The screening of immune-related biomarkers for prognosis of lung adenocarcinoma

The clinical significance of endoplasmic reticulum stress related genes in non-small cell lung cancer and analysis of single nucleotide polymorphism for CAV1

In recent years, protein homeostasis imbalance caused by endoplasmic reticulum stress has become a major hallmark of cancer. Studies have shown that endoplasmic reticulum stress is closely related to the occurrence, development, and drug resistance of non-small cell lung cancer, however, the role of various endoplasmic reticulum stress-related genes in non-small cell lung cancer is still unclear. In this study, we established an endoplasmic reticulum stress scores based on the Cancer Genome Atlas for non-small cell lung cancer to reflect patient features and predict prognosis. Survival analysis showed significant differences in overall survival among non-small cell lung cancer patients with different endoplasmic reticulum stress scores. In addition, endoplasmic reticulum stress scores was significantly correlated with the clinical features of non-small cell lung cancer patients, and can be served as an independent prognostic indicator. A nomogram based on endoplasmic reticulum stress scores indicated a certain clinical net benefit, while ssGSEA analysis demonstrated that there was a certain immunosuppressive microenvironment in high endoplasmic reticulum stress scores. Gene Set Enrichment Analysis showed that scores was associated with cancer pathways and metabolism. Finally, weighted gene co-expression network analysis displayed that CAV1 was closely related to the occurrence of non-small cell lung cancer. Therefore, in order to further analyze the role of this gene, Chinese non-smoking females were selected as the research subjects to investigate the relationship between CAV1 rs3779514 and susceptibility and prognosis of non-small cell lung cancer. The results showed that the mutation of rs3779514 significantly reduced the risk of non-small cell lung cancer in Chinese non-smoking females, but no prognostic effect was found. In summary, we proposed an endoplasmic reticulum stress scores, which was an independent prognostic factor and indicated immune characteristics in the microenvironment of non-small cell lung cancer. We also validated the relationship between single nucleotide polymorphism locus of core genes and susceptibility to non-small cell lung cancer.

Serum Protein Fishing for Machine Learning-Boosted Diagnostic Classification of Small Nodules of Lung

Diagnosis of benign and malignant small nodules of the lung remains an unmet clinical problem which is leading to serious false positive diagnosis and overtreatment. Here, we developed a serum protein fishing-based spectral library (ProteoFish) for data independent acquisition analysis and a machine learning-boosted protein panel for diagnosis of early Non-Small Cell Lung Cancer (NSCLC) and classification of benign and malignant small nodules. We established an extensive NSCLC protein bank consisting of 297 clinical subjects. After testing 5 feature extraction algorithms and six machine learning models, the Lasso algorithm for a 15-key protein panel selection and Random Forest was chosen for diagnostic classification. Our random forest classifier achieved 91.38% accuracy in benign and malignant small nodule diagnosis, which is superior to the existing clinical assays. By integrating with machine learning, the 15-key protein panel may provide insights to multiplexed protein biomarker fishing from serum for facile cancer screening and tackling the current clinical challenge in prospective diagnostic classification of small nodules of the lung.

Heat shock protein B7 (HSPB7) inhibits lung adenocarcinoma progression by inhibiting glycolysis

In the present study, it was aimed to investigate the effects and potential mechanisms of heat shock protein B7 (HSPB7) on lung adenocarcinoma (LUAD). Bioinformatic analysis was performed to explore the association between HSPB7 expression and patients with LUAD. MTT, colony formation, wound healing and Transwell assays were performed to examine the proliferative, migratory and invasive abilities of H1975 and A549 cells. Western blot analysis was conducted to determine the corresponding protein expression. Co‑Immunoprecipitation and Chromatin immunoprecipitation assays were carried out to reveal the interaction between HSPB7 and myelodysplastic syndrome 1 and ecotropic viral integration site 1 complex locus (MECOM). In addition, an animal model was conducted by the subcutaneous injection of A549 cells into BALB/c nude mice, and tumor weight and size were measured. HSPB7 was downregulated in LUAD tissues and cells, and its expression level correlated with patient prognosis. Cell functional data revealed that silencing of HSPB7 promoted lung cancer cell proliferation, migration, invasion and epithelial mesenchymal transition (EMT); whereas overexpression of HSPB7 led to the opposite results. Furthermore, bioinformatics analysis showed that HSPB7 inhibited glycolysis. HSPB7 decreased glucose consumption, lactic acid production, and lactate dehydrogenase A, hexokinase 2 and pyruvate kinase muscle isoform 2 protein levels. The results demonstrated that MECOM was a transcription factor of HSPB7. Collectively, these results suggested that HSPB7 is regulated by MECOM, and that HSPB7 attenuates LUAD cell proliferation, migration, invasion and EMT by inhibiting glycolysis.

Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma

Introduction

Lung cancer is a major cause of illness and death worldwide. Lung adenocarcinoma (LUAD) is its most common subtype. Metabolite-mRNA interactions play a crucial role in cancer metabolism. Thus, metabolism-related mRNAs are potential targets for cancer therapy.

Methods

This study constructed a network of metabolite-mRNA interactions (MMIs) using four databases. We retrieved mRNAs from the Tumor Genome Atlas (TCGA)-LUAD cohort showing significant expressional changes between tumor and non-tumor tissues and identified metabolism-related differential expression (DE) mRNAs among the MMIs. Candidate mRNAs showing significant contributions to the deep neural network (DNN) model were mined. Using MMIs and the results of function analysis, we created a subnetwork comprising candidate mRNAs and metabolites.

Results

Finally, 10 biomarkers were obtained after survival analysis and validation. Their good prognostic value in LUAD was validated in independent datasets. Their effectiveness was confirmed in the TCGA and an independent Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset by comparison with traditional machine-learning models.

Conclusion

To summarize, 10 metabolism-related biomarkers were identified, and their prognostic value was confirmed successfully through the MMI network and the DNN model. Our strategy bears implications to pave the way for investigating metabolic biomarkers in other cancers.

Identification of a novel intermittent hypoxia-related prognostic lncRNA signature and the ceRNA of lncRNA GSEC/miR-873-3p/EGLN3 regulatory axis in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is still the most prevalent type of respiratory cancer. Intermittent hypoxia can increase the mortality and morbidity associated with lung cancer. Long non-coding RNAs (lncRNAs) are crucial in lung adenocarcinoma. However, the effects of intermittent hypoxia-related long non-coding RNAs (IHRLs) on lung adenocarcinoma are still unknown.

Method

In the current research, eight IHRLs were selected to create a prognostic model. The risk score of the prognostic model was evaluated using multivariate and univariate analyses, and its accuracy and reliability were validated using a nomogram and ROC. Additionally, we investigated the relationships between IHRLs and the immune microenvironment.

Result

Our analysis identified GSEC, AC099850.3, and AL391001.1 as risk lncRNAs, while AC010615.2, AC010654.1, AL513550.1, LINC00996, and LINC01150 were categorized as protective lncRNAs. We observed variances in the expression of seven immune cells and 15 immune-correlated pathways between the two risk groups. Furthermore, our results confirmed the ceRNA network associated with the intermittent hypoxia-related lncRNA GSEC/miR-873-3p/EGLN3 regulatory pathway. GSEC showed pronounced expression in lung adenocarcinoma tissues and specific cell lines, and its inhibition resulted in reduced proliferation and migration in A549 and PC9 cells. Intriguingly, GSEC manifested oncogenic properties by sponging miR-873-3p and demonstrated a tendency to modulate EGLN3 expression favorably.

Conclusion

GSEC acts as an oncogenic lncRNA by interacting with miR-873-3p, modulating EGLN3 expression. This observation underscores the potential of GSEC as a diagnostic and therapeutic target for LUAD.

Cullin7 induces docetaxel resistance by regulating the protein level of the antiapoptotic protein Survivin in lung adenocarcinoma cells

Background

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC). Chemotherapy resistance is the main cause of chemotherapy failure. Cullin7 (Cul7) is highly expressed in LUAD and is associated with poor prognosis. Moreover, Cul7 is abnormally overexpressed in docetaxel-resistant LUAD cells. Therefore, further exploration of the role and molecular mechanism of Cul7 in LUAD docetaxel resistance is necessary.

Methods

We established docetaxel-resistant cell lines (A549DTX and H358DTX cell lines) by exposing cells to gradually increasing concentrations of docetaxel. Cell (A549, A549DTX, H358, and H358DTX cell lines) sensitivity to docetaxel was determined via a 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymmethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, inner salt (MTS) assay. And then quantitative polymerase chain reaction (qPCR) and Western blotting were performed to measure the expression of Cul7 and Survivin in A549, A549DTX, H358, and H358DTX cell lines. Subsequently, we knocked down Cul7 in docetaxel-resistant cells and overexpressed Cul7 in parental cells via lentiviral transduction to further validate the correlation between Cul7 and docetaxel resistance, while exploring the molecular mechanism of docetaxel resistance it caused. Immunofluorescence and immunohistochemical (IHC) staining were also used to evaluate the expression and cellular localization of Cul7. To confirm the effect of Cul7 expression on cell apoptosis, we used flow cytometry to detect the apoptosis rate of A549 and A549DTX cells with the same drug concentration.

