresses this gap by investigating the therapeutic effects and underlying mechanisms of Yiqi Sanjie formula in NSCLC.

Methods

We utilized network pharmacology to identify potential NSCLC drug targets of the Yiqi Sanjie formula via the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Compounds with favorable oral bioavailability and drug-likeness scores were selected. Molecular docking was conducted using AutoDock Vina with structural data from the Protein Data Bank and PubChem. Molecular dynamics (MD) simulations were performed with Desmond Molecular Dynamics System, analyzing interactions up to 500 nanoseconds using the OPLS4 force field. ADMET predictions were executed using SwissADME and ADMETlab 2.0, assessing pharmacokinetic properties.

Results

Using network pharmacology tools, we performed Search Tool for the Retrieval of Interaction Genes/Proteins (STRING) analysis for protein-protein interaction, Kyoto Encyclopedia of Genes and Genomes (KEGG) for pathway enrichment, and gene ontology (GO) for functional enrichment, identifying crucial signaling pathways and biological processes influenced by the hit compounds bifendate, xambioona, and hederagenin. STRING analysis indicated substantial connectivity among the targets, suggesting significant interactions within the cell cycle regulation and growth factor signaling pathways as outlined in our KEGG results. The GO analysis highlighted their involvement in critical biological processes such as cell cycle control, apoptosis, and drug response. Molecular docking simulations quantified the binding efficiencies of the identified compounds with their targets-CCND1, CDK4, and EGFR-selected based on high docking scores that suggest strong potential interactions crucial for NSCLC inhibition. Subsequent MD simulations validated the stability of these complexes, supporting their potential as therapeutic interventions. Additionally, the novel identification of ADH1B as a target underscores its prospective significance in NSCLC therapy, further expanded by our comprehensive bioinformatics approach.

Conclusions

Our research demonstrates the potential of integrating network pharmacology and computational biology to elucidate the mechanisms of the Yiqi Sanjie formula in NSCLC treatment. The identified compounds could lead to novel targeted therapies, especially for patients with overexpressed targets. The discovery of ADH1B as a therapeutic target adds a new dimension to NSCLC treatment strategies. Further studies, both in vitro and in vivo, are needed to confirm these computational findings and advance these compounds towards clinical trials.

Clinical significance and immune landscape of a novel immune cell infiltration-based prognostic model in lung adenocarcinoma

Tumor-infiltrating immune cells (TICs) play a central role in the tumor microenvironment, which can reflect the host anti-tumor immune response. However, few studies have explored TICs in predicting the prognosis of lung adenocarcinoma (LUAD). In our study, we enrolled 2470 LUAD patients from TCGA and GEO databases, and the normalized enrichment scores for 65 immune cell types were quantified for each patient. An immune-related risk score (IRRS) was built on the basis of 17 selected TICs using LASSO regression analysis, and the results showed that high-risk patients were correlated with shorter survival time for the LUAD cohorts. Correlation analyses between IRRS and clinical characteristics were also evaluated to validate the clinical use of IRRS. In addition, we analyzed the differences in the distribution of immune cell infiltration and immunoregulatory gene expression, which may facilitate individual immunotherapy. Based on the above result, we conclude that IRRS can act as a powerful predictor for risk stratification and prognosis prediction, and may facilitate the decision-making process for LUAD patients.

Global Gene Expression Profiling and Bioinformatics Analysis Reveal Downregulated Biomarkers as Potential Indicators for Hepatocellular Carcinoma

Objective: The study aimed to elucidate the significance of CLEC4G, CAMK2β, SLC22A1, CBFA2T3, and STAB2 in the prognosis of hepatocellular carcinoma (HCC) patients and their associated molecular biological characteristics. Additionally, the research sought to identify new potential biomarkers with therapeutic and diagnostic relevance for clinical applications. Methods and Materials: We utilized a publicly available high throughput phosphoproteomics and proteomics data set of HCC to focus on the analysis of 12 downregulated phosphoproteins in HCC. Our approach integrates bioinformatic analysis with pathway analysis, encompassing gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and the construction of a protein-protein interaction (PPI) network. Results: In total, we quantified 11547 phosphorylation sites associated with 4043 phosphoproteins from a cohort of 159 HCC patients. Within this extensive data set, our specific focus was on 19 phosphorylation sites displaying significant downregulation (log2 FC ≤ -2 with p-values < 0.0001). Remarkably, our investigation revealed distinct pathways exhibiting differential regulation across multiple dimensions, including the genomic, transcriptomic, proteomic, and phosphoproteomic levels. These pathways encompass a wide range of critical cellular processes, including cellular component organization, cell cycle control, signaling pathways, transcriptional and translational control, and metabolism. Furthermore, our bioinformatics analysis unveiled noteworthy insights into the subcellular localizations, biological processes, and molecular functions associated with these proteins and phosphoproteins. Within the context of the PPI network, we identified 12 key genes CLEC4G, STAB2, ADH1A, ADH1B, CAMK2B, ADH4, CHGB, PYGL, ADH1C, AKAP12, CBFA2T3, and SLC22A1 as the top highly interconnected hub genes. Conclusions: The findings related to CLEC4G, ADH1B, SLC22A1, CAMK2β, CBFA2T3, and STAB2 indicate their reduced expression in HCC, which is associated with an unfavorable prognosis. Furthermore, the results of KEGG and GO pathway analyses suggest that these genes may impact liver cancer by engaging various targets and pathways, ultimately promoting the progression of hepatocellular carcinoma. These results underscore the significant potential of CLEC4G, ADH1B, SLC22A1, CAMK2β, CBFA2T3, and STAB2 as key contributors to HCC development and advancement. This insight holds promise for identifying therapeutic targets and charting research avenues to enhance our understanding of the intricate molecular mechanisms underlying hepatocellular carcinoma.

