Literature DB >> 30288103

An expression signature model to predict lung adenocarcinoma-specific survival.

Xiaoshun Shi1,2, Haoming Tan3, Xiaobing Le4,5, Haibing Xian6, Xiaoxiang Li1, Kailing Huang4,5, Viola Yingjun Luo4,5, Yanhui Liu4,5, Zhuolin Wu7, Haiyun Mo8, Allen M Chen4,5, Ying Liang9, Jiexia Zhang1.   

Abstract

BACKGROUND: The current TNM staging system plays a central role in lung adenocarcinoma (LUAD) prognosis. However, it may not adequately stratify the risk of tumor recurrence. With the aid of gene expression profiling, we identified 31 lncRNAs whose expressions in tumor tissues could be used as a risk indicator for the guidance of lung cancer therapy. This exploratory analysis may shed new light on identification of potential prognostic factors.
MATERIALS AND METHODS: A survival prediction scoring model was developed from the data that are publicly available in The Cancer Genome Atlas (TCGA) LUAD RNA Sequencing dataset. Multivariate Cox regression analysis and Kaplan-Meier analysis were performed on a cohort of 254 stage I lung carcinoma patients with survival records.
RESULTS: Our model indicates that the panels comprising 31 lncRNAs are highly associated with overall survival (OS): 18.9% (95% CI: 10.4%-34.5%) and 89.5% (95% CI: 80.7%-99.2%) for the high- and low-risk group, respectively. The specificity and sensitivity of the model are verified, which show that the area under receiver operating characteristic curve yields 0.881, meaning our model has good accuracy and it is feasible for further applications.
CONCLUSION: The 31-lncRNA model might be able to predict OS in patients with LUAD with high accuracy. Its further applications in biomolecular experiments using clinical samples with independent cohorts of patients are needed to verify the results.

Entities:  

Keywords:  RNA-seq; lncRNA; lung adenocarcinoma; prognosis; signature; survival analysis

Year:  2018        PMID: 30288103      PMCID: PMC6161724          DOI: 10.2147/CMAR.S159563

Source DB:  PubMed          Journal:  Cancer Manag Res        ISSN: 1179-1322            Impact factor:   3.989


Background

Lung adenocarcinoma (LUAD) is the most commonly occurring histological type of lung cancer, which often has major driver oncogenes such as EGFR mutation1 and ALK fusion.2 Previous studies have shown that the genomic alterations in LUAD are different from those in other lung cancer subtypes.3,4 The current prediction systems for non-small-cell lung cancer (NSCLC) such as tumor, node, and metastases (TNM) staging5 and microarray-data-based prognostic modeling6 have not effectively distinguished lung cancer subtypes in terms of NSCLC patient survival. Moreover, the prognoses within the same TNM stage vary widely7 and the gene signature is yet limited in coding genes8 and microRNAs.6,9 Evidence provides the primary rationale to develop the lncRNA model for predicting lung cancer survival. lncRNAs are a class of RNA molecules with more than 200 nucleotides in length and have no evident open reading frames.10 These long molecules play key roles in gene regulation and carcinogenesis including proliferation, adhesion, migration and apoptosis.11 Current non-coding RNA profiling research reveals that lncRNAs are dysregulated among cancers12 and some may serve as promising therapeutic targets.13 For instance, Gutschner et al reported that elevated MALAT1 expression was associated with metastasis in multiple tumor types.14 Previous meta-analysis showed that MALAT1 may have a role in cancer prognosis.15 It is believed that the clinical value of lncRNA is not confined to candidate biomarkers for diagnostic and prognostic purposes. Also, lncRNA expression profiling by RNA sequencing (RNA-seq) might be useful in the classification of various cancers such as lung cancer; it is important for prognostic determinations as well.12 A large number of lncRNAs have been investigated in cancer research.11,16 Some of these lncRNAs are associated with patient survival,17,18 but most of the reports are only supported by clinical survival data from samples within a single institute19 or pooled clinical data with some het-erogeneities.20 Given the heterogeneity of LUAD and the susceptibility of non-coding RNA decay, a panel of the lncRNA biomarkers should be more precisely stable for LUAD prognosis determinations. However, using only a single lncRNA would yield unreliable results in predicting cancer survivals. Previous studies have also observed that gene signatures and clinical characteristics among patients are associated with overall survival (OS)21,22 aiming to overcome the limitations of the current TNM staging for predicting clinical outcomes. Several studies have reported that lncRNA expression profiles can be obtained from publicly available microarray data to perform analysis for the development of cancer survival models.23,24 However, RNA-seq-based prognostic lncRNA expression signature for the prediction of LUAD patient survival has not yet been investigated. Though a number of prognostic lncRNA biomarkers for NSCLC have been proposed,25–27 none of them have been successfully applied in real clinics. This is partially due to differences in the acquisition of samples and the usage of different systems for detection. In addition, the populations selected for lncRNA studies of cancer may vary and display inconsistencies. Moreover, the potential role of lncRNA as biomarkers for diagnosis and prognosis is better understood by the patterns of the lncRNA expression profile in the genome rather than as a single lncRNA expression abnormality. Notably, as we mentioned previously, the genomic characteristics of LUAD and squamous cancer distinguish greatly,4 and previous studies identifying the signature pattern of NSCLC may be modified further by separate analysis of each cancer subtype. To construct a reliable prognostic lncRNA signature that could improve the current staging system for predicting LUAD survival, we identified lncRNAs that can stratify the risk of LUAD recurrence through survival outcomes. RNA-seq data and corresponding clinical data were analyzed to identify lncRNAs that associate with the risk of LUAD recurrence in patients. A panel of key lncRNAs was identified by next generation sequencing technology, which could diagnose one of the major histological subtypes of lung cancer with fairly high specificity and sensitivity. We developed a risk score formula for predicting the OS time of LUAD patients. In summary, the use of the lncRNA signature provided a deeper insight into the parameters associated with LUAD than what is used exclusively for LUAD prognosis.

