TCN1 is a potential prognostic biomarker and correlates with immune infiltrates in lung adenocarcinoma

Nuclear protein NOP2 serves as a poor-prognosis predictor of LUAD and aggravates the malignancy of lung adenocarcinoma cells

Recent studies have shown that NOP2, a nucleolar protein, is up-regulated in various cancers, suggesting a potential link to tumor aggressiveness and unfavorable outcomes. This study examines NOP2's role in lung adenocarcinoma (LUAD), a context where its implications remain unclear. Utilizing bioinformatics, we assessed 513 LUAD and 59 normal tissue samples from The Cancer Genome Atlas (TCGA) to explore NOP2's diagnostic and prognostic significance in LUAD. Additionally, in vitro experiments compared NOP2 expression between Beas-2b and A549 cells. Advanced databases and analytical tools, including LINKEDOMICS, STRING, and TISIDB, were employed to further elucidate NOP2's association with LUAD. Our findings indicate a significantly higher expression of NOP2 mRNA and protein in A549 cells compared to Beas-2b cells (P < 0.001). In LUAD, elevated NOP2 levels were linked to decreased Overall Survival (OS) and advanced clinical stages. Univariate Cox analysis revealed that high NOP2 expression correlated with poorer OS in LUAD (P < 0.01), a finding independently supported by multivariate Cox analysis (P < 0.05). The relationship between NOP2 expression and LUAD risk was presented via a Nomogram. Additionally, Gene Set Enrichment Analysis (GSEA) identified seven NOP2-related signaling pathways. A focal point of our research was the interplay between NOP2 and tumor-immune interactions. Notably, a negative correlation was observed between NOP2 expression and the immune infiltration levels of macrophages, neutrophils, mast cells, Natural Killer (NK) cells, and CD8 + T cells in LUAD. Moreover, the expression of NOP2 was related to the sensitivity of various chemotherapeutic drugs. In vitro, we found that downregulating NOP2 can decrease the proliferation, migration and invasion of A549 cells. Furthermore, NOP2 can regulate Caspase3-mediated apoptosis. Collectively, particularly regarding prognosis, immune infiltration and vitro experiments, these findings suggest NOP2's potential of serving as a poor-prognostic biomarker for LUAD and aggravating the malignancy of lung adenocarcinoma cells.

Engineered hydrogel reveals contribution of matrix mechanics to esophageal adenocarcinoma and identifies matrix-activated therapeutic targets

Increased extracellular matrix (ECM) stiffness has been implicated in esophageal adenocarcinoma (EAC) progression, metastasis, and resistance to therapy. However, the underlying protumorigenic pathways are yet to be defined. Additional work is needed to develop physiologically relevant in vitro 3D culture models that better recapitulate the human tumor microenvironment and can be used to dissect the contributions of matrix stiffness to EAC pathogenesis. Here, we describe a modular, tumor ECM-mimetic hydrogel platform with tunable mechanical properties, defined presentation of cell-adhesive ligands, and protease-dependent degradation that supports robust in vitro growth and expansion of patient-derived EAC 3D organoids (EAC PDOs). Hydrogel mechanical properties control EAC PDO formation, growth, proliferation, and activation of tumor-associated pathways that elicit stem-like properties in the cancer cells, as highlighted through in vitro and in vivo environments. We also demonstrate that the engineered hydrogel serves as a platform for identifying potential therapeutic targets to disrupt the contribution of protumorigenic matrix mechanics in EAC. Together, these studies show that an engineered PDO culture platform can be used to elucidate underlying matrix-mediated mechanisms of EAC and inform the development of therapeutics that target ECM stiffness in EAC.

Construction of a genomic instability-derived predictive prognostic signature for non-small cell lung cancer patients

BACKGROUND

Genomic instability (GI) is an effective prognostic marker of cancer. Thus, in this work, we aimed to explore the impact of GI derived signature on prognosis in non-small cell lung cancer (NSCLC) patients using bioinformatics methods.

