Establishment of the Prognostic Index Reflecting Tumor Immune Microenvironment of Lung Adenocarcinoma Based on Metabolism-Related Genes

Abnormally High Expression of DNAJB6 Accelerates Malignant Progression of Lung Adenocarcinoma

DNAJB6, a major member of the DNAJ/HSP40 family, plays an important role in tumor development. We explored the effect of DNAJB6 expression on the prognosis of patients and its biological role in lung adenocarcinoma (LUAD). mRNA and clinical data were obtained from The Cancer Genome Atlas (TCGA). Enriched pathways were determined by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A nomogram incorporating DNAJB6 and three clinical features was constructed to predict the survival rate. DNAJB6 expression and function in LUAD were explored using immunohistochemistry, Western blotting, proliferation, cell cycle analysis, RNA sequencing, and xenograft tumor assays. DNAJB6 mRNA levels were elevated in the LUAD-TCGA dataset. DNAJB6 protein levels were higher in LUAD tumor tissues than in normal tissues. A high DNAJB6 level was an independent risk factor for poor prognosis in patients with LUAD. The proportion of tumor-infiltrating immune cells significantly differed between high and low DNAJB6 expression. DNAJB6 was associated with cell cycle pathways; therefore, its knockdown induced G2/M cell cycle arrest and inhibited LUAD cell proliferation. This is the first report of the DNAJB6 requirement for LUAD cell proliferation and its potentially crucial role in LUAD prognosis.

A second life for MAO inhibitors? From CNS diseases to anticancer therapy

Monoamine oxidases A and B (MAO A, B) are ubiquitous enzymes responsible for oxidative deamination of amine neurotransmitters and xenobiotics. Despite decades of studies, MAO inhibitors (MAOIs) find today limited therapeutic space as second-line drugs for the treatment of depression and Parkinson's disease. In recent years, a renewed interest in MAOIs has been raised up by several studies investigating the role of MAOs, particularly MAO A, in tumor insurgence and progression, and the efficacy of MAOIs as coadjutants in the therapy of chemoresistant tumors. In this survey, we highlight the implication of MAOs in the biochemical pathways of tumorigenesis and review the state-of-the-art of preclinical and clinical studies of MAOIs as anticancer agents used in monotherapy or in combination with antitumor chemotherapeutics.

GNPNAT1 is a Biomarker That Predicts a Poor Prognosis of Breast Cancer

Background

Breast cancer (BC) is increasingly becoming the primary reason for death in women, which sounded the alarm. Thus, finding a novel management target for BC is imminent.

Materials and Methods

The data on gene expression and clinicopathological characteristics were downloaded from The Cancer Genome Atlas (TCGA). The expression of GNPNAT1 in 40 paired breast cancer and adjacent tissues was measured by quantitative real-time polymerase chain reaction (qRT-PCR). Univariate and Multivariate logistic regression methodology was applied to analyze the prognostic factors for lymph node metastasis (LNM). Based on the status of breast cancer-relative receptors, patients were distributed into six groups, and then the Kaplan-Meier survival analysis with a Log rank test was applied to investigate the involvement among the expression of GNPNAT1 and overall survival (OS).

Results

We found higher expression of GNPNAT1 was connected with poor survival in breast cancer by COX regulation analysis. GO, KEGG, and GSEA analysis prompted that GNPNAT1 was connected with the defense mechanism of cells, cell proliferation, and division. Immunization infiltration analysis showed that high GNPNAT1 was negatively connected with 16 immunization infiltration cell types and positively connected with four immunization infiltration cell types.

Conclusion

As a whole, our results indicated that GNPNAT1 might be a probable biomarker for diagnosis and prognosis in breast cancer.

Identification of THSD7B and PRMT9 mutations as risk factors for familial lung adenocarcinoma: A case report

RATIONALE

Lung tumors arise from the unrestrained malignant growth of pulmonary epithelial cells. Lung cancer cases include both small and non-small cell lung cancers, with lung adenocarcinoma (LUAD) accounting for roughly half of all non-small cell lung cancer cases. Research focused on familial cancers suggests that approximately 8% of lung cancer cases are linked to genetic susceptibility or heritability. The precise genetic factors that underlie the onset of lung cancer, however, remain to be firmly established.

PATIENT CONCERNS

A 43-year-old presented with nodules in the lower left lung lobe. Following initial antibiotic treatment in a local hospital, these nodules remained present and the patient subsequently underwent the resection of the left lower lobe of the lung. The patient also had 4 family members with a history of LUAD.