Materials and methods

Lung adenocarcinoma RNA-seq data from TCGA

Level 3 RNA-seq data (HTSeq-FPKM-UQ) and the corresponding clinical data of 519 LUAD patients were obtained from the public The Cancer Genome Atlas data portal website (http://cancergenome.nih.gov). Clinicopathological parameters including age, gender, smoking history and TNM stage were also assessed. Patients with incomplete clinical data or OS of less than 1 month were excluded from the analysis. No correlations between patients’ gender as well as expression profile and OS were found; after data filtering and exclusions, a total of 462 LUAD samples comprising 250 females and 212 males were enrolled in the model.

Identification of differentially expressed lncRNAs in LUAD and normal lung tissue samples

To identify lncRNAs that are differentially expressed between LUAD and normal lung tissues, the raw counts of TCGA RNA-seq data (HTSeq-Counts) were downloaded for the analysis. Differential expression analysis was performed using the DESeq package in Bioconductor.28 The thresholds for screening the expression differences of lncRNAs were adjusted p-value <0.01 and |log2(fold change)|>2. The log2(fold change) indicates the fold change in the expression of each lncRNA between LUAD and normal lung tissue samples.

Cox regression analysis

First, the RNA-seq expression values of differentially expressed lncRNAs were normalized with log2 transformation. Afterward, the association between lncRNA expression and patient survival was determined by univariate Cox regression analysis. lncRNAs with a p-value less than 0.05 from the univariate Cox regression analysis were used for further mining potential lncRNAs that were associated with OS time, and they were fitted in a multivariate Cox regression analysis. The mathematical model was built based on the Akaike information criterion, which allows determination of the best trade-off between the complexity of model and its goodness of fit.29

Risk score and survival curve

Based on the multivariate Cox regression analysis, a formula (Equation 1) was built to predict the risk score for each patient. In Equation 1,G represents expression value of the ith lncRNA and Weight is the coefficient of each lncRNA from the Cox analysis results (Table 1). According to this risk scoring system, patients were divided into low-risk (< median risk score) and high-risk (> median risk score) groups. Subsequently, the log-rank statistical test was used to determine the differences in survivals between the low-risk and high-risk groups. A Kaplan–Meier OS curve was plotted against the two groups and the hazard ratio was calculated. Cox multivariate analysis was employed to test whether the risk score was independent of potential clinical risk factors including age, gender, smoking history and disease stage. The prognostic performance was measured by calculating the area under the receiver operating characteristic curve.
Table 1

31-lncRNA risk score model

Ensembl IDlncRNACoefficientsUnivariate p-valueMultivariate p-valueReferences
ENSG00000249364RP11-434D9.1−0.22951.44E-066.48E-0530
ENSG00000257654RP11-497G19.2−0.13813.35E-052.43E-05
ENSG00000183674LINC005180.16044.41E-052.02E-0531
ENSG00000247844CCAT10.087805.37E-057.05E-033234
ENSG00000238078LINC01352−0.15995.83E-057.40E-04
ENSG00000235997AC109642.10.27978.53E-055.92E-02
ENSG00000233760AC004947.20.10266.57E-041.66E-01
ENSG00000267123CTD-2357A8.30.31686.98E-041.62E04
ENSG00000253227RP11-383J24.1−0.065227.17E-045.18E-02
ENSG00000268388FENDRR0.44771.08E-033.77E-0335
ENSG00000237803LINC00211−0.076371.37E-037.11E-02
ENSG00000271830RP11-1C8.70.085412.23E-031.84E-03
ENSG00000245750DRAIC0.135672.33E-035.98E-0236
ENSG00000227307RP11-95I16.20.087322.62E-032.00E-02
ENSG00000257883RP11-497G19.10.071326.07E-036.61E-02
ENSG00000248538RP11-10A14.5−0.093447.01E-036.07E-02
ENSG00000223414LINC004730.060388.77E-035.45E-0225
ENSG00000180861LINC01559−0.047189.98E-031.44E-01
ENSG00000253288RP11-238K6.1−0.081471.04E-025.15E-02
ENSG00000270977AC015849.160.24141.13E-025.58E-03
ENSG00000236164RP11-268F1.30.057351.42E-029.19E-02
ENSG00000260468LINC01290−0.17281.58E-022.33E-03
ENSG00000268754RP11-514D23.2−0.056191.62E-028.82E-02
ENSG00000258474RP11-187E13.1−0.098781.82E-022.81E-03
ENSG00000241544RP11-6F2.50.16232.01E-024.25E-04
ENSG00000236452AC123023.10.044422.09E-021.32E-01
ENSG00000244649CTD-2377D24.6−0.078782.41E-021.14E-01
ENSG00000249241AC195454.1−0.12143.37E-025.00E-03
ENSG00000280776LINC012020.10153.37E-026.94E-03
ENSG00000274956UG0898H090.055794.16E-021.32E-01
ENSG00000225546RP11-328J2.1 LVCAT5−0.094124.84E-027.87E-03

Notes: The panel comprised 31 lncRNAs: RP11-434D9.1, RP11-497G19.2, LINC00518, CCAT1, LINC01352, AC109642.1, AC004947.2, CTD-2357A8.3, RP11-383J24.1, FENDRR, LINC00211, RP11-1C8.7, DRAIC, RP11-95I16.2, RP11-497G19.1, RP11-10A14.5, LINC00473, LINC01559, RP11-238K6.1, AC015849.16, RP11-268F1.3, LINC01290, RP11-514D23.2, RP11-187E13.1, RP11-6F2.5, AC123023.1, CTD-2377D24.6, AC195454.1, LINC01202, UG0898H09 and RP11-328J2.1.

Results

Differentially expressed lncRNAs in LUAD patients

The analysis of the lncRNA expression profiles in both LUAD tissues and normal lung tissues identified a total of 346 differentially expressed lncRNAs, which were used for subsequent survival analyses (Table S1). Compared to normal samples, 249 lncRNAs were overexpressed and 97 lncRNAs were underexpressed in LUAD samples. A cluster dendrogram was generated to ensure that the differentially expressed lncRNAs were good characterizations of LUAD (Figure S1).

