Identification of a novel gene expression signature associated with overall survival in patients with lung adenocarcinoma: A comprehensive analysis based on TCGA and GEO databases

Succinylation of 14-3-3 theta by CPT1A promotes survival and paclitaxel resistance in nasal type extranodal natural killer/T-cell lymphoma

BACKGROUND

The aggressive and refractory extranodal natural killer/T-cell lymphoma, nasal type (ENKTL-NT) is a subtype of non-Hodgkin's lymphoma. Succinylation promotes progression in a variety of tumors, but its mechanism in ENKTL-NT is unclear.

METHODS

Bioinformatic analysis was performed to screen differentially expressed genes in the ENKTL dataset. Cell transfection techniques were used for knockdown and overexpression of genes. The mRNA and protein expression were detected using RT-qPCR and western blot, respectively. Immunohistochemical staining was used to assess protein expression in situ. For the detection of cell proliferation activity, CCK-8, clonal formation, and EDU staining assays were used. Flow cytometry was employed to detect apoptosis. Co-immunoprecipitation was utilized for the identification of protein interactions and succinylation modifications.

RESULTS

Succinyltransferase CPT1A was highly elevated in ENKTL-NT and was associated with a dismal prognosis. CPT1A knockdown suppressed SNK-6 cells' proliferation and induced apoptosis, while these effects were reversed by the overexpression of 14-3-3theta. Co-immunoprecipitation results showed that CPT1A caused succinylation of 14-3-3theta at site of K85, thereby enhancing the protein stability. Suppression of CPT1A-induced succinylation of 14-3-3theta by ST1326 resulted in the inhibition of SNK-6 cell proliferation and increased apoptosis. Paclitaxel combined with knockdown of CPT1A significantly inhibited the proliferation of ENKTL-NT compared to paclitaxel alone.

CONCLUSION

CPT1A induces succinylation of 14-3-3theta at the K85 site, promoting ENKTL-NT proliferation. The anti-ENKTL activity of paclitaxel was improved when combined with CPT1A knockdown.

Comprehensive molecular analyses of cuproptosis-related genes with regard to prognosis, immune landscape, and response to immune checkpoint blockers in lung adenocarcinoma

BACKGROUND

Recent studies have emphasized the importance of the biological processes of different forms of cell death in tumor heterogeneity and anti-tumor immunity. Nonetheless, the relationship between cuproptosis and lung adenocarcinoma (LUAD) remains largely unexplored.

METHODS

Data for 793 LUAD samples and 59 normal lung tissues obtained from TCGA-LUAD cohort GEO datasets were used in this study. A total of 165 LUAD tissue samples and paired normal lung tissue samples obtained from our hospital were used to verify the prognostic value of dihydrolipoamide S-acetyltransferase (DLAT) and dihydrolipoamide branched chain transacylase E2 (DBT) for LUAD. The cuproptosis-related molecular patterns of LUAD were identified using consensus molecular clustering. Recursive feature elimination with random forest and a tenfold cross-validation method was applied to construct the cuproptosis score (CPS) for LUAD.

RESULTS

Bioinformatic and immunohistochemistry (IHC) analyses revealed that 13 core genes of cuproptosis were all significantly elevated in LUAD tissues, among which DBT and DLAT were associated with poor prognosis (DLAT, HR = 6.103; DBT, HR = 4.985). Based on the expression pattern of the 13 genes, two distinct cuproptosis-related patterns have been observed in LUAD: cluster 2 which has a relatively higher level of cuproptosis was characterized by immunological ignorance; conversely, cluster 1 which has a relatively lower level of cuproptosis is characterized by TILs infiltration and anti-tumor response. Finally, a scoring scheme termed the CPS was established to quantify the cuproptosis-related pattern and predict the prognosis and the response to immune checkpoint blockers of each individual patient with LUAD.

CONCLUSION

Cuproptosis was found to influence tumor microenvironment (TME) characteristics and heterogeneity in LUAD. Patients with a lower CPS had a relatively better prognosis, more abundant immune infiltration in the TME, and an enhanced response to immune checkpoint inhibitors.

Weighted Gene Co-expression Network Analysis and Machine Learning Validation for Identifying Major Genes Related to Sjogren's Syndrome

Sjogren's syndrome (SS) is an autoimmune disorder characterized by dry mouth and dry eyes. Its pathogenic mechanism is currently unclear. This study aims to integrate weighted gene co-expression network analysis (WGCNA) and machine learning to identify key genes associated with SS. We downloaded 3 publicly available datasets from the GEO database comprising the gene expression data of 231 SS and 78 control cases, including GSE84844, GSE48378 and GSE51092, and carried out WGCNA to elucidate differences in the abundant genes. Candidate biomarkers for SS were then identified using a LASSO regression model. Totally 6 machine-learning models were subsequently utilized for validating the biological significance of major genes according to their expression. Finally, immune cell infiltration of the SS tissue was assessed using the CIBERSORT algorithm. A weighted gene co-expression network was built to divide genes into 10 modules. Among them, blue and red modules were most closely associated with SS, and showed significant enrichment in type I interferon signaling, cellular response to type I interferon and response to virus, etc. Combined machine learning identified 5 hub genes, including OAS1, EIF2AK2, IFITM3, TOP2A and STAT1. Immune cell infiltration analysis showed that SS was associated with CD8+ T cell, CD4+ T cell, gamma delta T cell, NK cell and dendritic cell activation. WGCNA was combined with machine learning to uncover genes that may be involved in SS pathogenesis, which can be utilized for developing SS biomarkers and appropriate therapeutic targets.