DIAGNOSIS

Immunohistochemical staining results including cytokeratin 7 (+), TTF-1 (+), new aspartic proteinase A (+), CK5/6 (-), P63 (-), and Ki-67 (5%+) were consistent with a diagnosis of LUAD.

INTERVENTION

Whole exome sequencing analyses of 5 patients and 6 healthy family members were performed to explore potential mutations associated with familial LUAD.

OUTCOMES

Whole exome sequencing was conducted, confirming that the proband and their 4 other family members with LUAD harbored heterozygous THSD7B (c.A4000G:p.S1334G) mutations and homozygous PRMT9 (c.G40T:p.G14C) mutations, as further confirmed via Sanger sequencing. These mutations were predicted to be deleterious using the SIFT, PolyPhen2, and MutationTaster algorithms. Protein structure analyses indicated that the mutation of the serine at amino acid position 1334 in THSD7B to a glycine would reduce the minimum free energy from 8.08 kcal/mol to 68.57 kcal/mol. The identified mutation in the PRMT9 mutation was not present in the predicted protein structure. I-Mutant2.0 predictions indicated that both of these mutations (THSD7B:p.S1334G and PRMT9: p.G14C) were predicted to reduce protein stability.

LESSONS

Heterozygous THSD7B (c.A4000G:p.S1334G) and the homozygous PRMT9 (c.G40T:p.G14C) mutations were found to be linked to LUAD incidence in the analyzed family. Early analyses of these genetic loci and timely genetic counseling may provide benefits and aid in the early diagnosis of familial LUAD.

Integration of single sample and population analysis for understanding immune evasion mechanisms of lung cancer

A deep understanding of the complex interaction mechanism between the various cellular components in tumor microenvironment (TME) of lung adenocarcinoma (LUAD) is a prerequisite for understanding its drug resistance, recurrence, and metastasis. In this study, we proposed two complementary computational frameworks for integrating multi-source and multi-omics data, namely ImmuCycReg framework (single sample level) and L0Reg framework (population or subtype level), to carry out difference analysis between the normal population and different LUAD subtypes. Then, we aimed to identify the possible immune escape pathways adopted by patients with different LUAD subtypes, resulting in immune deficiency which may occur at different stages of the immune cycle. More importantly, combining the research results of the single sample level and population level can improve the credibility of the regulatory network analysis results. In addition, we also established a prognostic scoring model based on the risk factors identified by Lasso-Cox method to predict survival of LUAD patients. The experimental results showed that our frameworks could reliably identify transcription factor (TF) regulating immune-related genes and could analyze the dominant immune escape pathways adopted by each LUAD subtype or even a single sample. Note that the proposed computational framework may be also applicable to the immune escape mechanism analysis of pan-cancer.

Identification and validation of a novel phagocytosis regulators-related signature with potential prognostic and immunotherapeutic value in patients with lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a malignant tumor that seriously affects the prognosis of patients. Tumor-associated macrophages (TAMs) play a vital role in the tumor microenvironment and can be used as a potential target for tumor therapy, and phagocytosis regulators (PRs) are particularly important in this process. However, the PRs-related signature that can predict the potential prognostic and immunotherapeutic value in patients with LUAD has not been discovered.

METHODS

In this study, we mainly analyzed the effect of phagocytosis regulators on the prognosis of LUAD, and based on multiple screening analyses including differential analysis, univariate Cox analysis, and Lasso analysis, we constructed a prognostic risk model consisting of five genes. To verify the stability of the model, survival analysis and ROC curve verification were carried out through multiple data sets. In addition, we also combined many factors, such as immune infiltrating cells, clinical grouping characteristics, immune examination sites, pro-inflammatory factors, and other factors as well as in vitro cell experiments and clinical tissue samples for further validation analysis.

RESULTS

After identifying 29 differentially expressed PRs in LUAD samples, we further constructed a prognostic model consisting of five prognostic signatures (FURIN, KIF23, SASH3, GNPNAT1, and ITGAL). Further survival analysis tests, ROC verification, as well as univariate and multivariate Cox regression analysis showed that the risk score of the model could well predict the prognosis of LUAD patients and could be used as an independent prognostic factor. In addition, we further found that these phagocytic regulators-related signatures were closely related to the immune microenvironment and immunotherapy in LUAD patients, and could well predict the efficacy of immunotherapy in patients. In vitro cell experiments and Immunohistochemistry of clinical tissues showed that the expressions of FURIN, KIF23, SASH3, GNPNAT1 and ITGAL in normal lung cells/tissues and LUAD cells/tissues were consistent with bioinformatics results, and 3 of them had significant differences.