Results

Cul7 was highly expressed in A549DTX and H358DTX cells. However, when Cul7 expression was knocked down in A549DTX and H358DTX cells, cell sensitivity to docetaxel was significantly increased. In addition, we found that Cul7 was coexpressed with Survivin. Silencing Survivin reversed the docetaxel insensitivity caused by Cul7 overexpression. High expression of Cul7 and Survivin in docetaxel-resistant LUAD cells inhibited the intrinsic apoptosis pathway and promoted cell proliferation. Therefore, the Cul7/Survivin axis may play a role in inducing LUAD docetaxel chemoresistance.

Conclusions

Cul7 and Survivin were both highly expressed in docetaxel-resistant LUAD cells. Our results suggest that Cul7 may inhibit apoptosis and promote the proliferation of LUAD cells by increasing the Survivin protein level, which in turn contributes to docetaxel chemoresistance in LUAD.

Identification of key genes and biological pathways in lung adenocarcinoma by integrated bioinformatics analysis

BACKGROUND

The objectives of this study were to identify hub genes and biological pathways involved in lung adenocarcinoma (LUAD) via bioinformatics analysis, and investigate potential therapeutic targets.

AIM

To determine reliable prognostic biomarkers for early diagnosis and treatment of LUAD.

METHODS

To identify potential therapeutic targets for LUAD, two microarray datasets derived from the Gene Expression Omnibus (GEO) database were analyzed, GSE3116959 and GSE118370. Differentially expressed genes (DEGs) in LUAD and normal tissues were identified using the GEO2R tool. The Hiplot database was then used to generate a volcanic map of the DEGs. Weighted gene co-expression network analysis was conducted to cluster the genes in GSE116959 and GSE118370 into different modules, and identify immune genes shared between them. A protein-protein interaction network was established using the Search Tool for the Retrieval of Interacting Genes database, then the CytoNCA and CytoHubba components of Cytoscape software were used to visualize the genes. Hub genes with high scores and co-expression were identified, and the Database for Annotation, Visualization and Integrated Discovery was used to perform enrichment analysis of these genes. The diagnostic and prognostic values of the hub genes were calculated using receiver operating characteristic curves and Kaplan-Meier survival analysis, and gene-set enrichment analysis was conducted. The University of Alabama at Birmingham Cancer data analysis portal was used to analyze relationships between the hub genes and normal specimens, as well as their expression during tumor progression. Lastly, validation of protein expression was conducted on the identified hub genes via the Human Protein Atlas database.

RESULTS

Three hub genes with high connectivity were identified; cellular retinoic acid binding protein 2 (CRABP2), matrix metallopeptidase 12 (MMP12), and DNA topoisomerase II alpha (TOP2A). High expression of these genes was associated with a poor LUAD prognosis, and the genes exhibited high diagnostic value.

CONCLUSION

Expression levels of CRABP2, MMP12, and TOP2A in LUAD were higher than those in normal lung tissue. This observation has diagnostic value, and is linked to poor LUAD prognosis. These genes may be biomarkers and therapeutic targets in LUAD, but further research is warranted to investigate their usefulness in these respects.

Identification and integration analysis of a novel prognostic signature associated with cuproptosis-related ferroptosis genes and relevant lncRNA regulatory axis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a highly prevalent malignancy worldwide, and its clinical prognosis assessment and treatment is a major research direction. Both ferroptosis and cuproptosis are novel forms of cell death and are considered to be important factors involved in cancer progression. To further understand the correlation between the cuproptosis-related ferroptosis genes (CRFGs) and the prognosis of LUAD, we explore the molecular mechanisms related to the development of the disease. We constructed a prognostic signature containing 13 CRFGs, which, after grouping based on risk score, revealed that the LUAD high-risk group exhibited poor prognosis. Nomogram confirmed that it could be an independent risk factor for LUAD, and ROC curves and DCA validated the validity of the model. Further analysis showed that the three prognostic biomarkers (LIFR, CAV1, TFAP2A) were significantly correlated with immunization. Meanwhile, we found that a LINC00324/miR-200c-3p/TFAP2A regulatory axis could be involved in the progression of LUAD. In conclusion, our report reveals that CRFGs are well correlated with LUAD and provide new ideas for the construction of clinical prognostic tools, immunotherapy, and targeted therapy for LUAD.