Macrophage-Related Gene Signatures for Predicting Prognosis and Immunotherapy of Lung Adenocarcinoma by Machine Learning and Bioinformatics

Background

In recent years, the immunotherapy of lung adenocarcinoma has developed rapidly, but the good therapeutic effect only exists in some patients, and most of the current predictors cannot predict it very well. Tumor-infiltrating macrophages have been reported to play a crucial role in lung adenocarcinoma (LUAD). Thus, we want to build novel molecular markers based on macrophages.

Methods

By non-negative matrix factorization (NMF) algorithm and Cox regression analysis, we constructed macrophage-related subtypes of LUAD patients and built a novel gene signature consisting of 12 differentially expressed genes between two subtypes. The gene signature was further validated in Gene-Expression Omnibus (GEO) datasets. Its predictive effect on prognosis and immunotherapy outcome was further evaluated with rounded analyses. We finally explore the role of TRIM28 in LUAD with a series of in vitro experiments.

Results

Our research indicated that a higher LMS score was significantly correlated with tumor staging, pathological grade, tumor node metastasis stage, and survival. LMS was identified as an independent risk factor for OS in LUAD patients and verified in GEO datasets. Clinical response to immunotherapy was better in patients with low LMS score compared to those with high LMS score. TRIM28, a key gene in the gene signature, was shown to promote the proliferation, invasion and migration of LUAD cell.

Conclusion

Our study highlights the significant role of gene signature in predicting the prognosis and immunotherapy efficacy of LUAD patients, and identifies TRIM28 as a potential biomarker for the treatment of LUAD.

Screening of novel tumor-associated antigens for lung adenocarcinoma mRNA vaccine development based on pyroptosis phenotype genes

This study aimed to identify new pyroptosis-associated tumor antigens for use in mRNA vaccines and the screening of sensitive LUAD populations suitable for vaccination. The association between tumor immune infiltrating cell abundance and potential tumor antigens was investigated and visualized using the analysis modules of gene expression, clinical outcomes, and somatic copy number variation. In addition, the pyroptosis-related genes (PRGs) were clustered, the relative pyroptosis subtypes (PSs) and gene modules were identified, and the prognostic value of the PSs was examined. The expression of key PRGs in two lung adenocarcinoma cell lines was verified by RT-qPCR. Four tumor pyroptosis-associated antigens, CARD8, NAIP, NLRP1, and NLRP3, were screened as potential candidates for LUAD mRNA vaccine development. In the construction of consensus clusters for PRGs, two PSs, PS1 and PS2, were classified, in which patients with PS1 LUAD had a better prognosis. In contrast, patients with PS2 LUAD may have better responsiveness to mRNA vaccine treatment. The key PRGs can be regarded as biomarkers to predict the LUAD prognosis and identify patients suitable for mRNA vaccines. The RT-qPCR results showed that the expression levels of CSMD3, LRP1B, MUC16 and TTN were significantly increased in the two lung adenocarcinoma cell lines, while the expression levels of CARD8, TP53 and ZFHX4 were significantly reduced. The antigens CARD8, NAIP, NLRP1, and NLRP3, which are associated with tumor pyroptosis, could be candidate molecules for LUAD mRNA vaccine development. Patients with PS2 LUAD may be suitable candidates for mRNA vaccine treatment.

Computational Characterization of Undifferentially Expressed Genes with Altered Transcription Regulation in Lung Cancer

A transcriptome profiles the expression levels of genes in cells and has accumulated a huge amount of public data. Most of the existing biomarker-related studies investigated the differential expression of individual transcriptomic features under the assumption of inter-feature independence. Many transcriptomic features without differential expression were ignored from the biomarker lists. This study proposed a computational analysis protocol (mqTrans) to analyze transcriptomes from the view of high-dimensional inter-feature correlations. The mqTrans protocol trained a regression model to predict the expression of an mRNA feature from those of the transcription factors (TFs). The difference between the predicted and real expression of an mRNA feature in a query sample was defined as the mqTrans feature. The new mqTrans view facilitated the detection of thirteen transcriptomic features with differentially expressed mqTrans features, but without differential expression in the original transcriptomic values in three independent datasets of lung cancer. These features were called dark biomarkers because they would have been ignored in a conventional differential analysis. The detailed discussion of one dark biomarker, GBP5, and additional validation experiments suggested that the overlapping long non-coding RNAs might have contributed to this interesting phenomenon. In summary, this study aimed to find undifferentially expressed genes with significantly changed mqTrans values in lung cancer. These genes were usually ignored in most biomarker detection studies of undifferential expression. However, their differentially expressed mqTrans values in three independent datasets suggested their strong associations with lung cancer.

Screening and validation of plasma cell-derived, purinergic, and calcium signalling-related genetic signature to predict prognosis and PD-L1/PD-1 blockade responses in lung adenocarcinoma

BACKGROUND

This study aims at  screening and validation of prospective genetic signature for lung adenocarcinoma (LUAD) prognosis and treatment.

METHODS

The immune-related genes (IRGs) were obtained from The Cancer Genome Atlas (TCGA) dataset where a total of 535 LUAD and 59 control samples were included. A risk model was then developed for the risk stratification of LUAD patients.  The immune cell infiltration, clinical outcomes, and the therapeutic efficacy of programmed cell death protein 1 (PD-1) and its ligand (PD-L1) blockade were compared between high and low-risk groups. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to explore the biological processes and signalling pathways associated with the IRGs. Finally, IRGs mRNA levels were assayed by reverse transcription quantitative real-time PCR (RT-qPCR) in LUAD and relevant cell lines.

