Overall Survival Of Lung Squamous Cell Carcinoma Patients Research Articles

Establishment of a prognosis prediction model for lung squamous cell carcinoma related to PET/CT: basing on immunogenic cell death-related lncRNA

BackgroundImmunogenic cell death (ICD) stimulates adaptive immunity and holds significant promise in cancer therapy. Nevertheless, the influence of ICD-associated long non-coding RNAs (lncRNAs) on the prognosis of patients with lung squamous cell carcinoma (LUSC) remains unexplored.MethodsWe employed data from the The Cancer Genome Atlas (TCGA)database to identify ICD-related lncRNAs associated with the prognosis of LUSC using univariate Cox regression analysis. Subsequently, we utilized the LOSS regression model to construct a predictive risk model for assessing the prognosis of LUSC patients based on ICD-related lncRNAs. Our study randomly allocated187 TCGA patients into a training group and 184 patients for testing the predictive model. Furthermore, we conducted quantitative polymerase chain reaction (qPCR) analysis on 43 tumor tissues from LUSC patients to evaluate lncRNA expression levelsPearson correlation analysis was utilized to analyze the correlation of risk scores with positron emission tomography/computed tomography (PET/CT) parameters among LUSC patients.ResultsThe findings from the univariate Cox regression revealed 16 ICD-associated lncRNAs linked to LUSC prognosis, with 12 of these lncRNAs integrated into our risk model utilizing the LOSS regression. Survival analysis indicated a markedly higher overall survival time among patients in the low-risk group compared to those in the high-risk group. The area under the Receiver operating characteristic (ROC) curve to differentiate high-risk and low-risk patients was 0.688. Additionally, the overall survival rate was superior in the low-risk group compared to the high-risk group. Correlation analysis demonstrated a positive association between the risk score calculated based on the ICD-lncRNA risk model and the maximum standard uptake value (SUVmax) (r = 0.427, P = 0.0043) as well as metabolic volume (MTV)of PET-CT (r = 0.360, P = 0.0177) in 43 LUSC patients.ConclusionWe have successfully developed a risk model founded on ICD-related lncRNAs that proves effective in predicting the overall survival of LUSC patients.

Identification of specific prognostic markers for lung squamous cell carcinoma based on tumor progression, immune infiltration, and stem index.

Lung squamous cell carcinoma (LUSC) is a unique subform of nonsmall cell lung cancer (NSCLC). The lack of specific driver genes as therapeutic targets leads to worse prognoses in patients with LUSC, even with chemotherapy, radiotherapy, or immune checkpoint inhibitors. Furthermore, research on the LUSC-specific prognosis genes is lacking. This study aimed to develop a comprehensive LUSC-specific differentially expressed genes (DEGs) signature for prognosis correlated with tumor progression, immune infiltration,and stem index. RNA sequencing data for LUSC and lung adenocarcinoma (LUAD) were extracted from The Cancer Genome Atlas (TCGA) data portal, and DEGs analyses were conducted in TCGA-LUSC and TCGA-LUAD cohorts to identify specific DEGs associated with LUSC. Functional analysis and protein-protein interaction network were performed to annotate the roles of LUSC-specific DEGs and select the top 100 LUSC-specific DEGs. Univariate Cox regression and least absolute shrinkage and selection operator regression analyses were performed to select prognosis-related DEGs. Overall, 1,604 LUSC-specific DEGs were obtained, and a validated seven-gene signature was constructed comprising FGG, C3, FGA, JUN, CST3, CPSF4, and HIST1H2BH. FGG, C3, FGA, JUN, and CST3 were correlated with poor LUSC prognosis, whereas CPSF4 and HIST1H2BH were potential positive prognosis markers in patients with LUSC. Receiver operating characteristic analysis further confirmed that the genetic profile could accurately estimate the overall survival of LUSC patients. Analysis of immune infiltration demonstrated that the high risk (HR) LUSC patients exhibited accelerated tumor infiltration, relative to low risk (LR) LUSC patients. Molecular expressions of immune checkpoint genes differed significantly between the HR and LR cohorts. A ceRNA network containing 19 lncRNAs, 50 miRNAs, and 7 prognostic DEGs was constructed to demonstrate the prognostic value of novel biomarkers of LUSC-specific DEGs based on tumor progression, stemindex, and immune infiltration. In vitro experimental models confirmed that LUSC-specific DEG FGG expression was significantly higher in tumor cells and correlated with immune tumor progression, immune infiltration, and stem index. In vitro experimental models confirmed that LUSC-specific DEG FGG expression was significantly higher in tumor cells and correlated with immune tumor progression, immune infiltration, and stem index. Our study demonstrated the potential clinical implication of the 7- DEGs signature for prognosis prediction of LUSC patients based on tumor progression, immune infiltration, and stem index. And the FGG could be an independent prognostic biomarker of LUSC promoting cell proliferation, migration, invasion, THP-1 cell infiltration, and stem cell maintenance.

