Identification and Integrated Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer

Integrating network analysis with differential expression to uncover therapeutic and prognostic biomarkers in esophageal squamous cell carcinoma

Introduction: Esophageal squamous cell carcinoma (ESCC) accounts for over 90% of all esophageal tumors. However, the molecular mechanism underlying ESCC development and prognosis remains unclear, and there are still no effective molecular biomarkers for diagnosing or predicting the clinical outcome of patients with ESCC. Here, we used bioinformatics analysis to identify potential biomarkers and therapeutic targets for ESCC. Methodology: Differentially expressed genes (DEGs) between ESCC and normal esophageal tissue samples were obtained by comprehensively analyzing publicly available RNA-seq datasets from the TCGA and GTEX. Gene Ontology (GO) annotation and Reactome pathway analysis identified the biological roles of the DEGs. Moreover, the Cytoscape 3.10.1 platform and subsidiary tools such as CytoHubba were used to visualize the DEGs' protein-protein interaction (PPI) network and identify hub genes, Furthermore our results are validated by using Single-cell RNA analysis. Results: Identification of 2524 genes exhibiting altered expression enriched in pathways including keratinization, epidermal cell differentiation, G alpha(s) signaling events, and biological process of cell proliferation and division, extracellular matrix (ECM) disassembly, and muscle function. Moreover, upregulation of hallmarks E2F targets, G2M checkpoints, and TNF signaling. CytoHubba revealed 20 hub genes that had a valuable influence on the progression of ESCC in these patients. Among these, the high expression levels of four genes, CDK1 MAD2L1, PLK1, and TOP2A, were associated with critical dependence for cell survival in ESCC cell lines, as indicated by CRISPR dependency scores, gene expression data, and cell line metadata. We also identify the molecules targeting these essential hub genes, among which GSK461364 is a promising inhibitor of PLK1, BMS265246, and Valrubicin inhibitors of CDK1 and TOP2A, respectively. Moreover, we identified that elevated expression of MMP9 is associated with worse overall survival in ESCC patients, which may serve as potential prognostic biomarker or therapeutic target for ESCC. The single-cell RNA analysis showed MMP9 is highly expressed in myeloid, fibroblast, and epithelial cells, but low in T cells, endothelial cells, and B cells. This suggests MMP9's role in tumor progression and matrix remodeling, highlighting its potential as a prognostic marker and therapeutic target. Discussion: Our study identified key hub genes in ESCC, assessing their potential as therapeutic targets and biomarkers through detailed expression and dependency analyses. Notably, MMP9 emerged as a significant prognostic marker with high expression correlating with poor survival, underscoring its potential for targeted therapy. These findings enhance our understanding of ESCC pathogenesis and highlight promising avenues for treatment.

Anti-NSCLC role of SCN4B by negative regulation of the cGMP-PKG pathway: Integrated utilization of bioinformatics analysis and in vitro assay validation

Non-small cell lung cancer (NSCLC) is a malignant tumor with low overall cure and survival rates. Uncovering abnormally expressed genes is significantly important for developing novel targeted therapies in NSCLC. This study aimed to discover new differentially expressed genes (DEGs) of NSCLC. The DEGs of NSCLC were identified in eight data sets from Gene Expression Omnibus (GEO) database. The expression profiles and the prognostic significance of SCN4B in LUAD and LUSC were analyzed using GEPIA database. LinkedOmics was used to identify co-expressed genes with SCN4B, which were further subjected to KEGG pathway enrichment analysis. SCN4B-overexpressing plasmid (pcDNA/SCN4B) was transfected into A549 and NCI-H2170 cells to elevate the expression of SCN4B. MTT and TUNEL assays were performed to evaluate cell viability and apoptosis. Relying on the screened DEGs from GEO database, we identified that SCN4B was significantly downregulated in LUAD and LUSC. We confirmed the downregulation of SCN4B in NSCLC tissues using GEPIA database. SCN4B has a prognostic value in LUAD, but not LUSC. KEGG pathway enrichment analysis of SCN4B-related genes showed that cGMP-PKG signaling pathway might be involved in the role of SCN4B in NSCLC. Overexpression of SCN4B in A549 and NCI-H2170 cells inhibited the cell viability. Besides, SCN4B overexpression induced apoptosis of A549 and NCI-H2170 cells. SCN4B inhibited the expression of PKG1 and p-CREB in NSCLC cells. Moreover, the inhibitory effects of SCN4B on tumor malignancy were attenuated by the activator of PKG. In conclusion, integrated bioinformatical analysis proved that SCN4B was downregulated and had a prognostic significance in NSCLC. In vitro experimental studies demonstrated that SCN4B regulated NSCLC cells viability and apoptosis via inhibiting cGMP-PKG signaling pathway.

