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. 2018 Mar 16;17(5):7238–7248. doi: 10.3892/mmr.2018.8770

Long non-coding RNAs RP5-821D11.7, APCDD1L-AS1 and RP11-277P12.9 were associated with the prognosis of lung squamous cell carcinoma

Yanzhuo Luo 1,2,*, Zhaobo Xuan 3,*, Xiaofeng Zhu 4, Peng Zhan 4, Zhou Wang 1,✉
PMCID: PMC5928681  PMID: 29568882

Abstract

Lung squamous cell carcinoma (LUSC), a type of non-small cell lung carcinoma, has a poor therapeutic response, high relapse rate and poor prognosis. The present study was designed to reveal the key long non-coding RNAs (lncRNAs) associated with the prognosis of LUSC. The lncRNA expression profiles of LUSC and adjacent samples were downloaded from The Cancer Genome Atlas database. Based on the edgeR and DEseq packages, the differentially expressed lncRNAs (DELs) between LUSC and adjacent samples were obtained and the intersecting DELs were regarded as significant DELs. Subsequently, a prognostic risk model was established using Cox regression analysis and its classification effect was detected by survival analysis. Using survival analysis, the effect of the prognostic risk model was assessed in the validation set and other types of cancer. Finally, the co-expression genes of key lncRNAs were screened using the Multi-Experiment Matrix tool and the STRING database, and their functions were predicted via enrichment analysis using the Database for Annotation, Visualization and Integrated Discovery tool. A total of 2,041 significant DELs between LUSC and adjacent samples were screened. The prognostic risk model consisting of RP5-821D11.7, APCDD1L-AS1 and RP11-277P12.9 was established, which had a good classification effect. Cox multivariate regression analysis demonstrated that risk score may serve as an independent prognostic factor. Furthermore, certain co-expression genes of RP5-821D11.7 (including proliferating cell nuclear antigen), APCDD1L-AS1 (including semaphorin 5A, semaphorin 6D, ADAMTS like 1, ADAM metallopeptidase with thrombospondin type 1 motif 6, slit guidance ligand 3, and tenascin C) and RP11-277P12.9 (including Wnt family member 2B) were identified. Additionally, ‘positive regulation of cell migration’ and ‘proteinaceous extracellular matrix’ were enriched. In conclusion, the expression levels of the lncRNAs RP5-821D11.7, APCDD1L-AS1 and RP11-277P12.9 may affect the prognosis of LUSC.

Keywords: lung squamous cell carcinoma, long non-coding RNA, prognostic risk model, survival analysis, co-expression gene

Introduction

Lung cancer, including small cell lung carcinoma and non-small cell lung carcinoma (NSCLC), is the primary cause of cancer death globally (1). NSCLC consists of lung squamous cell carcinoma (LUSC), adenocarcinoma and large cell carcinoma (2), which are responsible for 80% of lung cancer mortality (3). LUSC is associated with tobacco smoking, and more frequently occurs in men (4). It has the characteristics of poor therapeutic response, high relapse rate and poor prognosis (5), inducing ~400,000 mortalities/year worldwide (6). Therefore, revealing the genes involved in the prognosis of LUSC is of great importance.

Long non-coding RNAs (lncRNAs) are a class of non-protein-coding RNAs that mediate gene expression, which play important roles in multiple biological processes and diverse carcinomas (7). Previous studies have identified a number of lncRNAs to be associated with prognosis of LUSC. For example, Zhang et al (8) reported that lncRNA 1133 (LINC01133) is overexpressed in patients with LUSC and may shorten their survival time, thus LINC01133 is a promising biomarker for LUSC. The lncRNA metastasis associated lung adenocarcinoma transcript 1 is reported to have tumor-promoting functions and is associated with the survival time of patients with NSCLC (9,10). The lncRNA HOX transcript antisense intergenic RNA (HOTAIR) mediates the cell invasion and metastasis of NSCLC by downregulating homeobox A5, indicating that HOTAIR may serve as a prognostic marker and therapeutic target in patients with NSCLC (11,12). By inhibiting the expression of Kruppel like factor 2 (KLF2) and p21, lncRNA antisense non-coding RNA in the INK4 locus promotes cell proliferation and suppresses cellular apoptosis NSCLC (13,14). Furthermore, lncRNA associated with microvascular invasion in HCC regulates NSCLC cell proliferation and invasion, thus its overexpression may be used as a prognostic biomarker for NSCLC (15). However, the lncRNAs associated with the prognosis of LUSC have not been completely elucidated.