RESULTS

Two IRGs, P2RX1 (purinergic receptor P2X 1) and PCP4 (Purkinje cell protein 4), were screened from a module that possesses the highest correlation with plasma cells. RT-qPCR verified the expression of the two IRGs in plasmacytoma cell RPMI 8226 but not in LUAD cells. A higher risk score is associated with a lower infiltration of immune cells. Kaplan-Meier and nomogram analysis showed that the high-risk group has a lower survival rate than the low-risk cohort. Furthermore, the high-risk group had a worse response rate to PD-L1/PD-1 blockade. GSVA and GSEA-GO results indicated that a lower risk score is linked to signalling pathways and biological functions promoting immune response and inflammation. In contrast, a higher risk score is associated with signalling cascades promoting tumour growth.

CONCLUSION

The immune-related prognostic model based on P2RX1 and PCP4 is conducive to predicting the therapeutic response of PD-L1/PD-1 blockade and clinical outcomes of LUAD.

Screening of potential biomarkers for polycystic ovary syndrome and identification of expression and immune characteristics

BACKGROUND

Polycystic ovary syndrome (PCOS) seriously affects the fertility and health of women of childbearing age. We look forward to finding potential biomarkers for PCOS that can aid clinical diagnosis.

METHODS

We acquired PCOS and normal granulosa cell (GC) expression profiles from the Gene Expression Omnibus (GEO) database. After data preprocessing, differentially expressed genes (DEGs) were screened by limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Set Enrichment Analysis (GSEA) were performed. Recursive feature elimination (RFE) algorithm and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to acquire feature genes as potential biomarkers. Time-dependent receiver operator characteristic curve (ROC curve) and Confusion matrix were used to verify the classification performance of biomarkers. Then, the expression characteristics of biomarkers in PCOS and normal cells were analyzed, and the insulin resistance (IR) score of samples was computed by ssGSEA. Immune characterization of biomarkers was evaluated using MCP counter and single sample gene set enrichment analysis (ssGSEA). Finally, the correlation between biomarkers and the scores of each pathway was assessed.

RESULTS

We acquired 93 DEGs, and the enrichment results indicated that most of DEGs in PCOS group were significantly enriched in immune-related biological pathways. Further screening results indicated that JDP2 and HMOX1 were potential biomarkers. The area under ROC curve (AUC) value and Confusion matrix of the two biomarkers were ideal when separated and combined. In the combination, the training set AUC = 0.929 and the test set AUC = 0.917 indicated good diagnostic performance of the two biomarkers. Both biomarkers were highly expressed in the PCOS group, and both biomarkers, which should be suppressed in the preovulation phase, were elevated in PCOS tissues. The IR score of PCOS group was higher, and the expression of JDP2 and HMOX1 showed a significant positive correlation with IR score. Most immune cell scores and immune infiltration results were significantly higher in PCOS. Comprehensive analysis indicated that the two biomarkers had strong correlation with immune-related pathways.

CONCLUSION

We acquired two potential biomarkers, JDP2 and HMOX1. We found that they were highly expressed in the PCOS and had a strong positive correlation with immune-related pathways.

Berberine targets KIF20A and CCNE2 to inhibit the progression of nonsmall cell lung cancer via the PI3K/AKT pathway

BACKGROUND

Nonsmall cell lung cancer (NSCLC) is the main type of lung cancer, accounting for approximately 85%. Berberine (BBR), a commonly used traditional Chinese medicine, has been reported to exhibit a potential antitumor effect in various cancers. In this research, we explored the function of BBR and its underlying mechanisms in the development of NSCLC.

METHODS

Cell Counting Kit-8 (CCK-8), 5-ethynyl-20-deoxyuridine (EdU), colony formation assays, flow cytometry, and transwell invasion assay were employed to determine cell growth, the apoptotic rate, cell invasion of NSCLC cells, respectively. Western blot was applied for detecting the protein expression of c-Myc, matrix metalloprotease 9 (MMP9), kinesin family member 20A (KIF20A), cyclin E2 (CCNE2), and phosphatidylinositol-3-kinase/protein kinase B (PI3K/AKT) pathway-related proteins. Glycolysis was evaluated by detecting glucose consumption, lactate production, and adenosine triphosphate/adenosine diphosphate (ATP/ADP) ratio with the matched kits. Real-time quantitative polymerase chain reaction (RT-qPCR) was conducted to analyze the level of KIF20A and CCNE2. Tumor model was established to evaluate the function of BBR on tumor growth in NSCLC in vivo. In addition, immunohistochemistry assay was employed to detect the level of KIF20A, CCNE2, c-Myc, and MMP9 in mice tissues.

RESULTS

BBR exhibited suppressive effects on the progression of NSCLC, as evidenced by inhibiting cell growth, invasion, glycolysis, and facilitating cell apoptosis in H1299 and A549 cells. KIF20A and CCNE2 were upregulated in NSCLC tissues and cells. Moreover, BBR treatment significantly decreased the expression of KIF20A and CCNE2. KIF20A or CCNE2 downregulation could repress cell proliferation, invasion, glycolysis, and induce cell apoptosis in both H1299 and A549 cells. The inhibition effects of BBR treatment on cell proliferation, invasion, glycolysis, and promotion effect on cell apoptosis were rescued by KIF20A or CCNE2 overexpression in NSCLC cells. The inactivation of PI3K/AKT pathway caused by BBR treatment in H1299 and A549 cells was restored by KIF20A or CCNE2 upregulation. In vivo experiments also demonstrated that BBR treatment could repress tumor growth by regulating KIF20A and CCNE2 and inactivating the PI3K/AKT pathway.