Bisphenol B and bisphenol AF exposure enhances uterine diseases risks in mouse

Bisphenol A (BPA) analogs, bisphenol B (BPB) and bisphenol AF (BPAF) have been widely detected in the environment and human products with increasing frequency. However, uterine health risks caused by BPB and BPAF exposure need to be further elucidated. The study aimed to explore whether BPB or BPAF exposure will induce adverse outcomes in uterus. Female CD-1 mice were continuously exposed to BPB or BPAF for 14 and 28 days. Morphological examination showed that BPB or BPAF exposure caused endometrial contraction, decreased epithelial height, and increased number of glands. Bioinformatics analysis indicated that both BPB and BPAF disturbed the immune comprehensive landscape of the uterus. In addition, survival and prognosis analysis of hub genes and tumor immune infiltration evaluation were performed. Finally, the expression of hub genes was verified by quantitative real-time PCR (qPCR). Disease prediction found that eight of the BPB and BPAF co-response genes, which participated in the immune invasion of the tumor microenvironment, were associated with uterine corpus endometrial carcinoma (UCEC). Importantly, the gene expression levels of Srd5a1 after 28-day BPB and BPAF exposure were 7.28- and 25.24-fold higher than those of the corresponding control group, respectively, which was consistent with the expression trend of UCEC patients, and its high expression was significantly related to the poor prognosis of patients (p = 0.003). This indicated that Srd5a1 could be a valuable signal of uterus abnormalities caused by BPA analogs exposure. Our study revealed the key molecular targets and mechanisms of BPB or BPAF exposure induced uterine injury at the transcriptional level, providing a perspective for evaluating the safety of BPA substitutes.

SHMT2 is Associated with Tumor Purity, CD8+ T Immune Cells Infiltration, and a Novel Therapeutic Target in Four Different Human Cancers

AIMS

This study was launched to identify the SHMT2 associated Human Cancer subtypes.

BACKGROUND

Cancer is the 2nd leading cause of death worldwide. Previous reports revealed the limited involvement of SHMT2 in human cancer. In the current study, we comprehensively analyzed the role of SHMT2 in 24 major subtypes of human cancers using in silico approach and identified a few subtypes that are mainly associated with SHMT2.

OBJECTIVE

We aim to comprehensively analyze the role of SHMT2 in 24 major subtypes of human cancers using in silico approach and identified a few subtypes that are mainly associated with SHMT2. Earlier, limited knowledge exists in the medical literature regarding the involvement of Serine Hydroxymethyltransferase 2 (SHMT2) in human cancer.

METHODS

In the current study, we comprehensively analyzed the role of SHMT2 in 24 major subtypes of human cancers using in silico approach and identified a few subtypes that are mainly associated with SHMT2. Pan-cancer transcriptional expression profiling of SHMT2 was done using UALCAN while further validation was performed using GENT2. For translational profiling of SHMT2, we utilized Human Protein Atlas (HPA) platform. Promoter methylation, genetic alteration, and copy number variations (CNVs) profiles were analyzed through MEXPRESS and cBioPortal. Survival analysis was carried out through Kaplan-Meier (KM) plotter platform. Pathway enrichment analysis of SHMT2 was performed using DAVID, while the gene-drug network was drawn through CTD and Cytoscape. Furthermore, in the tumor microenvironment, a correlation between tumor purity, CD8+ T immune cells infiltration, and SHMT2 expression was accessed using TIMER.

RESULTS

SHMT2 was found overexpressed in 24 different subtypes of human cancers and its overexpression was significantly associated with the reduced Overall survival (OS) and Relapse-free survival durations of Breast cancer (BRCA), Kidney renal papillary cell carcinoma (KIRP), Liver hepatocellular carcinoma (LIHC), and Lung adenocarcinoma (LUAD) patients. This implies that SHMT2 plays a significant role in the development and progression of these cancers. We further noticed that SHMT2 was also up-regulated in BRCA, KIRP, LIHC, and LUAD patients of different clinicopathological features. Pathways enrichment analysis revealed the involvement of SHMT2 enriched genes in five diverse pathways. Furthermore, we also explored some interesting correlations between SHMT2 expression and promoter methylation, genetic alterations, CNVs, tumor purity, and CD8+ T immune cell infiltrates.