The present study further examined the key lncRNAs associated with the prognosis of LUSC through a series of bioinformatics methods. The lncRNA expression profiles of LUSC were downloaded from The Cancer Genome Atlas, and the differentially expressed lncRNAs (DELs) between LUSC and adjacent samples were identified. Following prognosis-associated DEL screening, a prognostic risk model was established and evaluated. Finally, the co-expression genes of important lncRNAs were obtained and their functions were predicted. The present study may contribute to predicting the prognosis of LUSC and revealing novel molecules associated with the disease.

Materials and methods

Data source

The lncRNA expression profiles of LUSC and the relevant clinical data were downloaded from The Cancer Genome Atlas (TCGA; cancergenome.nih.gov) database (downloaded on April 18, 2017). Samples without clinical data were removed and the data of 501 patients were obtained (Table I). In addition, the data of 49 tumor-adjacent normal lung tissues were obtained.

Table I.

Clinical characteristics of patients (n=501) with lung squamous cell carcinoma in the present study.

Variable No. patients
Age, years
  ≤65 190
  >65 311
Sex
  Female 139
  Male 362
Pathological stage
  I 244
  II 162
  III 84
  IV 7
  NA 4
T stage
  T1 114
  T2 293
  T3 71
  T4 23
N stage
  N0 319
  N1 131
  N2-N3 45
  NA 6
M stage
  M0 411
  M1 7
  NA 83
Radiotherapy
  Yes 53
  No 384
  NA 64
Targeted molecular therapy
  Yes 133
  No 306
  NA 62
Residual tumor
  R0 398
  R1+R2 16
  NA 87
Neoplasm recurrence
  Yes 135
  No 285
  NA 81
Vital status
  Alive 289
  Deceased 212
  NA 6
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NA, data unavailable, tumor; N, node; M, metastasis.

DEL screening

Subsequent to obtaining the RNA-sequencing data of LUSC from TCGA, lncRNA annotation was performed using the GENCODE database (www.gencodegenes.org) (16). The lncRNAs with average expression values (counts/million) >0.1 were considered as sample expressing lncRNAs. The two independent R (version 3.1.0) (17) packages edgeR (version 3.8.5; www.bioconductor.org/packages/release/bioc/html/edgeR.html) (18) and DEseq (version 1.16.0; www.bioconductor.org/packages/release/bioc/html/DESeq.html) (19) were used for screening DELs between LUSC samples and adjacent samples. The adjusted P-value <0.05 and |log fold change (FC)| >1 were set as thresholds. The intersecting DELs predicted by the edgeR and DEseq packages were regarded as significant DELs.

Establishment of prognostic risk model

The DELs expressed in <10% of LUSC samples and patients with a survival time of <30 days were eliminated. The patients were randomly divided into a test set and validation set. For the test set, Cox univariate regression analysis was used to analyze the correlation between DELs and overall survival (OS). The lncRNAs with a P-value <0.05 was screened as prognosis-associated DELs. Subsequently, these prognosis-associated DELs were analyzed using Cox multivariate regression analysis to establish the prognostic risk model, with P-value <0.05 as the threshold. The prognosis risk score was calculated using the following formula (20):

Prognosis risk score=expression value of gene 1 × risk coefficient of gene 1 + expression value of gene 2 × risk coefficient of gene 2 + expression value of gene 3 × risk coefficient of gene 3.

In the test set, the expression levels of the prognosis-associated DELs in cancerous samples and adjacent samples were compared using the non-parametric Wilcoxon signed-rank test.

Detection of the classification effect of the prognostic risk model

The test set was divided into a high risk group and a low risk group, according to the median prognostic risk score. To assess the effect of prognostic risk score in determining the prognosis of patients, the difference between the survival curves of the high and low risk groups was analyzed by drawing receiver operating characteristic (ROC) curves using survivalROC package (version 1.0.3; https://cran.r-project.org/web/packages/survivalROC/index.html).

Determination of the correlation between prognostic risk score and prognosis

Using Cox univariate regression analysis, the correlation between prognostic risk score and clinical characteristics, including the OS of patients, were further analyzed. Subsequently, Cox multivariate regression analysis was applied to determinate whether the prognostic risk score was an independent prognostic factor. A P-value <0.05 was set as the threshold value. The hazard ratio and its 95% confidence intervals were used for evaluation. In order to assess the prediction accuracy of the prognostic risk model for time-dependent disease consequences, the survival ROC package (cran.r-project.org/web/packages/survivalROC/index.html) (21) in R was applied for drawing ROC curve. Furthermore, the OS differences among the patients in the high and low risk groups were evaluated by a log rank test using Kaplan Meier (KM) survival analysis (22). Based on a χ2 test (23), the correlations between DELs and clinical characteristics were detected. In addition, ROC curves were used to assess the prediction significance of the prognostic risk score following treatment, with the two-sided P-value <0.05 as the threshold.

