Cancer Metastasis Rev DOI 10.1007/s10555-015-9558-0
Global analysis of chromosome 1 genes among patients with lung adenocarcinoma, squamous carcinoma, large-cell carcinoma, small-cell carcinoma, or non-cancer Yong Zhang 1 & Haiyun Wang 2 & Jian Wang 1 & Lianming Bao 1 & Lingyan Wang 1 & Jiayuan Huo 1 & Xiangdong Wang 1
# Springer Science+Business Media New York 2015
Abstract The present study aimed at investigating genetic variations, specific signal pathways, or biological processes of chromosome 1 genes between subtypes and stages of lung cancer and prediction of selected targeting genes for patient survival rate. About 537 patients with lung adenocarcinoma (ADC), 140 with lung squamous carcinoma (SCC), 9 with lung large-cell carcinoma (LCC), 56 with small-cell lung cancer (SCLC), and 590 without caner were integrated from 16 databases and analyzed in the present study. Three (ASPM, CDC20, KIAA1799) or 28 genes significantly up- or downexpressed in four subtypes of lung cancer. The activated cell division and down-regulated immune responses were identified in patients with lung cancer. Keratinocyte development associated genes S100 and SPRR families dominantly upexpressed in SCC and AKT3 and NRAS in SCLC. Subtypespecific genes of ADC, SCC, LCC, or SCLC were also identified. C1orf106, CAPN8, CDC20, COL11A1, CRABP2, and NBPF9 up-expressed at four stages of ADC. Fifty six related with keratinocytes or potassium channels up-expressed in three stages of SCC. CDC20, IL10, ECM1, GABPB2, CRABP2, and COL11A1 significantly predicted the poor overall survival of ADC patients and S100A2 and TIMM17A in SCC patients. Our data indicate that a number of altered Electronic supplementary material The online version of this article (doi:10.1007/s10555-015-9558-0) contains supplementary material, which is available to authorized users. * Xiangdong Wang [email protected] 1
Zhongshan Hospital, Shanghai Institute of Clinical Bioinformatics, Fudan University Medical School, Shanghai, China
2
Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, China
chromosome 1 genes have the subtype and stage specificities of lung cancer and can be considered as diagnostic and prognosis biomarkers. Keywords Lung cancer . Genes . Bioinformatics . Data mining . Chromosome 1
1 Introduction Lung cancer becomes a leading one of diagnostic cancer cases, responsible for poor prognosis and the highest death rate in the world [1]. Lung cancer is mainly cataloged into lung adenocarcinoma (ADC), squamous-cell carcinoma (SCC), large-cell carcinoma (LCC), or small-cell lung carcinoma (SCLC). Lung cancer is a heterogeneous disease with a significant difference in the diagnosis and treatment in each subtype [2]. There is still a lack of knowledge of gene variations among lung cancer subtypes and stages for identification and validation of diagnosis and therapy, although few driven genes were proposed for lung cancer [3] Mapping and profiling of gene expression by microarray were used to detect specific genes expressed in lung cancer as diagnostic biomarkers and therapeutic targets [4–8]. It was indicated that the rationale and interaction of targeting oncogenic pathways and networks might be more important than a single oncogene [9]. The present study was to investigate and indentify genetic variations of chromosome 1 genes, specific signal pathways, and biological processes between subtypes and stages of lung cancer as potential candidates of biomarkers for future diagnosis, survival prediction, and therapy. The global analyses of different gene profile datasets and platforms [10] were performed with a special focus on chromosome 1 gene variations among ADC, SCC, LCC, SCLC, and with bioinformatics tools to systematically catalog the
Cancer Metastasis Rev
reference genes (ACTB, ALDOA, LDHA, NONO, and PGK1) for integration. Briefly, each gene expression data in a dataset was transformed to the ratio of expression data of itself with the average of reference genes. All data were integrated into one database, and a total of 3338 genes with official symbols in chromosome 1 were taken into study.
different expressions and functions of genes between lung cancer subtypes. Prediction of selected targeting genes in each subtype of lung cancer for prognosis of patients was furthermore evaluated in TCGA survival model.
