Comprehensive bioinformatics analysis identifies several potential diagnostic markers and potential roles of cyclin family members in lung adenocarcinoma

High expression of the circadian clock gene NPAS2 is associated with progression and poor prognosis of gastric cancer: A single-center study

BACKGROUND

The prognostic assessment of patients after surgical resection of gastric cancer (GC) patients is critical. However, the role of the circadian clock gene NPAS2 expression in GC remains unknown.

AIM

To explore the relationship between NPAS2 and the survival prognosis of GC patients and clarify its role in evaluating GC prognosis.

METHODS

The tumor tissues and clinical data of 101 patients with GC were collected retrospectively. Immunohistochemical staining (IHC) was used to detect the expression of NPAS2 protein in GC and adjacent tissues. Univariate and multivariate Cox regression analysis was used to determine the independent prognostic factors of GC, and a nomogram prediction model was established. The receiver operating characteristic (ROC) curve, the ROC area under the curve, the calibration curve, and C-index were used to evaluate the predictive effectiveness of the model. Kaplan Meier analysis was used to compare the risk stratification of subgroups according to the median score in the nomogram model of each patient.

RESULTS

Microarray IHC analysis showed that the positive rate of NPAS2 protein expression in GC tissues was 65.35%, which was significantly higher than 30.69% in adjacent tissues. The high expression of NPAS2 was correlated with tumor-node-metastasis (TNM) stage (P < 0.05), pN stage (P < 0.05), metastasis (P < 0.05), venous invasion (P < 0.05), lymphatic invasion (P < 0.05), and lymph node positive (P < 0.05) of GC. Kaplan Meier survival analysis showed that the 3-year overall survival (OS) of patients with high NPAS2 expression was significantly shortened (P < 0.0001). Univariate and multivariate COX regression analysis showed that TNM stage (P = 0.009), metastasis (P = 0.009), and NPAS2 expression (P = 0.020) were independent prognostic factors of OS in GC patients for 3 years. The nomogram prediction model based on independent prognostic factors has a C-Index of 0.740 (95%CI: 0.713-0.767). Furthermore, subgroup analysis showed that the 3-year OS time of the high-risk group was significantly lower than that of the low-risk group (P < 0.0001).

CONCLUSION

NPAS2 is highly expressed in GC tissues and is closely related to worse OS in patients. Therefore, the evaluation of NPAS2 expression may be a potential marker for GC prognosis evaluation. Notably, the nomogram model based on NPAS2 can improve the accuracy of GC prognosis prediction and assist clinicians in postoperative patient management and decision-making.

Integrated single-cell and transcriptome sequencing analyses determines a chromatin regulator-based signature for evaluating prognosis in lung adenocarcinoma

BACKGROUND

Accumulating evidence has highlighted the significance of chromatin regulator (CR) in pathogenesis and progression of cancer. However, the prognostic role of CRs in LUAD remains obscure. We aim to detect the prognostic value of CRs in LUAD and create favorable signature for assessing prognosis and clinical value of LUAD patients.

METHODS

The mRNA sequencing data and clinical information were obtained from TCGA and GEO databases. Gene consensus clustering analysis was utilized to determine the molecular subtype of LUAD. Cox regression methods were employed to set up the CRs-based signature (CRBS) for evaluating survival rate in LUAD. Biological function and signaling pathways were identified by KEGG and GSEA analyses. In addition, we calculated the infiltration level of immunocyte by CIBERSORT algorithm. The expressions of model hub genes were detected in LUAD cell lines by real-time polymerase chain reaction (PCR).