CONCLUSION

BBR treatment showed the suppressive impact on the progression of NSCLC by targeting KIF20A and CCNE2, thereby inhibiting the activation of the PI3K/AKT pathway.

Exosomal circ-ADRM1 promotes lung adenocarcinoma progression and induces macrophage M2 polarization through regulating MMP14 mRNA and protein

OBJECTIVE

Lung adenocarcinoma (LUAD) is one of the frequent subtypes of lung cancer, featuring high rates of incidence and mortality. Matrix metalloproteinase 14 (MMP14) is known as a regulator in multiple cancers, whereas its upstream molecular mechanism remains to be investigated. This study aims to reveal the upstream molecular mechanism of MMP14 in LUSC progression.

METHODS

Quantitative real-time polymerase chain reaction (qRT-PCR) and western blot were conducted to examine the levels of MMP14 mRNA and protein in LUAD cells, respectively. Cell counting kit-8 (CCK-8), transwell assay and wound healing assay were implemented to unveil LUAD cell proliferation, migration and invasion after indicated transfections. Flow cytometry analysis was applied to evaluate macrophage polarization. Mechanism experiments such as western blot, co-immunoprecipitation (Co-IP), RNA pulldown assay, luciferase reporter assay and RNA-binding protein immunoprecipitation (RIP) assay were used to explore relevant molecular mechanisms.

RESULTS

MMP14 facilitated LUAD cell proliferation, invasion and migration. MMP14 is the target gene of miR-1287-5p. Circ-ADRM1 upregulates MMP14 expression through sponging miR-1287-5p. Circ-ADRM1 recruits USP12 to impede the ubiquitination of MMP14 protein, thereby enhancing the stability of MMP14 protein. LUAD-derived exosomes induced macrophage M2 polarization by delivering circ-ADRM1.

CONCLUSIONS

Circ-ADRM1 promotes LUAD cell proliferation, invasion and migration through upregulating MMP14. Additionally, circ-ADRM1 induces macrophage M2 polarization in an exosome-dependent manner.

Wnt signaling pathway-derived score for predicting therapeutic resistance and tumor microenvironment in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Due to tumor heterogeneity, understanding the pathological mechanism of tumor progression helps to improve the diagnosis process and clinical treatment strategies of LUAD patients. Methods: The transcriptome pattern, mutant expression and complete clinical information were obtained from the cancer genome atlas (TCGA) database and microarray data from gene expression omnibus (GEO) database. Firstly, we used single sample Gene Set Enrichment Analysis (ssGSEA) to estimate the activation of Wnt signaling pathway in each sample. Consensus clustering algorithm was used to classify LUAD samples into different subgroups according to the transcription patterns of 152 Wnt signaling pathway related genes. Then, ESTIMATE, ssGSEA and Gene Set Variation Analysis (GSVA) algorithms were used to assess the biological pathways and immunocytes infiltration between different subtypes. LASSO-COX algorithm was conducted to construct prognostic model. Kaplan-Meier and multivariate Cox analysis were performed to evaluate the predictive performance of risk model. Gene features were further confirmed using external datasets. Finally, we conducted vitro assay for validating hub gene (LEF1). Results: Based on the transcription patterns of 152 Wnt signaling pathway related genes, four different subtypes of LUAD patients were screened out by consensus clustering algorithm. Subsequently, it was found that patients with cluster A and B had massive immunocytes infiltration, and the survival rate of patients with cluster B was better than that of other subgroups. According to the coefficients in the LASSO- Cox model and the transcriptome patterns of these 18 genes, the risk score was constructed for each sample. The degree of malignancy of LUAD patients with high-risk subgroup was remarkable higher than that of patients with low-risk subgroup (p < 0.001). Subsequently, five top prognostic genes (AXIN1, CTNNB1, LEF1, FZD2, FZD4.) were screened, and their expression values were different between cancer and normal tissues. FZD2 and LEF1 were negatively related to ImmunoScore, and AXIN1 was negatively related to ImmunoScore. The significant correlation between LUAD patient risk score and overall survival (OS) was verified in external datasets. In the A549 cell line, knockdown of LEF1 can reduce the invasive and proliferation ability of LUAD cells. Conclusion: A innovative 18 genes predictive feature based on transcriptome pattern was found in patients with lung adenocarcinoma. These investigations further promote the insight of the prognosis of lung adenocarcinoma and may contribute to disease management at risk stratification.