Downregulation of VRK1 Inhibits Progression of Lung Squamous Cell Carcinoma through DNA Damage.

Lung squamous cell carcinoma (LUSC) is a common malignancy. And the antitumor effect of bovine pox virus-associated kinase 1 (VRK1) is becoming a hot research topic. VRK1 expression and prognosis in LUSC were analyzed using the GEPIA database. The expression of VRK1 mRNA was detected in 25 LUSC clinical tissue samples by RT-PCR. VRK1 shRNA was transfected into LUSC NCI-H520 and SK-MES-1 cell lines to interfere with VRK1 expression, and the efficiency of VRK1 shRNA interference was detected by the western blot. The effects of VRK1 downregulation on LUSC cell viability, migration, cell cycle, and apoptosis were analyzed by the CCK8 assay, scratch assay, transwell assay, and flow cytometry. The effect of VRK1 downregulation on DNA damage response (DDR) was examined by immunofluorescence staining and western blot assays and further validated by in vivo experiments. VRK1 was highly expressed in both LUSC tissues and cells. Survival analysis showed that the overall survival of LUSC patients with high VRK1 expression was significantly lower than that of LUSC patients with low VRK1 expression (P=0.0026). The expression level of the VRK1 gene was significantly higher in cancer tissues of LUSC patients than in paracancerous tissues. After transfection of VRK1 shRNA in both LUSC cells, cell activity decreased (P < 0.001), migration ability started to be inhibited (P < 0.001), the ratio of G0/G1 phase cells increased (P < 0.001), and apoptosis rate increased (P < 0.001). Immunofluorescence and western blot results showed that shVRK1 increased the level of γ-H2A.X (P < 0.001) and promoted apoptosis of tumor cells (P < 0.001). In addition, the results of animal experiments showed that shVRK1 had antitumor effects (P < 0.001) and a combined effect with DOX (P < 0.001). The downregulation of VRK1 significantly affected the proliferation, apoptosis, migration, and cell cycle progression of LUSC cells via DDR, suggesting that VRK1 is a suitable target for potential LUSC therapy.

Comprehensive analyses of a CD8+ T cell infiltration related gene signature with regard to the prediction of prognosis and immunotherapy response in lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) is associated with a worse prognosis than other histological subtypes of non-small cell lung cancer. Due to the vital role of CD8+ T cells in anti-tumor immunity, the characterization of CD8+ T cell infiltration-related (CTLIR) gene signature in LUSC is worthy of in-depth exploration. In our study, tumor tissues of LUSC patients from Renmin Hospital of Wuhan University were stained by multiplex immunohistochemistry to evaluate the density of infiltrated CD8+ T cells and explore the correlation with immunotherapy response. We found that the proportion of LUSC patients who responded to immunotherapy was higher in the high density of CD8+ T cell infiltration group than in the low density of CD8+ T cell infiltration group. Subsequently, we collected bulk RNA-sequencing data from The Cancer Genome Atlas (TCGA) database. The abundance of infiltrating immune cells in LUSC patients was analyzed by using CIBERSORT algorithm, and weighted correlation network analysis was performed to identify the co-expressed gene modules related to CD8+ T cells. We then developed a prognostic gene signature based on CD8+ T cell co-expressed genes and calculated the CTLIR risk score, which stratified LUSC patients into high-risk and low-risk groups. With univariate and multivariate analyses, the gene signature was identified as an independent prognostic factor in LUSC patients. The overall survival of LUSC patients in the high-risk group was significantly shorter than that of the low-risk group in the TCGA cohort, which was validated in Gene Expression Omnibus datasets. We analyzed immune cell infiltration in the tumor microenviroment and found fewer CD8+ T cells and more regulatory T cell infiltration in the high-risk group, which is characterized as an immunosuppressive phenotype. Furthermore, the LUSC patients in the high-risk group were predicted to have a better response to immunotherapy than those in the low-risk group when treated with PD-1 and CTLA4 inhibitors. In conclusion, we performed a comprehensive molecular analysis of the CTLIR gene signature in LUSC and constructed a risk model for LUSC patients to predict prognosis and immunotherapy response.

