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Research Papers:

Eight potential biomarkers for distinguishing between lung adenocarcinoma and squamous cell carcinoma

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Oncotarget. 2017; 8:71759-71771. https://doi.org/10.18632/oncotarget.17606

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Jian Xiao, Xiaoxiao Lu, Xi Chen, Yong Zou, Aibin Liu, Wei Li, Bixiu He, Shuya He and Qiong Chen _

Abstract

Jian Xiao1, Xiaoxiao Lu1, Xi Chen2, Yong Zou1, Aibin Liu3, Wei Li4, Bixiu He1, Shuya He5 and Qiong Chen1

1Department of Geriatrics, Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, China

2Department of Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, China

3Department of Geriatrics, Xiangya Hospital of Central South University, Changsha 410008, China

4Department of Geriatrics, Clinical Laboratory, Xiangya Hospital of Central South University, Changsha 410008, China

5Department of Biochemistry & Biology, University of South China, Hengyang 421001, China

Correspondence to:

Qiong Chen, email: [email protected]

Keywords: lung cancer, adenocarcinoma, squamous cell carcinoma, biomarker, prognosis

Received: January 02, 2017    Accepted: March 29, 2017    Published: May 03, 2017

ABSTRACT

Lung adenocarcinoma (LADC) and squamous cell carcinoma (LSCC) are the most common non-small cell lung cancer histological phenotypes. Accurate diagnosis distinguishing between these two lung cancer types has clinical significance. For this study, we analyzed four Gene Expression Omnibus (GEO) datasets (GSE28571, GSE37745, GSE43580, and GSE50081). We then imported the datasets into the Gene-Cloud of Biotechnology Information online platform to identify genes differentially expressed in LADC and LSCC. We identified DSG3 (desmoglein 3), KRT5 (keratin 5), KRT6A (keratin 6A), KRT6B (keratin 6B), NKX2-1 (NK2 homeobox 1), SFTA2 (surfactant associated 2), SFTA3 (surfactant associated 3), and TMC5 (transmembrane channel-like 5) as potential biomarkers for distinguishing between LADC and LSCC. Receiver operating characteristic curve analysis suggested that KRT5 had the highest diagnostic value for discriminating between these two cancer types. Using the PrognoScan online survival analysis tool and the Kaplan-Meier Plotter, we found that high KRT6A or KRT6B levels, or low NKX2-1, SFTA3, or TMC5 levels correlated with unfavorable prognoses in LADC patients. Further studies will be needed to verify our findings in additional patient samples, and to elucidate the mechanisms of action of these potential biomarkers in non-small cell lung cancer.


INTRODUCTION

Non-small cell lung cancer (NSCLC) accounts for more than 85% of total lung cancer cases [1], and 5-year patient survival remains low at only 15.9% [1]. The most common NSCLC histological phenotypes are lung adenocarcinoma (LADC, ~50% of patients) and lung squamous cell carcinoma (LSCC, ~40% of patients) [1]. LADC cells commonly exhibit abnormal gene expression patterns and large numbers of gene mutations [2], and are characterized by specific biomarkers[37] and prognostic factors [810] that can be used to guide clinical diagnosis and treatment. LSCC cells also exhibit complex genomic alterations, including numerous gene mutations and copy number alterations [11], and are associated with particular biomarkers [1214] and prognostic factors [1517].

Accurate diagnosis of the LADC and LSCC cancer types has important significance for lung patient clinical treatment. While biomarkers that differentiate LADC from LSCC have been reported previously [1821], additional markers would help enhance diagnostic accuracy for these intractable malignant cancers. The present study identified differentially expressed genes (DEGs) between LADC and LSCC samples using comprehensive bioinformatics analyses. We identified eight potential biomarkers for discriminating LADC and LSCC, and assessed their prognostic values.

RESULTS

Study design

We imported four Gene Expression Omnibus (GEO) datasets (GSE28571, GSE37745, GSE43580, and GSE50081) into the Gene-Cloud of Biotechnology Information (GCBI) bioinformatics analysis platform (Figure 1). We extracted LADC and LSCC gene expression information from these datasets and identified DEGs between the two cancer types. From the top 10 down- or upregulated DEGs, we identified eight as potential biomarkers for discriminating LADC and LSCC. We assessed the prognostic values of these potential biomarkers using the survival analysis tools, PrognoScan and Kaplan-Meier Plotter.