Biochanin A inhibits lung adenocarcinoma progression by targeting ZEB1

Lung adenocarcinoma is the major subtype of lung cancer, accounting for approximately 40% of lung cancers. During clinical treatment, the emergence of chemotherapy resistance seriously affects the effectiveness of treatment. Thus, finding new chemotherapeutic sensitizers is considered to be one of the effective solutions. Biochanin A, as a naturally occurring isoflavone, has been demonstrated to exhibit anticancer effects in various tumors. However, the potential mechanisms of Biochanin A to inhibit tumor development have not been clarified. In the present study, we found that the combinational treatment of cisplatin and Biochanin A exhibited strong synergistic repression on lung adenocarcinoma growth and progression in vitro and in vivo. Considering that epithelial-mesenchymal transition (EMT) is recognized to be associated with both chemoresistance and metastasis, we examined the EMT-related markers and found that Biochanin A could specifically inhibit the expression of ZEB1. Importantly, Biochanin A chemosensitizes lung adenocarcinoma and inhibits cancer cell metastasis by suppressing ZEB1. At the molecular level, Biochanin A affects the stability of ZEB1 protein through the deubiquitination pathway and thereby influences the progression of lung adenocarcinoma. In conclusion, our finding elucidates the potential efficacy of Bichanin A as a chemosensitizer and provides new strategy for the chemotherapy of advanced lung adenocarcinoma.

Development and Validation of a Prognostic Nomogram for Lung Adenocarcinoma: A Population-Based Study

PURPOSE

To establish an effective and accurate prognostic nomogram for lung adenocarcinoma (LUAD). Patients and Methods. 62,355 LUAD patients from 1975 to 2016 enrolled in the Surveillance, Epidemiology, and End Results (SEER) database were randomly and equally divided into the training cohort (n = 31,179) and the validation cohort (n = 31,176). Univariate and multivariate Cox regression analyses screened the predictive effects of each variable on survival. The concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were used to examine and validate the predictive accuracy of the nomogram. Kaplan-Meier curves were used to estimate overall survival (OS).

RESULTS

10 prognostic factors associated with OS were identified, including age, sex, race, marital status, American Joint Committee on Cancer (AJCC) TNM stage, tumor size, grade, and primary site. A nomogram was established based on these results. C-indexes of the nomogram model reached 0.777 (95% confidence interval (CI), 0.773 to 0.781) and 0.779 (95% CI, 0.775 to 0.783) in the training and validation cohorts, respectively. The calibration curves were well-fitted for both cohorts. The AUC for the 3- and 5-year OS presented great prognostic accuracy in the training cohort (AUC = 0.832 and 0.827, respectively) and validation cohort (AUC = 0.835 and 0.828, respectively). The Kaplan-Meier curves presented significant differences in OS among the groups.

CONCLUSION

The nomogram allows accurate and comprehensive prognostic prediction for patients with LUAD.

Evaluating the Expression and Prognostic Value of Genes Encoding Microtubule-Associated Proteins in Lung Cancer

Microtubule-associated proteins (MAPs) play essential roles in cancer development. This study aimed to identify transcriptomic biomarkers among MAP genes for the diagnosis and prognosis of lung cancer by analyzing differential gene expressions and correlations with tumor progression. Gene expression data of patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) from the Cancer Genome Atlas (TCGA) database were used to identify differentially expressed MAP genes (DEMGs). Their prognostic value was evaluated by Kaplan-Meier and Cox regression analysis. Moreover, the relationships between alterations in lung cancer hallmark genes and the expression levels of DEMGs were investigated. The candidate biomarker genes were validated using three independent datasets from the Gene Expression Omnibus (GEO) database and by quantitative reverse transcription polymerase chain reaction (qRT-PCR) on clinical samples. A total of 88 DEMGs were identified from TCGA data. The 20 that showed the highest differential expression were subjected to association analysis with hallmark genes. Genetic alterations in TP53, EGFR, PTEN, NTRK1, and PIK3CA correlated with the expression of most of these DEMGs. Of these, six candidates-NUF2, KIF4A, KIF18B, DLGAP5, NEK2, and LRRK2-were significantly differentially expressed and correlated with the overall survival (OS) of the patients. The mRNA expression profiles of these candidates were consistently verified using three GEO datasets and qRT-PCR on patient lung tissues. The expression levels of NUF2, KIF4A, KIF18B, DLGAP5, NEK2, and LRRK2 can serve as diagnostic biomarkers for LUAD and LUSC. Moreover, the first five can serve as prognostic biomarkers for LUAD, while LRRK2 can be a prognostic biomarker for LUSC. Our research describes the novel role and potential application of MAP-encoding genes in clinical practice.

Development and validation of a prognostic 9-gene signature for colorectal cancer

Introduction

Colorectal cancer (CRC) is one of the most prevalent cancers globally with a high mortality rate. Predicting prognosis using disease progression and cancer pathologic stage is insufficient, and a prognostic factor that can accurately evaluate patient prognosis needs to be developed. In this study, we aimed to infer a prognostic gene signature to identify a functional signature associated with the prognosis of CRC patients.

Methods

First, we used univariate Cox regression, least absolute shrinkage and selection operator (lasso) regression, and multivariate Cox regression analyses to screen genes significantly associated with CRC patient prognosis, from colorectal cancer RNA sequencing data in The Cancer Genome Atlas (TCGA) database. We then calculated the risk score (RS) for each patient based on the expression of the nine candidate genes and developed a prognostic signature.

Results

Based on the optimal cut-off on the receiver operating characteristic (ROC) curve, patients were separated into high- and low-risk groups, and the difference in overall survival between the two groups was examined. Patients in the low-risk group had a better overall survival rate than those in the high-risk group. The results were validated using the GSE72970, GSE39582, and GSE17536 Gene Expression Omnibus (GEO) datasets, and the same conclusions were reached. ROC curve test of the RS signature also indicated that it had excellent accuracy. The RS signature was then compared with traditional clinical factors as a prognostic indicator, and we discovered that the RS signature had superior predictive ability.