Functional analysis of important lncRNAs

Associated genes (co-expression genes) of lncRNAs were obtained through the Human RNAseq expression data platform in the Multi-Experiment Matrix (MEM; biit.cs.ut.ee/mem/index.cgi) online tool (24). Using the STRING database (string-db.org) (25), a protein-protein interaction (PPI) network was established, with an associated score >0.4 and a number of associated nodes >3 set as the thresholds. Subsequently, the PPI network of lncRNAs and their associated genes was visualized using Cytoscape software 3.5.1 (www.cytoscape.org) (26). Additionally, Gene Ontology (GO; www.geneontology.org) (27) and Kyoto Encyclopedia of Genes and Genomes (KEGG; www.genome.ad.jp/kegg) (28) pathway enrichment analyses were performed based on the Database for Annotation, Visualization and Integrated Discovery (DAVID 6.7; david.ncifcrf.gov) (29) bioinformatics tool, with a P-value <0.05 set as the threshold.

Results

DEL screening

A total of 5,515 DELs were identified between LUSC samples and adjacent samples, including 2,537 DELs from the edgeR package and 2,048 DELs from the DEseq package. Finally, a total of 2,041 significant DELs were obtained by selecting the intersecting DELs predicted by the edgeR and DEseq packages. The heatmap of the 2,041 DELs is presented in Fig. 1, indicating that the lncRNA expression profiles of LUSC and adjacent samples were different.

Figure 1.

Figure 1.

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Heatmap of the 2,041 DELs. The horizontal axis and the vertical axis represent the DELs and the samples, respectively. DEL, differentially expressed long non-coding RNA.

Establishment of the prognostic risk model

A total of 489 samples and 1,468 DELs were included in the survival analysis. The patients were randomly divided into test (n=245) and validation (n=244) sets. Cox univariate regression analysis demonstrated that there were 68 prognosis-associated DELs in the test set. Subsequently, Cox multivariate regression analysis indicated that 3 prognosis-associated DELs (including RP5-821D11.7, APCDD1L-AS1 and RP11-277P12.9) had important prognostic value. The prognostic risk score was calculated using the following formula: Prognostic risk score=expression value of RP5-821D11.7 × (−0.392) + expression value of APCDD1L-AS1 × (0.101) + expression value of RP11-277P12.9 × (−0.114).

In the test set, the expression levels of the three prognosis-associated DELs in LUSC samples and adjacent samples were compared by non-parametric Wilcoxon signed-rank test. As presented in Fig. 2, the expression levels were significantly increased in LUSC samples compared with adjacent samples (P<0.05; Fig. 2). In addition, the expression levels of the three prognosis-associated DELs were able to be used to separate LUSC samples from the adjacent samples with an area under the ROC curve (AUC) >0.8 (Fig. 3A-C). Furthermore, the KM survival analysis demonstrated that the samples with high expression levels of the three prognosis-associated DELs had significantly lower OS compared with those with low expression levels (P<0.05; Fig. 3D-F).

Figure 2.

Figure 2.

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Expression levels of the three prognosis-associated differentially expressed lncRNAs in lung squamous cell carcinoma samples and adjacent samples of the test set. *P<0.05 (non-parametric Wilcoxon signed-rank test). lncRNA, long non-coding RNA.

Figure 3.

Figure 3.

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ROC curves and KM curves of the three prognosis-associated differentially expressed lncRNAs in lung squamous cell carcinoma samples and adjacent samples of the test set. (A) RP5-821D11.7 ROC curve, (B) APCDD1L-AS1 ROC curve, (C) RP11-277P12.9 ROC curve, (D) RP5-821D11.7 KM curve, (E) APCDD1L-AS1 KM curve and (F) RP11-277P12.9 KM curve. AUC, area under the curve; OS, overall survival; CI, confidence interval; ROC, receiver operating characteristic; KM, Kaplan-Meier.