Study design diagram.

Figure 1: Study design diagram. LADC: lung adenocarcinoma; LSCC: squamous cell carcinoma; DEGs: differentially expressed genes; GCBI: Gene-Cloud of Biotechnology Information.

DEGs in LADC and LSCC

Using GCBI, we identified 243, 210, 118, and 101 potential DEGs from GSE28571, GSE37745, GSE43580, and GSE50081, respectively (Figure 2, Supplementary Table 1–4). Removal of duplicate genes and expression values lacking specific gene symbols left 176 DEGs from GSE28571 (Supplementary Table 5), 153 from GSE37745 (Supplementary Table 6), 81 from GSE43580 (Supplementary Table 7) and 71 from GSE50081 (Supplementary Table 8).

Potential DEGs between LADC and LSCC.

Figure 2: Potential DEGs between LADC and LSCC. Heat maps for potential DEGs in GSE28571 (total n=243; LADC n=50; LSCC n=28) (A), GSE37745 (total n=210; LADC n=106; LSCC n=66) (B), GSE43580 (total n=118; LADC n=77; LSCC n=73) (C), and GSE50081 (total n=101; LADC n=128; LSCC n=43) (D).

Potential biomarkers for distinguishing between LADC and LSCC

Based on expression fold changes between LADC and LSCC, we selected the top 10 downregulated and upregulated DEGs from GSE28571 (Table 1), GSE37745 (Table 2), GSE43580 (Table 3), and GSE50081 (Table 4). We identified four downregulated DEGs (desmoglein 3, DSG3; keratin 5, KRT5; keratin 6A, KRT6A; keratin 6B, KRT6B) (Figure 3) and four upregulated DEGs (NK2 homeobox 1, NKX2-1; surfactant associated 2, SFTA2; surfactant associated 3, SFTA3; transmembrane channel-like 5, TMC5) (Figure 4) that were present in all four datasets. We achieved similar results via an integrated analysis based on all four datasets together (Supplementary Table 9–10). We assessed these eight genes as potential biomarkers for discriminating LADC and LSCC.

Table 1: Top 10 down- or upregulated DEGs between LADC and LSCC in lung cancer dataset, GSE28571

Probe set ID

Gene symbol

Gene description

Gene feature

Fold change

209125_at

KRT6A

keratin 6A

downregulation

-176.148978

206165_s_at

CLCA2

chloride channel accessory 2

downregulation

-90.443266

235075_at

DSG3

desmoglein 3

downregulation

-88.129812

201820_at

KRT5

keratin 5

downregulation

-82.362516

217272_s_at

SERPINB13

serpin peptidase inhibitor, clade B (ovalbumin), member 13

downregulation

-64.457025

213680_at

KRT6B

keratin 6B

downregulation

-52.540652

204455_at

DST

dystonin

downregulation

-46.258579

209863_s_at

TP63

tumor protein p63

downregulation

-45.820729

206032_at

DSC3

desmocollin 3

downregulation

-43.549951

204855_at

SERPINB5

serpin peptidase inhibitor, clade B (ovalbumin), member 5

downregulation

-39.535047

244056_at

SFTA2

surfactant associated 2

upregulation

31.032507

228979_at

SFTA3

surfactant associated 3

upregulation

27.153369

211024_s_at

NKX2-1

NK2 homeobox 1

upregulation

15.422392

219580_s_at

TMC5

transmembrane channel-like 5

upregulation

11.725501

229105_at

GPR39

G protein-coupled receptor 39

upregulation

6.443132

214033_at

ABCC6

ATP-binding cassette, sub-family C (CFTR/MRP), member 6

upregulation

6.288185

212328_at

LIMCH1

LIM and calponin homology domains 1

upregulation

6.28786

225822_at

TMEM125

transmembrane protein 125

upregulation

5.919894

230875_s_at

ATP11A

ATPase, class VI, type 11A

upregulation

5.787312

228806_at

RORC

RAR-related orphan receptor C

upregulation

5.335111

Table 2: Top 10 down- or upregulated DEGS between LADC and LSCC in lung cancer dataset, GSE37745