CONCLUSION

Our results suggested that overexpressed SHMT2 is correlated with the reduced OS and RFS of the BRCA, KIRP, LIHC, and LUAD patients and can be a potential diagnostic and prognostic biomarker for these cancers.

Assessing the Prognostic Capability of Immune-Related Gene Scoring Systems in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the commonest of the subtypes of lung cancer histologically. For this study, we intended to analyze the expression profiling of the immune-related genes (IRGs) from an independently available public database and developed a potent signature predictive of patients' prognosis.

Methods

Gene expression profiles and the clinical data of lung adenocarcinoma were gathered from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA), and the obtained data were split into a training set (n = 226), test set (n = 83), and validation set (n = 400). IRGs were then gathered from the ImmPort database. A prognostic model was constructed by analyzing the training set. Then the GO and KEGG analysis was performed, and a gene correlation prognostic nomogram was constructed. Finally, external validation, such as immune infiltration and immunohistochemistry, was performed.

Results

The 110 genes were significant by univariate Cox regression analysis and randomized survival forest algorithm for the training set and showed a good distinction between the low-risk-score and high-risk-score groups in the training set (P < 0.0001) by screening for four prognosis-related genes (HMOX1, ARRB1, ADM, PDIA3) and validated by the test set GSE30219 (P=0.0025) and TCGA dataset (P=0.00059). Multivariate Cox showed that the four gene signatures were an individual risk factor for LUAD. In addition, the genes in the signatures were externally verified using an online database. In particular, PDIA3 and HMOX1 are essential genes in the prognostic nomogram and play an important role in the model of immune-related genes.

Conclusion

Four immune-related genetic signatures are reliable prognostic indicators for patients with LUAD, providing a relevant theoretical basis and therapeutic rationale for immunotherapy.

An integrated analysis of prognostic and immune infiltrates for hub genes as potential survival indicators in patients with lung adenocarcinoma

Objective

Lung adenocarcinoma (LUAD) is one of the major subtypes of lung cancer that is associated with poor prognosis. The aim of this study was to identify useful biomarkers to enhance the treatment and diagnosis of LUAD.

Methods

GEO2R was used to identify common up-regulated differentially expressed genes (DEGs) in the GSE32863, GSE40791, and GSE75037 datasets. The DEGs were submitted to Metascape for gene ontology and pathway enrichment analysis as well as construction of the protein-protein interaction (PPI) network, while the molecular complex detection (MCODE) plug-in was employed to filter important subnetworks. The expression levels of the hub genes and their prognostic values were evaluated using the UALCAN, GEPIA2, and Kaplan-Meier plotter databases. The timer algorithm was utilized to determine the correlation between immune cell infiltration and the expression levels of hub genes in LUAD tissues. In addition, the hub gene mutation landscape and the correlation analysis with tumor mutational burden (TMB) score were evaluated using maftools package and ggstatsplot package in R software, respectively.

Results

We identified 156 common up-regulated DEGs, with gene ontology and pathway enrichment analysis indicating that they were mostly enriched in mitotic cell cycle process and cell cycle pathway. DEGs in the subnetwork with the largest number of genes were AURKB, CCNB2, CDC20, CDCA5, CDCA8, CENPF, and KNTC1. The seven hub genes were highly expressed in LUAD tissues and were associated with poor prognosis. These hub genes were negatively correlated with most immune cells. The somatic mutation landscape showed that AURKB, CDC20, CENPF, and KNTC1 had mutations and were positively correlated with TMB scores.

Conclusions

Our findings demonstrate that increased expression of seven hub genes is associated with poor prognosis for LUAD patients. Additionally, the TMB score indicates that the high expression of hub gene increases immune cell infiltration in patients with lung adenocarcinoma which may significantly improve response to immunotherapy.

Analysis of lung cancer-related genetic changes in long-term and low-dose polyhexamethylene guanidine phosphate (PHMG-p) treated human pulmonary alveolar epithelial cells

BACKGROUND

Lung injury elicited by respiratory exposure to humidifier disinfectants (HDs) is known as HD-associated lung injury (HDLI). Current elucidation of the molecular mechanisms related to HDLI is mostly restricted to fibrotic and inflammatory lung diseases. In our previous report, we found that lung tumors were caused by intratracheal instillation of polyhexamethylene guanidine phosphate (PHMG-p) in a rat model. However, the lung cancer-related genetic changes concomitant with the development of these lung tumors have not yet been fully defined. We aimed to discover the effect of long-term exposure of PHMG-p on normal human lung alveolar cells.