2 Application of data mining tools 2.1 GEO datasets mining
2.3 Comparisons
The search keywords of Blung cancer^ and organism as Bhuman^ were used to search GSE datasets in GEO microarray database. Among the 504 dataset results, there are 27 GSE datasets containing comparative gene expression profiling between lung cancer and non-cancer tissues. There are 21 GSE datasets with pathological definitions, including ADC, SCC, LCC, and SCLC. Eventually, 16 GSE datasets with the information of data normalizing methods were enrolled in the present study. There were totally 537 ADC, 140 SCC, 9 LCC, or 56 SCLC cases and 590 non-cancer cases (Table 1), where there were 148, 56, 55, or 7 cases at stages I, II, III, or IV in ADC, and 61, 17, or 13 cases at stages I, II, or III in SCC, respectively. The stages of LCC and SCLC were not taken into account in the present study due to the insufficient cases.
Gene expression profiling data of each lung cancer subtype was compared with non-cancer tissues, respectively, to indentify significantly differential genes with 2-folds and more than 2-folds up or down change. Differential genes identified from each lung cancer subtype were further compared with other lung cancer subtypes to indentify the specific differential genes. The gene expression data of ADC and SCC were sorted according to lung cancer stage, and the data at each stage was compared with non-cancer tissue.
2.4 Gene function analyses All differential genes were enrolled in further bioinformatics analyze. We used GenCLip 2.0 (http://ci.smu.edu.cn/ GenCLip/analysis.php)[11] for gene cluster analyze, Molecule Annotation System (MAS 3.0) (http://bioinfo. capitalbio.com/mas3/)to generate Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway [12] and Gene Ontology (GO) gene function annotation (Fig. 1).
2.2 Data normalization and integration Gene expression profiling data of each dataset were restored to original data according to the normalization rule of each. The original data of each dataset was re-normalized with
Table 1
The comparative gene expression datasets in GEO database (cases)
GEO series
Adenocarcinoma
Squamous carcinoma
Large-cell carcinoma
Small-cell carcinoma
Non-carcinoma
GSE10072 GSE12472
56 0
0 35
0 0
0 0
49 28
GSE18842 GSE19804 GSE1987 GSE29249 GSE31552 GSE32665 GSE3268 GSE32863 GSE33356 GSE40275 GSE43458 GSE4824 GSE6044 GSE7670 Total
14 60 7 3 35 87 0 58 60 8 80 26 16 27 537
32 0 17 3 25 0 5 0 0 4 0 4 15 0 140
0 0 0 0 0 0 0 0 0 2 0 6 0 1 9
0 0 0 0 0 0 0 0 0 25 0 22 9 0 56
45 60 7 6 66 92 5 58 60 43 30 8 5 28 590
Cancer Metastasis Rev Fig. 1 The study design and workflow to analyze genetic variations of chromosome 1 in lung cancer. Comparative gene expression profiles of lung cancer and non-cancer tissues were enrolled from global datasets. Data were mined, standardized, re-integrated, analyzed, and compared among subtypes of stages of lung cancer. Chromosome 1 genes were specially focused and analyzed for co-expressed, subtypespecific, or stage-specific genes of lung cancer which were then validated for patient survival prediction
Search GEO for gene expression profiling database on lung cancer 16 GSE datasets with gene expression profiling of 4 lung cancer subtype and non-cancer ssue
Data mining
Data re-integraon
Data standardizaon
Data analyses
3338 genes expression data in chromosome 1
GenClip 2.0
MAS system 3.0
Co-expressed genes of lung cancer
Gene cluster
Subtype-specific genes of lung cancer
Validaon and analyses of paent survival predicon
2.5 Survival analysis Selected target genes of ADC or SCC were further analyzed for survival prediction values. Univariate associations between expression profiles and survivals were assessed by Cox regression using the coxph function from the R statistical software package Bsurvival^ [13]. Differences between survival curves and log-rank pvalues were assessed using the survdiff function of the survival package. The normalized RNA-seq data from 528 ADC samples and 532 SCC samples were obtained from Broad GDAC FIREHOSE on 7th-Nov2013. In addition, the clinical survival data were pulled from the Cancer Genome Atlas (TCGA) FTP server (https://tcga-data.nci.nih.gov/tcgafiles/ftp_auth/distro_ ftpusers/anonymous/tumour) on 6 December 2013. These data were used to build survival models. Besides, another online survival prediction database including 1715 non-small-cell lung cancer samples of ten independent datasets [14] were also used to build survival models.