Prognostic and functional roles of EIF4G1 in lung squamous cell carcinoma

Eukaryotic translation initiation factor 4 gamma 1 (EIF4G1) is highly expressed in many cancers and affects their occurrence and development. However, the effect of EIF4G1 on the prognosis, biological function and the relevant mechanism in lung squamous cell carcinoma (LSCC) is unclear. Through clinical cases, Cox’s proportional hazard model and Kaplan–Meier plotter survival analysis, we find the expression levels of EIF4G1 are dependent on age and clinical stage, high expression of EIF4G1 could be used to predict the overall survival of LSCC patients. LSCC cell line NCI-H1703, NCI-H226 and SK-MES-1infected with EIF4G1 siRNA are used to detect the function of EIF4G1 with cell proliferation and tumorigenesis in vivo and vitro. The data show that EIF4G1 promotes tumor cell proliferation and the G1/S transition of cell cycle in LSCC, then the biological function of LSCC is effected by the AKT/mTOR pathway. Above all, these results have demonstrated that EIF4G1 promotes LSCC cell proliferation and may represent an indicator of prognosis in LSCC.

The development and validation of a m6A-lncRNAs based prognostic model for overall survival in lung squamous cell carcinoma.

No biomarkers have been identified for the prognosis of lung squamous cell carcinoma (LUSC). Risk models based on m6A-lncRNAs help to predict survival in some cancers. However, very few studies have reported m6A-lncRNA risk models in LUSC. We aimed to construct a prognostic model based on m6A-lncRNAs in LUSC. The clinical and RNA-sequencing information of 504 LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Prognostic m6A-lncRNAs were identified by a Pearson correlation analysis and univariate Cox regression analysis. The ConsensusClusterPlus algorithm was used to cluster the prognostic m6A-lncRNAs. The overall survival (OS) and clinicopathological characteristics of the 2 clusters were compared. A gene set enrichment analysis (GSEA) analysis was performed to analyze the genes enriched in the 2 clusters. A least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to construct the risk-score model. Two hundred and forty eight patients were randomly chosen from TCGA-LUSC cohort for the training set. The receiver operating characteristic (ROC) curve analysis was used to assess the predictive ability of the model. The clinical characteristics and OS in the high- and low-risk groups were compared. The independent prognostic value of the model was tested by Cox regression analyses. Thirteen m6A-lncRNAs were identified as prognostic lncRNAs and classified into cluster A and cluster B. The OS of patients in cluster A was better than that of patients in cluster B (P<0.001). Patients in cluster B had higher expressions of immune checkpoints. Immune score, stromal score, and ESTIMATE score were higher in cluster B (P<0.001). Seven of the 13 lncRNAs were used to construct the risk-score model. Patients in the high-risk group had a worse OS. ROC curves showed a under the curve (AUC) of 0.639 in the training set and 0.624 in the validation set. A high risk was associated with cluster B, a high immune score, and stage III-IV disease. Patients in the high-risk group had increased expressions of immune checkpoints. The Cox regression analyses showed that the risk-score model had independent prognostic value for OS. The risk-score model retained its prognostic value in different subgroups. The m6A-lncRNA risk-score model is an independent prognostic factor for OS in LUSC patients. However, the risk-score model need to be further tested clinically.

A Novel Prognosis Signature Based on Ferroptosis-Related Gene DNA Methylation Data for Lung Squamous Cell Carcinoma.