CONCLUSION

Our study identified a novel PRs-related signature that has potential application value in predicting the prognosis of LUAD patients and predicting the efficacy of immunotherapy. This provides a new basis for the prognosis assessment of LUAD patients and provides a novel target for immunotherapy of LUAD patients.

GNPNAT1 Predicts Poor Prognosis and Cancer Development in Non-Small Cell Lung Cancer

Background

Glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1) is a key enzyme in the biosynthetic pathway of uridine diphosphate-N-acetylglucosamine and is upregulated in multiple malignancies. However, its function in cancer biology remains unclear.

Methods

Using TCGA dataset, this study analysed GNPNAT1 expression in non-small cell lung cancer (NSCLC) and assessed the correlation between GNPNAT1 and NSCLC patient prognosis. MTT and transwell assays were performed to determine the effect of GNPNAT1 on the growth and metastatic ability of lung cancer cells. GNPNAT1 expression was detected using immunohistochemistry in 78 NSCLC patients, and we analysed the correlation among clinicopathological parameters, overall survival (OS) and GNPNAT1 levels. Transcription factors that potentially regulate GNPNAT1 were explored using database analysis. RNF2 expression was verified using immunohistochemistry in NSCLC tissues.

Results

The results indicated that GNPNAT1 was upregulated in NSCLC, and patients with high GNPNAT1 levels had a poor prognosis. GNPNAT1 overexpression promoted the proliferative and metastatic ability of lung cancer cells, whereas GNPNAT1 knockdown showed the opposite effect. GNPNAT1 expression was upregulated in NSCLC tissues compared to matched normal tissues as assessed by immunohistochemistry. Moreover, GNPNAT1 levels were positively correlated with histological type and pathological stage. The negative correlation between GNPNAT1 levels and OS was confirmed in 78 NSCLC patients. Aberrant RNF2 partly contributed to the upregulation of GNPNAT1 expression in NSCLC.

Conclusion

These findings suggested that GNPNAT1 was upregulated and played an important role in NSCLC. GNPNAT1 is expected to represent an effective prognostic biomarker for NSCLC patients.

Glucosamine-phosphate N-acetyltransferase 1 and its DNA methylation can be biomarkers for the diagnosis and prognosis of lung cancer

Objective

Lung cancer ranking high in the cancer‐related list has long perplexed patients, in which glucosamine‐phosphate N‐acetyltransferase 1 (GNPNAT1) is found to be highly expressed. Besides, DNA methylation is perceived as a biomarker to assess the prognosis of patients with various cancers. However, the correlation between GNPNAT1 and DNA methylation and the role of GNPNAT1 in lung cancer remain vague.

Methods

Principal component analysis (PCA), heatmap, volcano map, Venn diagram, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to screen out the candidate genes. The viability, migration, and invasion of lung cancer cells were detected by CCK‐8 and Transwell assays. An xenograft tumor mouse model was established. The relative expressions of GNPNAT1, E‐cadherin, vimentin, Matrix metalloproteinase‐2 (MMP‐2), tissue inhibitor of metalloproteinase‐2 (TIMP‐2), E2F1, and cyclin D1 in cells or xenograft tumor tissues were quantified by Western blot, RT‐qPCR, or immunohistochemistry assay.

Results

GNPNAT1 was screened as the research object. GNPNAT1 methylation was downregulated, while GNPNAT1 expression was upregulated in lung cancer tissues. The methylation and mRNA levels of GNPNAT1 were correlated with the patient prognosis. GNPNAT1 increased cell viability, migration and invasion, and promoted the xenograft tumor volume and weight, whereas shGNPNAT1 acted oppositely. Moreover, expressions of Vimentin, MMP‐2, E2F1, and cyclin D1 were increased, but E‐cadherin and TIMP‐2 expressions were decreased by overexpressed GNPNAT1, whilst GNPNAT1 knockdown ran conversely.

Conclusion

GNPNAT1 and methylated GNPNAT1 coverage are biomarkers for the diagnosis and prognosis of lung cancer.

