A novel messenger RNA signature as a biomarker for predicting early relapse in

non-small cell lung cancer

Authors: Jing Li, MD1, Xiaoxia Liu1, MD, Wenqian Xu, MD1, Xin Wang, MD1

Department: 1Departments of CyberKnife, Huashan Hospital, Fudan University,

Shanghai, China

Authors:

Jing Li, MD. Departments of CyberKnife, Huashan Hospital, Fudan University.

No.525, Hongfeng Road, Pudong District, Shanghai 200041, China. Tel: +86-021-

38719999. Fax: +86-021-38719999. Email: lijinghuashan@sina.com.

Xiaoxia Liu. Departments of CyberKnife, Huashan Hospital, Fudan University.

No.525, Hongfeng Road, Pudong District, Shanghai 200041, China. Tel: +86-021-

38719999. Fax: +86-021-38719999. Email: xiaoxia@ fudan.edu.cn .

Wenqian Xu, MD. Departments of CyberKnife, Huashan Hospital, Fudan University.

No.525, Hongfeng Road, Pudong District, Shanghai 200041, China. Tel: +86-021-

38719999. Fax: +86-021-38719999. Email: amy126simon@ 126.com .

Xin Wang, MD. Departments of CyberKnife, Huashan Hospital, Fudan University.

No.525, Hongfeng Road, Pudong District, Shanghai 200041, China. Tel: +86-021-

38719999. Fax: +86-021-38719999. Email: wangxinck@fudan.edu.cn.

Correspondence to:

Xin Wang, MD. Departments of CyberKnife, Huashan Hospital, Fudan University.

No.525, Hongfeng Road, Pudong District, Shanghai 200041, China. Tel: +86-021-

38719999. Fax: +86-021-38719999. Email: wangxinck@fudan.edu.cn.

Running title: RNA signature for predicting early relapse in NSCLC

Category: Original article

This study wasn’t based on a previous communication to a society or meeting.

Abstract

Background: High throughput gene expression profiling has showed great promise in

providing insight into molecular mechanisms. Recurrence-related mRNAs may

potentially enrich genes with the ability to predict cancer recurrence and survival,

therefore we attempted to build an early recurrence associated gene signature to

improve prognostic prediction of lung cancer.

Methods: Propensity score matching was conducted between patients in early relapse

group and long-term survival group from TCGA training series (N=579) and patients

were matched 1:1. Global transcriptome analysis was then performed between the

paired groups to identify tumor specific mRNAs. Finally, using LASSO Cox

regression model, we built a multi-gene early relapse classifier incorporating forty

mRNAs. The prognostic and predictive accuracy of the signature was internally

validated in another 193 lung cancer patients.

Results: Forty mRNAs were finally identified to build an early relapse classifier.

With specific risk score formula, patients were classified into a high-risk group and a

low-risk group. Relapse free survival was significantly different between the two

groups in both discovery (HR: 3.126, 95% CI: 2.249-4.346, P<0.001) and internal

validation (HR 1.806, 95% CI 1.077-3.030, P=0.025).Further analysis revealed that

the prognostic value of this signature was independent of tumor stage, histotype and

EGFR mutation (P<0.05). Receiver operating characteristic (ROC) analysis showed

that the area under ROC curve of this signature was higher than TNM stage alone

(0.771 vs 0.686, P<0.05).

Conclusions: Our forty-mRNA-based classifier provides a reliable model for

predicting early recurrence in non-small cell lung cancer after surgery. This model

may facilitate personalized therapy-decision making for these patients.

Keywords: Non-small cell lung cancer; Recurrence; Signature

Introduction

Lung cancer is one of the most frequent causes of cancer-related deaths worldwide[1,

2] .According to the latest update of cancer statistics in the United States in 2018, a

total of 234,030 estimated new lung and bronchus cancer cases will be diagnosed,

both the incidence of males and females are the second highest among all cancer

types.[2]. Non-small cell lung cancer (NSCLC) accounts for about 80% of lung

cancer cases at the time of initial diagnosis, and the standard treatment is curative

resection, which is associated with a higher chance of long-term survival. However,

even after curative resection of NSCLC, long-term survival is reported as <50%, with

33.1% of patients exhibiting recurrence within 2 years [3]. Early detection of

recurrence of primary lung cancer after surgery is associated with improved outcomes

and survival in patients received surgical resection. Thus, uncovering the underlying

mechanisms and precise biomarkers is urgently needed to facilitate early diagnosis

and treatment of lung cancer and predict and monitor cancer recurrence and

metastasis.

In fact, lung cancer is of a high heterogeneity, originating from complex interactions

between environmental and genetic factors [4]. Some critical genes, such as EGFR

[5].PD-1[6], NFS1 [7], and BRAF [8] are implicated in the initiation, progression, and

metastasis of lung cancer. Great efforts have been made to identify the molecular

markers for prognosis prediction. However, majority studies are focused on single

gene, and sometime demonstrated conflicting evidence as to the prognostic

significance of these genes. In recent years, many studies have focused on gene

expression profiles in lung cancer; these have shown great promise for predicting

prognosis in individual patients. Yu et al successfully developed a five microRNA

based signature that can effectively predicted survival and relapse in lung cancer[9].

Tomida et al. developed a signature that could identify adenocarcinoma patients at

very high risk for relapse, even those with cancer in the early stage[10]. However,

most of them are not used clinical practice. Thus, identifying a more powerful and

practical gene signature for prognosis prediction is urgent,

In the present study, we adopted The Cancer Genome Atlas (TCGA), and conducted

mRNA profiling on large cohorts of NSCLC patients. By using the sample-splitting

method and Cox regression analysis, a prognostic forty-mRNA signature was

identified from the discovery set, and validated in another cohort. This mRNA

signature may help identify the subset of NSCLC patients at high risk of early relapse.