Identification and validation of a novel predictive signature based on hepatocyte-specific genes in hepatocellular carcinoma by integrated analysis of single-cell and bulk RNA sequencing

BACKGROUND

Hepatocellular carcinoma represents a significant global burden in terms of cancer-related mortality, posing a substantial risk to human health. Despite the availability of various treatment modalities, the overall survival rates for patients with hepatocellular carcinoma remain suboptimal. The objective of this study was to explore the potential of novel biomarkers and to establish a novel predictive signature utilizing multiple transcriptome profiles.

METHODS

The GSE115469 and CNP0000650 cohorts were utilized for single cell analysis and gene identification. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets were utilized in the development and evaluation of a predictive signature. The expressions of hepatocyte-specific genes were further validated using the GSE135631 cohort. Furthermore, immune infiltration results, immunotherapy response prediction, somatic mutation frequency, tumor mutation burden, and anticancer drug sensitivity were analyzed based on various risk scores. Subsequently, functional enrichment analysis was performed on the differential genes identified in the risk model. Moreover, we investigated the expression of particular genes in chronic liver diseases utilizing datasets GSE135251 and GSE142530.

RESULTS

Our findings revealed hepatocyte-specific genes (ADH4, LCAT) with notable alterations during cell maturation and differentiation, leading to the development of a novel predictive signature. The analysis demonstrated the efficacy of the model in predicting outcomes, as evidenced by higher risk scores and poorer prognoses in the high-risk group. Additionally, a nomogram was devised to forecast the survival rates of patients at 1, 3, and 5 years. Our study demonstrated that the predictive model may play a role in modulating the immune microenvironment and impacting the anti-tumor immune response in hepatocellular carcinoma. The high-risk group exhibited a higher frequency of mutations and was more likely to benefit from immunotherapy as a treatment option. Additionally, we confirmed that the downregulation of hepatocyte-specific genes may indicate the progression of hepatocellular carcinoma and aid in the early diagnosis of the disease.

CONCLUSION

Our research findings indicate that ADH4 and LCAT are genes that undergo significant changes during the differentiation of hepatocytes into cancer cells. Additionally, we have created a unique predictive signature based on genes specific to hepatocytes.

Population-enriched innate immune variants may identify candidate gene targets at the intersection of cancer and cardio-metabolic disease

Both cancer and cardio-metabolic disease disparities exist among specific populations in the US. For example, African Americans experience the highest rates of breast and prostate cancer mortality and the highest incidence of obesity. Native and Hispanic Americans experience the highest rates of liver cancer mortality. At the same time, Pacific Islanders have the highest death rate attributed to type 2 diabetes (T2D), and Asian Americans experience the highest incidence of non-alcoholic fatty liver disease (NAFLD) and cancers induced by infectious agents. Notably, the pathologic progression of both cancer and cardio-metabolic diseases involves innate immunity and mechanisms of inflammation. Innate immunity in individuals is established through genetic inheritance and external stimuli to respond to environmental threats and stresses such as pathogen exposure. Further, individual genomes contain characteristic genetic markers associated with one or more geographic ancestries (ethnic groups), including protective innate immune genetic programming optimized for survival in their corresponding ancestral environment(s). This perspective explores evidence related to our working hypothesis that genetic variations in innate immune genes, particularly those that are commonly found but unevenly distributed between populations, are associated with disparities between populations in both cancer and cardio-metabolic diseases. Identifying conventional and unconventional innate immune genes that fit this profile may provide critical insights into the underlying mechanisms that connect these two families of complex diseases and offer novel targets for precision-based treatment of cancer and/or cardio-metabolic disease.