Ferroptosis-related genes regulating an iron- and lipid reactive oxygen species (ROS)-dependent form of programmed cell death suggest critical roles for ferroptosis in cancers. However, the prognostic value of ferroptosis-related epigenetic features such as DNA methylation in lung squamous cell carcinoma (LUSC) needs to be studied. Ferroptosis-related genes are collected from the FerrDb database, and the methylation data of these related genes in LUSC methylation data downloaded from the TCGA are retrieved. The DNA methylation data (362 LUSC samples) were analyzed to screen prognostic ferroptosis-related methylation sites. After patients with complete overall survival (OS) information were randomly separated into training cohort (n = 200) and validation cohort (n = 162), the least absolute shrinkage and selection operator (LASSO) and the Cox regression were used to establish and validate the prognostic signature. The time-dependent receiver operating characteristic (ROC) and Kaplan–Meier survival curve analyses, Harrell's concordance index (C-index), calibration analysis, and decision curve analysis (DCA) were performed to evaluate the risk signature and related nomogram. A series of other bioinformatics approaches such as mexpress, cbioportal, maftools, string, metascape, TIMER, and Kaplan–Meier survival curve analysis were also used to determine the methylation, mutation status, protein interaction network or functional enrichment, effects on immune cell infiltration, or expression level prognosis of those signature-related genes. A total of 137 DNA methylation sites were identified as prognostic predictors corresponding to 109 ferroptosis-related genes (FRGs). The methylation signature containing 31 methylation sites proved to be superior predictive efficiency in predicting the 1-, 3-, 5-, and 10-year OS. 8 out of 28 signature-related genes were significantly related to OS time or OS state in patients with LUSC. In addition, DUSP1, ZFN36, and ALOX5 methylation status also correlated with pathological M and ALOX5 methylation correlated with pathological N. The prognostic prediction efficiency of T, N, M, and the stage was inferior to that of the DNA methylation signature. LUSC patients in the high-risk group own a significantly larger number of variants of FRGs than those in the low-risk group. In addition, negative or positive correlation patterns were presented among the different infiltrating immune cells with risk scores or signature-related genes in patients with LUSC. The expression level of 15 signature-related genes showed a significant relationship with OS of LUSC patients. A novel prognostic nomogram survival model containing 4 factors including age, pathologic T, stage, and risk group was constructed and validated, AndC-index, decision curve analysis (DCA), and calibration analysis demonstrated its excellent predictive performance. The FRG DNA methylation data-based prognostic model acts as a powerful prognostic prediction indicator in LUSC patients and is advantageous over the traditional model based on T, N, M, and stage.

Identification of miRNA signature for predicting the prognostic biomarker of squamous cell lung carcinoma.

As explorations deepen, the role of microRNAs (miRNAs) in lung squamous cell carcinoma (LUSC), from its emergence to metastasis and prognosis, has elicited extensive concern. LUSC-related miRNA and mRNA samples were acquired from The Cancer Genome Atlas (TCGA) database. The data were initially screened and pretreated, and the R platform and series analytical tools were used to identify the specific and sensitive biomarkers. Seven miRNAs and 15 hub genes were found to be closely related to the overall survival of patients with LUSC. Determination of the expression of these miRNAs can help improve the overall survival of LUSC patients. The 15 hub genes correlated with overall survival (OS). The new miRNA markers were identified to predict the prognosis of LUSC. The findings of this study offer novel views on the evolution of precise cancer treatment approaches with high reliability.

N6-Methyladenosine RNA Methylation Regulator-Related Alternative Splicing (AS) Gene Signature Predicts Non-Small Cell Lung Cancer Prognosis.

Aberrant N6-methyladenosine (m6A) RNA methylation regulatory genes and related gene alternative splicing (AS) could be used to predict the prognosis of non–small cell lung carcinoma. This study focused on 13 m6A regulatory genes (METTL3, METTL14, WTAP, KIAA1429, RBM15, ZC3H13, YTHDC1, YTHDC2, YTHDF1, YTHDF2, HNRNPC, FTO, and ALKBH5) and expression profiles in TCGA-LUAD (n = 504) and TCGA-LUSC (n = 479) datasets from the Cancer Genome Atlas database. The data were downloaded and bioinformatically and statistically analyzed, including the gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. There were 43,948 mRNA splicing events in lung adenocarcinoma (LUAD) and 46,020 in lung squamous cell carcinoma (LUSC), and the data suggested that m6A regulators could regulate mRNA splicing. Differential HNRNPC and RBM15 expression was associated with overall survival (OS) of LUAD and HNRNPC and METTL3 expression with the OS of LUSC patients. Furthermore, the non–small cell lung cancer prognosis-related AS events signature was constructed and divided patients into high- vs. low-risk groups using seven and 14 AS genes in LUAD and LUSC, respectively. The LUAD risk signature was associated with gender and T, N, and TNM stages, but the LUSC risk signature was not associated with any clinical features. In addition, the risk signature and TNM stage were independent prognostic predictors in LUAD and the risk signature and T stage were independent prognostic predictors in LUSC after the multivariate Cox regression and receiver operating characteristic analyses. In conclusion, this study revealed the AS prognostic signature in the prediction of LUAD and LUSC prognosis.