 

Patients and Methods

Preprocessing of microarray data in TCGA database

The raw sequencing data and clinical information were downloaded from TCGA

database (Illumina HiSeq Systems) (https://cancergenome.nih.gov/), and were

normalized using Robust Multichip Average[11]. The samples were collected from

1991 to 2013. miRNAs whose expression was = 0 in more than 50% of the samples

data were removed and were then normalized by log2(X + 1). miRNAs with log 2 fold

change (log FC) < −1 or log FC > 1 (FDR adjusted P < 0.05) were considered to be

differentially expressed miRNAs and were included for subsequent analysis[12].

Datasets selection

The selection criterion for lung cancer datasets were as follows: (i) pathological

diagnosed with NSCLC; (ii) patients should have basic clinical information for

analysis; (iii) pathological diagnosed with stage I-III; (iv) with intact follow up

information of relapse free survival (RFS) interval and RFS status. Patients who

received neoadjuvant chemotherapy or radiation were excluded from the study.

Clinical data for all the patients used in this study were obtained from TCGA. RFS

times for patients who experienced tumor progression within the follow-up period

were obtained from the TCGA file for new tumor events. The patients were

randomized divided into discovery cohort and validation cohort with ratio 3:1.

Identification of early relapse associated genes

Early relapse was defined as the locoregional recurrence or distant metastasis within 1

year after primary resection[3]. Samples in the discovery set were selected and

divided into early relapse group and long-term survival group (no relapse after a

minimum of 5 years follow-up). Propensity score (PS) matching analysis was

performed between the two groups to adjust for stage and histotype, which were the

most significant clinical factors associated with early relapse. All patients were

matched 1:1. Finally, 31 paired patients in the discovery set were identified to identify

the changes of global gene expression profile between early relapse group and long-

term survival groups. The analysis of differentially expressed genes (DEGs) between

early relapse and long-term survival samples was conducted using the Linear Models

for Microarray data (LIMMA) method [13] The threshold for identification of DEGs

was set as P<0.05 and fold change>=1.25. Lastly, LASSO Cox regression

model[13]was used to select the most significantly relapse associated mRNAs of all

the differentially expressed genes.

Development of risk score and statistical analysis

Using LASSO Cox regression analysis, we identified a panel of genes and constructed

a multi-mRNA-based classifier for predicting the early relapse of patients with stage

I-III lung cancer in the discovery set. With specific risk score formula, patients from

different sets were divided into high-risk and low-risk groups by using the median

risk score of the discovery set as the cutoff point. Survival rate in the low-risk and

high-risk groups were estimated by the Kaplan-Meier estimate, and compared using

the log-rank test. Multivariate Cox regression analysis and data stratification analysis

were performed to test the independent prognostic role of risk score in predicting

RFS. Time-dependent ROC analysis was used to investigate the prognostic or

predictive accuracy of each feature and signature. All statistical analyses were

performed with use of R (version 2.15.0, www.r-project.org). All statistical tests were

2-sided, and P values<0.05 were considered statistically significant.

Results

Preparation of lung cancer data sets

A total of 772 eligible patients were identified in TCGA database, which included 419

(54.3%) cases at stage I, 236(30.6%) at stage II, and 117(15.2%) at stage III. 375

patients were diagnosed as squamous cell carcinoma, 397 were adenocarcinoma. Of

them, 579 patients were divided into discovery set and 193 patients were in internal

validation set using the median risk score as cutoff point.as described below. The

original data of the all patients included in analysis were listed in Table S1.

Development of early relapse signature in the discovery set

Patients in discovery set were divided into early relapse group and long-term survival

group with no relapse in five years. Patients’ clinicopathological characters before and

after PS matching were summarized in Table 1. Before the implement of PS analysis,

it is noticeable that tumor stage in early relapse group was significantly higher than

that in long-term survival group. Besides, there is high percentage of squamous cell

carcinoma in long time survival group. After PS matching, there were no significant

differences in tumor stage, histotype, and radiotherapy between early relapse and

long-term survival groups in each set (Table 1).

Changes of global mRNA expression profiles were analyzed between early relapse

and long-term survival groups. One-hundred and twenty six of them were

differentially expressed between the two groups (P <0.05, fold change>=2.0) (Fig.1A)

(Table 3). LASSO coefficient profiles of the 126 mRNAs were shown in Figure 1B. A

coefficient profile plot was produced against the log (λ) sequence. Vertical line was

drawn at the value selected using 10-fold cross-validation, and the minimize λ method

resulted in 40 optimal coefficients. Of these, fifteen mRNAs were down-regulated and

twenty-five were up-regulated in early relapse group compared with long-term

survival group (Table 4). Using Lasso Cox regression modeling, we derived a forty-

mRNA signature to calculate the risk score for every patient based on the expression

levels of the forty RNAs weighted by their regression coefficients: Risk score=

ADAMTS18*0.068+ADH1C*-0.006+AJAP1*0.092+AKAP12*0.041+C1orf186*-

0.112+CCR10*-0.087+CD177*-0.076+CLEC7A*-0.070+DPPA2*0.061+DUSP13*-

0.061+FGF19*0.023+ FTCD*-0.079+GLYATL2*0.003 + HOMER2*-0.115

+HSD17B13*-0.067 +HTR1B*0.059 + KIAA1875*-0.071+ KLB*-0.091

+LEFTY1*0.080 +LOC100131726*0.184 + MAGEA8*0.107 +MPPED1*-0.057

+NBPF4*-0.114 +PALM3*-.094 + PCDHA4*0.05 +PLA1A*0.133 + PLA2G4F*-

0.016+ PLEKHG4B*-0.044+ PLIN4*0.029+ PSORS1C2*-0.027+ PTPRR*-0.019+

RBM46*0.079+ RFPL3S*-0.109+ TMEM213*-0.089+ TMEM63C*-0.03+

TRIM58*0.044+ TSKS*-0.069+ ZDHHC11*-0.034+ ZFP42*0.086 + ZYG11A*-

0.075. Each gene represents its transcriptional expression levels.

The prognostic value of forty-mRNA signature in discovery, validation cohorts

The distribution of risk scores and RFS status was shown in Figure 2A (left panel).