Establishment of a prognostic model toward lung squamous cell carcinoma based on m7G-related genes in the cancer genome atlas

Lung squamous cell carcinoma (LUSC) is a non-small cell lung cancer with a poor prognosis owing to late diagnosis. New molecular markers are urgently needed to improve the diagnosis and prognosis of LUSC. 7-Methylguanosine (m7G) modifications, a tRNA modification, are common in eubacteria, eukaryotes, and a few archaea. These modifications promote the turnover and stability of some mRNAs to prevent mRNA decay, improve translation efficiency, and reduce ribosomal pausing but are associated with poor survival in human cancer cells. However, expression of m7G-related genes in LUSC and their association with prognosis remain unclear. In the present study, we identified nine differentially expressed genes related to prognosis by comparing the expression profiles of tumor tissues (502 LUSC reports) with normal tissues (49 adjacent nontumor lung tissue reports). The genes included six upregulated genes (KLK7, LCE3E, AREG, KLK6, ZBED2, and MAPK4) and three downregulated genes (ADH1C, NTS, and ERLIN2). Based on these nine genes, patients with LUSC were classified into low- and high-risk groups to analyze the trends in prognosis. We found that the nine m7G-related genes play important roles in immune regulation, hormone regulation, and drug sensitivity through pathways including antigen processing and presentation, adherent plaques, extracellular matrix receptor interactions, drug metabolism of cytochrome P-450, and metabolism of cytochrome P-450 to xenobiotics; the functions of these genes are likely accomplished in part by m6A modifications. The effect of m7G-related genes on the diagnosis and prognosis of LUSC was further indicated by population analysis.NEW & NOTEWORTHY Based on the differential expression of 7-methylguanosine (m7G) modification-associated genes between normal and lung squamous cell carcinoma (LUSC) tissues, and considering the performance of our m7G-related gene risk profiles as independent risk factors in predicting overall survival, we conclude that m7G modification is closely linked to the development of LUSC. In addition, this study offers a new genetic marker for predicting the prognosis of patients with LUSC and presents a crucial theoretical foundation for future investigations on the relationship between m7G modification-related genes, immunity, and drug sensitivity in LUSC.

Identification and validation of a tyrosine metabolism-related prognostic prediction model and characterization of the tumor microenvironment infiltration in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is an aggressive and heterogeneous disease characterized by high morbidity and mortality. The liver is the vital organ that participates in tyrosine catabolism, and abnormal tyrosine metabolism could cause various diseases, including HCC. Besides, the tumor immune microenvironment is involved in carcinogenesis and can influence the patients' clinical outcomes. However, the potential role of tyrosine metabolism pattern and immune molecular signature is poorly understood in HCC.

METHODS

Gene expression, somatic mutations, copy number variation data, and clinicopathological information of HCC were downloaded from The Cancer Genome Atlas (TCGA) database. GSE14520 from the Gene Expression Omnibus (GEO) databases was used as a validation dataset. We performed unsupervised consensus clustering of tyrosine metabolism-related genes (TRGs) and classified patients into distinct molecular subtypes. We used ESTIMATE algorithms to evaluate the immune infiltration. We then applied LASSO Cox regression to establish the TRGs risk model and validated its predictive performance.

RESULTS

In this study, we first described the alterations of 42 TRGs in HCC cohorts and characterized the clinicopathological characteristics and tumor microenvironmental landscape of the two distinct subtypes. We then established a tyrosine metabolism-related scoring system and identified five TRGs, which were highly correlated with prognosis and representative of this gene set, namely METTL6, GSTZ1, ADH4, ADH1A, and LCMT1. Patients in the high-risk group had an inferior prognosis. Univariate and multivariate Cox proportional hazards regression analysis also showed that the tyrosine metabolism-related signature was an independent prognostic indicator. Besides, receiver operating characteristic curve (ROC) analysis demonstrated the predictive accuracy of the TRGs signature that could reliably predict 1-, 3-, and 5-year survival in both TCGA and GEO cohorts. We also got consistent results by performing clone formation and invasion analysis, and immunohistochemical (IHC) assays. Moreover, we also discovered that the TRGs signature was significantly associated with the different immune landscapes and therapeutic drug sensitivity.

CONCLUSION

Our comprehensive analysis revealed the potential molecular signature and clinical utilities of TRGs in HCC. The model based on five TRGs can accurately predict the survival outcomes of HCC, improving our knowledge of TRGs in HCC and paving a new path for guiding risk stratification and treatment strategy development for HCC patients.

Genetic variant of ADH1C for predicting survival in esophageal squamous cell cancer patients who underwent postoperative radiotherapy

Background: Single nucleotide polymorphisms (SNPs) of essential enzymes for alcohol metabolism ADH1B, ADH1C, and ALDH2 are commonly regarded as genetic biomarkers for esophageal squamous cell carcinoma (ESCC) susceptibility. However, there have not been any reports on relations between SNPs of these genes and the prognosis of postoperative radiotherapy in ESCC. The current study aimed to understand the associations between gene variants of alcohol metabolism and adjuvant radiotherapy’s prognosis in ESCC.