Tumor mutation burden (TMB)-associated signature constructed to predict survival of lung squamous cell carcinoma patients

Lung squamous cell carcinoma (LUSC) is a common type of lung cancer with high incidence and mortality rate. Tumor mutational burden (TMB) is an emerging biomarker for selecting patients with non-small cell lung cancer (NSCLC) for immunotherapy. This study aimed to reveal TMB involved in the mechanisms of LUSC and develop a model to predict the overall survival of LUSC patients. The information of patients with LUSC were obtained from the cancer genome atlas database (TCGA). Differentially expressed genes (DEGs) between low- and the high-TMB groups were identified and taken as nodes for the protein–protein interaction (PPI) network construction. Gene oncology (GO) enrichment analysis and gene set enrichment analysis (GSEA) were used to investigate the potential molecular mechanism. Then, we identified the factors affecting the prognosis of LUSC through cox analysis, and developed a risk score signature. Kaplan–Meier method was conducted to analyze the difference in survival between the high- and low-risk groups. We constructed a nomogram based on the risk score model and clinical characteristics to predict the overall survival of patients with LUSC. Finally, the signature and nomogram were further validated by using the gene expression data downloaded from the Gene Expression Omnibus (GEO) database. 30 DEGs between high- and low-TMB groups were identified. PPI analysis identified CD22, TLR10, PIGR and SELE as the hub genes. Cox analysis indicated that FAM107A, IGLL1, SELE and T stage were independent prognostic factors of LUSC. Low-risk scores group lived longer than that of patients with high-risk scores in LUSC. Finally, we built a nomogram that integrated the clinical characteristics (TMN stage, age, gender) with the three-gene signature to predict the survival probability of LUSC patients. Further verification in the GEO dataset. TMB might contribute to the pathogenesis of LUSC. TMB-associated genes can be used to develope a model to predict the OS of lung squamous cell carcinoma patients.

HEDGEHOG/GLI Modulates the PRR11-SKA2 Bidirectional Transcription Unit in Lung Squamous Cell Carcinomas

We previously demonstrated that proline-rich protein 11 (PRR11) and spindle and kinetochore associated 2 (SKA2) constituted a head-to-head gene pair driven by a prototypical bidirectional promoter. This gene pair synergistically promoted the development of non-small cell lung cancer. However, the signaling pathways leading to the ectopic expression of this gene pair remains obscure. In the present study, we first analyzed the lung squamous cell carcinoma (LSCC) relevant RNA sequencing data from The Cancer Genome Atlas (TCGA) database using the correlation analysis of gene expression and gene set enrichment analysis (GSEA), which revealed that the PRR11-SKA2 correlated gene list highly resembled the Hedgehog (Hh) pathway activation-related gene set. Subsequently, GLI1/2 inhibitor GANT-61 or GLI1/2-siRNA inhibited the Hh pathway of LSCC cells, concomitantly decreasing the expression levels of PRR11 and SKA2. Furthermore, the mRNA expression profile of LSCC cells treated with GANT-61 was detected using RNA sequencing, displaying 397 differentially expressed genes (203 upregulated genes and 194 downregulated genes). Out of them, one gene set, including BIRC5, NCAPG, CCNB2, and BUB1, was involved in cell division and interacted with both PRR11 and SKA2. These genes were verified as the downregulated genes via RT-PCR and their high expression significantly correlated with the shorter overall survival of LSCC patients. Taken together, our results indicate that GLI1/2 mediates the expression of the PRR11-SKA2-centric gene set that serves as an unfavorable prognostic indicator for LSCC patients, potentializing new combinatorial diagnostic and therapeutic strategies in LSCC.

Four hub genes regulate tumor infiltration by immune cells, antitumor immunity in the tumor microenvironment, and survival outcomes in lung squamous cell carcinoma patients.

In this study, we performed bioinformatics analyses to identify hub genes that regulate tumor infiltration by immune cells and antitumor immunity in the lung squamous cell carcinoma (LUSC). We identified 1738 robust and stable differentially expressed genes (DEGs) in the LUSC tissues based on robust rank aggregation (RRA) analysis of RNA-sequencing data from 5 GEO-LUSC datasets. We then classified TCGA-LUSC patients based on ssGSEA and ESTIMATE analyses of LUSC tissues into high, medium and low immunity subgroups showing significant differences in tumor purity. Weighted gene co-expression network analysis of the robust DEGs revealed five immunity-related modules, including the brown module with 762 DEGs and 30 hub genes showing the highest correlation with the immunity-related LUSC patient subgroups and their clinicopathological characteristics. We selected four hub genes, LAPTM5, C1QC, CSF1R and SLCO2B1, for validation of the immunity status and prognosis of LUSC patients. High expression of these four genes correlated with increased infiltration of immune cell types, upregulation of the immunosuppressive TOX pathway genes, CD8+ T cell exhaustion, and shorter overall survival of LUSC patients. These findings demonstrate that four hub genes regulate tumor infiltration of immune cells, anti-tumor immunity, and survival outcomes in LUSC patients.