METHODS

The data of NSCLC patients were collected from The Cancer Genome Atlas. Totally 1794 immune related genes were downloaded from immport database. The optimal prognosis related genes were identified by univariate and LASSO Cox analyses. The risk score model was built to predict the NSCLC patients' prognosis. The immune cell infiltration was analyzed in CIBERSORT.

RESULTS

The 951 differentially expressed genes (DEGs) between the genomic stability (GS) and GI groups were enriched in 862 Gene ontology terms and 32 Kyoto Encyclopedia of Genes and Genomes pathways. Based on the 13 optimal genes, a prognostic risk score mode for NSCLC was established, and the high-risk patients exhibited worse overall survival. Moreover, the nomogram could reliably predict the clinical outcomes. The immune cell infiltration and checkpoints were significantly differential between the two groups (high-risk and low-risk).

CONCLUSION

The GI related 13-gene signature (TMPRSS11E, TNNC2, HLF, FOXM1, PKMYT1, TCN1, RGS20, SYT8, CD1B, LY6K, MFSD4A, KLRG2 APCDD1L) could reliably predict the prognosis of NSCLC patients.

Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism

Background: The progression of lung adenocarcinoma (LUAD) may be related to abnormal fatty acid metabolism (FAM). The present study investigated the relationship between FAM-related genes and LUAD prognosis. Methods: LUAD samples from The Cancer Genome Atlas were collected. The scores of FAM-associated pathways from the Kyoto Encyclopedia of Genes and Genomes website were calculated using the single sample gene set enrichment analysis. ConsensusClusterPlus and cumulative distribution function were used to classify molecular subtypes for LUAD. Key genes were obtained using limma package, Cox regression analysis, and six machine learning algorithms (GBM, LASSO, XGBoost, SVM, random forest, and decision trees), and a RiskScore model was established. According to the RiskScore model and clinical features, a nomogram was developed and evaluated for its prediction performance using a calibration curve. Differences in immune abnormalities among patients with different subtypes and RiskScores were analyzed by the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data, CIBERSORT, and single sample gene set enrichment analysis. Patients' drug sensitivity was predicted by the pRRophetic package in R language. Results: LUAD samples had lower scores of FAM-related pathways. Three molecular subtypes (C1, C2, and C3) were defined. Analysis on differential prognosis showed that the C1 subtype had the most favorable prognosis, followed by the C2 subtype, and the C3 subtype had the worst prognosis. The C3 subtype had lower immune infiltration. A total of 12 key genes (SLC2A1, PKP2, FAM83A, TCN1, MS4A1, CLIC6, UBE2S, RRM2, CDC45, IGF2BP1, ANGPTL4, and CD109) were screened and used to develop a RiskScore model. Survival chance of patients in the high-RiskScore group was significantly lower. The low-RiskScore group showed higher immune score and higher expression of most immune checkpoint genes. Patients with a high RiskScore were more likely to benefit from the six anticancer drugs we screened in this study. Conclusion: We developed a RiskScore model using FAM-related genes to help predict LUAD prognosis and develop new targeted drugs.

ELOVL6 is associated with immunosuppression in lung adenocarcinoma through bioinformatics analysis

The aim of this paper was to reveal the correlation between the expression of ELOVL fatty acid elongase 6 (ELOVL6) gene in lung adenocarcinoma (LUAD) and its clinical significance, immune cell infiltration level and prognosis. Expression profile data of ELOVL6 mRNA were collected from the cancer genome atlas database to analyze the differences in ELOVL6 mRNA expression in LUAD tissues and normal lung tissues, and to analyze the correlation between ELOVL6 and information on clinicopathological features. Based on TIMER database, TISDIB database and GEPIA2 database, the correlation between ELOVL6 expression and tumor immune cell infiltration in LUAD was analyzed. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses of ELOVL6-related co-expressed genes were performed to identify the involved signaling pathways and to construct their co-expressed gene protein interaction networks. Drugs affected by ELOVL6 expression were screened based on the Cell Miner database. These findings suggest that ELOVL6 plays an important role in the course of LUAD, and the expression level of this gene has a close relationship with clinicopathological characteristics and survival prognosis, and has the potential to become a prognostic marker and therapeutic target for LUAD.