Advancements in the diagnosis and treatment of sub‑centimeter lung cancer in the era of precision medicine (Review)

Lung cancer is the malignancy with the highest global mortality rate and imposes a substantial burden on society. The increasing popularity of lung cancer screening has led to increasing number of patients being diagnosed with pulmonary nodules due to their potential for malignancy, causing considerable distress in the affected population. However, the diagnosis and treatment of sub-centimeter grade pulmonary nodules remain controversial. The evolution of genetic detection technology and the development of targeted drugs have positioned the diagnosis and treatment of lung cancer in the precision medicine era, leading to a marked improvement in the survival rate of patients with lung cancer. It has been established that lung cancer driver genes serve a key role in the development and progression of sub-centimeter lung cancer. The present review aimed to consolidate the findings on genes associated with sub-centimeter lung cancer, with the intent of serving as a reference for future studies and the personalized management of sub-centimeter lung cancer through genetic testing.

Single-cell mRNA sequencing of giant panda (Ailuropoda melanoleuca) seminoma reveals the cellular and molecular characteristics of tumour cells

Testicular tumours are zoonoses that can occur in not only human, but other animals, include giant pandas. A middle-aged male giant panda named Fufu was diagnosed with a testicular tumour and underwent surgery to remove the entire left testis. The testis was mainly composed of three substantive parts: normal tissue on the outside, tumour tissue in the middle, and necrosis in the centre. HE stains revealed that the tumour was a seminoma. Single-cell mRNA sequence was applied to characterise cellular states and molecular circuitries of giant panda testicular seminoma. Only germ cell markers expressed in nearly all tumour cells, and the tumour cells appeared to be the same subtype of seminoma cells. We identified four clusters with unique genes expression. They were early apoptosis cells (EAC), inactive cells (IC), active cells subcluster 1 (AC-1) and active cells subcluster 2 (AC-2). We utilised monocle tools and found that IC cells was in the initiation stage, and EAC was one type of terminal stage, suggesting that tumour cells may undergo apoptosis in the future. AC-2 was another type of terminal stage, representing a group of progressive cells. Our study represents the first report to utilise scRNA-seq to characterise the cellular states and molecular circuitries of a giant panda testicular tumour. This investigation proposes CD117 and CD30 as dependable markers for future pathologic diagnosis. Our findings also suggest that CTSV and other genes with unique expression patterns in active and progressive giant panda seminoma cells may act as early prognostic biomarkers.

Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID-19 Using Bioinformatics Approaches

Several studies have been done to identify comorbidities of COVID-19. In this work, we developed an analytical bioinformatics framework to reveal COVID-19 comorbidities, their genomic associations, and molecular mechanisms accomplishing transcriptomic analyses of the RNA-seq datasets provided by the Gene Expression Omnibus (GEO) database, where normal and infected tissues were evaluated. Using the framework, we identified 27 COVID-19 correlated diseases out of 7,092 collected diseases. Analyzing clinical and epidemiological research, we noticed that our identified 27 diseases are associated with COVID-19, where hypertension, diabetes, obesity, and lung cancer are observed several times in COVID-19 patients. Therefore, we selected the above four diseases and performed assorted analyses to demonstrate the association between COVID-19 and hypertension, diabetes, obesity, and lung cancer as comorbidities. We investigated genomic associations with the cross-comparative analysis and Jaccard's similarity index, identifying shared differentially expressed genes (DEGs) and linking DEGs of COVID-19 and the comorbidities, in which we identified hypertension as the most associated illness. We also revealed molecular mechanisms by identifying statistically significant ten pathways and ten ontologies. Moreover, to understand cellular physiology, we did protein-protein interaction (PPI) analyses among the comorbidities and COVID-19. We also used the degree centrality method and identified ten biomarker hub proteins (IL1B, CXCL8, FN1, MMP9, CXCL10, IL1A, IRF7, VWF, CXCL9, and ISG15) that associate COVID-19 with the comorbidities. Finally, we validated our findings by searching the published literature. Thus, our analytical approach elicited interconnections between COVID-19 and the aforementioned comorbidities in terms of remarkable DEGs, pathways, ontologies, PPI, and biomarker hub proteins.