A prognostic 11-DNA methylation signature for lung squamous cell carcinoma.

BackgroundLung squamous cell carcinoma (LUSC), as the second frequent subtype of lung cancer, causes lots of mortalities primarily due to a lack of precise prognostic markers and timely treatment intervention. Previous studies have constructed several risk prognostic models based on DNA methylation sites in multiple tumors, whereas, DNA methylation signature of LUSC remains to be built, and its predictive value need to be evaluated.MethodsThe genome-wide DNA methylation data of LUSC samples was obtained from The Cancer Genome Atlas dataset. Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) were implemented to identify DNA methylation sites related to overall survival of LUSC patients. Thus, we performed multivariate Cox regression to establish a DNA methylation signature. The Kaplan-Meier (K-M) survival curves and time-dependent receiver operating characteristic (ROC) curves were plotted to estimate the prognostic power of the signature. Comparison with other known prognostic biomarkers, our DNA methylation signature showed higher predictive specificity and sensitivity. In addition, multivariate Cox regression screened out independent prognostic factors and constructed a nomogram.ResultsSeveral statistical methods were performed to construct an 11-DNA methylation signature. LUSC patients were divided into low- and high-risk group based on risk score, and high-risk group had a shorter survival time. According to the results of K-M and ROC analyses, the 11-DNA methylation signature showed significant sensitivity and specificity in predicting the LUSC patients’ overall survival. Finally, we integrated some independent prognostic factors (risk score, metastasis stage, and tobacco smoking history) to construct a nomogram, which has excellent prognostic power and may provide guidance for the therapeutic strategies.ConclusionsWe constructed the first risk prognosis model based on DNA methylation site in LUSC, which showed better predictive ability. In addition, a nomogram integrating the DNA methylation signature, metastasis stage, and tobacco smoking history was developed.

Genome-wide analysis of prognostic-related lncRNAs, miRNAs and mRNAs forming a competing endogenous RNA network in lung squamous cell carcinoma.

As a type of cancer with the highest morbidity and mortality, lung squamous cell carcinoma (LUSC) has a very poor prognosis. Long-non-coding RNA (lncRNA) has recently attracted attentions because it can play the role of competing endogenous RNA (ceRNA) to inhibit microRNA (miRNA) functions. In this study, we aimed to find prognosis-related lncRNAs, miRNAs and mRNAs and construct a prognosis-related ceRNA network. The original LUSC RNA-sequencing data and miRNA profiles data were downloaded from the cancer genome atlas (TCGA) database. Differentially expressed lncRNAs, miRNAs and mRNAs were then identified between patients with lymph node metastasis and no lymph node metastasis. Univariate Cox regression analysis was performed to find the survival-associated lncRNAs, miRNAs and mRNAs. Subsequently, prognostic-related ceRNA network was established. By multivariate Cox regression analysis, three lncRNA signatures and three mRNA signatures were developed and used for predicting LUSC patients' survival. A total of 224 lncRNAs, 160 miRNAs, 913 mRNAs were identified between samples with lymph node metastasis and no lymph node metastasis. Univariate Cox regression analysis showed that, among them, 28 lncRNAs, 8 miRNAs, 105 mRNAs were significantly associated with patients' overall survival time. Further pathway and enrichment analysis suggested that these mRNAs were associated with the regulation of transmembrane transport, regulation of blood circulation, plasma lipoprotein particle organization. Then we constructed a survival-related ceRNA network including 9 lncRNAs, 8 miRNAs and 23 mRNAs. Additionally, a multivariate Cox regression analysis demonstrated that three lncRNAs (AL161431.1, LINC02389, APCDD1L.DT) and three mRNAs (KLK6, SLITRK5, CCDC177) had a significant prognostic value. Risk score indicated that lncRNA signature and mRNA signature could independently predict overall survival in LUSC patients. The current study provided a better understanding of the ceRNA network in the progression of LUSC and laid a theoretical foundation for LUSC prognosis.