The association of lncRNA expressions and OS time

To identify the lncRNAs associated with patient survival in LUAD, univariate Cox regression analysis for the differentially expressed lncRNAs data was assessed. With the significance level threshold of 0.05, a set of 60 lncRNAs was selected (Table S2). These lncRNAs were used in stepwise multivariate Cox regression analysis and 31 lncRNAs were chosen. A cluster dendrogram for these 31 lncRNAs is shown in Figure 1. We conducted a risk score analysis using Equation 1 on the 31 lncRNAs to calculate risk scores for patients. The coefficients of the 31-lncRNA model for determining risk scores are listed in Table 1.
Figure 1

Hierarchical cluster dendrogram of selected 31 lncRNAs from TCGA LUAD RNA-seq dataset. The left vertical axis shows clusters of lncRNAs. The red rectangular strip in the upper portion of the picture represents normal tissue samples, and the light blue rectangular strip denotes LUAD samples. Red rectangles represent overexpressed genes, and green rectangles represent under-expressed lncRNAs. Black rectangles represent median-expressed lncRNAs.

Abbreviations: TCGA, The Cancer Genome Atlas; LUAD, lung adenocarcinoma; RNA-seq, RNA sequencing.

Stage prognostic classifiers

Tumor stage classification was significantly associated with OS of LUAD patients. In order to test whether the 31-lncRNA model is applicable for the prediction of LUAD survival in all stages, we calculated the risk scores for patients in all stages and divided the patients into high-risk and low-risk groups according to the median risk score of patients in all stages (value=1.111). Kaplan–Meier survival curves are displayed in Figure 2, showing that the 31-lncRNA model performs well for LUAD in all stages (p-value=8.508e-11).
Figure 2

Survival curves for LUAD patients in all stages using 31-lncRNA model. The differences between the high-risk (n=222) and low-risk (n=240) groups were determined by the log-rank test.

Abbreviation: LUAD, lung adenocarcinoma.

Survival times of low-risk and high-risk groups in stage I

Since tumor stage serves as an important factor that independently affects the survival of LUAD patients, we applied the 31-lncRNA model to stage I patients in order to test the effectiveness of the survival prediction. We divided the patients into high-risk and low-risk groups using the median risk score value 1.111, and found that the high-risk group correlated with poor prognoses for OS (log-rank test p-value 8.917e-13). Five-year OS was 18.9% (95% CI: 10.4%–34.5%) and 89.5% (95% CI: 80.7%–99.2%) for the high-risk and low-risk groups, respectively. The Kaplan–Meier OS curves as well as ROC curves (Figure 3) indicated that the AUC of the 31-lncRNA model was 0.881 (Figure 4), which demonstrated that the 31-lncRNA model has high specificity and sensitivity in predicting the OS time of LUAD patients.37
Figure 3

Survival curves for stage I LUAD patients through the 31-lncRNA model. The differences between the high-risk (n=126) and low-risk (n=126) groups were determined by the log-rank test.

Abbreviation: LUAD, lung adenocarcinoma.

Figure 4

ROC analysis of the 31-lncRNA model. AUC of the 31-lncRNA model was 0.881.

Abbreviations: ROC, receiver operating characteristic; AUC, area under the ROC curve.

Discussion

In present study, lncRNA expression data and clinical data were obtained from the public TCGA database (HTSeq-FPKM-UQ) of LUAD level 3 RNA-seq. A total of 254 stage I LUAD patients were included. lncRNAs associated with survival were identified by stepwise multivariate Cox regression analysis. Subsequently, an expression pattern of 31 lncRNAs was found to be significantly associated with OS of LUAD patients. The 31-lncRNA panel accurately predicted OS and was applied to conduct a risk score analysis for further investigation of the specificity and sensitivity. ROC analysis results indicate that the statistical power of 31-lncRNA model for high-risk and low-risk patients is formidable. Therefore, the 31-lncRNA signature predicts OS fairly well based on TCGA data sets, which shows its good predictive performance. The AUC was 0.881, which is better than previously reported results. Some of the lncRNAs that are predicted in our analysis were shown previously to function as potential biomarkers. LINC00473 has been found to correlate with poor NSCLC prognosis.25 Its overexpression is required for the growth and survival of LKB1-inactivated NSCLC cells through CREB-regulated transcription coactivator/CREB-mediated transcription.25 In addition, four lncRNAs including RP11-434D9.1 were found to be differentially expressed in microarray analyses.30 RP11-434D9.1 is correlated with TNBC occurrence, and is a potential biomarker for diagnosis for breast cancer treatment.30 Notably, one lncRNA, CCAT1, was reported to be associated with a variety of solid tumors. Abnormal expression of CCAT1 has been shown in a variety of tumors including lung cancers.34 Abnormally expressed CCAT1 can promote proliferation, migration and invasion in hepatocellular cells,32 gastric carcinoma cells38 and colon cancer cells.39 Currently, some evidences show that CCAT1 acts as a driver lncRNA of malignancy through miRNA sponging. CCAT1 also functions as a molecular sponge for let-7c in docetaxel-resistant LUAD cells,33 and miR-155 in acute myeloid leukemia HL-60 cells. Therefore, CCAT1 may hold an important role in the carcinogenesis of LUAD. Our lncRNA model includes cancer-associated lncRNAs that provide reference values for investigators and give eventuality of explorations in this perspective. The risk score of the 31-lncRNA signature model was found to have an independent correlation with OS (p-value <0.001) (Table 1). Moreover, we found that one of the clinicopathological parameters of LUAD in the TCGA data, i.e., tumor staging, was significantly associated with OS,5 which was consistent in clinical practice. Furthermore, tumor staging does not efficiently stratify the risk of early stage (stage I and II) LUAD patients. Since this parameter may affect the predictive performance of the model for cancer survival, we further tested the model in stage I LUAD patients. We calculated the risk score for each early stage patient, and the results indicate that the 31-lncRNA signature model is potentially a prognostic classifier for early stage LUAD patients (p-value <0.001). This suggests that patients in stage I and II could be divided into high-risk and low-risk groups by their lncRNA signatures. The results imply that patients with LUAD may benefit from this prognostic signature model, which could determine timeline of adjuvant chemotherapy treatments. There is a speculation that further treatments such as adjuvant therapy will alter the effectiveness of the predictions; however, relevant modifications could be adapted accordingly under the proposed framework. Using the updated TCGA data (June 2016 version), we were able to obtain 60,483 data points including both protein coding and non-coding genes. Many of these genes are known biomarkers and some are novel genes with survival records. This broadens our scope in gene signature modeling for cancer survival prediction. The prognostic power of the signature model in this study is applied for predicting OS of stage I patients. Moreover, since these lncRNAs might have a predicative role in the outcome of LUAD, further experimental studies to survey the biological roles of these lncRNAs in carcinogenesis are worthwhile to shed new light on specific investigations. Present results show that the current prognostic model is promising and further validations on independent datasets are still needed. In addition, co-regulatory relationship between these 31 lncRNAs will affect the model efficacy, which would be validated furthermore. Based on this work, further analysis such as expression network and co-expression analysis could be applied. This model is not without limitations. First of all, the data that we could access and analyze are still limited. The TCGA data involved fresh frozen samples, which were collected from top-notch institutions with robust tissue collection systems in place. Thus, only samples that were found to be of very high quality were included. Data in an average setting are expected to be applied by the model to further verify its robustness. For instance, further available data could be applied to analyze the correlation between the lncRNA profile and etiology such as smoking by patients or air pollutant profile of the local area of Guangzhou as well as the expression pattern. With more relevant data, a complete co-expression network could be derived.