Probe set ID

Gene symbol

Gene description

Gene feature

Fold change

209125_at

KRT6A

keratin 6A

downregulation

-140.927

235075_at

DSG3

desmoglein 3

downregulation

-86.646

206165_s_at

CLCA2

chloride channel accessory 2

downregulation

-84.9649

201820_at

KRT5

keratin 5

downregulation

-62.2157

213680_at

KRT6B

keratin 6B

downregulation

-53.2072

206032_at

DSC3

desmocollin 3

downregulation

-47.29

209863_s_at

TP63

tumor protein p63

downregulation

-44.3825

204455_at

DST

dystonin

downregulation

-38.1615

213796_at

SPRR1A

small proline-rich protein 1A

downregulation

-36.8294

217272_s_at

SERPINB13

serpin peptidase inhibitor, clade B (ovalbumin), member 13

downregulation

-36.3898

228979_at

SFTA3

surfactant associated 3

upregulation

33.59706

244056_at

SFTA2

surfactant associated 2

upregulation

27.97213

216623_x_at

TOX3

TOX high mobility group box family member 3

upregulation

21.41014

206239_s_at

SPINK1

serine peptidase inhibitor, Kazal type 1

upregulation

17.47105

211024_s_at

NKX2-1

NK2 homeobox 1

upregulation

16.6846

223806_s_at

NAPSA

napsin A aspartic peptidase

upregulation

14.23227

37004_at

SFTPB

surfactant protein B

upregulation

12.19793

240304_s_at

TMC5

transmembrane channel-like 5

upregulation

11.27782

204424_s_at

LMO3

LIM domain only 3 (rhombotin-like 2)

upregulation

10.23422

219612_s_at

FGG

fibrinogen gamma chain

upregulation

9.826917

Table 3: Top 10 down- or upregulated DEGs between LADC and LSCC in lung cancer dataset, GSE43580

Probe set ID

Gene symbol

Gene description

Gene feature

Fold change

209125_at

KRT6A

keratin 6A

downregulation

-53.2466

235075_at

DSG3

desmoglein 3

downregulation

-45.44

206165_s_at

CLCA2

chloride channel accessory 2

downregulation

-38.0985

209863_s_at

TP63

tumor protein p63

downregulation

-28.6096

213796_at

SPRR1A

small proline-rich protein 1A

downregulation

-27.828

201820_at

KRT5

keratin 5

downregulation

-26.5195

206032_at

DSC3

desmocollin 3

downregulation

-25.687

213680_at

KRT6B

keratin 6B

downregulation

-25.5837

217272_s_at

SERPINB13

serpin peptidase inhibitor, clade B (ovalbumin), member 13

downregulation

-22.7939

209351_at

KRT14

keratin 14

downregulation

-21.4751

216623_x_at

TOX3

TOX high mobility group box family member 3

upregulation

12.48837

228979_at

SFTA3

surfactant associated 3

upregulation

9.698342

244056_at

SFTA2

surfactant associated 2

upregulation

9.34222

220393_at

LGSN

lengsin, lens protein with glutamine synthetase domain

upregulation

7.272057

223806_s_at

NAPSA

napsin A aspartic peptidase

upregulation

6.387242

211024_s_at

NKX2-1

NK2 homeobox 1

upregulation

6.235382

240304_s_at

TMC5

transmembrane channel-like 5

upregulation

5.886752

229030_at

CAPN8

calpain 8

upregulation

5.558286

209016_s_at

KRT7

keratin 7

upregulation

5.197863

206239_s_at

SPINK1

serine peptidase inhibitor, Kazal type 1

upregulation

5.028636

Table 4: Top 10 down- or upregulated DEGs between LADC and LSCC in lung cancer dataset, GSE50081