RESULTS

KEGG analysis suggested the CRs were mainly involved in histone modification, nuclear division and DNA modification. Consensus clustering analysis identified a novel CRs-associated subtype which divided the combined LUAD cohort into two clusters (C1 = 217 and C2 = 296). We noticed that a remarkable discrepancy in survival rate among two clusters. Then, a total of 120 differentially expressed CRs were enrolled into stepwise Cox analyses. Four hub CRs (CBX7, HMGA2, NPAS2 and PRC1) were selected to create a risk signature which could accurately forecast patient outcomes and differentiate patient risk. GSEA unearthed that mTORC1 pathway, PI3K/Akt/mTOR and p53 pathway were greatly enriched in CRBS-high cohort. Moreover, the infiltration percentages of macrophage M0, macrophage M2, resting NK cells, memory B cells, dendritic cells and mast cells were statistically significantly different in the two groups. PCR assay confirmed the differential expression of four model biomarkers.

CONCLUSIONS

Altogether, our project developed a robust risk signature based on CRs and offered novel insights into individualized treatment for LUAD cases.

Advances in the Study of Circadian Genes in Non-Small Cell Lung Cancer

Circadian genes regulate several physiological functions such as circadian rhythm and metabolism and participate in the cytogenesis and progression of various malignancies. The abnormal expression of these genes in non-small cell lung cancer (NSCLC) is closely related to the clinicopathological features of NSCLC and may promote or inhibit NSCLC progression. Circadian rhythm disorders and clock gene abnormalities may increase the risk of lung cancer in some populations. We collected 15 circadian genes in NSCLC, namely PER1, PER2, PER3, TIMELESS, Cry1, Cry2, CLOCK, BMAL1/ARNTL-1, ARNTL2, NPAS2, NR1D1(REV-ERB), DEC1, DEC2, RORα, and RORγ, and determined their relationships with the clinicopathological features of patients and the potential mechanisms promoting or inhibiting NSCLC progression. We also summarized the studies on circadian rhythm disorders and circadian genes associated with lung cancer risk. The present study aimed to provide theoretical support for the future exploration of new therapeutic targets and for the primary prevention of NSCLC from the perspective of circadian genes. Interpretation of circadian rhythms in lung cancer could guide further lung cancer mechanism research and drug development that could lead to more effective treatments and improve patient outcomes.

Microdissection of the Bulk Transcriptome at Single-Cell Resolution Reveals Clinical Significance and Myeloid Cells Heterogeneity in Lung Adenocarcinoma

Background

Tumor infiltrating myeloid (TIM) cells constitute a vital element of the tumor microenvironment. The cell-type heterogeneity of TIM has yet to be fully investigated.

Methods

We used a time saving approach to generate a single-cell reference matrix, allowing quantification of cell-type proportions and cell-type-specific gene abundances in bulk RNA-seq data.

Results

Two distinct clusters, MSC1 and MSC2 (MSC subtype) were newly identified in lung adenocarcinoma (LUAD) patients, both significantly associated with overall survival and immune blockade therapy responses. Twenty myeloid cell types were detected. Thirteen of these had distinct enrichment patterns between MSC1 and MSC2. LAMP3+ dendritic cells, being a mature and transportable subtype of dendritic cell that may migrate to lymph nodes, were noted as associated with non-responsiveness to immunotargeted therapy. High infiltration level of IFIT3+ neutrophils was strongly related to the response to immune-targeted therapy and was seen to activate CD8+ T cells, partly through inflammasome activation. The infiltration levels of TIMP1+ macrophages and S100A8+ neutrophils were both significantly associated with poor prognosis. TIMP1+ macrophages were noted to recruit S100A8+ neutrophils via the CXCL5-CXCR2 axes and promote LUAD progression.

Conclusion

Altogether, we performed virtual microdissection of the bulk transcriptome at single-cell resolution and provided a promising TIM infiltration landscape that may shed new light on the development of immune therapy.