Detection of the classification effect of the prognostic risk model

The KM survival analysis indicated that the samples in the high risk group (OS, 769.1±713.3 days) had a significantly lower OS compared with those in the low risk group (OS, 1240.0±1030.1 days) (P=0.00097) (Fig. 4A). The ROC curve demonstrated that the prognostic risk scores were able to predict the 5-year survival of patients to a certain degree (AUC, 0.68; Fig. 4B).

Figure 4.

Figure 4.

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ROC curves and KM curves of the high and low risk groups classified by the prognostic risk score in the training dataset. (A) KM curve indicating that the samples in the high risk group had a significantly higher OS, and (B) ROC curve demonstrating that prognostic risk scores were able to predict the 5-year survival of patients. AUC, area under the curve; OS, overall survival; KM, Kaplan-Meier; ROC, Receiver operating characteristic.

Evaluation of prognostic risk as an independent prognostic factor

Cox univariate regression analysis demonstrated that certain clinical characteristics were associated with the OS of patients, including risk score (P<0.001), neoplasm recurrence (P<0.001), tumor-node-metastasis (TNM) stage (P<0.001), tobacco smoking history (P=0.028), M stage (P=0.030), and T stage (P=0.038) (Table II). However, Cox multivariate regression analysis suggested that only risk score (P<0.001), and tobacco smoking history (P=0.048) had potential as independent prognostic factors (Table III).

Table II.

Cox univariate regression analysis between prognostic risk scores/clinical characteristics and overall survival of patients.

Risk scores/clinical characteristics Hazard ratio Lower.95 Upper.95 P-value
Risk score 2.718 1.720 4.297 <0.001
Neoplasm recurrence 2.479 1.594 3.856 <0.001
TNM stage 2.254 1.402 3.625 <0.001
Tobacco smoking history 0.633 0.421 0.951 0.028
M stage 3.637 1.137 11.635 0.030
T stage 1.663 1.030 2.686 0.038
Target molecular therapy 0.689 0.409 1.160 0.161
Residual tumor 1.215 0.821 1.799 0.331
Radiotherapy 1.349 0.731 2.489 0.338
N stage 1.188 0.781 1.806 0.421
Gender 1.200 0.744 1.935 0.455
Age 1.016 0.671 1.538 0.939
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T, tumor; N, node; M, metastasis.

Table III.

Cox multivariate regression analysis between prognostic risk scores/clinical characteristics and overall survival of patients.

Risk scores/clinical characteristics Hazard ratio Lower.95 Upper.95 P-value
Risk score 3.747 1.765 7.957 <0.001
Tobacco smoking history 0.585 0.344 0.995 0.048
Neoplasm recurrence 1.486 0.871 2.535 0.146
M stage 3.026 0.550 16.640 0.203
TNM stage 1.711 0.658 4.451 0.271
T stage 0.838 0.325 2.161 0.714
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T, tumor; N, node; M, metastasis.

In addition, the correlation between prognostic risk scores and other clinical characteristics were evaluated. ROC curves demonstrated that prognostic risk scores were correlated with distant metastasis (pM) (P<0.001; Fig. 5A), tumor recurrence (P=0.038; Fig. 5B) and residual tumor (P=0.001; Fig. 5C). In particular, pM had the highest correlation with prognostic risk scores, indicating that the three prognosis-associated DELs may serve important roles in the distant metastasis of LUSC.

Figure 5.

Figure 5.

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Correlation between prognostic risk scores and other clinical characteristics. Receiver operating characteristic curves demonstrating that prognostic risk scores had a degree of correlation with (A) distant metastasis, (B) tumor recurrence and (C) residual tumor. AUC, area under the curve.

Assessment of the prognostic values of prognostic risk scores in the validation and universal sets

The three prognosis-associated DELs were validated in the validation and universal sets (including the test set and the validation set). In the validation set (P=0.0014; Fig. 6A) and the universal set (P<0.0001; Fig. 6B), the OS of patients in the high risk group was significantly decreased compared with the low risk group. Additionally, the ROC curves illustrated that the prognostic risk scores of the three prognosis-associated DELs were able to predict the 5-year survival of patients in the validation set (AUC, 0.61; Fig. 6C) and the universal set (AUC, 0.63; Fig. 6D). Though the AUCs of the ROCs predicting 5-year survival were small (0.61 and 0.63; Fig. 6C and D), the KM curves did illustrate an association between the three DEL-signature and survival (P=0.0014 and P<0.0001; Fig. 6A and B).

Figure 6.

Figure 6.