Methods: This study retrospectively analyzed 110 ESCC patients from our institution who received adjuvant radiotherapy after surgery. The SNPs of ADH1B rs1229984, ADH1C rs1789924, and ALDH2 rs671 were detected by Sanger sequencing using formalin-fixed paraffin-embedded tumor samples. A nomogram was drawn based on prognostic factors associated with overall survival (OS).

Results: ADH1C rs1789924 (C&gt;T) was associated with poor DFS and OS in ESCC patients undergoing adjuvant radiotherapy. Multivariate analysis showed that ADH1C rs1789924 (C&gt;T) was one of the independent prognosis factors of DFS and OS. However, the genotypes of ADH1B SNP rs1229984 and ALDH2 rs671 were not associated with differences in the PFS and OS of these patients. Compared with the AJCC staging system, the nomogram containing the ADH1C genotype can more effectively and accurately predict the survival time of ESCC after surgery and adjuvant radiotherapy.

Conclusion: ADH1C rs1789924 might be a prognostic genetic biomarker for ESCC patients undergoing surgery and postoperative radiotherapy.

Identification of Energy Metabolism-Related Gene Signatures From scRNA-Seq Data to Predict the Prognosis of Liver Cancer Patients

The increasingly common usage of single-cell sequencing in cancer research enables analysis of tumor development mechanisms from a wider range of perspectives. Metabolic disorders are closely associated with liver cancer development. In recent years, liver cancer has been evaluated from different perspectives and classified into different subtypes to improve targeted treatment strategies. Here, we performed an analysis of liver cancer from the perspective of energy metabolism based on single-cell sequencing data. Single-cell and bulk sequencing data of liver cancer patients were obtained from GEO and TCGA/ICGC databases, respectively. Using the Seurat R package and protocols such as consensus clustering analysis, genes associated with energy metabolism in liver cancer were identified and validated. An energy metabolism-related score (EM score) was established based on five identified genes. Finally, the sensitivity of patients in different scoring groups to different chemotherapeutic agents and immune checkpoint inhibitors was analyzed. Tumor cells from liver cancer patients were found to divide into nine clusters, with cluster 4 having the highest energy metabolism score. Based on the marker genes of this cluster and TCGA database data, the five most stable key genes (ADH4, AKR1B10, CEBPZOS, ENO1, and FOXN2) were identified as energy metabolism-related genes in liver cancer. In addition, drug sensitivity analysis showed that patients in the low EM score group were more sensitive to immune checkpoint inhibitors and chemotherapeutic agents AICAR, metformin, and methotrexate.

Alcohol dehydrogenase expression patterns in normal prostate, benign prostatic hyperplasia, and prostatic adenocarcinoma in African American and Caucasian men

BACKGROUND

In situ metabolism of ethanol by alcohol dehydrogenases (ADHs) contributes to oxidative damage of cells and DNA and has been linked to carcinogenesis in numerous epithelial tissues. The goal of this study was to determine expression patterns of ADH1 and ADH7 isozymes in normal, hyperplastic (benign prostatic hyperplasia [BPH]) and neoplastic (prostate cancer [PCa]) prostate. Furthermore, racial differences in ADH expression between African Americans and Caucasians were investigated.

METHODS

ADH expression patterns were characterized by density analysis of ADH immunohistochemistry (n = 21) and real-time RT-PCR of total RNAs by laser-capture microdissection (n = 10) and whole tissue formalin-fixed paraffin embedded prostate biopsies (n = 63).

RESULTS

ADH protein is found in normal prostate and is primarily associated with glandular epithelium. Transcripts of ADH1B are suppressed in PCa compared to BPH (p = 0.0095). Racial differences in ADH7 transcripts exist between African American and Caucasian men. A total of 57.6% of biopsies from African American prostates have detectable ADH7 messenger RNA (mRNA) transcripts compared to the 13.3% of Caucasian prostate biopsies with detectable transcripts (p = 0.0005). This increased frequency of detection contributes to higher mean ADH7 mRNA transcript levels in African Americans (p = 0.001).

CONCLUSIONS

To our knowledge this study is the first to report downregulation of ADH1B in neoplastic prostate at the transcriptional level, suggesting protective regulatory functions. ADH7 transcripts were not detectable in all samples and was found in higher frequency and amount in our African American samples. Racial differences in ADH7 within the prostate is a novel finding and should be investigated further.

An advanced network pharmacology study to explore the novel molecular mechanism of Compound Kushen Injection for treating hepatocellular carcinoma by bioinformatics and experimental verification

Background

Compound Kushen Injection (CKI) is a Chinese patent drug that exerts curative effects in the clinical treatment of hepatocellular carcinoma (HCC). This study aimed to explore the targets and potential pharmacological mechanisms of CKI in the treatment of HCC.