Subtype-dependent regulation of Gβγ signalling

G protein-coupled receptors (GPCRs) transmit information to the cell interior by transducing external signals to heterotrimeric G protein subunits, Gα and Gβγ subunits, localized on the inner leaflet of the plasma membrane. Though the initial focus was mainly on Gα-mediated events, Gβγ subunits were later identified as major contributors to GPCR-G protein signalling. A broad functional array of Gβγ signalling has recently been attributed to Gβ and Gγ subtype diversity, comprising 5 Gβ and 12 Gγ subtypes, respectively. In addition to displaying selectivity towards each other to form the Gβγ dimer, numerous studies have identified preferences of distinct Gβγ combinations for specific GPCRs, Gα subtypes and effector molecules. Importantly, Gβ and Gγ subtype-dependent regulation of downstream effectors, representing a diverse range of signalling pathways and physiological functions have been found. Here, we review the literature on the repercussions of Gβ and Gγ subtype diversity on direct and indirect regulation of GPCR/G protein signalling events and their physiological outcomes. Our discussion additionally provides perspective in understanding the intricacies underlying molecular regulation of subtype-specific roles of Gβγ signalling and associated diseases.

Genetic network and gene set enrichment analyses identify MND1 as potential diagnostic and therapeutic target gene for lung adenocarcinoma

This study aimed to characterize the key survival-specific genes for lung adenocarcinoma (LUAD) using machine-based learning approaches. Gene expression profiles were download from gene expression omnibus to analyze differentially expressed genes (DEGs) in LUAD tissues versus healthy lung tissue and to construct protein–protein interaction (PPI) networks. Using high-dimensional datasets of cancer specimens from clinical patients in the cancer genome atlas, gene set enrichment analysis was employed to assess the independent effect of meiotic nuclear divisions 1 (MND1) expression on survival status, and univariate and multivariate Cox regression analyses were applied to determine the associations of clinic-pathologic characteristics and MND1 expression with overall survival (OS). A set of 495 DEGs (145 upregulated and 350 downregulated) was detected, including 63 hub genes with ≥ 10 nodes in the PPI network. Among them, MND1 was participated in several important pathways by connecting with other genes via 17 nodes in lung cancer, and more frequently expressed in LUAD patients with advancing stage (OR = 1.68 for stage III vs. stage I). Univariate and multivariate Cox analyses demonstrated that the expression level of MND1 was significantly and negatively correlated with OS. Therefore, MND1 is a promising diagnostic and therapeutic target for LUAD.

G protein gamma 7 suppresses progression of lung adenocarcinoma by inhibiting E2F transcription factor 1

G protein gamma 7 (GNG7) has been found to be aberrantly expressed in some kinds of malignant tumors. In this study, we mainly discuss the antitumor role of it in lung adenocarcinoma (LUAD) cells. Protein levels of GNG7 in LUAD tissues were measured by western blot and immunohistochemical analysis. Cell proliferation, invasion and migration were detected by CCK-8 assay, 5-ethynyl-2'-deoxyuridine (EdU), and Transwell assay. In our study, GNG7 was down-regulated in LUAD, which significantly correlated with survival of LUAD patients. Functional experiments revealed that GNG7 significantly inhibited LUAD cell proliferation, migration, and invasion in vitro and E2F1 overexpression reversed these properties. GNG7 suppressed xenograft tumorigenesis in nude mice models in vivo. In conclusion, GNG7 functions as a tumor suppressor in LUAD cells through inhibiting E2F1.

Identification of a four-gene panel predicting overall survival for lung adenocarcinoma

Background

Lung cancer is the most frequently diagnosed carcinoma and the leading cause of cancer-related mortality. Although molecular targeted therapy and immunotherapy have made great progress, the overall survival (OS) is still poor due to a lack of accurate and available prognostic biomarkers. Therefore, in this study we aimed to establish a multiple-gene panel predicting OS for lung adenocarcinoma.