Conclusion

In this study, we developed a signature model (Table 1) that is associated with OS in LUAD patients. Patients with a high-risk score from the model have shorter survival time, and this lncRNA panel could help to serve as a prognostic classifier for LUAD. The results of this study suggest that these lncRNAs may play specific roles in the carcinogenesis of LUAD and be of potential prognostic values. Further experiments are needed to verify the connection of 31 lncRNAs to cell survival and apoptosis linking with DNA repair. This model is LUAD specific, and proper adjustments could be made for reference of other disease-associated models.

Availability of data and material

All data generated or analyzed during this study are enclosed in this article and the supplementary information files. Hierarchical cluster dendrogram of all differentially expressed lncRNAs from TCGA LUAD RNA-seq dataset. The horizontal axis shows clusters of samples, and the left vertical axis shows clusters of lncRNAs. The red rectangular strip in the upper portion of the picture represents normal tissue samples, and the light blue rectangular strip denotes LUAD samples. Red rectangles represent overexpressed genes, and green rectangles represent underexpressed lncRNAs. Black rectangles represent median-expressed lncRNAs. Abbreviations: TCGA, The Cancer Genome Atlas; LUAD, lung adenocarcinoma; RNA-seq, RNA sequencing. Differentially expressed lncRNAs Abbreviations: LUAD, lung adenocarcinoma; pval, p-value; padj, adjusted p-value; Inf, infinity. Univariate Cox regression result Abbreviations: HR, hazard ratio; Pr, probability.
Table S1