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Assessment of the prognostic values of prognostic risk scores in the validation and universal sets. Kaplan-Meier curves indicating that the OS of patients in the high risk group was significantly decreased for (A) the validation set and (B) the universal set, and receiver operating characteristic curves demonstrating that the three prognosis-associated differentially expressed long non-coding RNAs were able to predict the 5-year survival of patients in (C) the validation set and (D) the universal set. OS, overall survival; AUC, area under the curve.

Functional analysis of important lncRNAs

Using the MEM online tool and STRING database, co-expression genes of RP5-821D11.7 [including proliferating cell nuclear antigen (PCNA)], APCDD1L-AS1 [including semaphorin 5A (SEMA5A), semaphorin 6D (SEMA6D), ADAMTS like 1 (ADAMTSL1), ADAM metallopeptidase with thrombospondin type 1 motif 6 (ADAMTS6), slit guidance ligand 3 (SLIT3) and tenascin-C (TNC)] and RP11-277P12.9 [including Wnt family member 2B (WNT2B)] were screened. Enrichment analysis demonstrated that only the co-expression genes of APCDD1L-AS1 were enriched in certain GO terms and KEGG pathways (Table IV). Notably, ‘positive regulation of cell migration’ (P=0.048, involving SEMA5A and SEMA6D) and ‘proteinaceous extracellular matrix’ (P=0.017, involving ADAMTSL1 and ADAMTS6) were enriched.

Table IV.

The Gene Ontology terms and pathways enriched for the co-expression genes of APCDD1L-AS1.

Category Term P-value Genes
Pathway Axon guidance <0.001 SEMA5A, SEMA6D, NTNG1, NFATC4, SLIT3
ECM-receptor interaction 0.013 TNC, COL6A3, ITGA11
Olfactory transduction 0.043 OR4C13, OR5J2, OR1L6, OR8H2
Biological process Cell adhesion <0.001 NRP2, SEMA5A, COL7A1, TNC, COL6A3, ITGA11, COL8A1, CDH6
Negative chemotaxis <0.001 NRP2, SEMA5A, SEMA6D, SLIT3
Extracellular matrix organization <0.001 COL7A1, TNC, COL6A3, ITGA11, COL8A1
Semaphorin-plexin signaling pathway 0.002 SEMA5A, PLXNA4, SEMA6D
Collagen catabolic process 0.007 COL7A1, COL6A3, COL8A1
Proteolysis 0.014 CPA4, ADAMTS6, ADAMTSL1, PAPPA2, HTRA3
Facial nerve structural organization 0.017 NRP2, PLXNA4
Axon extension involved in axon guidance 0.023 NRP2, SLIT3
Detection of chemical stimulus involved in sensory perception of smell 0.046 OR4C13, OR5J2, OR1L6, OR8H2
Heart development 0.047 NRP2, FOXL1, NFATC4
Positive regulation of cell migration 0.048 SEMA5A, SEMA6D, LRRC15
Negative regulation of axon extension involved in axon guidance 0.048 SEMA5A, SEMA6D
Cellular component Extracellular matrix 0.003 COL7A1, TNC, COL6A3, COL8A1, SSC5D
Endoplasmic reticulum lumen 0.007 ADAMTSL1, COL7A1, COL6A3, COL8A1
Extracellular region 0.014 NRP2, PTHLH, COL7A1, TNC, COL6A3, PAPPA2, HTRA3, COL8A1, SLIT3
Proteinaceous extracellular matrix 0.017 ADAMTS6, ADAMTSL1, COL6A3, SLIT3
Semaphorin receptor complex 0.022 NRP2, PLXNA4
Molecular function Metallopeptidase activity 0.010 ADAMTS6, ADAMTSL1, PAPPA2
Syndecan binding 0.011 SEMA5A, TNC
Semaphorin receptor activity 0.023 NRP2, PLXNA4
Semaphorin receptor binding 0.043 SEMA5A, SEMA6D
Laminin binding 0.046 LRRC15, SSC5D
Olfactory receptor activity 0.047 OR4C13, OR5J2, OR1L6, OR8H2
Fibronectin binding 0.048 LRRC15, SSC5D
Chemorepellent activity 0.050 SEMA5A, SEMA6D
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Discussion

In the present study, a total of 2,041 significant DELs between LUSC and adjacent samples were identified. A prognostic risk model involving three prognosis-associated DELs (including RP5-821D11.7, APCDD1L-AS1 and RP11-277P12.9) was established. The prognostic risk scores were able to predict the 5-year survival of patients to a certain degree, indicating that the classification effect of the prognostic risk model was good. Cox multivariate regression analysis suggested that only prognostic risk score and tobacco smoking history had the potential to be used as independent prognostic factors. Besides, the prognostic risk model was validated in the validation set and the universal set. In addition, certain co-expression genes of RP5-821D11.7 (including PCNA), APCDD1L-AS1 (including SEMA5A, SEMA6D, ADAMTSL1, ADAMTS6, SLIT3 and TNC) and RP11-277P12.9 (including WNT2B) were screened.