METHODS

We investigated whether PHMG-p could increase distorted homeostasis of oncogenes and tumor-suppressor genes, with long-term and low-dose treatment, in human pulmonary alveolar epithelial cells (HPAEpiCs). Total RNA sequencing was performed with cells continuously treated with PHMG-p and harvested after 35 days.

RESULTS

After PHMG-p treatment, genes with transcriptional expression changes of more than 2.0-fold or less than 0.5-fold were identified. Within 10 days of exposure, 2 protein-coding and 5 non-coding genes were selected, whereas in the group treated for 27-35 days, 24 protein-coding and 5 non-coding genes were identified. Furthermore, in the long-term treatment group, 11 of the 15 upregulated genes and 9 of the 14 downregulated genes were reported as oncogenes and tumor suppressor genes in lung cancer, respectively. We also found that 10 genes of the selected 24 protein-coding genes were clinically significant in lung adenocarcinoma patients.

CONCLUSIONS

Our findings demonstrate that long-term exposure of human pulmonary normal alveolar cells to low-dose PHMG-p caused genetic changes, mainly in lung cancer-associated genes, in a time-dependent manner.

Long non-coding RNA FAM230B is a novel prognostic and diagnostic biomarker for lung adenocarcinoma

Long non-coding RNA (lncRNA) FAM230B has been reported to participate in gastric cancer and papillary thyroid cancer, while its role in other cancers has not been reported. We then explored the role of FAM230B in lung adenocarcinoma (LA). This study enrolled a total of 60 LA patients, 60 patients with gastric reflux disease (GRD), 60 cases of chronic obstructive pulmonary disease (COPD), 60 cases of asthma and 60 cases of healthy controls. LA and paired non-tumor tissues were donated by all LA patients. Plasma samples were donated by all participants. Expression of FAM230B in these samples was determined by RT-qPCR. The 60 LA patients were followed up for 5 years to evaluate the prognostic value of plasma FAM230B for LA. Diagnostic value of FAM230B for LA was analyzed with ROC curve analysis. FAM230B was highly expressed in LA tissues compared to that in non-tumor samples. In addition, plasma FAM230B was specifically upregulated in LA patients, but not in GRD, COPD and asthma patients. High expression levels of FAM230B in plasma samples were closely correlated with poor survival. Plasma FAM230B effectively separated LA patients from GRD, COPD, asthma and control groups. Plasma FAM230B was closely correlated with tumor size, but not other clinical factors of LA patients. Therefore, FAM230B is highly upregulated in LA and may serve as a potential diagnostic and prognostic biomarker for LA.

MicroRNA‑20a promotes non‑small cell lung cancer proliferation by upregulating PD‑L1 by targeting PTEN

Non-small cell lung cancer (NSCLC) remains one of the most common malignant tumors worldwide. The aim of the present study was to investigate the possibility of microRNA-20a (miR-20a) as a biomarker and therapeutic target for the diagnosis and treatment of NSCLC. Bioinformatics prediction, together with functional validation, confirmed miR-20a bound to programmed death ligand-1 (PD-L1) 3′-untranslated region to upregulate PD-L1 expression. Both miR-20a and PD-L1 could promote the proliferation of NSCLC cells. The expression level of PD-L1 was controlled by PTEN; however, further upstream regulation of PD-L1 expression was largely unknown. The present study showed that miR-20a could not restore the inhibition of PD-L1 expression levels by PTEN. Knockdown of PTEN expression upregulated the expression level of PD-L1 and promoted the proliferation of NSCLC cells. PTEN negatively regulated the Wnt/β-catenin signaling pathway by inhibiting β-catenin and Cyclin D1. Interestingly, PTEN could reverse miR-20a-mediated proliferation of NSCLC cells and the inhibitory effect was similar to that of XAV-939. miR-20a promotes the proliferation of NSCLC cells by inhibiting the expression level of PTEN and upregulating the expression level of PD-L1. It is suggested that miR-20a could be used as a biomarker and therapeutic target for the treatment of NSCLC.