Probe set ID

Gene symbol

Gene description

Gene feature

Fold change

209125_at

KRT6A

keratin 6A

downregulation

-57.006103

213680_at

KRT6B

keratin 6B

downregulation

-39.001783

201820_at

KRT5

keratin 5

downregulation

-37.082683

207935_s_at

KRT13

keratin 13

downregulation

-23.955773

210020_x_at

CALML3

calmodulin-like 3

downregulation

-22.527441

235075_at

DSG3

desmoglein 3

downregulation

-21.167905

213796_at

SPRR1A

small proline-rich protein 1A

downregulation

-20.461997

221854_at

PKP1

plakophilin 1 (ectodermal dysplasia/skin fragility syndrome)

downregulation

-18.214428

205157_s_at

JUP

junction plakoglobin

downregulation

-17.594235

209351_at

KRT14

keratin 14

downregulation

-16.96603

228979_at

SFTA3

surfactant associated 3

upregulation

13.36924

244056_at

SFTA2

surfactant associated 2

upregulation

13.198138

211024_s_at

NKX2-1

NK2 homeobox 1

upregulation

11.03073

240304_s_at

TMC5

transmembrane channel-like 5

upregulation

8.335526

206239_s_at

SPINK1

serine peptidase inhibitor, Kazal type 1

upregulation

7.171856

209016_s_at

KRT7

keratin 7

upregulation

6.780702

204124_at

SLC34A2

solute carrier family 34 (sodium phosphate), member 2

upregulation

6.362828

204437_s_at

FOLR1

folate receptor 1 (adult)

upregulation

6.138674

229177_at

C16orf89

chromosome 16 open reading frame 89

upregulation

6.035951

204424_s_at

LMO3

LIM domain only 3 (rhombotin-like 2)

upregulation

5.987309

Venn diagram showing downregulated DEGs common to all four GEO datasets.

Figure 3: Venn diagram showing downregulated DEGs common to all four GEO datasets.

Venn diagram showing upregulated DEGs common to all four GEO datasets.

Figure 4: Venn diagram showing upregulated DEGs common to all four GEO datasets.

Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic values of DSG3, KRT5, KRT6A, KRT6B, NKX2-1, SFTA2, SFTA3, and TMC5. The four downregulated DEGs had similar areas under the curve (AUC): 0.9188 for DSG3, 0.9386 for KRT5, 0.9333 for KRT6A, and 0.9229 for KRT6B (Figure 5A). The four upregulated DEGs also had similar AUCs: 0.8723 for NKX2-1, 0.8559 for SFTA2, 0.8108 for SFTA3, and 0.8442 for TMC5 (Figure 5B). AUC results showed that KRT5 had the highest diagnostic value for discriminating LADC and LSCC.

Figure 5:

Figure 5: ROC curves for downregulated (A) and upregulated DEGs (B) in distinguishing between LADC and LSCC. TPR: true positive rate; FPR: false positive rate; AUC: area under the curve.

PrognoScan identified potential prognostic factors for LADC and LSCC patients

We assessed the prognostic values of the eight potential biomarkers using the bioinformatics analysis platform, PrognoScan. P<0.05 was considered significant in Cox regression analyses. We found that high DSG3, KRT6A, or KRT6B levels (Table 5), or low NKX2-1, SFTA3, or TMC5 levels (Table 6), were associated with unfavorable prognosis in LADC patients. However, only low NKX2-1 expression was associated with unfavorable prognosis in LSCC patients (Table 6). We speculated that DSG3, KRT6A, KRT6B, NKX2-1, SFTA3, and TMC5 might be LADC patient prognostic factors, and NKX2-1 might be an LSCC patient prognostic factor. Because each lung cancer microarray dataset in PrognoScan contained limited cases (Table 56), we verified these findings using Kaplan-Meier Plotter.