2.6 Statistical analysis All values are expressed as the mean±standard error. Statistical analyses were performed using SPSS software (SPSS 18.0, SPSS Inc., Chicago, IL, USA). Values between lung cancer subtypes or between stages were measured and statistically compared using Student’s t test and Mann–Whitney U test. The statistical significance was expressed as p value less than 0.05.
KEGG pathway and GO annotaon
Stage-specific genes of lung ADC and SCC
Selected biomarkers
3 Stage-specific, type-specific, and co-expressed genes 3.1 Differential genes The number of up-expressed genes more than 2-folds in patients with ADC, SCC, LCC, or SCLC, as compared with non-cancer were 176, 379, 69, or 145, respectively. The number of down-expressed genes less than 2-folds of patients with ADC, SCC, LCC, or SCLC were 120, 253, 635, or 629, respectively, as shown in Fig. 2. More details of the information on genes are listed in Supplementary Table 1. Three genes: ASPM, CDC20, and KIAA1799, significantly up-expressed over 2-fold, while 28 genes significantly down-expressed over 2-fold in four subtypes of lung cancer (Supplementary Table S2) 3.2 Gene clusters Gene clusters associated with chromosome segregation or mitosis significantly up-expressed in four subtypes of lung cancer (Fig. 3); spindle pole in ADC (Fig. 3a), SCC (Fig. 3b), and LCC (Fig. 3c); M phase in ADC, LCC, and SCLC (Fig. 3d); cell division in SCC, LCC, and SCLC; or RNA processing in ADC and LCC. Up-expressed genes were also associated with ligase in ADC; channel activity (e.g., ion, metal, or potassium channels), cornified envelope, epidermal differentiation complex, gap junction, or keratinocyte differentiation and growth in SCC; C terminal domain, centrosomes, kinetochore, or spindle-related (assembly, checkpoint, microtubule) in LCC; as well as cell cycle arrest, DNA replication, or S phase in SCLC. Significantly down-expressed gene clusters were
Cancer Metastasis Rev
A
ADC Non-ca SCC
LCC
SCLC
B
SCC Non-ca ADC LCC
SCLC
C
LCC
Non-ca ADC SCC
SCLC
D
SCLC Non-ca ADC SCC
LCC
Fig. 2 Gene expression more than up- or down-expressed 2-folds in patients with lung adenocarcinoma (ADC), lung squamous carcinoma (SCC), lung large-cell carcinoma (LCC), or small-cell lung cancer (SCLC), as compared with non-tumor tissue. a Up- or down-expressed
genes in ADC expressed in others, b up- or down-expressed genes in SCC expressed in others, c up- or down-expressed genes in LCC expressed in others, or d up- or down-expressed genes in SCLC expressed in others
immune system and cell surface in four subtypes of lung cancer; tumor necrosis factor, inflammatory response, cell adhesion in ADC (Fig. 3e), LCC (Fig. 3g), and SCLC (Fig. 3h); extracellular matrix in SCC (Fig. 3f), LCC, and SCLC; immune response and cell activation in LCC and SCLC; cell differentiation in ADC and LCC; or cell growth in ADC and SCC. Mitogen-activated protein, wound healing, or endothelial growth factor were only indentified in ADC; focal adhesion, receptor tyrosine kinase, kinase inhibitor, or phospholipase C in SCC; or plasma membrane and transforming growth factor in LCC.