The SLIT/ROBO signaling pathway acts by regulating tumor cell metastasis, and SLIT3 serves as a promising tumor suppressor gene in lung adenocarcinoma (LAD) (30,31). The expression of TNC, S100A10 and S100A11 may predict survival in patients with LAD, indicating that these factors may be promising markers for better diagnosis and therapy in LAD (32,33). TNC expression is markedly elevated in patients with NSCLC, implying that TNC may serve as a prognostic marker for NSCLC (34,35). Semaphorins are a large number of transmembrane, glycosylphosphatidylinositol-linked and secreted proteins that are able to suppress and promote tumors (36). Downregulated SEMA5A is correlated with poor survival in patients with NSCLC, thus SEMA5A may be a prognostic marker for the disease (37). In the present study, enrichment analysis suggested that SEMA5A and SEMA6D were enriched in ‘positive regulation of cell migration’. These results indicated that APCDD1L-AS1 may affect the prognosis of LUSC by affecting the expression levels of SEMA5A, SEMA6D, SLIT3 and TNC.

Functional enrichment analysis demonstrated that ADAMTSL1 and ADAMTS6 were enriched in ‘proteinaceous extracellular matrix’. The ADAMTS enzymes are zinc metalloendopeptidases that affect the structure and function of the extracellular matrix (ECM) (38). ADAMTSL-1 belongs to the family of ADAMTS-like proteins and may serve important roles in the ECM (39). The ECM is important in mesenchymal cancer cells; in particular, its compositional and structural alterations may affect metastasis of mesenchymal lung cancer cells (40). Dysregulation of certain ADAMTS proteinases may be directly implicated in tumor development and metastasis (41). Therefore, APCDD1L-AS1 may be associated with the prognosis of LUSC through ADAMTSL1 and ADAMTS6.

A previous study reported that PCNA is specifically targeted by miR-363-3p, and may inhibit tumor growth in LAD (42). PCNA may be inhibited by the small molecule AOH1160, which suppresses the growth of SCLC cells without inducing any unacceptable side-effects and may be used as a potential anticancer therapy (43). Dysregulated Wnt signaling functions in the progression and metastasis of lung cancer, and inhibitors of WNT signaling are promising for the treatment of the disease (44,45). WNT2 may promote NSCLC cell growth by activating the Wnt/β-catenin signaling pathway, suggesting that WNT2 may be a marker for the diagnosis and prognosis of NSCLC (46). Thus, RP5-821D11.7 (co-expressed with PCNA) and RP11-277P12.9 (co-expressed with WNT2B) may be used for predicting the prognosis of LUSC.

There are certain limitations to the present study. First, these findings resulted from bioinformatics analysis and require further validation. Since the sample and experimental conditions are insufficient, it is not possible to perform verification experiments at present. Furthermore, it was identified from the Cox univariate regression analysis that only neoplasm recurrence, risk score and T stage were associated with the overall survival of patients. Therefore, only these three indicators were included in the Cox multivariate regression analysis, and only neoplasm recurrence and risk score were associated with the overall survival of patients. It was hypothesized that this may result from the relatively small sample size and its unbalanced distribution. For example, the number of patients with T3 and T4 stages was significantly lower compared with T1 and T2 stages. Similarly, the number of patients with N2-N3 stages was significantly lower compared with N0 and N1 stages. In addition, only seven patients were M1, while 411 patients were M0. However, the present prognosis risk model may be a valuable tool for further research.

In conclusion, a total of 2,041 significant DELs were identified in LUSC samples. RP5-821D11.7 (co-expressed with PCNA), APCDD1L-AS1 (co-expressed with SEMA5A, SEMA6D, ADAMTSL1, ADAMTS6, SLIT3 and TNC) and RP11-277P12.9 (co-expressed with WNT2B) may be important lncRNAs associated with the prognosis of LUSC.

Acknowledgements

Not applicable.

Funding

No funding was received.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Authors' contributions

ZW conceived and designed the study. YL and ZX designed and performed data analyses. XZ collected the data and wrote the manuscript. PZ provided critical suggestion and organized the literature. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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