The Role of CEP55 Expression in Tumor Immune Response and Prognosis of Patients with Non-small Cell lung Cancer

Background: With the continuous advancement of diagnostic methods, more and more early-stage Non-small cell lung cancer (NSCLC) patients are diagnosed. Although many scholars have devoted substantial efforts to investigate the pathogenesis and prognosis of NSCLC, its molecular mechanism is still not well explained. Methods: We retrieved three gene datasets GSE10072, GSE19188 and GSE40791 from the Gene Expression Omnibus (GEO) database and screened and identified differentially expressed genes (DEGs). Then, we performed KEGG and GO functional enrichment analysis, survival analysis, risk analysis and prognosis analysis on the selected hub genes. We constructed a protein-protein interaction (PPI) network, and used the STRING database and Cytoscape software. Results: The biological process analysis showed that these genes were mainly enriched in cell division and nuclear division. Survival analysis showed that the genes of CEP55 (centrosomal protein 55), NMU (neuromedin U), CAV1 (Caveolin 1), TBX3 (T-box transcription factor 3), FBLN1 (fibulin 1) and SYNM (synemin) may be involved in the development, invasion or metastasis of NSCLC (P<0.05, logFC>1). Prognostic analysis and independent prognostic analysis showed that the expression of these hub gene-related mRNAs was related to the prognostic risk of NSCLC. Risk analysis showed that the selected hub genes were closely related to the overall survival time of patients with NSCLC. Conclusion: The DEGs and hub genes screened and identified in this study will help us to understand the molecular mechanisms of NSCLC, and CEP55 expression affects the survival and prognosis of patients with NSCLC, and participates in tumor immune response.

Identification of Pathologic and Prognostic Genes in Prostate Cancer Based on Database Mining

Background: Prostate cancer (PCa) is an epithelial malignant tumor that occurs in the urinary system with high incidence and is the second most common cancer among men in the world. Thus, it is important to screen out potential key biomarkers for the pathogenesis and prognosis of PCa. The present study aimed to identify potential biomarkers to reveal the underlying molecular mechanisms.

Methods: Differentially expressed genes (DEGs) between PCa tissues and matched normal tissues from The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset were screened out by R software. Weighted gene co-expression network analysis was performed primarily to identify statistically significant genes for clinical manifestations. Protein–protein interaction (PPI) network analysis and network screening were performed based on the STRING database in conjunction with Cytoscape software. Hub genes were then screened out by Cytoscape in conjunction with stepwise algorithm and multivariate Cox regression analysis to construct a risk model. Gene expression in different clinical manifestations and survival analysis correlated with the expression of hub genes were performed. Moreover, the protein expression of hub genes was validated by the Human Protein Atlas database.

Results: A total of 1,621 DEGs (870 downregulated genes and 751 upregulated genes) were identified from the TCGA-PRAD dataset. Eight prognostic genes [BUB1, KIF2C, CCNA2, CDC20, CCNB2, PBK, RRM2, and CDC45] and four hub genes (BUB1, KIF2C, CDC20, and PBK) potentially correlated with the pathogenesis of PCa were identified. A prognostic model with good predictive power for survival was constructed and was validated by the dataset in GSE21032. The survival analysis demonstrated that the expression of RRM2 was statistically significant to the prognosis of PCa, indicating that RRM2 may potentially play an important role in the PCa progression.