Table 5: DSG3, KRT5, KRT6A, and KRT6B prognostic values in LADC and LSCC as assessed by PrognoScan

Gene symbol

LADC

LSCC

Dataset

Case

HR (95% CIs)

P-value

Dataset

Case

HR (95% CIs)

P-value

DSG3

MICHIGAN-LC

86

2.54 (1.22–5.32)

0.013244

-

-

-

>0.05

KRT5

-

-

-

>0.05

-

-

-

>0.05

KRT6A

jacob-00182-HLM

79

1.24 (1.06–1.45)

0.006974

-

-

-

>0.05

jacob-00182-MSK

104

1.28 (1.06–1.53)

0.008562

GSE31210

204

1.39 (1.18–1.63)

0.000083

KRT6B

jacob-00182-MSK

104

1.26 (1.07–1.47)

0.005120

-

-

-

>0.05

GSE31210

204

1.47 (1.23–1.75)

0.000017

Table 6: NKX2-1, SFTA2, SFTA3, and TMC5 prognostic values in LADC and LSCC as assessed by PrognoScan

Gene symbol

LADC

LSCC

Dataset

Case

HR (95% CIs)

P-value

Dataset

Case

HR (95% CIs)

P-value

NKX2-1

jacob-00182-CANDF

82

0.78 (0.64–0.96)

0.020132

GSE17710

56

0.71 (0.52–0.97)

0.029764

jacob-00182-HLM

79

0.78 (0.63–0.97)

0.027745

MICHIGAN-LC

86

0.56 (0.36–0.87)

0.009902

GSE31210

204

0.62 (0.43–0.88)

0.008218

jacob-00182-UM

178

0.81 (0.68–0.97)

0.021112

SFTA2

-

-

-

>0.05

-

-

-

-

SFTA3

GSE13213

117

0.89 (0.79–1.00)

0.048445

-

-

-

-

GSE31210

204

0.62 (0.46–0.85)

0.003019

TMC5

jacob-00182-HLM

79

0.45 (0.24–0.84)

0.012012

-

-

-

>0.05

GSE31210

204

0.30 (0.13–0.68)

0.004014

Kaplan-meier plotter verified five LADC prognostic factors

Using Kaplan-Meier Plotter, we verified that high KRT6A (Hazard ratio, HR=1.66; 95% confidence intervals, 95% CIs: 1.31–2.11; P=1.90E-05) or KRT6B (HR=1.76; 95% CIs: 1.39–2.22; P=1.90E-06) (Figure 6, Table 7), or low NKX2-1 (HR=0.66; 95% CIs: 0.52–0.84; P=0.00051), SFTA3 (HR=0.55; 95% CIs: 0.43–0.70; P=1.20E-06), or TMC5 (HR=0.51; 95% CIs: 0.41–0.65; P=3.30E-08) (Figure 7, Table 7) levels correlated with unfavorable prognosis in LADC patients. However, no DEGs correlated with LSCC patient prognosis (Table 7). Unlike the scattered results obtained by PrognoScan, Kaplan-Meier Plotter gained the meta-analysis results and we therefore draw our conclusions based on the Kaplan-Meier Plotter findings.

Kaplan-Meier survival curves for KRT6A and KRT6B expression in LADC patients.

Figure 6: Kaplan-Meier survival curves for KRT6A and KRT6B expression in LADC patients.

Table 7: Verification of potential prognostic indicators via Kaplan-Meier Plotter

Gene symbol

LADC

LSCC

Case

HR (95% CIs)

P-value

Case

HR (95% CIs)

P-value

DSG3

673

1.09 (0.86–1.39)

0.48

271

0.86 (0.63–1.18)

0.35

KRT6A

720

1.66 (1.31–2.11)

1.90E-05

524

0.99 (0.78–1.25)

0.92

KRT6B

720

1.76 (1.39–2.22)

1.90E-06

524

0.94 (0.75–1.20)

0.63

NKX2-1

720

0.66 (0.52–0.84)

0.00051

524

0.82 (0.65–1.04)

0.11

SFTA3

673

0.55 (0.43–0.70)

1.20E-06

271

0.82 (0.60–1.11)

0.20

TMC5

720

0.51 (0.41–0.65)

3.30E-08

524

1.02 (0.8–1.29)

0.88

Kaplan-Meier survival curves for NKX2-1, SFTA3, and TMC5 expression in LADC patients.

Figure 7: Kaplan-Meier survival curves for NKX2-1, SFTA3, and TMC5 expression in LADC patients.