TCN1 Expression Is Increased in Asthma

INTRODUCTION

Asthma is a chronic disease that affects populations worldwide. The purpose of this study was to investigate the expression of TCN1 in sputum and its correlation with inflammation and lung function in asthma.

METHODS

We recruited 141 subjects, detected TCN1 mRNA level by quantitative reverse transcription polymerase chain reaction, detected TCN1 protein expression by Western blot, detected TCN1 protein level by enzyme-linked immunosorbent assay, and analyzed the correlation between TCN1 and fraction of exhaled nitric oxide (FeNO), IgE, EOS%, lung functions, and some Th2 cytokines. The diagnostic value of TCN1 was evaluated by receiver operating characteristics curve. The expression of TCN1 was further confirmed by human bronchial epithelial cell in vitro.

RESULTS

Compared with the health group, the expression of TCN1 in induced sputum cells increased in asthma group and was correlated with FeNO, IgE, and EOS%. TCN1 level was also elevated in the induced sputum supernatant of asthma patients. The protein level of TCN1 in induced sputum supernatant was correlated with FeNO, IgE and PC-20, forced expiratory volume in the first second (FEV1)%pred, FEV1/FVC, and some cytokines (IL-4, IL-5, IL-10, IL-13, MUC5AC). TCN1 was also differentially expressed in patients with different severity of asthma. Four weeks after ICS treatment, the expression of TCN1 in induced sputum supernatant increased. In vitro, the protein level of TCN1 in human bronchial epithelial cells' supernatant increased after stimulated with IL-4 and IL-13.

CONCLUSION

The expression of TCN1 was increased in asthma patients' sputum, and was positively correlated with some inflammatory markers, negatively correlated with lung function. TCN1 may be used as a potential biomarker for the diagnosis and treatment of asthma.

Drug resistance related genes in lung adenocarcinoma predict patient prognosis and influence the tumor microenvironment

Lung adenocarcinoma (LUAD) is the predominant type of non-small lung cancer (NSCLC) with strong invasive ability and poor prognosis. The drug resistance related genes are potentially associated with prognosis of LUAD. Our research aimed to identify the drug resistance related genes and explore their potential prognostic value in LUAD patients. The data used in this study were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Firstly, we screened drug resistance related genes in LUAD by differential gene analysis, univariate Cox regression and drug sensitivity analyses. Subsequently, we constructed a risk score model using LASSO Cox regression analysis, and verified whether the risk score can predict the survival of LUAD patients independent of other factors. Moreover, we explored the immune infiltration of 22 immune cells between high-risk and low-risk patients. Totally 10 drug-resistance positively related genes (PLEK2, TFAP2A, KIF20A, S100P, GDF15, HSPB8, SASH1, WASF3, LAMA3 and TCN1) were identified in LUAD. The risk score model of LUAD constructed with these 10 genes could reliably predict the prognosis of LUAD patients. 18 pathways were significantly activated in high-risk group compared with low-risk group. In addition, the infiltration proportion of multiple immune cells was significantly different between high-risk and low-risk groups, and the proportion of M1 phagocytes was significantly higher in the high-risk group compared with the low-risk group. The drug resistance related genes (PLEK2, TFAP2A, KIF20A, S100P, GDF15, HSPB8, SASH1, WASF3, LAMA3 and TCN1) could predict the prognosis of LUAD patients. Clarifying the roles and mechanisms of these 10 genes in regulating drug resistance in LUAD will help to improve individualized clinical treatment protocols and predict patient sensitivity to treatment.