interaction, calcium signaling pathway, ECM-receptor interaction, small-cell lung cancer, cytokine-cytokine receptor interaction, synthesis and degradation of ketone bodies in SCC; starch and sucrose metabolism, olfactory transduction in LCC; or thyroid cancer, tight junction, renal cell carcinoma, pyrimidine metabolism, gap junction, MAPK signaling pathway in SCLC (Table 2). Down-regulated KEGG pathways indentified included drug metabolism cytochrome P450 and systemic lupus erythematosus (please check this with the above) in four subtypes of lung cancer; cytokine-cytokine receptor interaction, and hematopoietic cell lineage in ADC, LCC, and SCLC; complement and coagulation cascades in SCC, LCC, and SCLC; arachidonic acid metabolism, axon guidance, and MAPK signaling pathway in LCC and SCLC; or cell adhesion molecules in ADC and SCLC. The unique down-regulated KEGG pathways included hedgehog signaling pathway, TGF-beta signaling pathway, Jak-STAT signaling pathway, neuroactive ligand-receptor interaction in ADC; asthma, Fc epsilon RI signaling pathway, glycerolipid metabolism, natural killer cell-mediated cytotoxicity, pathogenic Escherichia coli
3.3 KEGG pathways Up-regulated KEGG pathways were indentified in the MAS system, including systemic lupus erythematosus in ADC, SCC, and SCLC, or cell cycle in ADC and SCLC. Other pathways included adipocytokine signaling pathway, Parkinson’s disease, ubiquitin-mediated proteolysis, focal adhesion, regulation of actin cytoskeleton in ADC; complement and coagulation cascades, neuroactive ligand-receptor
Cancer Metastasis Rev
Fig. 3 The alterations of gene clusters in patients with lung adenocarcinoma (ADC), lung squamous carcinoma (SCC), lung largecell carcinoma (LCC), or small-cell lung cancer (SCLC). The up-
expression gene cluster key word of ADC (a), SCC (b), LCC (c), or SCLC (d), and the down-expression gene cluster key word of ADC (e), SCC (f), LCC (g), or SCLC (h)
infection–EHEC and EPEC, pyrimidine metabolism, starch and sucrose metabolism in SCC; calcium signaling pathway and linoleic acid metabolism in LCC; or olfactory transduction in SCLC (Table 3).
translation initiation factor activity, or ligase activity in ADC; calcium ion binding in SCC; or alpha-amylase activity, transcription factor activity, olfactory receptor activity, magnesium ion binding, or chloride ion binding in LCC. The GO biological processes of up-expressed genes mainly included cell division, DNA-dependent regulation of transcription, or mitosis in four subtypes of lung cancer (Fig. 4b); cell cycle in ADC, LCC, and SCLC; response to drug in ADC and SCLC; signal transduction in SCC and LCC; or transcription in SCC and SCLC. Up-expressed genes in the GO biological processes were also associated with RNA splicing, regulation of mRNA processing, tRNA processing, phospholipid transfer to membrane, or nuclear pore distribution in ADC; keratinization, keratinocyte differentiation, epidermis development, cell adhesion, or protein amino acid phosphorylation in SCC; G-protein-coupled receptor protein signaling pathway, sensory perception of smell, carbohydrate metabolism, microtubule-based movement, or DNA protection in LCC;