Conclusion

The RS signature developed in this study has excellent predictive power for the prognosis of patients with CRC and broad applicability as a prognostic indicator for patients.

Ferroptosis-Related Gene GCLC Is a Novel Prognostic Molecular and Correlates with Immune Infiltrates in Lung Adenocarcinoma

Ferroptosis, a newly discovered iron-dependent type of cell death, has been found to play a crucial role in the depression of tumorigenesis. However, the prognostic value of ferroptosis-related genes (FRGs) in lung adenocarcinoma (LUAD) remains to be further elucidated. Differential expression analysis and univariate Cox regression analysis were utilized in this study to search for FRGs that were associated with the prognosis of LUAD patients. The influences of candidate markers on LUAD cell proliferation, migration, and ferroptosis were evaluated by CCK8, colony formation, and functional experimental assays in association with ferroptosis. To predict the prognosis of LUAD patients, we constructed a predictive signature comprised of six FRGs. We discovered a critical gene (GCLC) after intersecting the prognostic analysis results of all aspects, and its high expression was associated with a bad prognosis in LUAD. Correlation research revealed that GCLC was related to a variety of clinical information from LUAD patients. At the same time, in the experimental verification, we found that GCLC expression was upregulated in LUAD cell lines, and silencing GCLC accelerated ferroptosis and decreased LUAD cell proliferation and invasion. Taken together, this study established a novel ferroptosis-related gene signature and discovered a crucial gene, GCLC, that might be a new prognostic biomarker of LUAD patients, as well as provide a potential therapeutic target for LUAD patients.

MDFI is a novel biomarker for poor prognosis in LUAD

Background

Approximately 80% of lung cancers are non-small cell lung cancers (NSCLC). Lung adenocarcinoma (LUAD) is the main subtype of NSCLC. The incidence and mortality of lung cancer are also increasing yearly. Myogenic differentiation family inhibitor (MDFI) as a transcription factor, its role in lung cancer has not yet been clarified.

Methods

LUAD data were downloaded from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO), analyzed and plotted using the R language. Associations between Clinical information and MDFI expression were assessed using logistic regression analyses to explore the effects of MDFI on LUAD. Two sets of tissue microarrays (TMAs) further confirmed the overexpression of MDFI in LUAD and its impact on prognosis. In addition, we examined the correlation between MDFI and immune infiltration. To investigate the effect of MDFI on the biological behavior of LUAD tumor cells by GSEA and GO/KEGG analysis. The survival status and somatic mutational characteristics of patients according to MDFI levels were depicted and analyzed.

Results

Expression of high MDFI in LUAD tissues via analyzing TCGA dataset (P <0.001). Kaplan-Meier survival analysis indicated a poor prognosis for those patients with LUAD who had upregulated MDFI expression levels (P <0.001). This was also verified by two groups of TMAs (P=0.024). Using logistic statistics analysis, MDFI was identified as an independent predictive factor and was associated with poor prognosis in LUAD (P <0.001, P =0.021). Assessment of clinical characteristics, tumor mutation burden (TMB), and tumor microenvironment (TME) between high- and low-expression score groups showed lower TMB, richer immune cell infiltration, and better prognosis in the low-risk group.

Conclusion

This study showed that MDFI was overexpressed in LUAD and was significantly associated with poor prognosis, indicating that MDFI may be used as a potential novel biomarker for the diagnosis and prognosis of LUAD. MDFI is associated with immune infiltration of LUAD and it is reasonable to speculate that it plays an important role in tumor proliferation and spread. In view of the significant differences in MDFI expression between different biological activities, LUAD patients with MDFI overexpression may obtain more precise treatment strategies in the clinic.

Bioinformatic Analysis Identifies of Potential miRNA-mRNA Regulatory Networks Involved in the Pathogenesis of Lung Cancer

Objective. The purpose of the present study was to explore the biomarkers related to lung cancer based on the bioinformatics method, which might be new targets for lung cancer treatment. Methods. GSE17681 and GSE18842 were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed miRNAs (DEMs) and genes (DEGs) in lung cancer samples were screened via the GEO2R online tool. DEMs were submitted to the mirDIP website to predict target genes. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted via uploading DEGs to the DAVID database. The protein-protein interaction network (PPI) of the DEGs was analyzed by STRING’s online tool. Then, the PPI network was visualized using Cytoscape 3.8.0. Results. 46 DEMs were identified in GSE17681, and the website predicted that there were 873 target genes of these DEMs. 1029 DEGs were identified in the GSE18842 chip. GO analysis suggested that the co-DEGs participated in the canonical Wnt signaling pathway, regulation of the Wnt signaling pathway, a serine/threonine kinase signaling pathway, the Wnt signaling pathway, and cell-cell signaling by Wnt. KEGG analysis results showed the co-DEGs of GSE17681 and GSE18842 were related to the Hippo signaling pathway and adhesion molecules. In addition, six hub genes that were related to lung cancer were identified as hub genes, including mTOR, NF1, CHD7, ETS1, IL-6, and COL1A1. Conclusions. The present study identified six hub genes that were related to lung cancer, including mTOR, NF1, CHD7, ETS1, IL-6, and COL1A1, which might be a potential target for lung cancer.