Methods

In this study, network pharmacology was used in combination with molecular biology experiments to predict and verify the molecular mechanism of CKI in the treatment of HCC. The constituents of CKI were identified by UHPLC-MS/MS and literature search. The targets corresponding to these compounds and the targets related to HCC were collected based on public databases. To screen out the potential hub targets of CKI in the treatment of HCC, a compound-HCC target network was constructed. The underlying pharmacological mechanism was explored through the subsequent enrichment analysis. Interactive Gene Expression Profiling Analysis and Kaplan-Meier plotter were used to examine the expression and prognostic value of hub genes. Furthermore, the effects of CKI on HCC were verified through molecular docking simulations and cell experiments in vitro.

Results

Network analysis revealed that BCHE, SRD5A2, EPHX2, ADH1C, ADH1A and CDK1 were the key targets of CKI in the treatment of HCC. Among them, only CDK1 was highly expressed in HCC tissues, while the other 5 targets were lowly expressed. Furthermore, the six hub genes were all closely related to the prognosis of HCC patients in survival analysis. Molecular docking revealed that there was an efficient binding potential between the constituents of CKI and BCHE. Experiments in vitro proved that CKI inhibited the proliferation of HepG2 cells and up-regulated SRD5A2 and ADH1A, while down-regulated CDK1 and EPHX2.

Conclusions

This study revealed and verified the targets of CKI on HCC based on network pharmacology and experiments and provided a scientific reference for further mechanism research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12906-022-03530-3.

Identification of risk model based on glycolysis-related genes in the metastasis of osteosarcoma

BACKGROUND

Glycolytic metabolic pathway has been confirmed to play a vital role in the proliferation, survival, and migration of malignant tumors, but the relationship between glycolytic pathway-related genes and osteosarcoma (OS) metastasis and prognosis remain unclear.

METHODS

We performed Gene set enrichment analysis (GSEA) on the osteosarcoma dataset in the TARGET database to explore differences in glycolysis-related pathway gene sets between primary osteosarcoma (without other organ metastases) and metastatic osteosarcoma patient samples, as well as glycolytic pathway gene set gene difference analysis. Then, we extracted OS data from the TCGA database and used Cox proportional risk regression to identify prognosis-associated glycolytic genes to establish a risk model. Further, the validity of the risk model was confirmed using the GEO database dataset. Finally, we further screened OS metastasis-related genes based on machine learning. We selected the genes with the highest clinical metastasis-related importance as representative genes for in vitro experimental validation.

RESULTS

Using the TARGET osteosarcoma dataset, we identified 5 glycolysis-related pathway gene sets that were significantly different in metastatic and non-metastatic osteosarcoma patient samples and identified 29 prognostically relevant genes. Next, we used multivariate Cox regression to determine the inclusion of 13 genes (ADH5, DCN, G6PD, etc.) to construct a prognostic risk score model to predict 1- (AUC=0.959), 3- (AUC=0.899), and 5-year (AUC=0.895) survival under the curve. Ultimately, the KM curves pooled into the datasets GSE21257 and GSE39055 also confirmed the validity of the prognostic risk model, with a statistically significant difference in overall survival between the low- and high-risk groups (P<0.05). In addition, machine learning identified INSR as the gene with the highest importance for OS metastasis, and the transwell assay verified that INSR significantly promoted OS cell metastasis.

CONCLUSIONS

A risk model based on seven glycolytic genes (INSR, FAM162A, GLCE, ADH5, G6PD, SDC3, HS2ST1) can effectively evaluate the prognosis of osteosarcoma, and in vitro experiments also confirmed the important role of INSR in promoting OS migration.

Investigation of miR-21-5p Key Target Genes and Pathways in Head and Neck Squamous Cell Carcinoma Based on TCGA Database and Bioinformatics Analysis

Aim: Head and neck squamous cell carcinoma (HNSCC) is the sixth most commonly diagnosed malignancy worldwide. Overexpressed of microRNA-21-5p (miR-21-5p) has been reported to be involved in the development of HNSCC. However, the role of miR-21-5p in HNSCC is still not fully elucidated. The purpose of this study was to explore the underlying molecular mechanisms of miR-21-5p in HNSCC. Methods: RT-qPCR was used to determine the differential expression levels of miR-21-5p in tissue samples of HNSCC patients. Meta-analysis was performed based on miRNA expression data collected from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA), and published articles to evaluate the expression of miR-21-5p in HNSCC. We investigated the biological function of miR-21-5P by gene ontology enrichment and target prediction analysis. Furthermore, RT-qPCR and IHC were conducted to verify the expression of target genes. Finally, Kaplan–Meier survival analysis was performed to assessed the prognostic value of the putative miR-21-5p target genes. Results: MiR-21-5p was significantly overexpressed in HNSCC compared to healthy tissues (P < .05) and showed potent predictive power with a summary receiver operating characteristic of 0.90. Meanwhile, the expression of miR-21-5p was significantly correlated with tumor stage, T stage and smoking in HNSCC (P < .05). A total of 71 down-regulated genes, both HNSCC-related and miR-21-p5-related, were obtained from the analytical integration. Two predicted genes (ADH7, RDH12) were down-regulated in HNSCC, and significantly negatively correlated with miR-21-5p. IHC and RT-qPCR demonstrated that the expression of ADH7 and RDH12 in HNSCC samples was significantly lower than control. And high expression of ADH7 was associated with better DFS of HNSCC patients. Conclusions: miR-21-5p may target at ADH7, RDH12 and participate in regulation of retinol metabolism, which might affect the prognosis of HNSCC. High expression of ADH7 may indicate better prognosis in HNSCC patients.