The chance of recurrence raised steadily ad score increased. Time-dependent ROC

analyses at 1 year, 3 year and 5 year were conducted to assess the prognostic accuracy

of the forty-mRNA based classifier (Fig.2A, middle panel). The 1-year, 3-year, 5-year

RFS rates for patients with low-risk scores were 95.3%, 80.8%, and 67.4%, compared

with 79.0%,49.7%, and 37.8% for patients with high-risk scores, respectively (HR:

3.244, 95% CI: 2.338-4.500, P<0.001, Fig.2A, right panel).

We then did the same analyses in the internal validation cohort. The prognostic score

showed same clinical significance as in discovery set. The 1-year, 3-year and 5-year

RFS was 90.3%, 66.3%, and 62.1% for the low-risk group, and 80.1%, 54.3%, and

37.8% for the high-risk group (HR 1.970, 95% CI 1.181-3.289, P=0.009, Fig. 2B).

Furthermore, in the entire dataset analysis, risk score-based classification yielded

similar results (Fig. 2C). Patients with lung cancer can be divided into low and high

risk with significantly different RFS and the signature showed the best predicting

accuracy at one year after surgery.

Independence and accuracy of the signature in predicting RFS

After multivariate analysis adjusted by clinicopathological variables that were

significance in univariate survival analysis, the forty-mRNA-based signature

remained a powerful and independent prognostic factor in both the discovery and

internal validation cohorts (Table 2). Stratified analysis suggested that the forty-

mRNA-based classifier was still a statistically significant prognostic model in stage

IA (Fig.3A), stage IB (Fig.3B), stage II (Fig.3C), stage III (Fig.3D), patients

diagnosed with adenocarcinoma (Fig.3E) or squamous cell carcinoma (Fig.3F),

patients with or without KRAS mutation (Fig.3G and 3F).

To further confirm that the forty-mRNA-based signature had higher efficacy in

predicting early relapse, time-dependent ROC was used, which suggested that the

forty-mRNA-based classifier had significantly higher prognostic accuracy than tumor

stage at 1 year. Combined TNM stage and the signature provided more accurate

survival prediction than TNM stage or forty-mRNA-based signature alone (Fig. 4).

Identification of forty-mRNA signature associated biological signaling pathway

To further identify the biologically meaningful pathways that the forty gene were

involved, we performed GSEA analysis in TCGA database to identify associated

biological signaling pathway. Significant gene sets (FDR < 5%) were visualized as

Enrichment Map (Fig. 5). The risk score was accompanied with exceptional

regulation of several important cancer-related networks, namely Selenoamino acid

metabolism, One carbon pool by folate, Amyotrophic lateral sclerosis (ALS), and

Drug metabolism cytochrome (P450).

 

Discussion

Surgery is the optimal treatment to cure lung cancer. However, nearly 50% of patients

with NSCLC experience recurrence and have a poor prognosis despite curative

resection [14, 15].TNM staging indicates the serious of disease and recurrence

potential of primary lung cancer [16, 17]. However, even patients diagnosed at the

same stage are split between the recurrent and non-recurrent group after curative

resection. Therefore, the current TNM staging system has its limitation in clinical

practice. Accurately predicting the cases in which disease is likely to recur can help to

personalize therapy and follow-up strategies. Several studies have indicated that the

risk factors associated with postoperative recurrence include tumor differentiation,

and vessel invasion1, adenocarcinoma, visceral pleural invasion, the serum

carcinoembryonic antigen (CEA) level[4, 18-20]. In addition, novel predictors of lung

cancer, such as maximal standardized uptake values (SUVs) of tumors on positron

emission tomography (PET), the status of epidermal growth factor receptor (EGFR)

and KRAS were also associated with postoperative outcomes[20-23]. These study

mainly based on single clinicopathological factor or single gene status, and the results

show variety depending on sample size or patient selection. Little attention has been

paid to mRNAs expression pattern and its clinical significance in the prediction of

early relapse in stage I-III NSCLC using high-throughput expression profile datasets.

In the present study, we developed a novel prognostic classifier based on forty

mRNAs to improve the prediction accurate of early relapse and RFS for NSCLC after

surgical resection. By applying the forty-mRNA signature to the patients in the TCGA

discovery set, a clear difference was observed in survival for patients with low and

high-risk score. And it was internally validated in the validation series, suggesting the

good reproducibility of this signature in lung cancer. After stratified by AJCC stage,

histotype and EGFR status, the forty-mRNA-based signature remains a good

prognostic model, implying that the mRNA signature can be used to refining the

current staging system. Furthermore, the time-dependent ROC at 1 year suggested

that this forty-mRNA-signature has considerable prognostic accuracy in predicting

tumor relapse within the first year after initial resection of lung cancer. Therefore, our

study identified a forty-mRNAs signature that could help identify patients with high

risk of early relapse and guide individualized treatment of patients with lung cancer,

which is credible to be applied to clinic[13].

Most of genes included in the signature have been demonstrated to be linked with

cancer. The GSEA analysis found that the risk score based gene exceptional regulated

several important cancer-related networks, including Selenoamino acid metabolism,

One carbon pool by folate, ALS, and Drug metabolism cytochrome (P450). ALS is a

progressive disease characterized by degeneration of motor neurons that results in

increasing weakness and death[24]. An increased risk of ALS was observed during the

first year after cancer diagnosis, and in contrast, a lower risk of cancer was observed

in ALS patients after diagnosis compared with ALS-free individuals [25, 26]. Some

drugs for ALS was shown to induce anti-cancer effects on cancer [25, 27, 28]. Most of

genes involved in ALS turned out to be related to various cancers using survival

analysis, pathway enrichment analysis, and TF enrichment analysis[26]. The one

carbon pool by folate pathway has been noted as a predominant pathway in cancer

cell survival and progression for many years[29].It has been proven to contribute to

genome instability and cancer development[30], cell proliferation, DNA synthesis

control, and cell migration[31, 32], mitochondrial folate metabolism[33].