Differentially expressed lncRNAs

IDNormal meanLUAD meanFold changelog2 fold changepvalpadj
FENDRR2981.044549202.04540630.067776715−3.883066479657062.06E-582.35E-55
C14orf1324536.086751729.84252310.16089695−2.6357911168621.26E-364.41E-34
LINC012722806.558749499.38033160.177933326−2.490591353208542.22E-264.10E-24
RP3-340N1.28.089904837537.042803566.384316556.0527705371.41E-221.94E-20
RP11-88I21.2158.6252165.0821948660.03203901−4.96402662464491.01E-211.29E-19
LINC00511137.63153051157.5829668.4107396143.0722326724.17E-183.69E-16
RP11-141J13.5193.01159158.8682274790.045946606−4.443897905238021.19E-179.84E-16
PCAT191231.988315230.53316460.18712285−2.417942355271043.12E-162.18E-14
FAM83H-AS1394.5773061902.4089474.82138462.2694475177.75E-154.72E-13
LINC00968385.907781133.728476920.087400355−3.516217056118033.04E-141.72E-12
AP002856.5115.04952236.6472086220.057776934−4.11336254941811.39E-137.17E-12
RP11-284F21.96.141918547250.677217340.814155275.3509976931.49E-137.64E-12
RP1-78O14.1574.649188463.413313620.11035135−3.179823817895571.96E-139.89E-12
RP11-287F9.234.988635241.4248147830.040722217−4.61804009674344.68E-132.24E-11
GS1-600G8.5327.013583329.481150080.090152677−3.47148585151935.48E-132.61E-11
RP11-805I24.377.737134934.5911157550.059059493−4.081687224933317.25E-133.42E-11
MIR3945HG257.201385821.764166650.084619166−3.562871730187018.24E-133.83E-11
PVT1109.9042973731.34044746.654338962.7342953561.61E-127.22E-11
LINC01314581.679782266.862033920.114946464−3.120966013442353.42E-121.48E-10
CASC93.877102467177.208169245.706341465.5143224393.74E-121.61E-10
LINC00665225.05987831100.5602914.8900776972.2898573885.82E-122.46E-10
LINC0108242.5767072.2704634380.053326422−4.229005662101139.14E-123.78E-10
AC011286.118.263333480.757094580.041454348−4.592332764249821.90E-117.58E-10
AC128709.235.809408141.72014110.04803601−4.379739857966223.36E-111.28E-09
AC109642.1701.5189391129.61155140.184758449−2.436287751940374.68E-111.73E-09
RP11-401P9.4736.6771455152.0740510.206432427−2.276258483936776.33E-112.32E-09
RP11-89K21.10.76418720480.81937877105.7586136.7246313519.96E-113.54E-09
RP5-839B4.8302.515535737.55048440.124127458−3.010105810565473.03E-101.00E-08
RP11-81H3.20.33209987549.41633002148.79960447.2172268816.11E-101.93E-08
CH17-360D5.2501.271812685.98825720.17154018−2.543381551426952.02E-095.82E-08
CTD-2139B15.50.04996209925.1743678503.86929648.9769057372.84E-097.97E-08
LINC00551114.744477313.634090130.118821319−3.073134387710136.51E-091.73E-07
LINC0155242.825761794.1104386910.095980515−3.381114628949936.91E-091.83E-07
LINC009731.12176760282.021518473.118102416.1921567248.97E-092.31E-07
LINC0016348.62141474.528373180.093135364−3.424527111341831.79E-084.34E-07
CTD-2008P7.80.16878907125.75784117152.60372627.2536463791.80E-084.35E-07
BARX1-AS10.20011969130.13316326150.57570337.2343451872.10E-085.02E-07
AC004947.293.7951564211.538950410.123022881−3.023001422609122.19E-085.22E-07
LINC0065638.021050373.3226977590.087391004−3.516371415495052.27E-085.39E-07
AC005256.1011.56605236InfInf2.37E-085.59E-07
AC008268.11184.201055267.4151880.225819076−2.146760732241542.67E-086.24E-07
RP11-400N13.23.932808365107.659825327.374795634.7747762893.36E-087.74E-07
RP11-211G23.20.47189847138.796205682.213035256.3612952533.56E-088.16E-07
RP11-2N1.310.64618690.6614859020.062133598−4.008482579994293.79E-088.62E-07
AC098973.20.40339982136.8049946491.237012846.5115473083.82E-088.69E-07
CTD-2527I21.150.88452219455.0471450262.233763515.9596255894.17E-089.39E-07
CTD-3010D24.32.06192590882.656144540.086864525.3250576734.24E-089.50E-07
LINC00961259.020469939.366544360.151982368−2.718024134518794.56E-081.02E-06
CTB-43E15.148.609210985.1106977580.105138464−3.249637537549137.42E-081.61E-06
MNX1-AS12.12858507378.367232536.816584645.2022838937.67E-081.66E-06
RP5-826L7.123.565761432.1829867740.092633832−3.432317002309429.18E-081.95E-06
MIR548XHG0.0544899413.88864612254.88459127.9937003491.35E-072.80E-06
CTD-2591A6.20.01297967210.89487903839.38016389.713180561.46E-073.00E-06
RP11-474D1.30.71763177152.3136505212.24485457.7295857681.58E-073.20E-06
BLACAT132.50389347250.16562047.6964816732.9441990911.66E-073.36E-06
RP11-253E3.3516.4366306115.95018520.224519676-2.155086211.70E-073.43E-06
LINC011940.29528210131.6709472107.25657646.7449222981.82E-073.65E-06
LINC008581.7045354660.0346396335.220528445.138344652.13E-074.23E-06
RP11-353N14.22.49426036378.100597831.312127224.9686496182.98E-075.76E-06
C11orf97107.060252319.126087940.178647888−2.484809237840063.06E-075.89E-06
AC011294.32.37638352671.2764143829.993649424.9065851653.64E-076.86E-06
LUCAT127.88216156254.27140429.1195011423.1889549083.68E-076.94E-06
LINC009424.510955617430.156250995.358120846.5752839014.14E-077.73E-06
LINC004910.43479170326.1225731160.080661475.9088287914.25E-077.92E-06
RP11-209K10.20.05375138911.90773641221.53355737.7913814414.73E-078.73E-06
RP4-594A5.10.0975643413.18263545135.11735377.0780691685.00E-079.19E-06
XXbac-BPG27H4.822.549557281.9923591950.088354692−3.500549434975475.30E-079.70E-06
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RP11-496D24.20.2592060887.07385810727.290478244.7703257720.00052140.004506692
CTD-2319I12.57.1855325931.1599485840.161428338−2.631034236041410.0005280.004557892
RP11-108K3.10.3473765067.63322456121.97392294.4577205450.0005290.004563319
LINC005010.54584666626.3644289548.30006415.5939531990.00053010.004571455