Methods

We obtained the mRNA expression and clinical data of lung adenocarcinoma (LUAD) from TCGA database for further integrated bioinformatic analysis. Lasso regression and Cox regression were performed to establish a prognosis model based on a multi-gene panel. A nomogram based on this model was constructed. The receiver operating characteristic (ROC) curve and the Kaplan–Meier curve were used to assess the predicted capacity of the model. The prognosis value of the multi-gene panel was further validated in TCGA-LUAD patients with EGFR, KRAS and TP53 mutation and a dataset from GEO. Gene set enrichment analysis (GSEA) was performed to explore potential biological mechanisms of a novel prognostic gene signature.

Results

A four-gene panel (including DKK1, GNG7, LDHA, MELTF) was established for LUAD prognostic indicator. The ROC curve revealed good predicted performance in both test cohort (AUC = 0.740) and validation cohort (AUC = 0.752). Each patient was calculated a risk score according to the model based on the four-gene panel. The results showed that the risk score was an independent prognostic factor, and the high-risk group had a worse OS compared with the low-risk group. The nomogram based on this model showed good prediction performance. The four-gene panel was still good predictors for OS in LUAD patients with TP53 and KRAS mutations. GSEA revealed that the four genes may be significantly related to the metabolism of genetic material, especially the regulation of cell cycle pathway.

Conclusion

Our study proposed a novel four-gene panel to predict the OS of LUAD, which may contribute to predicting prognosis accurately and making the clinical decisions of individual therapy for LUAD patients.

The ADAM17 protease promotes tobacco smoke carcinogen-induced lung tumorigenesis

Lung cancer is the leading cause of cancer-related mortality, with most cases attributed to tobacco smoking, in which nicotine-derived nitrosamine ketone (NNK) is the most potent lung carcinogen. The ADAM17 protease is responsible for the ectodomain shedding of many pro-tumorigenic cytokines, growth factors and receptors, and therefore is an attractive target in cancer. However, the role of ADAM17 in promoting tobacco smoke carcinogen-induced lung carcinogenesis is unknown. The hypomorphic Adam17ex/ex mice-characterized by reduced global ADAM17 expression-were backcrossed onto the NNK-sensitive pseudo-A/J background. CRISPR-driven and inhibitor-based (GW280264X, and ADAM17 prodomain) ADAM17 targeting was employed in the human lung adenocarcinoma cell lines A549 and NCI-H23. Human lung cancer biopsies were also used for analyses. The Adam17ex/ex mice displayed marked protection against NNK-induced lung adenocarcinoma. Specifically, the number and size of lung lesions in NNK-treated pseudo-A/J Adam17ex/ex mice were significantly reduced compared with wild-type littermate controls. This was associated with lower proliferative index throughout the lung epithelium. ADAM17 targeting in A549 and NCI-H23 cells led to reduced proliferative and colony-forming capacities. Notably, among select ADAM17 substrates, ADAM17 deficiency abrogated shedding of the soluble IL-6 receptor (sIL-6R), which coincided with the blockade of sIL-6R-mediated trans-signaling via ERK MAPK cascade. Furthermore, NNK upregulated phosphorylation of p38 MAPK, whose pharmacological inhibition suppressed ADAM17 threonine phosphorylation. Importantly, ADAM17 threonine phosphorylation was significantly upregulated in human lung adenocarcinoma with smoking history compared with their cancer-free controls. Our study identifies the ADAM17/sIL-6R/ERK MAPK axis as a candidate therapeutic strategy against tobacco smoke-associated lung carcinogenesis.

Expression of collagen type 1 alpha 1 indicates lymph node metastasis and poor outcomes in squamous cell carcinomas of the lung

Background

Squamous cell carcinomas of the lung are an extremely common and deadly form of non-small cell lung cancers. Clinical management of the disease is dependent on staging and metastatic status. Metastasis to the lymph node is especially crucial to diagnose as it occurs at an earlier stage. However, lymphadenectomies are invasive and tumor cells may be overlooked during evaluation.There are limited approved biomarkers for predicting lymph node metastasis with squamous cell carcinomas of the lung (LSCC).