Conclusion: The present study implied that RRM2 was associated with prognosis and could be used as a potential therapeutic target for PCa clinical treatment.

microRNA-1321 and microRNA-7515 contribute to the progression of non-small cell lung cancer by targeting CDC20

Cell division cycle 20 (CDC20) and microRNAs (miRNAs) are differentially expressed in non-small cell lung cancer (NSCLC). The current study aimed to investigate the role of miR-1321 and miR-7515 regulation in CDC20 during NSCLC development. CDC20 expression in paracancerous and tumor tissues was assessed using quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The relationship between CDC20 expression and prognosis of patients was analyzed using the TCGA database. The expression profile of CDC20 in healthy lung cells and NSCLC cells was detected using qRT-PCR and western blotting. After the knockdown of CDC20 in NSCLC cells, the cell proliferation, apoptosis, migration, invasion, and cell cycle changes were investigated by CCK8, EdU, flow cytometry, wound healing, and Transwell assays. The miRNAs targeting CDC20 were predicted using two bioinformatics websites and validated using dual-luciferase assays. CDC20 was enhanced in NSCLC tissues and cells, thus predicting the poor prognosis in NSCLC patients. After CDC20 inhibition, the malignant phenotype of NSCLC cells was reverted. miR-1321 and miR-7515 targeted CDC20 and exhibited the same anti-tumor effects as CDC20 silencing. Functional rescue experiments showed that CDC20 overexpression averted the anti-tumor effects of miR-1321 and miR-7515 on NSCLC cells. miR-1321 and miR-7515 inhibited NSCLC development by targeting CDC20. Thus, the current study has implications in NSCLC treatment and provides novel insights into NSCLC management.

Identification of HMMR as a prognostic biomarker for patients with lung adenocarcinoma via integrated bioinformatics analysis

Background

Lung adenocarcinoma (LUAD) is the most prevalent tumor in lung carcinoma cases and threatens human life seriously worldwide. Here we attempt to identify a prognostic biomarker and potential therapeutic target for LUAD patients.

Methods

Differentially expressed genes (DEGs) shared by GSE18842, GSE75037, GSE101929 and GSE19188 profiles were determined and used for protein-protein interaction analysis, enrichment analysis and clinical correlation analysis to search for the core gene, whose expression was further validated in multiple databases and LUAD cells (A549 and PC-9) by quantitative real-time PCR (qRT-PCR) and western blot analyses. Its prognostic value was estimated using the Kaplan-Meier method, meta-analysis and Cox regression analysis based on the Cancer Genome Atlas (TCGA) dataset and co-expression analysis was conducted using the Oncomine database. Gene Set Enrichment Analysis (GSEA) was performed to illuminate the potential functions of the core gene.

Results

A total of 115 shared DEGs were found, of which 24 DEGs were identified as candidate hub genes with potential functions associated with cell cycle and FOXM1 transcription factor network. Among these candidates, HMMR was identified as the core gene, which was highly expressed in LUAD as verified by multiple datasets and cell samples. Besides, high HMMR expression was found to independently predict poor survival in patients with LUAD. Co-expression analysis showed that HMMR was closely related to FOXM1 and was mainly involved in cell cycle as suggested by GSEA.

Conclusion

HMMR might be served as an independent prognostic biomarker for LUAD patients, which needs further validation in subsequent studies.

ASPM combined with KIF11 promotes the malignant progression of hepatocellular carcinoma via the Wnt/β-catenin signaling pathway

To investigate the molecular mechanism of assembly factor for spindle microtubules (ASPM) in the regulation of the malignant progression of hepatocellular carcinoma (HCC), bioinformatics analysis was utilized to analyze the role of ASPM in the malignant progression of HCC and its potential interaction with the kinesin family member 11 (KIF11) gene. The expression levels of ASPM and KIF11 were detected by reverse transcription-quantitative PCR and western blotting. Following knockdown of ASPM expression, Cell Counting Kit-8/colony formation assays were performed to detect cell viability and proliferation. Wound healing and Transwell assays were employed to detect cell migration and invasion. Additionally, a co-immunoprecipitation (CO-IP) assay was used to detect whether there was an interaction between ASPM and KIF11. KIF11 overexpression was performed to verify if ASPM exerted its effects via KIF11. ASPM was highly expressed in HCC tissues and cells, and was closely associated with a poor prognosis of patients with HCC. Interference with ASPM expression markedly inhibited the viability, proliferation, invasion and migration of HCC cells. Using a CO-IP assay, it was revealed that there was an interaction between ASPM and KIF11. Rescue experiments subsequently revealed the regulatory effects of ASPM on the activity, proliferation, invasion and migration of HCC cells via KIF11. Finally, western blot analysis demonstrated that ASPM in combination with KIF11 promoted the malignant progression of HCC by regulating the activity of the Wnt/β-catenin signaling pathway. Therefore, the present study demonstrated that ASPM may interact with KIF11 in HCC cells to promote the malignant progression of HCC via the Wnt/β-catenin signaling pathway.