AC145343.26.88866098757.919714778.4079786893.0717590130.00053360.004597971
LINC012970.0271772253.503684652128.91988257.0103309690.00053770.0046262
AC005537.22.3385202829.9991253612.828251113.6812525940.00056270.004812372
AC092415.10.0938880984.07426856643.394942175.4394549960.00056380.004819731
RP5-907D15.40.93534097914.8277677315.852793863.9866652150.00056750.004844316
RP11-53B5.11.67084942320.0764275312.015701273.5868489450.00058470.004966619
CTD-2515H24.245.6383862910.576239170.23173999−2.109421073772290.00058750.00498714
CCAT11.57287057873.3509284746.635069365.5433433580.00058920.004996469
RP11-1070N10.70.1842493985.28701627528.694890374.8427219570.00060080.005081898
RP11-161I6.21.25904923220.4402836316.234697674.0210086130.00060380.005106069
RP11-13E5.201.439087312InfInf0.00060960.005152781
LINC0003217.365341033.7404164730.215395509−2.214939924144320.00065770.005502384
UG0898H091.36506554534.2851199325.116097944.6505404380.00065770.005502384
RP11-114H21.20.0246376552.19091557388.925492056.4745251470.00066040.005520144
RP11-6F2.50.3929109877.81866164919.899320474.3146472610.00066740.005571454
RP11-57A19.24.44816380639.381558818.8534416743.1462383950.0006840.00568161
AF131215.825.386611525.401865290.212784021−2.23253827605690.00070260.005814437
LINC008802.83311666428.8737140910.191501983.349294780.00070410.005824498
RP11-412P11.10.0130771854.423395808338.25289988.4019584920.00070790.005852146
RP11-416I2.14.15394136339.736196129.565902033.2579010160.00071340.005890063
AC027119.10.080122862.9298262636.566671135.1924573910.00073560.006052435
CTD-2532K18.20.1523145485.16711969833.924006375.0842346550.00074930.006147782
RP11-608O21.10.1273403484.02494492631.60777394.9822075260.0007510.0061573
LINC012080.1599661715.49852296534.373035975.1032053770.00075120.006157921
LINC013850.0248401815.014651394201.87660077.6573298890.00075820.006200559
RP11-335L23.519.402229313.873567070.199645464−2.324487802424420.00078930.006420716
CTD-2066L21.30.3268989317.46837769722.846136794.5138783250.00079410.006453602
RP11-527D7.10.345135897.52486053421.802602244.4464284320.00080170.006510187
CTD-2357A8.38.52705432462.082089457.280602082.8640577610.00080230.006512962
WASIR22.34919553927.019806811.501727443.5237786510.00081210.006572905
CTD-3224I3.35.7617090051.0858245570.188455293−2.407705781803680.00082450.006656382
LINC001601.20849053816.4989628313.652537863.7710972520.00082440.006656382
RP11-1070N10.50.3793871948.07513587421.284682254.4117436480.00082880.006685208
RP11-431M3.101.27573314InfInf0.00083020.006693217
RP11-439L18.117.51533543.4414070640.196479655−2.347548159426620.00085720.006878542
CTD-2227E11.17.43669894655.333026737.4405360682.8954065670.00085960.006894349
AC073316.20.80312201812.8809401616.038584274.0034748950.00086360.006922829
AP000696.20.1223087343.88845579531.792134984.9905979980.00089160.007112504
AC113617.10.0342734782.53229175273.884878366.207207220.00089270.007118458
CTD-3193K9.1116.92766943.6464997930.215416529−2.214799139466230.00090940.007223302
LINC002210.77510309129.9302964338.61460085.2710745520.00094620.007463356
AC009236.201.201393756InfInf0.00095770.00754877
RP1-15D23.27.5181561561.3498565650.179546226−2.47757276596960.00096740.007613436
RP11-396O20.20.3528268396.43801848318.246963574.1895845040.00097190.007642504
RP11-180I4.410.413793312.0944116290.201118993−2.313878760201480.00098380.007723993
RP11-44F21.533.20806617153.80972044.63169762.2115410640.00099390.007794766
RP11-1038A11.11.08388372314.9810380913.821628433.7888556960.0010230.007985538
CTD-2384A14.10.2536550095.85117974123.067471674.527787980.00103180.0080419
RP11-297L17.20.0837417997.53974766490.035654246.4924245190.00103780.008074388
RP11-57A1.10.487858619.63851856919.756786844.3042764270.00105880.008208608
C2orf484.0144213932.87556888.1893666883.0337518880.00106740.008269797
RP11-237N19.30.1605425714.90064668730.525527654.9319443280.00108170.008358608
LINC0021111.413444772.3604409740.206812318−2.273605974822630.00108230.008359735
LINC0122414.433768183.982664775.8184851112.5406435850.00115960.00886389
LINC0116610.835874712.0295003020.187294552−2.416619158131360.00117170.008935899
AE000662.930.3394974076.71788501519.787735884.3065346430.00119330.009078381
RP11-542G1.10.1375757424.00579972829.117049734.8637922780.00119580.009090409
RP4-712E4.10.1646680154.76142941428.915326564.853762490.00120480.009148073
AC123023.115.037461873.1826789610.21165001−2.240247535213890.00120520.009148937
LINC0116926.070346035.9091363810.226661218−2.141390530411610.00120910.009161157
LINC015976.843536565120.529736617.612200284.138503250.00121020.009165339
XX-C2158C6.30.195736414.6383975423.697162694.5666424270.00123790.009354967
AC012531.250.88073150212.401313414.080696993.8156468430.00126030.0094977
RP11-356N1.226.459554156.4503649880.243782074−2.036336050284180.00128280.009626315
CTA-384D8.346.6735563348.209967727.2240294882.8528037820.00129110.009682676
RP11-815M8.128.95577384129.77641634.4818838912.1641052740.00129710.009713388
LINC004703.49816186131.803289769.0914288763.1845070570.00132140.009880132
RP11-414H23.30.1208943885.069478641.933117845.3900181970.00132240.009886058
LINC011688.1849133161.4978087140.182996283−2.450113749800040.00132930.009929169
RP11-513G11.20.2457117275.52218135322.474227924.4901996490.00134060.009996557