3.4 GO gene function annotation Ten most frequent GO terms were selected for GO molecular function and biological process analysis in the present study. In GO molecular function, genes related with protein binding, nucleotide binding, ATP binding, and transferase activity were up-expressed in four subtypes of lung cancer (Fig. 4a). Genes with DNA binding were up-expressed in ADC, SCC, and SCLC; zinc ion binding, receptor activity, metal ion binding, hydrolase activity in SCC and SCLC; or microtubule motor activity in LCC and SCLC. Up-expressed genes in GO molecular function (Fig. 4a) also included RNA binding, transporter activity, cyclin-dependent protein kinase activity,
Cancer Metastasis Rev
Fig. 3 (continued)
or DNA replication, response to DNA damage stimulus, negative regulation of cell growth, or mitotic spindle checkpoint in SCLC. GO molecular functions of mainly down-expressed genes included nucleotide binding, protein binding, or receptor activity in four subtypes of lung cancer (Fig. 4c); ATP binding or calcium ion binding in ADC, LCC, and SCLC; sugar binding or dimethylaniline monooxygenase (N-oxide-forming) activity in ADC and SCC; or zinc ion binding or metal ion binding in SCC, LCC, and SCLC. Down-expressed genes were associated with monooxygenase activity, NADP binding, or receptor binding in ADC; IgE receptor activity, alpha-amylase activity, or GTPase activator activity in SCC; transcription factor activity, transferase activity, or electron carrier activity in LCC; or olfactory receptor activity, GTP binding, or oxidoreductase activity in SCLC. The GO biological processes of down-expressed genes included signal transduction, oxidation reduction, DNA-dependent regulation of transcription, or development in four subtypes of lung cancer (Fig. 4d);
keratinization in ADC, LCC, and SCLC; G-protein-coupled receptor protein signaling pathway in SCC and SCLC; or cell adhesion or immune response in LCC and SCLC. Downexpressed genes were associated negative regulation of angiogenesis, xenobiotic metabolism, protein amino acid phosphorylation, or neuron differentiation in ADC; complement activation, classical pathway, positive regulation of type I hypersensitivity, innate immune response, positive regulation of mast cell degranulation, activation of JNK activity, or negative regulation of signal transduction in SCC; cell differentiation, immune response, or ion transport in LCC; or sensory perception of smell, or complement activation-classical pathway in SCLC. 3.5 Subtype-specific genes Subtype-specific genes were defined when the expression of genes in a subtype of lung cancer different from non-cancer and other lung cancer subtypes more than 1.5-fold changes
Cancer Metastasis Rev Table 2
Activated pathway of lung cancer subtype in KEGG
Pathway ADC Adipocytokine signaling pathway Cell cycle Parkinson’s disease Ubiquitin-mediated proteolysis Systemic lupus erythematosus Focal adhesion Regulation of actin cytoskeleton SCC Complement and coagulation cascades Neuroactive ligand-receptor interaction Systemic lupus erythematosus Calcium signaling pathway ECM-receptor interaction Small-cell lung cancer Cytokine-cytokine receptor interaction Synthesis and degradation of ketone bodies LCC Starch and sucrose metabolism Olfactory transduction SCLC Cell cycle Thyroid cancer Tight junction Systemic lupus erythematosus Renal cell carcinoma Pyrimidine metabolism Gap junction MAPK signaling pathway