Identifying the Key Genes in Mouse Liver Regeneration After Partial Hepatectomy by Bioinformatics Analysis and in vitro/vivo Experiments

Background

The liver is the only organ that can completely regenerate after various injuries or tissue loss. There are still a large number of gene functions in liver regeneration that have not been explored. This study aimed to identify key genes in the early stage of liver regeneration in mice after partial hepatectomy (PH).

Materials and Methods

We first analyzed the expression profiles of genes in mouse liver at 48 and 72 h after PH from Gene Expression Omnibus (GEO) database. Gene ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) analysis were performed to identify key genes in liver regeneration. Finally, we validated key genes in vivo and in vitro.

Results

We identified 46 upregulated genes and 19 downregulated genes at 48 h after PH, and 223 upregulated genes and 40 downregulated genes at 72 h after PH, respectively. These genes were mainly involved in cell cycle, DNA replication, and p53 signaling pathway. Among of these genes, cycle-related genes (Ccna2, Cdkn1a, Chek1, and Mcm5) and Ube2c were highly expressed in the residual liver both at 48 and 72 h after PH. Furthermore, Ube2c knockdown not only caused abnormal expression of Ccna2, Cdkn1a, Chek1, and Mcm5, but also inhibited transition of hepatocytes from G1 to S phase of the cell cycle in vitro.

Conclusion

Mouse hepatocytes enter the proliferation phase at 48 h after PH. Ube2c may mediate cell proliferation by regulating or partially regulating Ccna2, Cdkn1a, Chek1, and Mcm5.

Genetic variants of CHEK1, PRIM2 and CDK6 in the mitotic phase-related pathway are associated with nonsmall cell lung cancer survival

The mitotic phase is a vital step in cell division and may be involved in cancer progression, but it remains unclear whether genetic variants in mitotic phase-related pathways genes impact the survival of these patients. Here, we investigated associations between 31 032 single nucleotide polymorphisms (SNPs) in 368 mitotic phase-related pathway genes and overall survival (OS) of patients with nonsmall cell lung cancer (NSCLC). We assessed the associations in a discovery data set of 1185 NSCLC patients from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial and validated the findings in another data set of 984 patients from the Harvard Lung Cancer Susceptibility Study. As a result, we identified three independent SNPs (ie, CHEK1 rs76744140 T>C, PRIM2 rs6939623 G>T and CDK6 rs113181986 G>C) to be significantly associated with NSCLC OS with an adjusted hazard ratio of 1.29 (95% confidence interval = 1.11-1.49, P = 8.26 × 10^-4 ), 1.26 (1.12-1.42, 1.10 × 10^-4 ) and 0.73 (0.63-0.86, 1.63 × 10^-4 ), respectively. Moreover, the number of combined unfavorable genotypes of these three SNPs was significantly associated with NSCLC OS and disease-specific survival in the PLCO data set (P_trend  < .0001 and .0003, respectively). Further expression quantitative trait loci analysis showed that the rs76744140C allele predicted CHEK1 mRNA expression levels in normal lung tissues and that rs113181986C allele predicted CDK6 mRNA expression levels in whole blood tissues. Additional analyses indicated CHEK1, PRIM2 and CDK6 may impact NSCLC survival. Taken together, these findings suggested that these genetic variants may be prognostic biomarkers of patients with NSCLC.

High KIF11 expression is associated with poor outcome of NSCLC

PURPOSE

To clarify the correlation between KIF11 (kinesin family member 11) and clinicopathologic characteristics of non-small cell lung cancer (NSCLC) and identify the prognostic value of KIF11 in patients with NSCLC.