Abbreviations: LUAD, lung adenocarcinoma; pval, p-value; padj, adjusted p-value; Inf, infinity.

Table S2

Univariate Cox regression result

IDCoefficientHR95% lower confidence limit95% upper confidence limitz-scorePr(>|z-score|)
RP11-434D9.1−0.09667419723750590.9078517390.8728529090.944253918−4.819601246048181.44E-06
RP11-497G19.2−0.04734394610210220.9537592990.9326613580.975334502−4.148230782025113.35E-05
LINC005180.0624261221.0644158191.0330045621.0967822184.0846246114.41E-05
CCAT10.0615249251.0634570021.0321763971.0956855824.0390300455.37E-05
LINC01352−0.07752595865212170.9254030020.8910744730.961054033−4.019651079467295.83E-05
AC109642.1−0.1986048268485370.8198738210.7425424290.90525882−3.929106490365938.53E-05
RP11-21B23.20.0474483811.0485920721.0230325341.0747901933.7685606270.000164192
LINC00968−0.1124569592194120.8936358090.842577790.947787811−3.746436561765880.000179364
C20orf197−0.1003366124752740.904532890.8559761720.955844071−3.564152775548240.000365033
RP5-839B4.8−0.05579506140013760.9457329330.916259240.976154719−3.453988421203030.000552361
AC004947.2−0.07067757120134290.9317622710.8946357930.97042946−3.406838668991990.0006572
RP11-89K21.10.0594346351.0612363911.0255172621.0981996293.4024017720.000667964
CTD-2357A8.30.133697011.1430464361.0580239251.2349013333.3901869230.00069845
RP11-383J24.10.0454364931.0464845431.0192969771.074397283.3830769370.000716785
RP1-78O14.1−0.1158179844832360.8906373180.8318974080.953524828−3.327058057406350.000877681
RP11-805I24.3−0.05985645322699070.9418997310.9090162630.97597275−3.301356935979980.000962184
RP11-284F21.90.0849833991.0886989931.0350895421.1450849883.2986014240.000971678
FENDRR−0.1354642970170850.8733103420.805174590.9472119−3.268485921980450.001081246
CTD-2515H24.2−0.06557505616166840.9365287520.9002296330.974291526−3.251293459876590.001148812
RP11-403A3.3−0.05707177826297880.944526270.9123762110.977809225−3.230006649049040.001237873
LINC00211−0.05619091550822470.9453586350.9133780130.978459012−3.200164487865220.001373492
RP11-297L17.20.0435637021.044526531.0157862061.0740800233.0602449190.002211561
RP11-1C8.70.0346381561.0352450431.0125141541.058486243.0578591170.002229244
LINC008570.2489790021.2827150981.0931488011.5051546713.0515261470.002276812
DRAIC−0.08004540622345640.9230744320.8767209860.971878649−3.045086031642890.002326138
RP11-95I16.2−0.04137771581154120.9594666560.9339516030.985678766−3.008904578231070.002621915
RP11-290F5.1−0.1334211686966280.8750964510.7996642720.957644134−2.900981836522880.003719954
AC018647.3−0.09136804298353090.9126817430.8564565750.972598014−2.81641835057130.004856238
RP11-414H23.30.0385433251.0392957551.011152991.0682217992.751833470.005926265
RP11-497G19.1−0.03485324203546570.9657471370.9420018240.990091005−2.743987814401220.006069779
RP11-10A14.50.0759599511.0789193641.020963911.1401646852.6964557990.00700817
LINC004730.0329408861.0334894441.0083406191.05926552.6207965670.008772459
LINC015590.0405810861.041415751.0097581111.0740659112.5765063270.009980438
RP11-238K6.1−0.04075670823817660.9600626770.9306097420.99044777−2.563715193668810.010355848
RP11-244M2.10.0889627841.0930399771.0204231041.170824522.5363699280.011200834
AC015849.160.0938473811.0983920981.0214728851.18110352.5335117250.011292596
RP11-523L20.2−0.04170821047982680.959149610.9280422020.991299718−2.479433937247250.01315911
RP11-268F1.3−0.03251675021904280.9680062350.9431614430.99350549−2.451117811592340.014241333
RP11-396O20.2−0.03476106591470380.965836160.9391711350.993258261−2.433534218306560.014952222
RP11-384F7.2−0.03188296894977010.9686199340.9438882710.993999613−2.416031735763390.015690694
LINC01290−0.06316077237695020.938792530.8918449830.988211439−2.413013460811640.015821235
CTA-384D8.35−0.1160059760072620.8904699020.8102005980.978691756−2.406835094723740.016091434
RP11-514D23.2−0.0363185854732270.9643330220.9362147920.993295754−2.405504371065660.016150159
RP11-187E13.10.0352380671.0358662851.0060078161.0666109592.3613515170.018208462
CTD-2147F2.10.0278806911.0282729951.0045872711.0525171712.3448919720.019032593
RP11-57A1.10.0474635641.0486079941.0075547571.0913339622.329328860.019841651
RP11-6F2.50.0389289271.0396965871.0061318111.0743810912.3250752420.020067937
AC123023.1−0.03187902377761020.9686237550.9427693690.995187169−2.309470456471770.020917489
LINC01447−0.04122055916566680.9596174540.9263355770.9940951−2.288811057716810.022090333
CTD-2377D24.60.0529513691.0543783681.0069618561.1040276612.2554787790.024103293
CTD-2118P12.10.0310794561.0315674651.0039169041.0599795962.2419639050.024963705
RP11-203H2.2−0.03845773407611040.9622723750.9299021990.995769367−2.202802477030620.027608674
RP11-400N13.30.0454093521.0464561411.0042639511.0904209552.1626011220.030571872
RP11-253E3.30.1613378561.175081911.0151454641.3602163872.1613366380.030669345
AC145343.2−0.1158996036996020.8905646280.8016194270.989378912−2.158861039975890.030860949
AC195454.1−0.03386471237181740.9667022790.9369595270.997389181−2.123923128623890.033676572
LINC012020.0315961351.0321005921.002440831.0626379142.123832030.033684192
UG0898H090.0342348391.0348275961.0013012961.0694764472.0373553120.041614449
RP11-401P9.4−0.08867013812464910.9151473960.8393212070.997823896−2.009329819315330.044502168
RP11-328J2.1−0.03280484746512870.9677273960.9367141230.999767473−1.973957372926720.04838659

Abbreviations: HR, hazard ratio; Pr, probability.

  38 in total

1.  A long noncoding RNA activated by TGF-β promotes the invasion-metastasis cascade in hepatocellular carcinoma.

Authors:  Ji-hang Yuan; Fu Yang; Fang Wang; Jin-zhao Ma; Ying-jun Guo; Qi-fei Tao; Feng Liu; Wei Pan; Tian-tian Wang; Chuan-chuan Zhou; Shao-bing Wang; Yu-zhao Wang; Yuan Yang; Ning Yang; Wei-ping Zhou; Guang-shun Yang; Shu-han Sun
Journal:  Cancer Cell       Date:  2014-04-24       Impact factor: 31.743

2.  The lncRNA DRAIC/PCAT29 Locus Constitutes a Tumor-Suppressive Nexus.

Authors:  Kouhei Sakurai; Brian J Reon; Jordan Anaya; Anindya Dutta
Journal:  Mol Cancer Res       Date:  2015-02-20       Impact factor: 5.852

3.  Application of Akaike's information criterion (AIC) in the evaluation of linear pharmacokinetic equations.

Authors:  K Yamaoka; T Nakagawa; T Uno
Journal:  J Pharmacokinet Biopharm       Date:  1978-04

4.  First-line crizotinib versus chemotherapy in ALK-positive lung cancer.

Authors:  Benjamin J Solomon; Tony Mok; Dong-Wan Kim; Yi-Long Wu; Kazuhiko Nakagawa; Tarek Mekhail; Enriqueta Felip; Federico Cappuzzo; Jolanda Paolini; Tiziana Usari; Shrividya Iyer; Arlene Reisman; Keith D Wilner; Jennifer Tursi; Fiona Blackhall
Journal:  N Engl J Med       Date:  2014-12-04       Impact factor: 91.245

Review 5.  CCAT1: a pivotal oncogenic long non-coding RNA in human cancers.