GPR87 promotes tumor cell invasion and mediates the immunogenomic landscape of lung adenocarcinoma

The purpose of this study is to examine the association between G protein-coupled receptor 87 (GPR87) and lung adenocarcinoma (LUAD) metastasis and immune infiltration. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets extract clinical data. According to the TCGA database, increased GPR87 expression predicts poor overall survival, progression-free interval, and disease-specific survival in LUAD patients. The meta-analysis also reveals a significant association between high GPR87 expression and poor overall survival. Moreover, functional experiments demonstrate that GPR87 silencing reduces LUAD cell invasion and migration. Immunoblotting shows that GPR87 knockdown decreased Vimentin and N-cadherin expression and increased E-cadherin expression in LUAD cells. GPR87 expression in LUAD is positively correlated with immune infiltration. In addition, GPR87 expression is associated with immune and chemotherapy resistance in LUAD patients. Our findings indicate that GPR87 promotes tumor progression and is correlated with immune infiltration, suggesting GPR87 as a possible biomarker for prognosis prediction in LUAD.

GPR87 is reported as a central player in lung adenocarcinoma and in resistance to immunotherapy, by promoting tumor cell invasion and mediating the immunogenomic landscape.

Identification of glycolysis genes signature for predicting prognosis in malignant pleural mesothelioma by bioinformatics and machine learning

BACKGROUND

Glycolysis-related genes as prognostic markers in malignant pleural mesothelioma (MPM) is still unclear. We hope to explore the relationship between glycolytic pathway genes and MPM prognosis by constructing prognostic risk models through bioinformatics and machine learning.

METHODS

The authors screened the dataset GSE51024 from the GEO database for Gene set enrichment analysis (GSEA), and performed differentially expressed genes (DEGs) of glycolytic pathway gene sets. Then, Cox regression analysis was used to identify prognosis-associated glycolytic genes and establish a risk model. Further, the validity of the risk model was evaluated using the dataset GSE67487 in GEO database, and finally, a specimen classification model was constructed by support vector machine (SVM) and random forest (RF) to further screen prognostic genes.

RESULTS

By DEGs, five glycolysis-related pathway gene sets (17 glycolytic genes) were identified to be highly expressed in MPM tumor tissues. Also 11 genes associated with MPM prognosis were identified in TCGA-MPM patients, and 6 (COL5A1, ALDH2, KIF20A, ADH1B, SDC1, VCAN) of them were included by Multi-factor COX analysis to construct a prognostic risk model for MPM patients, with Area under the ROC curve (AUC) was 0.830. Further, dataset GSE67487 also confirmed the validity of the risk model, with a significant difference in overall survival (OS) between the low-risk and high-risk groups (P < 0.05). The final machine learning screened the five prognostic genes with the highest risk of MPM, in order of importance, were ALDH2, KIF20A, COL5A1, ADH1B and SDC1.

CONCLUSIONS

A risk model based on six glycolytic genes (ALDH2, KIF20A, COL5A1, ADH1B, SDC1, VCAN) can effectively predict the prognosis of MPM 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.

Exploration of the prognostic signature reflecting tumor microenvironment of lung adenocarcinoma based on immunologically relevant genes

Lung adenocarcinoma (LUAD) represents the major histological type of lung cancer with high mortality globally. Due to the heterogeneous nature, the same treatment strategy to various patients may result in different therapeutic responses. Hence, we aimed to elaborate an effective signature for predicting patient survival outcomes. The TCGA-LUAD cohort from the TCGA portal was used as a training dataset. The GSE26939 and GSE68465 cohorts from the GEO database were taken as validation datasets. All immunologically relevant genes were extracted from the ImmPort. The ESTIMATE algorithm was employed to explore LUAD microenvironment in the training dataset. Further, the DEGs were picked out based on the immune-associated genes reflecting different statuses in the immune context of TME. Univariate/multivariate Cox regression was performed to determine six prognosis- specific genes (PIK3CG, BTK, VEGFD, INHA, INSL4, and PTPRC) and established a risk predictive signature. The time-dependent ROC indicated that AUC values were all greater than 0.70 at 1-, 3-, and 5- year intervals. Corresponding RiskScore of each LUAD patient was calculated from the signature, and they were stratified into the high- and low-risk groups by the median value of RiskScore. K-M curves and Log-rank test demonstrated significant survival differences between the two groups (P < 0.05). Similar results were exhibited in the validation datasets. The RiskScore was incredibly relevant to clinicopathological factors like gender, AJCC stage, and T stage. Also, it can mirror the distribution state of 15 kinds of TIICs and have some predictive value for the sensitivity of therapeutic drugs.