DISCUSSION

In this study, we imported four GEO datasets into the GCBI comprehensive analysis platform to extract LADC and LSCC gene expression data. We identified DEGs between LADC and LSCC samples through differential expression analysis in GCBI, and found that DSG3, KRT5, KRT6A, KRT6B, NKX2-1, SFTA2, SFTA3, and TMC5 were potential biomarkers for distinguishing the two cancer types. According to ROC analyses, KRT5 had the highest diagnostic value for discriminating LADC and LSCC. Finally, using the survival analysis platforms, PrognoScan and Kaplan-Meier Plotter, we found that high KRT6A or KRT6B, or low NKX2-1, SFTA3, or TMC5 levels correlated with unfavorable prognoses in LADC patients.

Previous studies reported that DSG3 [18, 21, 22], KRT5 [23], KRT6A [24], and KRT6B [24] levels were higher in LSCC than in LADC, and that NKX2-1 [2527], SFTA3 [21], and TMC5 [21] levels were higher in LADC than in LSCC, suggesting that these genes were biomarkers for differentiating between LSCC and LADC. In agreement with this, our results showed that DSG3, KRT5, KRT6A, and KRT6B were downregulated in LADC compared to LSCC, and that NKX2-1, SFTA3, and TMC5 were upregulated in LADC compared to LSCC. Our study also identified SFTA2 as a novel biomarker upregulated in LADC.

The potential biomarker, NKX2-1, binds DNA damage-binding protein 1 (DDB1) and degrades check-point kinase 1 (CHK1) to facilitate lung adenocarcinoma progression [28]. Through modulating IKKβ/NF-κB pathway activation, NKX2-1 also modulates lung adenocarcinoma by directly regulating p53 transcription [29]. However, the molecular mechanisms by which DSG3, KRT5, KRT6A, KRT6B, SFTA2, SFTA3, and TMC5 regulate NSCLC development remain unclear. DSG3 promotes epidermoid carcinoma progression by regulating activation of protein kinase C-dependent Ezrin and activator protein 1 [30]. KRT5 combines with transforming growth factor beta receptor 3 (TGFBR3) and transcription factor JunD to promote breast cancer cell growth [31]. KRT6B interacts with notch1 to promote renal carcinoma development [32]. Studies to elucidate the mechanisms of action of these biomarkers in NSCLC development and progression are warranted.

Lu C, et al. [33] and Tian [34] also extracted gene expression data from GEO profiles to identify DEGs between LADC and LSCC. Based on the GSE6044 and GSE50081 datasets, these groups identified 19 and 33 DEGs, respectively, that might discriminate between LADC and LSCC. However, these genes were not identified based on expression fold changes between LADC and LSCC. Fold change is important for detecting DEGs [3537] and guiding further research [38, 39], and our eight potential biomarkers for differentiating between LADC and LSCC were identified based on this measurement type in the GSE28571, GSE37745, GSE43580, and GSE50081 datasets. Consequently, the biomarkers reported here differ from those identified in previous studies [33, 34]. This indicates that different gene expression dataset screening methods may produce different results and the differences of molecule expression between LADC and LSCC may be far more complicated than we thought.

Previous studies have identified prognostic biomarkers in patients with LADC [10, 4044] or LSCC [4549]. While we did not identify any LSCC prognostic indictors, we found that high KRT6A or KRT6B levels, or low NKX2-1, SFTA3, or TMC5 levels correlated with an unfavorable prognosis in LADC patients. Of these prognostic factors, only NKX2-1, thought to be a tumor suppressor [50], was previously associated with LADC prognosis [26, 51]. The prognostic values of KRT6A, KRT6B, SFTA3, and TMC5 in LADC are reported here for the first time. Both KRT6A and KRT6B are type II cytokeratins and keratin 6 isoforms [52, 53]. KRT6A and KRT6B are associated with pachyonychia congenita [54, 55], as well as renal carcinoma [32] and breast cancer [56] progression. SFTA3 is an immunoregulatory protein that protects lung tissue during inflammation and is likely a lung surfactant protein family member [57]. SFTA3 is also downregulated in anaplastic thyroid carcinoma compared with normal thyroid tissue [58]. TMC5 is a transmembrane protein with at least eight membrane-spanning domains that belongs to a novel group of transporters, ion channels, or modifiers of such [59]. TMC5 is upregulated in chromophobe renal cell carcinoma [60] and intrahepatic cholangiocarcinoma [61].