AURKA, TOP2A and MELK are the key genes identified by WGCNA for the pathogenesis of lung adenocarcinoma

The comprehensive analysis of single or multiple microarray datasets is currently available in Gene Expression Omnibus (GEO) databases, with several studies having identified genes strongly associated with the development of lung adenocarcinoma (LUAD). However, the mechanisms of LUAD development remain largely unknown and has not yet been systematically studied; thus, further studies are required in this field. In the present study, weighted gene co-expression network analysis (WGCNA) was used for the evaluation of key genes with potential high risk of LUAD, and to provide more reliable evidence concerning its pathogenesis. The GSE140797 dataset from the high-throughput GEO database was downloaded and was first analyzed using the Limma package in the R language in order to determine the differentially expressed genes. The dataset was then analyzed using the WGCNA package to analyze the co-expressed genes, and the modular genes with the highest correlation with the clinical phenotype were identified. Subsequently, the pathogenic genes shared in common between the result of the two analyses were imported into the STRING database for protein-protein interaction network analysis. The hub genes were screened out using Cytoscape, and then The Cancer Genome Atlas analysis, receiver operating characteristic analysis and survival analysis were subsequently performed. Finally, the key genes were evaluated using reverse transcription-quantitative PCR and western blot analysis. Bioinformatics analysis of the GSE140797 dataset revealed eight key genes: AURKA, BUB1, CCNB1, CDK1, MELK, NUSAP1, TOP2A and PBK. Finally, the AURKA, TOP2A and MELK genes were evaluated in samples from patients with lung cancer using WGCNA and RT-qPCR, western blot analysis experiments, providing basis for further research on the mechanisms of LUAD development and targeted therapy.

Gastric Cancer Biomarker Candidates Identified by Machine Learning and Integrative Bioinformatics: Toward Personalized Medicine

Gastric cancer (GC) is among the leading causes of cancer-related deaths worldwide. The discovery of robust diagnostic biomarkers for GC remains a challenge. This study sought to identify biomarker candidates for GC by integrating machine learning (ML) and bioinformatics approaches. Transcriptome profiles of patients with GC were analyzed to identify differentially expressed genes between the tumor and adjacent normal tissues. Subsequently, we constructed protein-protein interaction networks so as to find the significant hub genes. Along with the bioinformatics integration of ML methods such as support vector machine, the recursive feature elimination was used to select the most informative genes. The analysis unraveled 160 significant genes, with 88 upregulated and 72 downregulated, 10 hub genes, and 12 features from the variable selection method. The integrated analyses found that EXO1, DTL, KIF14, and TRIP13 genes are significant and poised as potential diagnostic biomarkers in relation to GC. The receiver operating characteristic curve analysis found KIF14 and TRIP13 are strongly associated with diagnosis of GC. We suggest KIF14 and TRIP13 are considered as biomarker candidates that might potentially inform future research on diagnosis, prognosis, or therapeutic targets for GC. These findings collectively offer new future possibilities for precision/personalized medicine research and development for patients with GC.

A network-based dynamic criterion for identifying prediction and early diagnosis biomarkers of complex diseases

Lung adenocarcinoma (LUAD) seriously threatens human health and generally results from dysfunction of relevant module molecules, which dynamically change with time and conditions, rather than that of an individual molecule. In this study, a novel network construction algorithm for identifying early warning network signals (IEWNS) is proposed for improving the performance of LUAD early diagnosis. To this end, we theoretically derived a dynamic criterion, namely, the relationship of variation (RV), to construct dynamic networks. RV infers correlation [Formula: see text] statistics to measure dynamic changes in molecular relationships during the process of disease development. Based on the dynamic networks constructed by IEWNS, network warning signals used to represent the occurrence of LUAD deterioration can be defined without human intervention. IEWNS was employed to perform a comprehensive analysis of gene expression profiles of LUAD from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. The experimental results suggest that the potential biomarkers selected by IEWNS can facilitate a better understanding of pathogenetic mechanisms and help to achieve effective early diagnosis of LUAD. In conclusion, IEWNS provides novel insight into the initiation and progression of LUAD and helps to define prospective biomarkers for assessing disease deterioration.