Monoamine Oxidase (MAO) as a Potential Target for Anticancer Drug Design and Development

Monoamine oxidases (MAOs) are oxidative enzymes that catalyze the conversion of biogenic amines into their corresponding aldehydes and ketones through oxidative deamination. Owing to the crucial role of MAOs in maintaining functional levels of neurotransmitters, the implications of its distorted activity have been associated with numerous neurological diseases. Recently, an unanticipated role of MAOs in tumor progression and metastasis has been reported. The chemical inhibition of MAOs might be a valuable therapeutic approach for cancer treatment. In this review, we reported computational approaches exploited in the design and development of selective MAO inhibitors accompanied by their biological activities. Additionally, we generated a pharmacophore model for MAO-A active inhibitors to identify the structural motifs to invoke an activity.

Identification and validation of a novel redox-related lncRNA prognostic signature in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the main causes of cancer deaths globally. Redox is emerging as a crucial contributor to the pathophysiology of LUAD, which can be regulated by long non-coding RNAs (lncRNAs). The aim of our research is to identify a novel redox-related lncRNA prognostic signature (redox-LPS) for better prediction of LUAD prognosis. 535 LUAD samples from The Cancer Genome Atlas (TCGA) database and 226 LUAD samples from the Gene Expression Omnibus (GEO) database were included in our study. 67 redox genes and 313 redox-related lncRNAs were identified. After performing LASSO-Cox regression analysis, a redox-LPS consisting of four lncRNAs (i.e., CRNDE, CASC15, LINC01137, and CYP1B1-AS1) was developed and validated. Our redox-LPS was superior to another three established models in predicting survival probability of LUAD patients. Univariate and multivariate Cox regression analysis revealed that risk score and stage were independent prognostic indicators. A nomogram plot including risk score and stage was constructed to predict survival probability of LUAD patients; this was further verified by calibration curves. Functional enrichment analysis and gene set enrichment analysis, were performed to determine the differences in cellular processes and signaling pathways between the high – and low-risk subgroups. A variety of algorithms (such as single-sample gene set enrichment analysis and CIBERSOFT) were conducted to uncover the landscape of tumor immune microenvironment in the high- and low-risk subgroups. In conclusion, a novel independent redox-LPS was constructed and validated for LUAD patients, which might provide new insights for clinical decision-making and precision medicine.

A Personalized Genomics Approach of the Prostate Cancer

Decades of research identified genomic similarities among prostate cancer patients and proposed general solutions for diagnostic and treatments. However, each human is a dynamic unique with never repeatable transcriptomic topology and no gene therapy is good for everybody. Therefore, we propose the Genomic Fabric Paradigm (GFP) as a personalized alternative to the biomarkers approach. Here, GFP is applied to three (one primary—“A”, and two secondary—“B” & “C”) cancer nodules and the surrounding normal tissue (“N”) from a surgically removed prostate tumor. GFP proved for the first time that, in addition to the expression levels, cancer alters also the cellular control of the gene expression fluctuations and remodels their networking. Substantial differences among the profiled regions were found in the pathways of P53-signaling, apoptosis, prostate cancer, block of differentiation, evading apoptosis, immortality, insensitivity to anti-growth signals, proliferation, resistance to chemotherapy, and sustained angiogenesis. ENTPD2, AP5M1 BAIAP2L1, and TOR1A were identified as the master regulators of the “A”, “B”, “C”, and “N” regions, and potential consequences of ENTPD2 manipulation were analyzed. The study shows that GFP can fully characterize the transcriptomic complexity of a heterogeneous prostate tumor and identify the most influential genes in each cancer nodule.

Predicting lung adenocarcinoma prognosis with a novel risk scoring based on platelet-related gene expression

Lung adenocarcinoma is the most common subtype of non-small cell lung cancer, and platelet receptor-related genes are related to its occurrence and progression. A new prognostic indicator based on platelet receptor-related genes was developed with multivariate COX analysis. Prognostic markers based on platelet-related risk score perform moderately in prognosis prediction. The functional annotation of this risk model in high-risk patients shows that the pathways related to cell cycle, glycolysis and platelet-derived related factors are rich. It is worth noting that somatic mutation analysis shows that TTN and MUC16 have higher mutation burdens in high-risk patients. Moreover, the differential genes of high- and low-risk groups are regulated by copy number variation and miRNA. And we provide a free online nomogram web tool based on clinical factors and the risk score (https://wsxzaq.shinyapps.io/wsxzaq_nomogram/). The score has been verified among three independent external cohorts (GSE13213, GSE68465 and GSE72094), and is still an independent risk factor for lung adenocarcinoma. In addition, among the other 6 cancers, the OS prognosis of high and low-risk groups of PRS is different (P < 0.05). Our research results have screened multiple platelet differential genes with clinical significance and constructed a meaningful prognostic risk score (PRS).