Long Noncoding RNA 00472: A Novel Biomarker in Human Diseases

Long non-coding RNAs (lncRNAs) play important roles in human diseases. They control gene expression levels and influence various biological processes through multiple mechanisms. Functional abnormalities in lncRNAs are strongly associated with occurrence and development of various diseases. LINC00472, which is located on chromosome 6q13, is involved in several human diseases, particularly cancers of the breast, lung, liver, osteosarcoma, bladder, colorectal, ovarian, pancreatic and stomach. Importantly, LINC00472 can be used as a biomarker for breast cancer cell sensitivity to chemotherapeutic regimens, including doxorubicin. LINC00472 is regulated by microRNAs and several signaling pathways. However, the significance of LINC00472 in human diseases has not been clearly established. In this review, we elucidate on the significance of LINC00472 in various human diseases, indicating that LINC00472 may be a diagnostic, prognostic as well as therapeutic target for these diseases.

Constructing a new prognostic signature of gastric cancer based on multiple data sets

In order to explore new prediction methods and key genes for gastric cancer. Firstly, we downloaded the 6 original sequencing data of gastric cancer on the Illumina HumanHT-12 platform from Array Expression and Gene Expression Omnibus, and used bioinformatics methods to identify 109 up-regulated genes and 271 down-regulated genes. Further, we performed univariate Cox regression analysis of prognostic-related genes, then used Lasso regression to remove collinearity, and finally used multivariate Cox regression to analyze independent prognostic genes (MT1M, AKR1C2, HEYL, KLK11, EEF1A2, MMP7, THBS1, KRT17, RPESP, CMTM4, UGT2B17, CGNL1, TNFRSF17, REG1A). Based on these, we constructed a prognostic risk proportion signature, and found that patients with high-risk gastric cancer have a high degree of malignancy. Subsequently, we used the GSE15459 data set to verify the signature. By calculating the area under the recipient operator characteristic curve of 5-year survival rate, the test set and verification set are 0.739 and 0.681, respectively, suggesting that the prognostic signature has a moderate prognostic ability. The nomogram is used to visualize the prognostic sig-nature, and the calibration curve verification showed that the prediction accuracy is higher. Finally, we verified the expression and prognosis of the hub gene, and suggested that HEYL, MMP7, THBS1, and KRT17 may be potential prognostic biomarkers.

Identification of a novel prognosis-associated ceRNA network in lung adenocarcinoma via bioinformatics analysis

Background

Lung adenocarcinoma (LUAD) is the most common subtype of nonsmall-cell lung cancer (NSCLC) and has a high incidence rate and mortality. The survival of LUAD patients has increased with the development of targeted therapeutics, but the prognosis of these patients is still poor. Long noncoding RNAs (lncRNAs) play an important role in the occurrence and development of LUAD. The purpose of this study was to identify novel abnormally regulated lncRNA–microRNA (miRNA)–messenger RNA (mRNA) competing endogenous RNA (ceRNA) networks that may suggest new therapeutic targets for LUAD or relate to LUAD prognosis.

Methods

We used the SBC human ceRNA array V1.0 to screen for differentially expressed (DE) lncRNAs and mRNAs in four paired LUAD samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate the DE lncRNAs and mRNAs. R bioinformatics packages, The Cancer Genome Atlas (TCGA) LUAD database, and Kaplan–Meier (KM) survival analysis tools were used to validate the microarray data and construct the lncRNA–miRNA–mRNA ceRNA regulatory network. Then, quantitative real-time PCR (qRT-PCR) was used to validate the DE lncRNAs in 7 LUAD cell lines.

Results

A total of 2819 DE lncRNAs and 2396 DE mRNAs (P < 0.05 and fold change ≥ 2 or ≤ 0.5) were identified in four paired LUAD tissue samples. In total, 255 of the DE lncRNAs were also identified in TCGA. The GO and KEGG analysis results suggested that the DE genes were most enriched in angiogenesis and cell proliferation, and were closely related to human cancers. Moreover, the differential expression of ENST00000609697, ENST00000602992, and NR_024321 was consistent with the microarray data, as determined by qRT-PCR validation in 7 LUAD cell lines; however, only ENST00000609697 was associated with the overall survival of LUAD patients (log-rank P = 0.029). Finally, through analysis of ENST00000609697 target genes, we identified the ENST00000609697–hsa-miR-6791-5p–RASL12 ceRNA network, which may play a tumor-suppressive role in LUAD.

Conclusion

ENST00000609697 was abnormally expressed in LUAD. Furthermore, downregulation of ENST00000609697 and its target gene RASL12 was associated with poor prognosis in LUAD. The ENST00000609697–hsa-miR-6791-5p–RASL12 axis may play a tumor-suppressive role. These results suggest new potential prognostic and therapeutic biomarkers for LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12938-021-00952-x.