Selenoamino acid metabolism play critical role in reactive oxygen species-mediated

DNA damage, apoptosis and drug resistance in human cancer [34-36]. P450 in the

tumor is relevant to cancer susceptibility, drug response, and progression. Thus, it is

not surprising that our signature has a good prediction of early recurrence after

surgery for NSCLC.

Albeit we successfully developed a prognostic model for cancer prognosis using a

biology-driven approach in large NSCLC cases, the limitations of our study should be

addressed. Firstly, there is no external validation for the signature. Before the

signature can be applied as a clinical-grade assay, the external validation is essential.

Secondly, the information of several other important clinicopathological features and

therapy strategies is not available in TCGA database, thus, we cannot adjust these

factor when built the signature. Thirdly, our study was based on the data from a

public-available datasets without testing prospectively in a clinical trial, which may

have some inherit limitation as retrospective study.

In conclusion, our study demonstrated that the forty-mRNA prognostic model can

effectively distinguish NSCLC patients with low early recurrence risk from those with

high early recurrence risk, regardless of TNM stage, histotype and EGFR status. Since

our forty-mRNA-based classifier can make a good supplementary to traditional TNM

stage, clinicians may be able to recommend less aggressive therapy for low-risk

individuals and intensive care for high risk individuals in directing personalized

therapy. Therefore, this model may facilitate personalized clinical decision making for

lung cancer patients.

AbbreviationsNSCLC: Non-small cell lung cancer

TCGA: The Cancer Genome Atlas

RFS: Relapse free survival

PS: Propensity score

DEGs: Differentially expressed genes

LIMMA: Linear Models for Microarray data

Acknowledgments

None

Funding support

This research was supported by the National Science Foundation of China (No.

81802374). The funders had no role in the study design, data collection and analysis,

decision to publish, or preparation of the manuscript.

Authors' contributions

JL and XW conceived this study. XXL, XW and WQX improved the study design and

contributed to the interpretation of results. JL and XW performed the study. JL and

XXL performed data processing and statistical analysis. JL wrote the manuscript. XW

revised the manuscript. All authors read and approved the final manuscript.

Competing InterestsThe authors have declared that no competing interest exists.

References

1. Global Burden of Disease Cancer C, Fitzmaurice C, Allen C, Barber RM,

Barregard L, Bhutta ZA, et al. Global, Regional, and National Cancer Incidence,

Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted

Life-years for 32 Cancer Groups, 1990 to 2015: A Systematic Analysis for the Global

Burden of Disease Study. JAMA Oncol. 2017; 3: 524-48.

2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin. 2018;

68: 7-30.

3. Vendrell JA, Mau-Them FT, Beganton B, Godreuil S, Coopman P, Solassol J.

Circulating Cell Free Tumor DNA Detection as a Routine Tool forLung Cancer

Patient Management. Int J Mol Sci. 2017; 18.

4. Chen YY, Huang TW, Tsai WC, Lin LF, Cheng JB, Chang H, et al. Risk factors of

postoperative recurrences in patients with clinical stage I NSCLC. World J Surg

Oncol. 2014; 12: 10.

5. Blakely CM, Watkins TBK, Wu W, Gini B, Chabon JJ, McCoach CE, et al.

Evolution and clinical impact of co-occurring genetic alterations in advanced-stage

EGFR-mutant lung cancers. Nat Genet. 2017; 49: 1693-704.

6. Akbay EA, Koyama S, Carretero J, Altabef A, Tchaicha JH, Christensen CL, et al.

Activation of the PD-1 pathway contributes to immune escape in EGFR-driven lung

tumors. Cancer Discov. 2013; 3: 1355-63.

7. Alvarez SW, Sviderskiy VO, Terzi EM, Papagiannakopoulos T, Moreira AL,

Adams S, et al. NFS1 undergoes positive selection in lung tumours and protects cells

from ferroptosis. Nature. 2017; 551: 639-43.

8. Damsky WE, Curley DP, Santhanakrishnan M, Rosenbaum LE, Platt JT, Gould

Rothberg BE, et al. beta-catenin signaling controls metastasis in Braf-activated Pten-

deficient melanomas. Cancer Cell. 2011; 20: 741-54.

9. Yu SL, Chen HY, Chang GC, Chen CY, Chen HW, Singh S, et al. MicroRNA

signature predicts survival and relapse in lung cancer. Cancer Cell. 2008; 13: 48-57.

10. Tomida S, Takeuchi T, Shimada Y, Arima C, Matsuo K, Mitsudomi T, et al.

Relapse-related molecular signature in lung adenocarcinomas identifies patients with

dismal prognosis. J Clin Oncol. 2009; 27: 2793-9.

11. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, et

al. Exploration, normalization, and summaries of high density oligonucleotide array

probe level data. Biostatistics. 2003; 4: 249-64.

12. Fu Q, Yang F, Xiang T, Huai G, Yang X, Wei L, et al. A novel microRNA

signature predicts survival in liver hepatocellular carcinoma after hepatectomy. Sci

Rep. 2018; 8: 7933.

13. Dai W, Li Y, Mo S, Feng Y, Zhang L, Xu Y, et al. A robust gene signature for the

prediction of early relapse in stage I-III colon cancer. Mol Oncol. 2018; 12: 463-75.