Methods

Genome data of 60 tumor-adjacent samples were downloaded from Genome Expression Omnibus. We identified over-expressed HUB genes using Cytoscape as key prognostic markers. The selected markers were further evaluated based on gene ontology and overall expression levels compared to normal tissue using The Cancer Genome Atlas. We further validated these results using clinical biopsy tissue taken from squamous cell carcinoma patients.

Results

Analysis of the genome expression data resulted in 13 relevant hub genes that were differentially expressed in cancerous samples. All of these genes are associated with collagen biosynthesis within the tumor microenvironment. We chose Collagen Type 1 Alpha 1 (COL1A1) as the most relevant prognostic marker due to its high number of pathway connections and over expression in the tumor microenvironment compared to the other 12 genes. Additionally, based on analysis of The Cancer Genome Atlas, tumors with higher levels of COL1A1 expression are associated with poorer overall survival. Finally, evaluation of clinical biopsy samples suggests that overexpression of COL1A1 in the LSCC microenvironment highly correlates with lymph node metastasis. These results suggest COL1A1 is a clinically relevant marker that should be used to justify lymphadenectomies.

Screening of Hub Gene Targets for Lung Cancer via Microarray Data

BACKGROUND

Lung cancer is one of the malignancies exhibiting the fastest increase in morbidity and mortality, but the cause is not clearly understood. The goal of this investigation was to screen and identify relevant biomarkers of lung cancer.

METHODS

Publicly available lung cancer data sets, including GSE40275 and GSE134381, were obtained from the GEO database. The repeatability test for data was done by principal component analysis (PCA), and a GEO2R was performed to screen differentially expressed genes (DEGs), which were all subjected to enrichment analysis. Protein-protein interactions (PPIs), and the significant module and hub genes were identified via Cytoscape. Expression and correlation analysis of hub genes was done, and an overall survival analysis of lung cancer was performed. A receiver operating characteristic (ROC) curve analysis was performed to test the sensitivity and specificity of the identified hub genes for diagnosing lung cancer.

RESULTS

The repeatability of the two datasets was good and 115 DEGs and 10 hub genes were identified. Functional analysis revealed that these DEGs were associated with cell adhesion, the extracellular matrix, and calcium ion binding. The DEGs were mainly involved with ECM-receptor interaction, ABC transporters, cell-adhesion molecules, and the p53 signaling pathway. Ten genes including COL1A2, POSTN, DSG2, CDKN2A, COL1A1, KRT19, SLC2A1, SERPINB5, DSC3, and SPP1 were identified as hub genes through module analysis in the PPI network. Lung cancer patients with high expression of COL1A2, POSTN, DSG2, CDKN2A, COL1A1, SLC2A1, SERPINB5, and SPP1 had poorer overall survival times than those with low expression (p <0.05). The CTD database showed that 10 hub genes were closely related to lung cancer. Expression of POSTN, DSG2, CDKN2A, COL1A1, SLC2A1, SERPINB5, and SPP1 was also associated with a diagnosis of lung cancer (p<0.05). ROC analysis showed that SPP1 (AUC = 0.940, p = 0.000*, 95%CI = 0.930-0.973, ODT = 7.004), SLC2A1 (AUC = 0.889, p = 0.000*, 95%CI = 0.791-0.865, ODT = 7.123), CDKN2A (AUC = 0.730, p = 0.000*, 95%CI = 0.465-1.000, ODT = 6.071) were suitable biomarkers.

CONCLUSION

Microarray technology represents an effective method for exploring genetic targets and molecular mechanisms of lung cancer. In addition, the identification of hub genes of lung cancer provides novel research insights for the diagnosis and treatment of lung cancer.