An integrative analysis of DNA methylation and gene expression to predict lung adenocarcinoma prognosis

Background: Abnormal DNA methylation of gene promoters is an important feature in lung adenocarcinoma (LUAD). However, the prognostic value of DNA methylation remains to be further explored. Objectives. We sought to explore DNA methylation characteristics and develop a quantifiable criterion related to DNA methylation to improve survival prediction for LUAD patients.

Methods: Illumina Human Methylation450K array data, level 3 RNA-seq data and corresponding clinical information were obtained from TCGA. Cox regression analysis and the Akaike information criterion were used to construct the best-prognosis methylation signature. Receiver operating characteristic curve analysis was used to validate the prognostic ability of the DNA methylation-related feature score. qPCR was used to measure the transcription levels of the identified genes upon methylation.

Results: We identified a set of DNA methylation features composed of 11 genes (MYEOV, KCNU1, SLC27A6, NEUROD4, HMGB4, TACR3, GABRA5, TRPM8, NLRP13, EDN3 and SLC34A1). The feature score, calculated based on DNA methylation features, was independent of tumor recurrence and TNM stage in predicting overall survival. Of note, the combination of this feature score and TNM stage provided a better overall survival prediction than either of them individually. The transcription levels of all the hypermethylated genes were significantly increased after demethylation, and the expression levels of 3 hypomethylated proteins were significantly higher in tumor tissues than in normal tissues, as indicated by immunohistochemistry data from the Human Protein Atlas. Our results suggested that these identified genes with prognostic features were regulated by DNA methylation of their promoters.

Conclusion: Our studies demonstrated the potential application of DNA methylation markers in the prognosis of LUAD.

Identification of G6PC as a potential prognostic biomarker in hepatocellular carcinoma based on bioinformatics analysis

Hepatocellular carcinoma (HCC) has high mortality and incidence rates around the world with limited therapeutic options. There is an urgent need for identification of novel therapeutic targets and biomarkers for early diagnosis and predicting patient survival with HCC. Several studies (GSE102083, GSE29722, GSE101685, and GSE112790) from the GEO database in HCC were screened and analyzed by GEO2R, gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were conducted with the Database for Annotation, Visualization and Integrated Discovery. The protein-protein interaction network was plotted and the module analysis was performed using Search Tool for the Retrieval of Inter-acting Genes/Proteins database and Cytoscape. The expression and survival of key genes were identified using UALCAN, Kaplan-Meier Plotter and ONCOMINE online databases, and the immune infiltration level of key genes was analyzed via the Tumor Immune Estimation Resource (TIMER) database. Through database analysis, eight key genes were finally screened out, and the expressions of cyclin-dependent kinase regulatory subunit 2 and glucose-6-phosphatase catalytic (G6PC), which were closely related to the survival of HCC patients, was detected by using UALCAN. Further analysis on the differential expression of G6PC in multiple cancerous tumors and normal tissues revealed low expression in many solid tumors by Oncomine and TIMER. In addition, Kaplan-Meier plotter and UALCAN database analysis to access diseases prognosis suggested that low expression of G6PC was significantly associated with poor overall survival in HCC patients. Finally, TIMER database analysis showed a significant negative correlation between G6PC and infiltration levels of six kinds of immune cells. The somatic copy number alterations of G6PC were associated with B cells, CD8+ T cells, CD4+ T cells, macrophages, dentritic cells and neutrophils. These bioinformatic data identified G6PC as a potential key gene in the diagnosis and prognosis of HCC.

Clustering analysis and prognostic signature of lung adenocarcinoma based on the tumor microenvironment

Because of immunotherapy failure in lung adenocarcinoma, we have tried to find new potential biomarkers for differentiating different tumor subtypes and predicting prognosis. We identified two subtypes based on tumor microenvironment-related genes in this study. We used seven methods to analyze the immune cell infiltration between subgroups. Further analysis of tumor mutation load and immune checkpoint expression among different subgroups was performed. The least absolute shrinkage and selection operator Cox regression was applied for further selection. The selected genes were used to construct a prognostic 14-gene signature for LUAD. Next, a survival analysis and time-dependent receiver operating characteristics were performed to verify and evaluate the model. Gene set enrichment analyses and immune analysis in risk groups was also performed. According to the expression of genes related to the tumor microenvironment, lung adenocarcinoma can be divided into cold tumors and hot tumors. The signature we built was able to predict prognosis more accurately than previously known models. The signature-based tumor microenvironment provides further insight into the prediction of lung adenocarcinoma prognosis and may guide individualized treatment.

Deciphering the tumor cell-infiltrating landscapes reveal microenvironment subtypes and therapeutic potentials for nonsquamous NSCLC

Recent studies highlighted the clinicopathologic importance of tumor microenvironment (TME) in delineating molecular attributes and therapeutic potentials. However, the overall TME cell-infiltration landscape in non-squamous NSCLC have not been comprehensively recognized. In this study, we employed consensus non-negative matrix factorization (NMF) molecular subtyping to determine the TME cell infiltration patterns and identified three TME clusters (TME-C1, -C2, -C3) characterized by distinct clinicopathologic features, infiltrating cells, and biological processes. Proteomics analyses revealed that cGAS-STING immune signaling mediated protein and phosphorylation level were significantly upregulated in inflamed-related TME-C2 clusters. The TMEsig-score extracted from the TME-related signature divided NSCLC patients into high- and low-score subgroups, where a high score was associated with favorable prognosis and immune infiltration. Genomic landscape revealed that patients with low TMEsig-score harbored greater somatic copy number alternations and higher mutation frequency of driver genes involving STK11, KEAP1 and SMARCA4 et al. Drug sensitivity analyses suggested that tumors with high TMEsig-score were responsible for favorable clinical response to immune check-point inhibitors (ICI) treatment. In summary, this study highlights that comprehensive recognizing of the TME cell infiltration landscape will contribute to enhance our understanding of TME immune regulation and promote effectiveness of precision biotherapy strategies.