14. Hoffman PC, Mauer AM, Vokes EE. Lung cancer. Lancet. 2000; 355: 479-85.

15. al-Kattan K, Sepsas E, Fountain SW, Townsend ER. Disease recurrence after

resection for stage I lung cancer. Eur J Cardiothorac Surg. 1997; 12: 380-4.

16. Yamashita T, Uramoto H, Onitsuka T, Ono K, Baba T, So T, et al. Association

between lymphangiogenesis-/micrometastasis- and adhesion-related molecules in

resected stage I NSCLC. Lung Cancer. 2010; 70: 320-8.

17. Uramoto H, Tanaka F. Prediction of recurrence after complete resection in

patients with NSCLC. Anticancer Res. 2012; 32: 3953-60.

18. Koo HK, Jin SM, Lee CH, Lim HJ, Yim JJ, Kim YT, et al. Factors associated

with recurrence in patients with curatively resected stage I-II lung cancer. Lung

Cancer. 2011; 73: 222-9.

19. Maeda R, Yoshida J, Ishii G, Hishida T, Nishimura M, Nagai K. Risk factors for

tumor recurrence in patients with early-stage (stage I and II) non-small cell lung

cancer: patient selection criteria for adjuvant chemotherapy according to the seventh

edition TNM classification. Chest. 2011; 140: 1494-502.

20. Lee SH, Jo EJ, Eom JS, Mok JH, Kim MH, Lee K, et al. Predictors of Recurrence

after Curative Resection in Patients with Early-Stage Non-Small Cell Lung Cancer.

Tuberc Respir Dis (Seoul). 2015; 78: 341-8.

21. Higashi K, Ueda Y, Arisaka Y, Sakuma T, Nambu Y, Oguchi M, et al. 18F-FDG

uptake as a biologic prognostic factor for recurrence in patients with surgically

resected non-small cell lung cancer. J Nucl Med. 2002; 43: 39-45.

22. Liu WS, Zhao LJ, Pang QS, Yuan ZY, Li B, Wang P. Prognostic value of

epidermal growth factor receptor mutations in resected lung adenocarcinomas. Med

Oncol. 2014; 31: 771.

23. Nadal E, Chen G, Prensner JR, Shiratsuchi H, Sam C, Zhao L, et al. KRAS-G12C

mutation is associated with poor outcome in surgically resected lung adenocarcinoma.

J Thorac Oncol. 2014; 9: 1513-22.

24. Rowland LP, Shneider NA. Amyotrophic lateral sclerosis. N Engl J Med. 2001;

344: 1688-700.

25. Fang F, Al-Chalabi A, Ronnevi LO, Turner MR, Wirdefeldt K, Kamel F, et al.

Amyotrophic lateral sclerosis and cancer: a register-based study in Sweden.

Amyotroph Lateral Scler Frontotemporal Degener. 2013; 14: 362-8.

26. Taguchi YH, Wang H. Genetic Association between Amyotrophic Lateral

Sclerosis and Cancer. Genes (Basel). 2017; 8.

27. Oberley LW, Buettner GR. Role of superoxide dismutase in cancer: a review.

Cancer Res. 1979; 39: 1141-9.

28. Seol HS, Lee SE, Song JS, Lee HY, Park S, Kim I, et al. Glutamate release

inhibitor, Riluzole, inhibited proliferation of human hepatocellular carcinoma cells by

elevated ROS production. Cancer Lett. 2016; 382: 157-65.

29. Locasale JW. Serine, glycine and one-carbon units: cancer metabolism in full

circle. Nat Rev Cancer. 2013; 13: 572-83.

30. Yang M, Vousden KH. Serine and one-carbon metabolism in cancer. Nat Rev

Cancer. 2016; 16: 650-62.

31. Lehtinen L, Ketola K, Makela R, Mpindi JP, Viitala M, Kallioniemi O, et al.

High-throughput RNAi screening for novel modulators of vimentin expression

identifies MTHFD2 as a regulator of breast cancer cell migration and invasion.

Oncotarget. 2013; 4: 48-63.

32. Gustafsson Sheppard N, Jarl L, Mahadessian D, Strittmatter L, Schmidt A,

Madhusudan N, et al. The folate-coupled enzyme MTHFD2 is a nuclear protein and

promotes cell proliferation. Sci Rep. 2015; 5: 15029.

33. Nilsson R, Jain M, Madhusudhan N, Sheppard NG, Strittmatter L, Kampf C, et

al. Metabolic enzyme expression highlights a key role for MTHFD2 and the

mitochondrial folate pathway in cancer. Nat Commun. 2014; 5: 3128.

34. Chen T, Wong YS. Selenocystine induces reactive oxygen species-mediated

apoptosis in human cancer cells. Biomed Pharmacother. 2009; 63: 105-13.

35. Fan C, Chen J, Wang Y, Wong YS, Zhang Y, Zheng W, et al. Selenocystine

potentiates cancer cell apoptosis induced by 5-fluorouracil by triggering reactive

oxygen species-mediated DNA damage and inactivation of the ERK pathway. Free

Radic Biol Med. 2013; 65: 305-16.

36. Wang K, Fu XT, Li Y, Hou YJ, Yang MF, Sun JY, et al. Induction of S-Phase

Arrest in Human Glioma Cells by Selenocysteine, a Natural Selenium-Containing

Agent Via Triggering Reactive Oxygen Species-Mediated DNA Damage and

Modulating MAPKs and AKT Pathways. Neurochem Res. 2016; 41: 1439-47.

Figure Legends

Figure 1. (A) Heat map showed One-hundred and twenty six differentially expressed

mRNAs in NSCLC between early relapse and long-term survival group in discovery

set. (B) LASSO coefficient profiles of the 126 early relapse associated mRNAs. A

vertical line is drawn at the value chosen by 10-fold cross-validation.