In conclusion, we identified DSG3, KRT5, KRT6A, KRT6B, NKX2-1, SFTA2, SFTA3, and TMC5 as potential biomarkers for distinguishing between LADC and LSCC. Additionally, high KRT6A or KRT6B levels, or low NKX2-1, SFTA3, or TMC5 levels correlated with unfavorable LDAC patient prognosis. Further studies are required to verify our findings in additional patient samples, and to elucidate the mechanisms of action of these potential biomarkers in NSCLC.

MATERIALS AND METHODS

Gene expression omnibus datasets

The Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/gds) is a public repository at the National Center of Biotechnology Information for storing high throughput gene expression datasets. We screened potential GEO datasets according to the following inclusion criteria: 1) Homo sapiens NSCLC specimens classified as LADC or LSCC; 2) expression profiling by array; 3) performed on the GPL570 platform ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array); and 4) ≥100 samples. Datasets with specimens from other organisms, expression profiling by RT-PCR (or genome variation profiling by SNP array/SNP genotyping by SNP array), analyses on platforms other than GPL570, or sample size <100 were excluded.

We used the search terms, “((lung cancer [Title]) AND GPL570 [Related Series]) AND Homo sapiens [Organism] AND (squamous cell carcinoma [Description] OR adenocarcinoma [Description]),” to identify potential datasets within GEO. Screening using the aforementioned inclusion criteria identified four datasets (GSE28571, GSE37745, GSE43580, and GSE50081) for use in analyses of DEGs between LADC and LSCC. These datasets contained 361 LADC (50 in GSE28571, 106 in GSE37745, 77 in GSE43580, and 128 in GS50081) and 210 LSCC (28 in GSE28571, 66 in GSE37745, 73 in GSE43580, and 43 in GSE50081) fresh-frozen specimens (Tables S11–S14).

Gene-cloud of biotechnology information

Gene-Cloud of Biotechnology Information (GCBI; https://www.gcbi.com.cn/gclib/html/index), is an online comprehensive bioinformatics analysis platform that can systematically analyze GEO dataset-derived gene expression information [62]. After flagged data normalization, filtering, and quality control, we identified genes differentially expressed by >5 fold between LADC and LSCC, with the cutoff values P<0.05 and Q<0.05 using GCBI.

Prognoscan

The PrognoScan (http://www.prognoscan.org/) online database provides a powerful platform for exploring therapeutic targets, tumor markers, and prognostic factors in cancer patients [63], and contains cancer microarray datasets with corresponding clinical data. PrognoScan automatically calculates HRs, 95% CIs, and Cox P-values according to a given gene’s mRNA level (high or low).

Kaplan-meier plotter

Kaplan-Meier Plotter (http://kmplot.com/analysis/) is an online database of published microarray datasets for four cancer types (breast, ovarian, lung, and gastric cancer), and includes clinical data and gene expression information for 2,437 lung cancer patients [64]. Kaplan-Meier Plotter is useful for assessing new biomarkers related to lung cancer patient survival.

Receiver operating characteristic curve analyses

Receiver operating characteristic (ROC) curves were constructed to compare biomarker diagnostic values. Curves are created by plotting true positive rates (TPR, sensitivity) against false positive rates (FPR, 1-specificity). The area under the curve (AUC) is used to determine diagnostic accuracy. An AUC value close to 1.0 indicates high accuracy [65].

ACKNOWLEDGMENTS

We thank Qingqing LYU, Lang Ma, and Donglin Cheng from the GCBI Center for providing assistance with statistical analysis methods.

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

GRANT SUPPORT

This work was supported by the National Natural Science Foundation of China (Grant No. 81572284) and the Important Research and Development Plan of Hunan Provincial Science and Technology Department (Grant No. 2015SK20662).

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