METHODS

For investigating the expression of KIF11 in NSCLC, two tissue microarrays (TMAs: one contained 60 paired NSCLC tissues and paratumor tissues, the other contained 140 NSCLC tissues and 10 normal lung tissues) were constructed, stained, and scored. The Cancer Genome Atlas (TCGA) datasets were used to explore the differential expression level of KIF11 between NSCLC and paratumor. Kaplan-Meier survival curves were plotted and multivariate analysis were carried out.

RESULTS

The staining of KIF11 mainly distributed throughout the cytoplasm of tumor cells. Its expression was higher in NSCLC than paratumor cells, and similar results were obtained from TCGA datasets. We found that high expression of KIF11 had a significant correlation with lymph node metastases (p = 0.024) and pathologic stage (p = 0.018); that significant difference was not found in any other clinicopathologic characteristic. As univariate and multivariate analysis showed, KIF11 expression was significantly correlated with overall survival time of NSCLC (p = 0.002, p = 0.025, respectively). High KIF11 expression was found to significantly associate with overall survival of stage II-III (p = 0.001) and lung adenocarcinoma (p = 0.036).

CONCLUSION

High KIF11 expression predicts poor outcome in NSCLC. KIF11 is expected to be a viable prognostic biomarker for NSCLC.

Abnormal spindle-like microcephaly-associated (ASPM) gene expression in posterior fossa brain tumors of childhood and adolescence

PURPOSE

In neurogenesis, ASPM (abnormal spindle-like microcephaly-associated) gene is expressed mainly in the ventricular zone of posterior fossa and is the major determinant in the cerebral cortex. Besides its role in embryonic development, ASPM overexpression promotes tumor growth, including central nervous system (CNS) tumors. This study aims to investigate ASPM expression levels in most frequent posterior fossa brain tumors of childhood and adolescence: medulloblastoma (MB), ependymoma (EPN), and astrocytoma (AS), correlating them with clinicopathological characteristics and tumor solid portion size.

METHODS

Quantitative reverse transcription (qRT-PCR) is used to quantify ASPM mRNA levels in 80 pre-treatment tumor samples: 28 MB, 22 EPN, and 30 AS. The tumor solid portion size was determined by IOP-GRAACC Diagnostic Imaging Center. We correlated these findings with clinicopathological characteristics and tumor solid portion size.

RESULTS

Our results demonstrated that ASPM gene was overexpressed in MB (p = 0.007) and EPN (p = 0.0260) samples. ASPM high expression was significantly associated to MB samples from patients with worse overall survival (p = 0.0123) and death due to disease progression (p = 0.0039). Interestingly, two patients with AS progressed toward higher grade showed ASPM overexpression (p = 0.0046). No correlation was found between the tumor solid portion size and ASPM expression levels in MB (p = 0.1154 and r = - 0.4825) and EPN (p = 0.1108 and r = - 0.3495) samples.

CONCLUSION

Taking in account that ASPM gene has several functions to support cell proliferation, as mitotic defects and premature differentiation, we suggest that its overexpression, presumably, plays a critical role in disease progression of posterior fossa brain tumors of childhood and adolescence.

Identification of LINC02310 as an enhancer in lung adenocarcinoma and investigation of its regulatory network via comprehensive analyses

BACKGROUND

Lung adenocarcinoma (LADC) is a major subtype of non-small cell lung cancer and has one of the highest mortality rates. An increasing number of long non-coding RNAs (LncRNAs) were reported to be associated with the occurrence and progression of LADC. Thus, it is necessary and reasonable to find new prognostic biomarkers for LADC among LncRNAs.

METHODS

Differential expression analysis, survival analysis, PCR experiments and clinical feature analysis were performed to screen out the LncRNA which was significantly related to LADC. Its role in LADC was verified by CCK-8 assay and colony. Furthermore, competing endogenous RNA (ceRNA) regulatory network construction, enrichment analysis and protein-protein interaction (PPI) network construction were performed to investigate the downstream regulatory network of the selected LncRNA.