Prognostic value of AKAP13 methylation and expression in lung squamous cell carcinoma.

Aim: This study aimed to analyze the prognostic value and clinical significance of AKAP13 mRNA expression and AKAP13 methylation in lung squamous cell carcinoma (LUSC). Materials & methods: The mRNA expression and methylation of AKAP13 data of LUSC patients were downloaded from the Broad GDAC Firehose database and analyzed. Results:AKAP13 mRNA expression was downregulated and methylation was upregulated in LUSC tissue. Three CpG sites of AKAP13 were associated with overall survival. Combination of AKAP13 mRNA and methylation revealed 11 CpG sites associated with overall survival of LUSC patients. AKAP13 mRNA expression was associated with distant metastasis of LUSC, no associations were found between methylation status of CpG sites and clinical features. Conclusion:AKAP13 mRNA and its methylated CpG sites are potential prognostic indicators in LUSC patients.

Integrative analysis of DNA methylation-driven genes for the prognosis of lung squamous cell carcinoma using MethylMix.

Background: DNA methylation acts as a key component in epigenetic modifications of genomic function and functions as disease-specific prognostic biomarkers for lung squamous cell carcinoma (LUSC). This present study aimed to identify methylation-driven genes as prognostic biomarkers for LUSC using bioinformatics analysis.Materials and Methods: Differentially expressed RNAs were obtained using the edge R package from 502 LUSC tissues and 49 adjacent non-LUSC tissues. Differentially methylated genes were obtained using the limma R package from 504 LUSC tissues and 69 adjacent non-LUSC tissues. The methylation-driven genes were obtained using the MethylMix R package from 500 LUSC tissues with matched DNA methylation data and gene expression data and 69 non-LUSC tissues with DNA methylation data. Gene ontology and ConsensusPathDB pathway analysis were performed to analyze the functional enrichment of methylation-driven genes. Univariate and multivariate Cox regression analyses were performed to identify the independent effect of differentially methylated genes for predicting the prognosis of LUSC.Results: A total of 44 methylation-driven genes were obtained. Univariate and multivariate Cox regression analyses showed that twelve aberrant methylated genes (ATP6V0CP3, AGGF1P3, RP11-264L1.4, HIST1H4K, LINC01158, CH17-140K24.1, CTC-523E23.14, ADCYAP1, COX11P1, TRIM58, FOXD4L6, CBLN1) were entered into a Cox predictive model associated with overall survival in LUSC patients. Methylation and gene expression combined survival analysis showed that the survival rate of hypermethylation and low-expression of DQX1 and WDR61 were low. The expression of DQX1 had a significantly negatively correlated with the methylation site cg02034222.Conclusion: Methylation-driven genes DQX1 and WDR61 might be potential biomarkers for predicting the prognosis of LUSC.

A nine-long non-coding RNA signature for prognosis prediction of patients with lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) is malignant disease with poor therapeutic response and unfavourable prognosis. This study aims to develop a long non-coding RNA (lncRNA) signature for survival prediction in patients with LUSC. We obtained lncRNA expression profiles of 493 LUSC cases from The Cancer Genome Atlas, and randomly divided the samples into a training set (n= 296) and a testing set (n= 197). Univariate Cox regression and random survival forest algorithm were performed to select optimum survival-related lncRNAs. A lncRNA-focused risk score model was then constructed for prognosis prediction in the training set and further validated in the testing set and the entire set. Finally, bioinformatics analysis was carried out to explore the potential signaling pathways associated with the prognostic lncRNAs. A set of 9 lncRNAs were found to be strongly correlated with overall survival of LUSC patients. These 9 lncRNAs were integrated into a prognostic signature, which could separate patients into high- and low-risk groups with significantly different survival times in the training set (median: 30.5 vs. 80.5 months, log-rank P< 0.001). This signature was also confirmed in the testing set and the entire set. Besides, the prognostic value of the 9-lncRNA signature was independent of clinical features and maintained stable in stratified analyses. Functional enrichment study suggested that the 9 lncRNAs may be mainly involved in metabolism-related pathways, phosphatidylinositol signaling system, p53 signaling pathway, and notch signaling pathway. Our study demonstrated the potential clinical implication of the 9-lncRNA signature for survival prediction of LUSC patients.