Authors:  Yu Xin; Zheng Li; Jianxiong Shen; Matthew T V Chan; William Ka Kei Wu
Journal:  Cell Prolif       Date:  2016-05-01       Impact factor: 6.831

6.  A multigene assay is prognostic of survival in patients with early-stage lung adenocarcinoma.

Authors:  Dan J Raz; M Roshni Ray; Jae Y Kim; Biao He; Miquel Taron; Marcin Skrzypski; Mark Segal; David R Gandara; Rafael Rosell; David M Jablons
Journal:  Clin Cancer Res       Date:  2008-09-01       Impact factor: 12.531

7.  C-Myc-activated long noncoding RNA CCAT1 promotes colon cancer cell proliferation and invasion.

Authors:  Xiaolu He; Xueming Tan; Xiang Wang; Heiying Jin; Li Liu; Limei Ma; Hong Yu; Zhining Fan
Journal:  Tumour Biol       Date:  2014-09-04

Review 8.  Role of MALAT1 as a Prognostic Factor for Survival in Various Cancers: A Systematic Review of the Literature with Meta-Analysis.

Authors:  Yao Wei; Ben Niu
Journal:  Dis Markers       Date:  2015-09-02       Impact factor: 3.434

9.  Low expression of long noncoding RNA PANDAR predicts a poor prognosis of non-small cell lung cancer and affects cell apoptosis by regulating Bcl-2.

Authors:  L Han; E-b Zhang; D-d Yin; R Kong; T-p Xu; W-m Chen; R Xia; Y-q Shu; W De
Journal:  Cell Death Dis       Date:  2015-02-26       Impact factor: 8.469

10.  Long noncoding RNA CCAT1 acts as an oncogene and promotes chemoresistance in docetaxel-resistant lung adenocarcinoma cells.

Authors:  Jing Chen; Kai Zhang; Haizhu Song; Rui Wang; Xiaoyuan Chu; Longbang Chen
Journal:  Oncotarget       Date:  2016-09-20
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  17 in total

1.  Single-cell cloning of human T-cell lines reveals clonal variation in cell death responses to chemotherapeutics.

Authors:  Kathleen Hanlon; Alex Thompson; Lorena Pantano; John N Hutchinson; Arshed Al-Obeidi; Shu Wang; Meghan Bliss-Moreau; Jennifer Helble; Gabriela Alexe; Kimberly Stegmaier; Daniel E Bauer; Ben A Croker
Journal:  Cancer Genet       Date:  2019-06-12

2.  Identification of crucial long non-coding RNAs and mRNAs along with related regulatory networks through microarray analysis in esophageal carcinoma.

Authors:  Yaowen Zhang; Huitao Wang; Fuyou Zhou; Anlin Hao; Ningtao Dai; Haijun Yang; Anping Zheng
Journal:  Funct Integr Genomics       Date:  2021-04-16       Impact factor: 3.410

3.  A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies.

Authors:  Yin Li; Di Ge; Jie Gu; Fengkai Xu; Qiaoliang Zhu; Chunlai Lu
Journal:  BMC Cancer       Date:  2019-09-05       Impact factor: 4.430

4.  Characterization of lncRNA-Associated ceRNA Network to Reveal Potential Prognostic Biomarkers in Lung Adenocarcinoma.

Authors:  Yang Wang; Ruyi He; Lixin Ma
Journal:  Front Bioeng Biotechnol       Date:  2020-04-17

5.  IRGS: an immune-related gene classifier for lung adenocarcinoma prognosis.

Authors:  Xiaoshun Shi; Ruidong Li; Xiaoying Dong; Allen Menglin Chen; Xiguang Liu; Di Lu; Siyang Feng; He Wang; Kaican Cai
Journal:  J Transl Med       Date:  2020-02-04       Impact factor: 5.531

6.  7-lncRNA Assessment Model for Monitoring and Prognosis of Breast Cancer Patients: Based on Cox Regression and Co-expression Analysis.

Authors:  Huayao Li; Chundi Gao; Lijuan Liu; Jing Zhuang; Jing Yang; Cun Liu; Chao Zhou; Fubin Feng; Changgang Sun
Journal:  Front Oncol       Date:  2019-12-03       Impact factor: 6.244

7.  Long non‑coding RNA RP11‑400N13.3 promotes the progression of colorectal cancer by regulating the miR‑4722‑3p/P2RY8 axis.

Authors:  Hongju Yang; Qian Li; Yanrui Wu; Jianlong Dong; Yaling Lao; Zheng Ding; Changyan Xiao; Jinxiao Fu; Song Bai
Journal:  Oncol Rep       Date:  2020-09-07       Impact factor: 3.906

8.  Development and Validation of a 12-Gene Immune Relevant Prognostic Signature for Lung Adenocarcinoma Through Machine Learning Strategies.

Authors:  Liang Xue; Guoshu Bi; Cheng Zhan; Yi Zhang; Yunfeng Yuan; Hong Fan
Journal:  Front Oncol       Date:  2020-05-27       Impact factor: 6.244

9.  Construction of ceRNA Coexpression Network and Screening of Molecular Targets in Colorectal Cancer.

Authors:  Zhao Hui; Wang Zhanwei; Yang Xi; Liu Jin; Zhuang Jing; Han Shuwen
Journal:  Dis Markers       Date:  2020-04-21       Impact factor: 3.434

10.  Development and validation of a robust immune-related prognostic signature in early-stage lung adenocarcinoma.

Authors:  Pancheng Wu; Yi Zheng; Yanyu Wang; Yadong Wang; Naixin Liang
Journal:  J Transl Med       Date:  2020-10-07       Impact factor: 5.531

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