Identification of the Signature Associated With m6A RNA Methylation Regulators and m6A-Related Genes and Construction of the Risk Score for Prognostication in Early-Stage Lung Adenocarcinoma

Background

N6-methyladenosine (m⁶A) RNA modification is vital for cancers because methylation can alter gene expression and even affect some functional modification. Our study aimed to analyze m⁶A RNA methylation regulators and m⁶A-related genes to understand the prognosis of early lung adenocarcinoma.

Methods

The relevant datasets were utilized to analyze 21 m⁶A RNA methylation regulators and 5,486 m⁶A-related genes in m⁶Avar. Univariate Cox regression analysis, random survival forest analysis, Kaplan-Meier analysis, Chi-square analysis, and multivariate cox analysis were carried out on the datasets, and a risk prognostic model based on three feature genes was constructed.

Results

Respectively, we treated GSE31210 (n = 226) as the training set, GSE50081 (n = 128) and TCGA data (n = 400) as the test set. By performing univariable cox regression analysis and random survival forest algorithm in the training group, 218 genes were significant and three prognosis-related genes (ZCRB1, ADH1C, and YTHDC2) were screened out, which could divide LUAD patients into low and high-risk group (P < 0.0001). The predictive efficacy of the model was confirmed in the test group GSE50081 (P = 0.0018) and the TCGA datasets (P = 0.014). Multivariable cox manifested that the three-gene signature was an independent risk factor in LUAD. Furthermore, genes in the signature were also externally validated using the online database. Moreover, YTHDC2 was the important gene in the risk score model and played a vital role in readers of m⁶A methylation.

Conclusion

The findings of this study suggested that associated with m⁶A RNA methylation regulators and m⁶A-related genes, the three-gene signature was a reliable prognostic indicator for LUAD patients, indicating a clinical application prospect to serve as a potential therapeutic target.

TNMplot.com: A Web Tool for the Comparison of Gene Expression in Normal, Tumor and Metastatic Tissues

Genes showing higher expression in either tumor or metastatic tissues can help in better understanding tumor formation and can serve as biomarkers of progression or as potential therapy targets. Our goal was to establish an integrated database using available transcriptome-level datasets and to create a web platform which enables the mining of this database by comparing normal, tumor and metastatic data across all genes in real time. We utilized data generated by either gene arrays from the Gene Expression Omnibus of the National Center for Biotechnology Information (NCBI-GEO) or RNA-seq from The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and The Genotype-Tissue Expression (GTEx) repositories. The altered expression within different platforms was analyzed separately. Statistical significance was computed using Mann–Whitney or Kruskal–Wallis tests. False Discovery Rate (FDR) was computed using the Benjamini–Hochberg method. The entire database contains 56,938 samples, including 33,520 samples from 3180 gene chip-based studies (453 metastatic, 29,376 tumorous and 3691 normal samples), 11,010 samples from TCGA (394 metastatic, 9886 tumorous and 730 normal), 1193 samples from TARGET (1 metastatic, 1180 tumorous and 12 normal) and 11,215 normal samples from GTEx. The most consistently upregulated genes across multiple tumor types were TOP2A (FC = 7.8), SPP1 (FC = 7.0) and CENPA (FC = 6.03), and the most consistently downregulated gene was ADH1B (FC = 0.15). Validation of differential expression using equally sized training and test sets confirmed the reliability of the database in breast, colon, and lung cancer at an FDR below 10%. The online analysis platform enables unrestricted mining of the database and is accessible at TNMplot.com.

Prognostic implications of alcohol dehydrogenases in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a malignancy with high incidence and mortality rates worldwide. Alcohol dehydrogenases (ADHs) are huge family of dehydrogenase enzymes and associated with the prognosis of various cancers. However, comprehensive analysis of prognostic implications related to ADHs in HCC is still lacking and largely unknown.

Methods

The expression profiles and corresponding clinical information of HCC were obtained from The Cancer Genome Atlas (TCGA). Wilcoxon signed-rank test was employed to evaluate the expression of ADHs. Cox regression and Kaplan-Meier analyses were used to investigate the association between clinicopathological characteristics and survival. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analyses were performed and visualized using R/BiocManager package.