circPlekha7 suppresses renal fibrosis via targeting miR-493-3p/KLF4

Aims: The authors aim to investigate the function of circPlekha7 in renal fibrosis. Methods: Human renal tissues from chronic kidney disease patients, kidney cell line and primary cultured renal tubular epithelial cells were used. TGF-β1-treated human kidney 2 cells/tubular epithelial cells and a unilateral ureteral obstruction mouse model were employed to study renal fibrosis. Results: circPlekha7 was diminished in renal tissues from chronic kidney disease patients and TGF-β1-treated human kidney 2 cells and tubular epithelial cells, while miR-493-3p was upregulated. Overexpression of circPlekha7 or knockdown of miR-493-3p suppressed TGF-β1 induced enhancements on epithelial to mesenchymal transition and fibrogenesis, as well as attenuated renal fibrosis and injury in mice subjected to unilateral ureteral obstruction. circPlekha7 bound with miR-493-3p, which directly targeted KLF4. Conclusion: circPlekha7 inhibits epithelial to mesenchymal transition of renal tubular epithelial cells and fibrosis via targeting miR-493-3p to de-repress KLF4/mitofusin2 expression.

Immune Checkpoint Gene Expression Profiling Identifies Programmed Cell Death Ligand-1 Centered Immunologic Subtypes of Oral and Squamous Cell Carcinoma With Favorable Survival

Objective

This study aimed to identify the programmed death ligand-1 (PDL1, also termed as CD274) and its positively correlated immune checkpoint genes (ICGs) and to determine the immune subtypes of CD274-centered ICG combinations in oral and squamous cell carcinoma (OSCC).

Materials and Methods

Firstly, the 95 ICGs obtained via literature reviews were identified in the Cancer Genome Atlas (TCGA) database in relation to OSCC, and such 88 ICG expression profiles were extracted. ICGs positively correlated with CD274 were utilized for subsequent analysis. The relationship between ICGs positively correlated with CD274 and immunotherapy biomarkers (tumor mutation burden (TMB), and adaptive immune resistance pathway genes) was investigated, and the relationships of these genes with OSCC clinical features were explored. The prognostic values of CD274 and its positively correlated ICGs and also their associated gene pairs were revealed using the survival analysis.

Results

Eight ICGs, including CTLA4, ICOS, TNFRSF4, CD27, B- and T-lymphocyte attenuator (BTLA), ADORA2A, CD40LG, and CD28, were found to be positively correlated with CD274. Among the eight ICGs, seven ICGs (CTLA4, ICOS, TNFRSF4, CD27, BTLA, CD40LG, and CD28) were significantly negatively correlated with TMB. The majority of the adaptive immune resistance pathway genes were positively correlated with ICGs positively correlated with CD274. The survival analysis utilizing the TCGA-OSCC data showed that, although CD274 was not significantly associated with overall survival (OS), the majority of ICGs positively correlated with CD274 (BTLA, CD27, CTLA4, CD40LG, CD28, ICOS, and TNFRSF4) were significantly correlated with OS, whereby their low-expression predicted a favorable prognosis. The survival analysis based on the gene pair subtypes showed that the combination subtypes of CD274_low/BTLA_low, CD274_low/CD27_low, CD274_low/CTLA4_low, CD8A_high/BTLA_low, CD8A_high/CD27_low, and CD8A_high/CTLA4_low predicted favorable OS.

Conclusion

The results in this study provide a theoretical basis for prognostic immune subtyping of OSCC and highlight the importance of developing future immunotherapeutic strategies for treating oral cancer.

A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets

Background

Gastric cancer (GC) is one of the most common carcinomas of the digestive tract, and the prognosis for these patients may be poor. There is evidence that some long non-coding RNAs(lncRNAs) can predict the prognosis of patients with GC. However, few lncRNA signatures have been used to predict prognosis. Herein, we aimed to construct a risk score model based on the expression of five lncRNAs to predict the prognosis of patients with GC and provide new potential therapeutic targets.

Methods

We performed differentially expressed and survival analyses to identify differentially expressed survival-ralated lncRNAs by using GC patient expression profile data from The Cancer Genome Atlas (TCGA) database. We then established a formula including five lncRNAs to predict the prognosis of patients with GC. In addition, to verify the prognostic value of this risk score model, two independent Gene Expression Omnibus (GEO) datasets, GSE62254 (N = 300) and GSE15459 (N = 200), were employed as validation groups.

Results

Based on the characteristics of five lncRNAs, patients with GC were divided into high or low risk subgroups. The prognostic value of the risk score model with five lncRNAs was confirmed in both TCGA and the two independent GEO datasets. Furthermore, stratification analysis results showed that this model had an independent prognostic value in patients with stage II-IV GC. We constructed a nomogram model combining clinical factors and the five lncRNAs to increase the accuracy of prognostic prediction. Enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the five lncRNAs are associated with multiple cancer occurrence and progression-related pathways.

Conclusion

The risk score model including five lncRNAs can predict the prognosis of patients with GC, especially those with stage II-IV, and may provide potential therapeutic targets in future.