Gene count
Gene symbol
2 2 1 1 2 2 2
FRAP1; SLC2A1 ORC1L; ORC1L; CDC20 hCG_25371 CDC20 HIST2H4B; HIST2H4A COL11A1; LOC646821 LOC646821; IQGAP3
6 9 7 7 5 5 7 2
CR2; SERPINC1; MASP2; F5; C8A; C4BPB HTR6; AVPR1B; ADORA1; PTGER3; HTR1D; TSHB; PTAFR; PTGFR; S1PR1 ACTN2; FCGR1A; HIST3H3; C8A; FCGR2B; FCGR2C; HIST2H2BE HTR6; AVPR1B; CACNA1E; PTGER3; PTAFR; PTGFR; CACNA1A LAMB3; SV2A; COL11A1; LAMC2; SDC3 CKS1B; LAMB3; PIK3R3; LAMC2; TRAF5 TNFSF4; XCL2; IL24; IL6R; TNFSF13B; XCL1; TNFRSF25 HMGCL; HMGCS2
2 2
AMY2A; AMY2B OR2T8; OR2T6
5 2 3 3 2 2 2 3
CDC20; CDKN2C; MAD2L2; ORC1L; CDC7; ORC1L NTRK1; NRAS RAB3B; NRAS; ASH1L HIST2H2AB; HIST2H2AC; HIST3H2A SLC2A1; NRAS CTPS; UCK2 NRAS; GJD2 STMN1; NTRK1; NRAS
with statistical significance. The up-expression numbers of subtype-specific genes of ADC, SCC, LCC, or SCLC were 6, 422, 1, or 35, respectively. ADC-specific up-expression genes were CTSE, RAB25, PLA2G4A, PTCH2, C1orf106, and SLC50A1. SCC-specific up-expression genes included SPRR family (SPRR1A, SPRR1B, SPRR2B, SPRR2C, and SPRR3), and S100 family (S100A1, S100A11, S100A13, S100A2, S100A7, S100A8, S100A9). GLRX2 was the only one LCC-specific up-expression gene indentified. SCLCspecific up-expression genes include PRAMEF12, STMN1, MLLT11, DLEU2L, SYT11, TRIT1, PROX1, GNG4, BEND5 NKAIN1, and others, as listed in Supplementary Table 3. Subtype-specific down-expression gene numbers of SCC, LCC, or SCLC were 7, 166, or 14, respectively. SCC-specific down-expression genes were TSNAX-DISC1, SLC35E2B, LINC00115, NBPF10, CROCCP2, or ATP5G2P1. LCCspecific down-expression genes include MMP23A/B, GJC2,
FCGR3B/FCGR3A, CELF3, SLC35E2, CLCNKB/CLCNKA, or HIST2H2AA3. SCLC-specific down-expression genes included S100 family (S100A10, S100A11, S100A4, S100A6), BCAR3, GLTPD1, GNG12, LMNA, MT1HL1, NOTCH2NL, RAB13, RHOC, or RPS14P3 (Supplementary Table 3). 3.6 Differential genes of lung cancer stages About 157, 10, 21, and 28, or 429, 519, 521, and 642 genes significantly up-expressed or down-expressed at stages I, II, III, and IVof ADC, respectively. Of those, C1orf106, CAPN8, CDC20, COL11A1, CRABP2, or NBPF9 significantly upexpressed at all four stages of ADC, ABCA4 at stages I, II, and III, or ECM1, GABPB2, IL10, or PLA2G4A at two stages (Fig. 5). There were 154 significantly up-expressed genes at stage I of ADC as the early diagnostic markers for ADC. Ten genes, e.g., FOXO6, RAB42, AMY1C, NBPF1, GBAP1,
Cancer Metastasis Rev Table 3
Inactivated pathways of lung cancer subtype in KEGG
Pathway
Gene count
Gene symbol
ADC Hedgehog signaling pathway
2
BMP8A; WNT3A
Drug metabolism—cytochrome P450
2
FMO2; FMO4
TGF-beta signaling pathway
2
BMP8A; ID3
Hematopoietic cell lineage
2
CD34; CSF3R
Cell adhesion molecules (CAMs)
2
SELE; CD34
Systemic lupus erythematosus
2
HIST3H3; FCGR3B
Jak-STAT signaling pathway
2
LEPR; CSF3R
Neuroactive ligand-receptor interaction
2
S1PR1; LEPR; S1PR1
Cytokine-cytokine receptor interaction
2
LEPR; CSF3R
SCC Complement and coagulation cascades
4
C1QA; C1QC; C4BPA; C1QB