ADAM17 selectively activates the IL-6 trans-signaling/ERK MAPK axis in KRAS-addicted lung cancer

Oncogenic KRAS mutations are major drivers of lung adenocarcinoma (LAC), yet the direct therapeutic targeting of KRAS has been problematic. Here, we reveal an obligate requirement by oncogenic KRAS for the ADAM17 protease in LAC In genetically engineered and xenograft (human cell line and patient-derived) Kras ^G12D-driven LAC models, the specific blockade of ADAM17, including with a non-toxic prodomain inhibitor, suppressed tumor burden by reducing cellular proliferation. The pro-tumorigenic activity of ADAM17 was dependent upon its threonine phosphorylation by p38 MAPK, along with the preferential shedding of the ADAM17 substrate, IL-6R, to release soluble IL-6R that drives IL-6 trans-signaling via the ERK1/2 MAPK pathway. The requirement for ADAM17 in Kras ^G12D-driven LAC was independent of bone marrow-derived immune cells. Furthermore, in KRAS mutant human LAC, there was a significant positive correlation between augmented phospho-ADAM17 levels, observed primarily in epithelial rather than immune cells, and activation of ERK and p38 MAPK pathways. Collectively, these findings identify ADAM17 as a druggable target for oncogenic KRAS-driven LAC and provide the rationale to employ ADAM17-based therapeutic strategies for targeting KRAS mutant cancers.

Elevated SLC2A1 Expression Correlates with Poor Prognosis in Patients with Surgically Resected Lung Adenocarcinoma: A Study Based on Immunohistochemical Analysis and Bioinformatics

Lung adenocarcinoma (LUAD) accounts for an increasing proportion of non-small-cell lung cancer and an increasing number of cancer-related deaths worldwide. However, few biomarkers are available for prognosis and patient stratification. In all eight datasets from the Oncomine and The Cancer Genome Atlas (TCGA) LUAD cohorts, solute carrier family 2 member 1 (SLC2A1) was significantly more highly expressed in LUAD tissue than in normal lung tissue. High SLC2A1 expression was also significantly (p < 0.05) associated with a poor prognosis in stage I, II, and III subgroups using the Kaplan-Meier plotter. In the National Cancer Center of China (NCC) cohort, SLC2A1 expression correlated significantly (p < 0.05) with several parameters, including sex, smoking history, tumor size, tumor differentiation, T stage, N stage, and pathologic TNM stage. Moreover, multivariate Cox regression indicated SLC2A1 to be an independent prognostic factor (p < 0.05) in both TCGA and NCC cohorts. Eleven hallmark pathways were significantly enriched (p < 0.01, false discovery rate <0.25) in the high-SLC2A1 expression group. SLC2A1 is a promising biomarker that can be used to predict the prognosis of LUAD.

Identification of key genes in non-small cell lung cancer by bioinformatics analysis

Background

Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors in the world, and it has become the leading cause of death of malignant tumors. However, its mechanisms are not fully clear. The aim of this study is to investigate the key genes and explore their potential mechanisms involving in NSCLC.

Methods

We downloaded gene expression profiles GSE33532, GSE30219 and GSE19804 from the Gene Expression Omnibus (GEO) database and analyzed them by using GEO2R. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were used for the functional and pathway enrichment analysis. We constructed the protein-protein interaction (PPI) network by STRING and visualized it by Cytoscape. Further, we performed module analysis and centrality analysis to find the potential key genes. Finally, we carried on survival analysis of key genes by GEPIA.

Results

In total, we obtained 685 DEGs. Moreover, GO analysis showed that they were mainly enriched in cell adhesion, proteinaceous extracellular region, heparin binding. KEGG pathway analysis revealed that transcriptional misregulation in cancer, ECM-receptor interaction, cell cycle and p53 signaling pathway were involved in. Furthermore, PPI network was constructed including 249 nodes and 1,027 edges. Additionally, a significant module was found, which included eight candidate genes with high centrality features. Further, among the eight candidate genes, the survival of NSCLC patients with the seven high expression genes were significantly worse, including CDK1, CCNB1, CCNA2, BIRC5, CCNB2, KIAA0101 and MELK. In summary, these identified genes should play an important role in NSCLC, which can provide new insight for NSCLC research.