Identification of lncRNA biomarkers in lung squamous cell carcinoma using comprehensive analysis of lncRNA mediated ceRNA network

Long non-coding RNAs (lncRNAs) act as a member of competing endogenous RNAs (ceRNAs) and plays a significant role in tumorigenesis. The aim of this study was to identify potential lncRNA biomarkers for predicting the prognosis of lung squamous cell carcinoma (LUSC) using a comprehensive analysis of lncRNA mediated ceRNA network. Differentially expressed RNAs datasets were obtained using edge R package in 502 LUSC tissues and 49 adjacent non-LUSC tissues from the Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to identify functional enrichment implication of lncRNA related differentially expressed mRNAs. Survival analysis was used Kaplan-Meier curve method. Univariate and multivariate Cox regression analysis were performed to construct a predictive model with lncRNA biomarkers. A total of 2185 lncRNAs, 170 miRNAs and 2053 mRNAs were differentially expressed between LUSC tissues and adjacent non-LUSC tissues. The novel constructed ceRNA network incorporated 184 LUSC-specific lncRNAs, 18 miRNAs, and 49 mRNAs. About 11 of 184 differentially expressed lncRNAs and 1 of 18 differentially expressed miRNAs and 5 of 49 differentially expressed mRNAs were conspicuously related to overall survival (p < .05). Univariate and multivariate cox regression analysis showed that 6 lncRNAs were retrieved to construct a predictive model to predict the overall survival in LUSC patients. In conclusion, CeRNAs contributed to the progression of LUSC and a model with 6 lncRNAs might be potential biomarker for predicting the prognosis of LUSC.

Identification of a progression-associated long non-coding RNA signature for predicting the prognosis of lung squamous cell carcinoma.

Long non-coding RNAs (lncRNAs) have been indicated to have prognostic roles in various cancer types. However, the association between lncRNAs and lung squamous cell carcinoma (LSCC) progression, and the prognostic value of lncRNAs as a marker for early detection of LSCC have not been systematically investigated. The present study performed a genome-wide comparative analysis in order to determine the expression profiles of 10,207 lncRNAs to investigate the expression patterns between patients with early stages of LSCC (stage I–II) and those with late-stage disease (stage III–IV). It was found that 114 lncRNAs were significantly differentially expressed between late- and early-stage LSCC and therefore associated with the progression of the malignancy. By focusing on progression-associated lncRNAs, eight lncRNAs were found to be significantly associated with overall survival of LSCC patients according to univariate Cox proportional hazards regression analysis. These eight prognostic lncRNAs were integrated into a progression-associated eight-lncRNA signature, which stratified patients into two groups with significantly different overall survival (median survival, 2.25 vs. 7.68 years; P=2.06×10−5). In addition, the prognostic value of the progression-associated eight-lncRNA signature was independent of known clinical factors. Functional analysis suggested that eight prognostic lncRNAs may be involved in adipocytokine signaling pathway and glycerophospholipid metabolism. Taken together, the progression-associated eight-lncRNA signature identified by our study not only represents a candidate prognostic biomarker for LSCC patients but also provides insight into the molecular mechanisms in the progression of LSCC.

Integrated analysis of competing endogenous RNA network revealing lncRNAs as potential prognostic biomarkers in human lung squamous cell carcinoma.

Accumulating evidence shows the important role of long non-coding RNAs (lncRNAs) in competing endogenous RNA (ceRNA) networks for predicting survival in tumor patients. However, prognostic biomarkers for lung squamous cell carcinoma (LUSC) are still lacking. The objective of this study is to identify a lncRNA signature for evaluation of overall survival (OS) in 474 LUSC patients from The Cancer Genome Atlas (TCGA) database. A total of 474 RNA sequencing profiles in LUSC patients with clinical data were obtained, providing a large sample of RNA sequencing data, and 83 LUSC-specific lncRNAs, 26 miRNAs, and 85 mRNAs were identified to construct the ceRNA network (fold change>2, P<0.05). Among these above 83 LUSC-specific lncRNAs, 22 were assessed as closely related to OS in LUSC patients using a univariate Cox proportional regression model. Meanwhile, two (FMO6P and PRR26) of the above 22 OS-related lncRNAs were identified using a multivariate Cox regression model to construct a risk score as an independent indicator of the prognostic value of the lncRNA signature in LUSC patients. LUSC patients with low-risk scores were more positively correlated with OS (P<0.001). The present study provides a deeper understanding of the lncRNA-related ceRNA network in LUSC and suggests that the two-lncRNA signature could serve as an independent biomarker for prognosis of LUSC.