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

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

Identification of fibronectin 1 (FN1) and complement component 3 (C3) as immune infiltration-related biomarkers for diabetic nephropathy using integrated bioinformatic analysis

Immune cell infiltration (ICI) plays a pivotal role in the development of diabetic nephropathy (DN). Evidence suggests that immune-related genes play an important role in the initiation of inflammation and the recruitment of immune cells. However, the underlying mechanisms and immune-related biomarkers in DN have not been elucidated. Therefore, this study aimed to explore immune-related biomarkers in DN and the underlying mechanisms using bioinformatic approaches. In this study, four DN glomerular datasets were downloaded, merged, and divided into training and test cohorts. First, we identified 55 differentially expressed immune-related genes; their biological functions were mainly enriched in leukocyte chemotaxis and neutrophil migration. The CIBERSORT algorithm was then used to evaluate the infiltrated immune cells; macrophages M1/M2, T cells CD8, and resting mast cells were strongly associated with DN. The ICI-related gene modules as well as 25 candidate hub genes were identified to construct a protein-protein interactive network and conduct molecular complex detection using the GOSemSim algorithm. Consequently, FN1, C3, and VEGFC were identified as immune-related biomarkers in DN, and a related transcription factor-miRNA-target network was constructed. Receiver operating characteristic curve analysis was estimated in the test cohort; FN1 and C3 had large area under the curve values (0.837 and 0.824, respectively). Clinical validation showed that FN1 and C3 were negatively related to the glomerular filtration rate in patients with DN. Six potential therapeutic small molecule compounds, such as calyculin, phenamil, and clofazimine, were discovered in the connectivity map. In conclusion, FN1 and C3 are immune-related biomarkers of DN.

Prognostic and immunological significance of metastasis associated lung adenocarcinoma transcript 1 among different kinds of cancers

LncRNAs belong to the type of noncoding RNA transcripts, which exceed 200 nucleotides in size. MALAT1 as one of the earlier identified lncRNAs in cancer is investigated by more and more scientific researchers. Expression, clinical significance and function of MALAT1 in pan-cancer exist as big difference. To detect the expression and clinical significance of MALAT1 gene precisely and comprehensively among different kinds of cancers, some classical databases such as GEPIA, TIMER, KM Plotter, and PrognoScan were fully applied. An immunological role of MALAT1 among different kinds of cancers was also determined in TIMER database. Our results showed that MALAT1 was differently expressed in different kinds of cancers using GEPIA, Oncomine, and TIMER databases to analyze. Especially, MALAT1 high RNA level was related to the early stage in lung and gastric cancer patients. MALAT1 expression was closely related to prognosis among different cancer patients. Furthermore, expression of MALAT1 was related to tumor immune cell infiltrating. Expression level of MALAT1 was also related to immune makers such as macrophage, T cell, NK cells, and so on. These findings indicate that MALAT1 could be a potential prognostic biomarker of some kinds of cancer and was significantly correlated with tumor-infiltrating immune cells in a wide variety of cancers.

Construction of an immune-related lncRNA pairs model to predict prognosis and immune landscape of lung adenocarcinoma patients

The model of immune-related lncRNA pairs (IRLPs) seems to be an available predictor in lung adenocarcinoma (LUAD) patients. The aim of our study was to construct a model with IRLPs to predict the survival status and immune landscape of LUAD patients. Based on TCGA-LUAD dataset, a risk assessment model with IRLPs was established. Then, ROC curves were used to assess the predictive accuracy and effectiveness of our model. Next, we identified the difference of survival, immune cell infiltration, immune checkpoint inhibitor-related (ICI-related) biomarkers, and chemotherapeutics between high-risk group and low-risk group. Finally, A nomogram was built for predicting the survival rates of LUAD patients. 464 LUAD samples were randomly and equally divided into a training set and a test set. Six IRLPs were screened out to construct a risk model. K-M analysis and risk-plot suggested the prognosis of high-risk group was worse than low-risk group (p < 0.001). Multivariate analysis shows risk score was independent risk factor of LUAD (p < 0.001). In addition, the expression of immune cell infiltration, ICI-related biomarkers, chemotherapeutics all demonstrate significant difference in two groups. A nomogram was built that could predict the 1-, 3-, and 5-year survival rates of LUAD patients. Our immune-related lncRNA pairs risk model is expected to be a reliable model for predicting the prognosis and immune landscape of LUAD patients.