Figure 2. Distribution of risk score(left panel), time dependent ROC curves at 1, 3

and 5 years(middle panel) and Kaplan-Meier survival analysis between patients at low

and high risk of relapse(right panel) in discovery set (A), internal validation set (B),

and entire dataset (C).

Figure 3. Kaplan-Meier survival analysis for patients based on the fifteen-mRNA-

based signature stratified by clinicopathological risk factors. (A) stage IA, P<0.001;

(B) stage IB, P<0.001; (C) stage II, P=0.001; (D) stage III, P=0.002; (E)

adenocarcinoma, P<0.001; (F) squamous cell carcinoma, P<0.001; (G) EGFR wild

type, P=0.003; (H) EGFR mutation, P=0.003.

Figure 4. Time-dependent ROC curves at 1 year compare the prognostic accuracy in

predicting early relapse of the forty-mRNA signature with TNM staging system (A) in

the entire cohorts with stage I-III lung cancer (N=772). Decision curve analysis at 12

months for the tumor stage, r integrated mRNA signature and the two combined

model (B). The y-axis measures the net benefit.

Figure 5. Gene Set Enrichment Analysis Delineates biological pathways associated

with risk score

Table 1 Clinical-pathological features of patients in early relapse and long-term survival groups before and after propensity score matching.

Variable Training SetBefore matching After matching

early relapse

long-term survival

p early relapse

long-term survival

p

Age(mean,IQR) 65.1 62.4 0.29 65.2 62.6 0.397(58.7-72.0)

(59.5-72.3) (57.0-71.0)

(59.0-71.0)

Gender 0.717 0.793 female 22 19 5 10 male 44 33 19 18Stage <0.001 1 I 17 38 5 10 II 25 8 19 18 III 24 6 6 2T stage <0.001 0.296 T1 5 20 5 10 T2 41 29 19 18 T3 18 2 6 2 T4 2 1 1 1N stage <0.001 0.744 N0 27 40 20 19 N1 23 7 8 7 N2 16 5 3 5Histological type 0.046 1adenocarcinom

a35 18 15 15

squamous cell carcinoma

31 34 16 16

Radiation therapy 0.009 0.125No 48 50 22 29

Yes 14 2 7 2 Unknown 4 0 2 0Total 66 52 31 31

Table 2 Univariable and multivariable Cox regression analysis in lung cancer

Discovery set(N=579)

Variables Univariate Analysis Multivariate Analysis

HR(95%CI) p HR(95%CI) p

Age 1.005(0.988-1.021) 0.576Gender female Reference 0.580 male 0.918(0.679-1.242)Stage <0.001 <0.001 I Reference Reference II 1.993(1.415-2.809) 1.936(1.369-2.736) III 2.414(1.630-3.577) 1.994(1.308-3.039)histotype 0.045 0.114 Squamous cell carcinoma Reference Reference Adenocarcinoma 1.362(1.007-1.843) 1.281(0.942-1.742)Radiotherapy <0.001 <0.001 No Reference Reference Yes 2.146(1.457-3.160) 1.607(1.059-2.439)Unknown 3.523(1.925-6.447) 3.006(1.633-5.536)15 gene risk score <0.001 <0.001 Low Reference Reference High 3.244(2.338-4.500) 3.126(2.249-4.346)

Validation set(N=193)

Variables Univariate Analysis Multivariate Analysis

HR(95%CI) p HR(95%CI) p

Age 0.998(0.971-1.025) 0.869Gender female Reference 0.068 male 1.590 (0.967-2.613)Stage 0.103 I Reference II 1.251(0.721-2.172) III 2.071(1.060-4.044)histotype 0.002 0.005 Squamous cell carcinoma Reference Reference Adenocarcinoma 2.277(1.347-3.849) 2.119(1.249-3.594)Radiotherapy 0.055 No Reference Yes 2.081(1.123-3.858)

Unknown 0.746(0.181-3.071)15 gene risk score 0.009 0.025 Low Reference Reference High 1.970(1.181-3.289) 1.806(1.077-3.030)

Table 3. Differentially expressed genes between early relapse and long-term survival

groups (P <0.05, fold change>=2.0).