Results

We found that the expression of ADH1A, ADH1B, ADH1C, ADH4, and ADH6 was significantly downregulated in HCC samples compared to normal liver samples. Our univariate and multivariate Cox regression analyses results showed that high expression of ADH1A, ADH1B, ADH1C, ADH4, and ADH6 was considered as an independent factor with an improved prognosis for the survival of HCC patients. Moreover, our Kaplan-Meier analysis results also revealed that high expression of AHD1A, ADH1B, ADH1C, ADH4, and ADH6 was significantly associated with good survival rate in HCC patients. In addition, GO, KEGG, and GSEA analyses unveiled several oncogenic signaling pathways were negatively associated high expression of ADHs in HCC.

Conclusion

In the present study, our results provide the potential prognostic biomarkers or molecular targets for the patients with HCC.

The prognostic value of the lysyl oxidase family in ovarian cancer

BACKGROUND

Our study intended to evaluate the prognostic value of lysyl oxidase (LOX) and its four relevant members, the lysyl oxidase-like genes (LOXL1-4), in ovarian cancer (OC) patients.

MATERIAL AND METHODS

The Kaplan-Meier plotter (KM plotter) database was used to investigate the prognostic power of the LOX family for OC patients. Overall survival (OS) and progression-free survival (PFS) were the clinical endpoints. The prognostic roles of the LOX family in OC patients were also analyzed according to various clinicopathological characteristics, including histological subtypes, clinical stages, pathological grades, and chemotherapeutic treatments.

RESULTS

Overexpression of LOX, LOXL1, LOXL2, and LOXL3 mRNA indicated poor OS and PFS in OC patients, particularly in serous and grade II + III OC patients. Overexpression of LOXL4 mRNA resulted in worse PFS in OC patients. Overexpression of LOX and LOXL1 mRNA showed worse OS and PFS in stage III + IV OC patients, and overexpression of LOXL3 mRNA indicated worse OS and PFS in stage I + II OC patients. Overexpression of LOX, LOXL3, and LOXL4 mRNA indicated worse OS and PFS among OC patients who received platinum, taxol, and taxol + platinum chemotherapy. Overexpression of LOXL1 and LOXL2 mRNA was related to lower OS and PFS in OC patients who received platinum chemotherapy.

CONCLUSION

LOX, LOXL1, LOXL2, and LOXL3 may become potential predictive markers for negative outcomes in OC patients. Moreover, the LOX family can serve as new molecular predictors for the efficiency of platinum-based chemotherapy in OC patients.

The Influence of Met Receptor Level on HGF-Induced Glycolytic Reprogramming in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) is known to overexpress a variety of receptor tyrosine kinases, such as the HGF receptor Met. Like other malignancies, HNSCC involves a mutual interaction between the tumor cells and surrounding tissues and cells. We hypothesized that activation of HGF/Met signaling in HNSCC influences glucose metabolism and therefore substantially changes the tumor microenvironment. To determine the effect of HGF, we submitted three established HNSCC cell lines to mRNA sequencing. Dynamic changes in glucose metabolism were measured in real time by an extracellular flux analyzer. As expected, the cell lines exhibited different levels of Met and responded differently to HGF stimulation. As confirmed by mRNA sequencing, the level of Met expression was associated with the number of upregulated HGF-dependent genes. Overall, Met stimulation by HGF leads to increased glycolysis, presumably mediated by higher expression of three key enzymes of glycolysis. These effects appear to be stronger in Methigh-expressing HNSCC cells. Collectively, our data support the hypothesized role of HGF/Met signaling in metabolic reprogramming of HNSCC.

A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin

BACKGROUND

The influence of the Warburg phenomenon on chemotherapy response is unknown. Saccharomyces cerevisiae mimics the Warburg effect, repressing respiration in the presence of adequate glucose. Yeast phenomic experiments were conducted to assess potential influences of Warburg metabolism on gene-drug interaction underlying the cellular response to doxorubicin. Homologous genes from yeast phenomic and cancer pharmacogenomics data were analyzed to infer evolutionary conservation of gene-drug interaction and predict therapeutic relevance.

METHODS

Cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library were measured by quantitative high-throughput cell array phenotyping (Q-HTCP), treating with escalating doxorubicin concentrations under conditions of respiratory or glycolytic metabolism. Doxorubicin-gene interaction was quantified by departure of CPPs observed for the doxorubicin-treated mutant strain from that expected based on an interaction model. Recursive expectation-maximization clustering (REMc) and Gene Ontology (GO)-based analyses of interactions identified functional biological modules that differentially buffer or promote doxorubicin cytotoxicity with respect to Warburg metabolism. Yeast phenomic and cancer pharmacogenomics data were integrated to predict differential gene expression causally influencing doxorubicin anti-tumor efficacy.