RESULTS

A total of 2431 differentially expressed LncRNAs (DELncRNAs) and 2227 differentially expressed mRNAs (DEmRNAs) were from The Cancer Genome Atlas database. Survival analysis results indicated that lnc-YARS2-5, lnc-NPR3-2 and LINC02310 were significantly related to overall survival. Their overexpression indicated poor prognostic. PCR experiments and clinical feature analysis suggested that LINC02310 was significantly correlated with TNM-stage and T-stage. CCK-8 assay and colony formation assay demonstrated that LINC02310 acted as an enhancer in LADC. In addition, 3 targeted miRNAs of LINC02310 and 414 downstream DEmRNAs were predicted. The downstream DEmRNAs were then enriched in 405 Gene Ontology terms and 11 Kyoto Encyclopedia of Genes and Genomes pathways, which revealed their potential functions and mechanisms. The PPI network showed the interactions among the downstream DEmRNAs.

CONCLUSIONS

This study verified LINC02310 as an enhancer in LADC and performed comprehensive analyses on its downstream regulatory network, which might benefit LADC prognoses and therapies.

Establishment of a Gene Signature to Predict Prognosis for Patients with Lung Adenocarcinoma

Accumulating evidence indicates that the reliable gene signature may serve as an independent prognosis factor for lung adenocarcinoma (LUAD) diagnosis. Here, we sought to identify a risk score signature for survival prediction of LUAD patients. In the Gene Expression Omnibus (GEO) database, GSE18842, GSE75037, GSE101929, and GSE19188 mRNA expression profiles were downloaded to screen differentially expressed genes (DEGs), which were used to establish a protein-protein interaction network and perform clustering module analysis. Univariate and multivariate proportional hazards regression analyses were applied to develop and validate the gene signature based on the TCGA dataset. The signature genes were then verified on GEPIA, Oncomine, and HPA platforms. Expression levels of corresponding genes were also measured by qRT-PCR and Western blotting in HBE, A549, and PC-9 cell lines. The prognostic signature based on eight genes (TTK, HMMR, ASPM, CDCA8, KIF2C, CCNA2, CCNB2, and MKI67) was established, which was independent of other clinical factors. The risk model offered better discrimination between risk groups, and patients with high-risk scores tended to have poor survival rate at 1-, 3- and 5-year follow-up. The model also presented better survival prediction in cancer-specific cohorts of age, gender, clinical stage III/IV, primary tumor 1/2, and lymph node metastasis 1/2. The signature genes, moreover, were highly expressed in A549 and PC-9 cells. In conclusion, the risk score signature could be used for prognostic estimation and as an independent risk factor for survival prediction in patients with LUAD.

POLE2 Serves as a Prognostic Biomarker and Is Associated with Immune Infiltration in Squamous Cell Lung Cancer

Background

Squamous cell lung cancer is the main cause of cancer-associated mortality. The discovery of promising prognostic biomarkers for predicting the survival of patients with squamous cell lung cancer remains a challenge.

Material/Methods

Gene expression profiles of GSE33479 and GSE51855, including 42 squamous cell lung cancer tissues and 17 normal tissues, from the GEO database were assessed to find common differentially expressed genes (DEGs) via the GEO2R online tool and Venn diagram software. Then, gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analyses were conducted. The key protein-protein interaction (PPI) network within those common DEGs was subsequently illustrated through a combination of Search Tool for Retrieval of Interacting Genes (STRING) and Cytoscape software. Finally, core genes associated with survival and levels of immune infiltration were demonstrated by the Kaplan-Meier plotter and Tumor Immune Estimation Resource (TIMER) online database, respectively.

Results

In total, 483 DEGs were involved, including 216 upregulated genes enriched in “cell division”, “DNA replication”, and “DNA repair pathway” and 267 downregulated genes enriched in “cell adhesion”, “oxidation-reduction process”, and “cell-cell signaling”. The 75 core genes were selected by Molecular Complex Detection applied in Cytoscape. Four genes – MND1, FOXM1, CDC6, and POLE2 – were found to be significantly associated with survival. Further analysis of the KEEG pathway and TIMER database revealed that only POLE2 was enriched in “DNA replication” and its higher expression was negatively associated with survival and immune infiltration.

Conclusions

Higher expression of POLE2 is a prognosis-related biomarker for worse survival and is negatively associated with immune infiltration in squamous cell lung cancer.