Gene symbol logFCAveExp

rt P.Value

adj.P.Val

B

FGF19 -2.728092.26286

1-4.0604

0.000142

0.3553590.81506

2

PLA2G4F1.57673

26.88418

93.75653

60.00038

70.533308 -0.02272

ENHO1.52796

13.30991

33.61162

90.00061

60.56877 -0.40911

RFPL3S1.09244

22.59591

13.57433

60.00069

40.56877 -0.50709

ANXA10 -2.551362.78635

3-3.55941

0.000727

0.56877 -0.54613

TPPP1.28446

17.93489

83.40547

90.00117

30.56877 -0.94277

HLF 1.58857.21671

13.34167

0.001426

0.56877 -1.10392

CYP17A11.32116

11.72539 3.32559

0.001497

0.56877 -1.14421

LOC4418691.12463

58.63022

43.30838

30.00157

70.56877 -1.18719

RHCE1.00455

83.70646

33.29067

40.00166

40.56877 -1.23127

LOC619207 1.25754.05008

23.26740

40.00178

40.56877 -1.28896

HOXD8 -1.456455.53551

3-3.22267 0.00204 0.56877 -1.39909

TMEM63C1.72192

66.18397

63.21800

60.00206

80.56877 -1.4105

TRIM58 -1.422512.94306

3-3.21418

0.002092

0.56877 -1.41987

PCDHA4 -1.664714.90585

6-3.13664 0.00263 0.56877 -1.60798

C1orf1861.22376

83.09153

93.03380

80.00354

50.620294 -1.85253

PLIN51.12535

25.04104 3.01553

0.003736

0.620294 -1.8954

SOHLH2 -1.75995 3.30674 -2.977390.00416

50.628829 -1.98427

PLIN41.01004

24.95335

32.96686

60.00429

20.633915 -2.00864

C1orf511.13394

86.84631

2.948407

0.004522

0.640287 -2.05125

STC2 -1.108598.93618

1-2.9375

0.004663

0.641611 -2.07634

KIAA18751.23297

74.07180

82.93715

80.00466

80.641611 -2.07712

S100A11.13021

94.43977

72.93330

50.00471

90.641611 -2.08596

SLCO1B1 -1.108021.35699

2-2.90743

0.005075

0.661301 -2.14515

BLK1.55353

23.89343

12.87549

90.00554

80.670049 -2.21765

RBM46 -1.483231.17057

1-2.80072

0.006821

0.684867 -2.38515

CLEC7A1.04540

38.31229

52.77386 0.00734 0.6894 -2.44451

PLEKHG4B1.52708

77.22096

92.76882

30.00744

10.6894 -2.45559

ABHD12B -1.055981.75937

6-2.75763

0.007671

0.6894 -2.48016

AKAP12 -1.23528.91873

5-2.7548 0.00773 0.6894 -2.48638

NBPF4 -1.094431.40113

1-2.7474

0.007887

0.6894 -2.50256

STC1 -1.08794 9.05034 -2.73360.00818

70.6894 -2.53267

HTR1B -1.023071.59102

6-2.73162

0.008231

0.6894 -2.53699

HAS2AS -1.04622.59579

7-2.72026

0.008487

0.6894 -2.56169

MIOX1.38254

52.09935

32.71589

90.00858

70.6894 -2.57114

ZDHHC111.18308

45.84667

12.71322

10.00864

90.6894 -2.57694

PCDHB6 -1.423584.72073

1-2.69684

0.009037

0.6894 -2.61232

FTCD1.15463

21.96121

2.647279

0.010311

0.705841 -2.71842

LRRTM1 -1.198051.43468

4-2.63601

0.010623

0.714931 -2.74233

CKMT21.10296

52.78022

12.62670

10.01088

70.714931 -2.76203

KCNA4 -1.081921.45096

9-2.62538

0.010925

0.714931 -2.76482

BMPER -1.14934.42606

5-2.59193

0.011927

0.714931 -2.83512

CCL191.63379

77.03591

82.55109

50.01326

20.714931 -2.91996

CA31.08452

63.86080

82.53882

10.01369 0.721945 -2.94526

GOLGA8C1.06653

91.64043

72.51801

40.01444

20.730697 -2.98793

C12orf39 -1.24491.33122

7-2.51232

0.014655

0.730697 -2.99957

NOS1 -1.358672.70115

3-2.50965

0.014755

0.730697 -3.00501

MAGEA1 -2.40993.85353

1-2.50031

0.015112

0.730697 -3.02402

PABPC1L1.00731

98.40477

32.49826

0.015191

0.730697 -3.02818

LOC100131726

-1.217964.18668

5-2.49475

0.015328

0.730697 -3.03532

GABRA2 -1.0841.16890

2-2.49389

0.015362

0.730697 -3.03706

LGALS21.08298

73.79723

92.48955

20.01553

20.730697 -3.04585

ADAMTS19 -1.165570.83188

4-2.48395

0.015756

0.730697 -3.05719

ADH1C2.17976

55.68457

32.48276

40.01580

30.730697 -3.05958

SLC30A10 -1.05045 1.32916 -2.482220.01582

50.730697 -3.06068

PSORS1C2 1.34559 2.361862.46967

70.01633

70.730697 -3.08598

ZYG11A 1.28215.50069

82.46860

60.01638

10.730697 -3.08813

TSKS1.05059

71.79009

52.46790

70.01641 0.730697 -3.08954

CCR101.03771

93.94787

62.46528

20.01652 0.730697 -3.09482

TM7SF41.14242

33.94772

42.45201

80.01708

30.730697 -3.12142

UNC5D -1.31481.50740

2-2.44054

0.017584

0.730958 -3.14435

PASD1 -1.328951.05877

9-2.43685

0.017748

0.730958 -3.15171

DUSP131.41696

83.18931

62.43644

0.017767

0.730958 -3.15252

NCCRP1 -1.598315.79897

1-2.43215

0.017959

0.730958 -3.16106

HLA-DQB21.22656

17.49600

62.43194

10.01796

90.730958 -3.16147

MST1P21.03910

36.51342

62.42479

90.01829

30.731066 -3.17566

ACSM51.00356

52.65786 2.4221

0.018418

0.731224 -3.18101

MUC151.37181

36.48726

82.40441

10.01925 0.737346 -3.21596

GLI2 -1.02179 6.76626 -2.397050.01960

60.738871 -3.23045

TSPYL5 -1.10624 8.92384 -2.371780.02087

40.738871 -3.27989

LCT1.29624

21.72813

42.36352

80.02130

30.742647 -3.29595

PCDHB17 -1.04442.77495

3-2.35544

0.021732

0.743464 -3.31163

GLYATL21.62424

22.43453