RESULTS

Yeast compromised for genes functioning in chromatin organization, and several other cellular processes are more resistant to doxorubicin under glycolytic conditions. Thus, the Warburg transition appears to alleviate requirements for cellular functions that buffer doxorubicin cytotoxicity in a respiratory context. We analyzed human homologs of yeast genes exhibiting gene-doxorubicin interaction in cancer pharmacogenomics data to predict causality for differential gene expression associated with doxorubicin cytotoxicity in cancer cells. This analysis suggested conserved cellular responses to doxorubicin due to influences of homologous recombination, sphingolipid homeostasis, telomere tethering at nuclear periphery, actin cortical patch localization, and other gene functions.

CONCLUSIONS

Warburg status alters the genetic network required for yeast to buffer doxorubicin toxicity. Integration of yeast phenomic and cancer pharmacogenomics data suggests evolutionary conservation of gene-drug interaction networks and provides a new experimental approach to model their influence on chemotherapy response. Thus, yeast phenomic models could aid the development of precision oncology algorithms to predict efficacious cytotoxic drugs for cancer, based on genetic and metabolic profiles of individual tumors.

Long Non-Coding RNAs (lncRNAs) Tumor-Suppressive Role of lncRNA on Chromosome 8p12 (TSLNC8) Inhibits Tumor Metastasis and Promotes Apoptosis by Regulating Interleukin 6 (IL-6)/Signal Transducer and Activator of Transcription 3 (STAT3)/Hypoxia-Inducible Factor 1-alpha (HIF-1α) Signaling Pathway in Non-Small Cell Lung Cancer

BACKGROUND Long non-coding RNAs (lncRNAs) exert various functions in human cancers. However, the biological functions of lncRNAs in non-small cell lung cancer (NSCLC) are unknown. In the present study we investigated the tumor-suppressive role of lncRNA on chromosome 8p12 (TSLNC8) in the pathogenesis and progression of NSCLC. MATERIAL AND METHODS qRT-PCR was carried out to evaluate the expression of TSLNC8 in lung cancer cell lines. The effects of TSLNC8 on A549 cells proliferation, migration, and invasion were analyzed using CCK-8 assay, wound healing assay, Transwell assay, and Western blot analysis. We used flow cytometry to assess cell apoptosis, and cell autophagy was assessed by Western blot analysis and immunofluorescence staining. Levels of proteins in the IL-6/STAT3/HIF-1alpha pathway were measured by Western blot analysis. RESULTS The results revealed that TSLNC8 was significantly downregulated in lung cancer cells compared to normal bronchial epithelial cells. Further experiments showed that overexpression of TSLNC8 in A549 cells significantly inhibited proliferation in a time-dependent manner and promoted cell apoptosis. We found that TSLNC8 overexpression suppressed cell migration and invasion, and upregulation of TSLNC8 regulated the protein levels of Beclin-1, p62, ATG14, and LC3-II and inhibited the IL-6/STAT3/HIF-1alpha signaling pathway. CONCLUSIONS lncRNA TSLNC8 remarkably inhibited the proliferation and migration and accelerated apoptosis of lung cancer cells by targeting the IL-6/STAT3/HIF-1alpha signaling pathway. TSLNC8 may be a potential therapeutic target for the diagnosis and treatment of NSCLC.

Genome-wide analysis reveals alcohol dehydrogenase 1C and secreted phosphoprotein 1 for prognostic biomarkers in lung adenocarcinoma

To seek out novel promising biomarkers for predicting lung adenocarcinoma (LUAD) prognosis, we conducted this study. First, 279 upregulated and 37 downregulated differentially expressed genes were obtained from LUAD and para-carcinoma tissues by the Affymetrix GeneChip Human Transcriptome Array. Then, we randomly classified samples of LUAD data set GSE31210 as training and testing sets in a 1:1 ratio. Alcohol dehydrogenase 1C (ADH1C) and secreted phosphoprotein 1 (SPP1) were finally identified correlating with the LUAD survival through least absolute shrinkage and selection operator penalized Cox proportion hazards regression model, and applied to build a 2-gene signature related to prognosis in training set. Univariate and multivariable survival analyses suggested that overall survival (OS) and relapse-free survival (RFS) in the 2-gene signature low-risk group were better than the high-risk group. Kaplan-Meier curves proved that elevated ADH1C expression and reduced SPP1 expression were related to better OS and RFS. Besides, the SPP1 expressed higher in LUAD than para-carcinoma tissues using quantitative reverse transcription polymerase chain reaction assay. Finally, the association between the two genes and clinicopathological parameters in 80 LUAD were analyzed, it is suggested that SPP1 was relevant to epidermal growth factor receptor mutation. These findings indicated that ADH1C and SPP1 might be novel promising biomarkers for predicting LUAD prognosis.