Identification of a TGF-β signaling-related gene signature for prediction of immunotherapy and targeted therapy for lung adenocarcinoma

BACKGROUND

Transforming growth factor (TGF)-β signaling functions importantly in regulating tumor microenvironment (TME). This study developed a prognostic gene signature based on TGF-β signaling-related genes for predicting clinical outcome of patients with lung adenocarcinoma (LUAD).

METHODS

TGF-β signaling-related genes came from The Molecular Signature Database (MSigDB). LUAD prognosis-related genes were screened from all the genes involved in TGF-β signaling using least absolute shrinkage and selection operator (LASSO) Cox regression analysis and then used to establish a risk score model for LUAD. ESTIMATE and CIBERSORT analyzed infiltration of immune cells in TME. Immunotherapy response was analyzed by the TIDE algorithm.

RESULTS

A LUAD prognostic 5-gene signature was developed based on 54 TGF-β signaling-related genes. Prognosis of high-risk patients was significantly worse than low-risk patients. Both internal validation and external dataset validation confirmed a high precision of the risk model in predicting the clinical outcomes of LUAD patients. Multivariate Cox analysis demonstrated the model independence in OS prediction of LUAD. The risk model was significantly related to the infiltration of 9 kinds of immune cells, matrix, and immune components in TME. Low-risk patients tended to respond more actively to anti-PD-1 treatment, while high-risk patients were more sensitive to chemotherapy and targeted therapy.

CONCLUSIONS

The 5-gene signature based on TGF-β signaling-related genes showed potential for LUAD management.

UCHL1 acts as a potential oncogene and affects sensitivity of common anti-tumor drugs in lung adenocarcinoma

Background

Lung adenocarcinoma is the leading cause of cancer death worldwide. Recently, ubiquitin C-terminal hydrolase L1 (UCHL1) has been demonstrated to be highly expressed in many tumors and plays the role of an oncogene. However, the functional mechanism of UCHL1 is unclear in lung adenocarcinoma progression.

Methods

We analyzed the differential expression of the UCHL1 gene in lung adenocarcinoma and normal lung tissues, and the correlation between the UCHL1 gene and prognosis was also analyzed by the bioinformatics database TCGA. Meanwhile, we detected and analyzed the expression of UCHL1 and Ki-67 protein in a tissue microarray (TMA) containing 150 patients with lung adenocarcinoma by immunohistochemistry (IHC) and clinicopathological characteristics by TCGA database. In vitro experiments, we knocked down the UCHL1 gene of A549 cells and detected the changes in cell migration, invasion, and apoptosis. At the same time, we analyzed the effect of UCHL1 on anti-tumor drug sensitivity of lung adenocarcinoma by a bioinformatics database. In terms of the detection rate of lung adenocarcinoma indicators, we analyzed the impact of UCHL1 combined with common clinical indicators on the detection rate of lung adenocarcinoma through a bioinformatics database.

Results

In this study, the analysis of UCHL1 protein expression in lung adenocarcinoma proved that obviously higher UCHL1 protein level was discovered in lung adenocarcinoma tissues. The expression of UCHL1 was closely related to poor clinical outcomes. Interestingly, a significantly positive correlation between the expression of UCHL1 and Ki-67-indicated UCHL1 was associated with tumor migration and invasion. Through executing loss of function tests, we affirmed that silencing of UCHL1 expression significantly inhibited migration and invasion of lung adenocarcinoma cells in vitro. Furthermore, lung adenocarcinoma cells with silenced UCHL1 showed a higher probability of apoptosis. In terms of the detection rate of lung adenocarcinoma indicators, we discovered UCHL1 could improve the detection rate of clinical lung adenocarcinoma and affect drug sensitivity.

Conclusion

In lung adenocarcinoma, UCHL1 promotes tumor migration, invasion, and metastasis by inhibiting apoptosis and has an important impact on the clinical drug treatment of lung adenocarcinoma. In addition, UCHL1 can improve the detection rate of clinical lung adenocarcinoma. Above all, UCHL1 may be a new marker for the diagnosis of lung adenocarcinoma and provide a new target for the treatment of clinical diseases.