72.35394

80.02181

20.743464 -3.31453

NCRNA001051.02824

85.67992

72.34902

70.02207

70.747975 -3.32405

PCDHGB5 -1.331085.78508

7-2.34329

0.022391

0.752388 -3.33513

PLA1A1.10802

65.76255

52.33568

40.02281

20.752388 -3.34979

HOMER21.01279

77.60315

32.33328

0.022946

0.752388 -3.35441

C1orf161 -1.11253.94988

1-2.32805

0.023242

0.752388 -3.36446

BNIPL1.16021

97.01440

32.31594

0.023938

0.752388 -3.38764

COL9A21.20008

18.09042

72.30279

0.024716

0.758232 -3.41272

HSF41.01086

16.87678

92.29818

30.02499

30.758232 -3.42147

LEFTY1 -1.05562.87726

3-2.29633

0.025106

0.758232 -3.42498

CYP4Z11.00704

21.83374

2.279884

0.026124

0.758232 -3.45611

MESP11.04374

55.16345

62.25891

20.02747

40.758232 -3.49552

GPD11.07032

94.54613

92.25055

10.02803 0.758232 -3.51115

MAGEC1 -1.686442.76814

2-2.25025 0.02805 0.758232 -3.51171

MAPK151.10389

45.13425

2.243043

0.028537

0.758232 -3.52514

NTSR1 -1.43173 2.21385 -2.2370.02895

20.758232 -3.53638

PRODH1.21901

68.85877

32.23556

60.02905 0.758232 -3.53903

PTPRR -1.05591 4.19124 -2.234330.02913

60.758232 -3.54133

ZFP42 -1.743832.60912

9-2.22849

0.029543

0.758232 -3.55215

CXCL131.44642

38.17065

2.219555

0.030177

0.758448 -3.56866

TMEM2131.30518

73.10531

2.206322

0.031137

0.759638 -3.59302

KLHL4 -1.116663.75531

9-2.20077

0.031548

0.759638 -3.6032

EDAR -1.246393.79978

1-2.1996

0.031635

0.759638 -3.60534

CD1E1.05726

83.99941

12.19221

10.03219 0.76227 -3.61885

AJAP1 -1.18159 2.5557 -2.184890.03274

80.765075 -3.63221

GATA4 -1.497042.61174

2-2.17205

0.033749

0.769278 -3.65553

DPPA2 -1.157191.11764

2-2.16566

0.034256

0.772019 -3.66709

ALB1.03334

81.72862

32.15837

40.03484

30.772992 -3.68024

CYP2D61.04964

24.15136

92.14756

40.03573 0.775971 -3.69968

ECEL1 -1.520794.22018

7-2.14435

0.035998

0.775971 -3.70544

EPHA6 -1.153792.42628

1-2.13713

0.036605

0.775971 -3.71837

PALM31.18574

55.75340

22.13100

50.03712

70.77623 -3.7293

CTAG2 -1.926752.87426

1-2.13011

0.037204

0.776248 -3.73089

CD1771.63878

15.48399

72.12932

60.03727

10.776873 -3.73229

DNAJB131.10238

74.29773

92.1277

0.037412

0.777084 -3.73519

KLB1.01903

94.17403

52.11834

20.03822

70.779243 -3.75182

FGF5 -1.079481.79446

6-2.1181

0.038249

0.779243 -3.75225

PCDHB5 -1.167426.46210

5-2.11422

0.038592

0.779243 -3.75913

CYP4F121.25064

53.75651

32.09189

60.04061

70.779243 -3.79849

C4orf71.83416

54.27980

52.08670

30.04110

20.779243 -3.80759

HSD17B131.12819

72.98134

2.083463

0.041406

0.779243 -3.81326

GDF5 1.147293.70068

72.08280

60.04146

80.779243 -3.81441

MAGEA8 -1.277571.67556

9-2.07528

0.042185

0.779567 -3.82756

PCK1 -1.424831.76076

6-2.06872

0.042818

0.781907 -3.83897

LOC100133469

-1.642811.90315

2-2.06833

0.042855

0.781907 -3.83965

MST1P91.18695

86.00719

22.05775

50.04389

40.783221 -3.85799

ANKRD1 -1.038763.81991

1-2.05739 0.04393 0.783221 -3.85863

MPPED11.43048

11.97852

42.05332

60.04433

50.783221 -3.86565

TFF1 -1.461873.22649

5-2.03256

0.046456

0.78566 -3.90137

PCDHA1 -1.21527 3.55384 -2.016450.04816

20.788116 -3.92887

ADAMTS18 -1.110483.52257

9-2.01617

0.048191

0.788116 -3.92934

UCA1 -1.454253.68230

5-2.01463

0.048357

0.788473 -3.93196

C10orf811.56456

85.89625

52.00942

10.04892

20.789294 -3.9408

PCDHGB1 -1.153024.59705

5-2.00327

0.049597

0.790617 -3.95122

Table 4. Forty differentially expressed mRNA included in the signature.

Gene symbol logFC P.Valuedown-regulated 　 　

FGF19 -2.72809 0.000142ZFP42 -1.74383 0.029543

PCDHA4 -1.66471 0.00263RBM46 -1.48323 0.006821TRIM58 -1.42251 0.002092

MAGEA8 -1.27757 0.042185AKAP12 -1.2352 0.00773

LOC100131726 -1.21796 0.015328AJAP1 -1.18159 0.032748DPPA2 -1.15719 0.034256

ADAMTS18 -1.11048 0.048191NBPF4 -1.09443 0.007887PTPRR -1.05591 0.029136

LEFTY1 -1.0556 0.025106HTR1B -1.02307 0.008231

up-regulated 　 　PLIN4 1.010042 0.004292

HOMER2 1.012797 0.022946KLB 1.019039 0.038227

CCR10 1.037719 0.01652CLEC7A 1.045403 0.00734

TSKS 1.050597 0.01641RFPL3S 1.092442 0.000694PLA1A 1.108026 0.022812

HSD17B13 1.128197 0.041406FTCD 1.154632 0.010311

ZDHHC11 1.183084 0.008649PALM3 1.185745 0.037127

C1orf186 1.223768 0.003545KIAA1875 1.232977 0.004668ZYG11A 1.2821 0.016381

TMEM213 1.305187 0.031137PSORS1C2 1.34559 0.016337

DUSP13 1.416968 0.017767MPPED1 1.430481 0.044335

PLEKHG4B 1.527087 0.007441PLA2G4F 1.576732 0.000387GLYATL2 1.624242 0.021812

CD177 1.638781 0.037271TMEM63C 1.721926 0.002068

ADH1C 2.179765 0.015803

Supplementary files:

Table S1. The original data of the all patients included in analysis.