Systemic lupus erythematosus
4
HIST2H3D; C1QA; C1QC; C1QB
Asthma
2
FCER1G; FCER1A
Glycerolipid metabolism
2
ALDH9A1; PPAP2B
Starch and sucrose metabolism
2
AMY1C; AMY2A
Pathogenic Escherichia coli infection—EHEC
2
ARHGEF2; ARPC5
Pathogenic Escherichia coli infection—EPEC
2
ARHGEF2; ARPC5
Drug metabolism—cytochrome P450
2
FMO2; FMO4
Fc epsilon RI signaling pathway
2
FCER1G; FCER1A
Pyrimidine metabolism
2
POLR3GL; CMPK1
Natural killer cell-mediated cytotoxicity
2
FCER1G; SH2D1B
LCC Complement and coagulation cascades
10
C1QA; C1QB; SERPINC1; C4BPA; CFH; C8B; CR2; F13B; CD55; C8A
Hematopoietic cell lineage
10
CD1D; CSF3R; IL6R; CD1C; CSF1; CD2; FCGR1A; CD1B; CR2; CD55
Systemic lupus erythematosus
11
C1QA; HIST3H3; HIST2H2AA3; C1QB; FCGR3B; IL10; FCGR2C; ACTN2; FCGR1A; C8B; C8A
Axon guidance
10
EFNA1; SEMA4A; PLXNA2; SRGAP3; SRGAP2; SEMA6C; CDC42; EFNA3; NTNG1; EFNA4
MAPK signaling pathway
13
PLA2G2A; JUN; MAP3K6; NGF; RPS6KA1; PLA2G2D; PLA2G5; HSPA6; CDC42; CACNA1E; MAPKAPK2; CACNA1A; PTPN7
Arachidonic acid metabolism
6
PLA2G2A; CYP2J2; PLA2G2D; PLA2G5; CYP4A22; CYP4A11
Cytokine-cytokine receptor interaction
11
TNFRSF14; TNFRSF1B; IL10; XCL2; TNFSF4; CSF3R; IL6R; CSF1; XCL1; MPL; TNFRSF25
Calcium signaling pathway
9
RYR2; AVPR1B; ITPKB; ATP2B4; HTR6; CACNA1E; CHRM3; PTAFR; CACNA1A
Drug metabolism—cytochrome P450
6
FMO3; FMO2; FMO4; FMO1; GSTM5; FMO5
Linoleic acid metabolism
4
PLA2G2A; CYP2J2; PLA2G2D; PLA2G5
26
OR11L1; OR6Y1; OR6N2; OR2M5; OR2L3; OR10Z1; OR10J3; OR2T10; OR2B11; OR6K2; OR2T4; OR2T33; OR2G6; OR10K2; OR2M2; OR6F1; OR6K6; OR10J5; OR2L8; OR2G2; OR2W3; OR6N1; OR10T2; CLCA2; OR2G3; OR2T12
SCLC Olfactory transduction
Complement and coagulation cascades
11
C4BPA; CD55; C1QA; CFH; C1QB; C1QC; F3; SERPINC1; C8B; F5; CD46
Systemic lupus erythematosus
12
HIST3H3; C1QA; C1QB; FCGR2A; FCGR2C; HIST2H2AA3; IL10; C1QC; FCGR3B; C8B; FCGR1A; HIST2H2BF
Hematopoietic cell lineage
8
CD55; CSF3R; IL6R; CD1D; CD2; CSF1; CD1C; FCGR1A
Drug metabolism—cytochrome P450
7
FMO2; FMO4; FMO3; FMO5; GSTM1; GSTM5; GSTM2
Cytokine-cytokine receptor interaction
11
XCL2; TNFRSF14; CSF3R; TNFRSF1B; IL6R; IL10; TNFSF4; TGFB2; CSF1; IL24; IL28RA
Cell adhesion molecules (CAMs)
7
SELE; PTPRC; CD58; SELL; CNTN1; CD2; SELP
MAPK signaling pathway
9
GNG12; MAP3K6; TGFB2; CDC42; RPS6KA1; HSPA6; PLA2G2A; PLA2G5; NGF
Axon guidance
6
EFNA1; EPHA2; CDC42; SEMA4A; SRGAP3; SRGAP2
Arachidonic acid metabolism
4
PTGS2; CYP2J2; PLA2G2A; PLA2G5
Cancer Metastasis Rev
Fig. 4 Molecular function of chromosome 1 genes in patients with lung adenocarcinoma (ADC), lung squamous carcinoma (SCC), lung largecell carcinoma (LCC), or small-cell lung cancer (SCLC), including up-
expressed genes in GO molecular function (a) or biological processes (b) and down-expressed genes in GO molecular function (c) or biological processes (d)
Cancer Metastasis Rev
Fig. 4 (continued)
Cancer Metastasis Rev
C1orf106
CAPN8
0.25 0.2
CDC20
0.2 **
**
**
**
**
0.15 **
0.15
0.15
0.1
**
0.1 0.05
0.05
0
0
0
CRABP2 ** **
0.25 0.2 0.15 0.1 0.05 0
**
PLA2G4A 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0
**
NBPF9
** **
**
0.1
0.05
0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0
*
0.2
**
*
**
**
1 0.8 0.6 0.4 0.2 0
0.07 0.06 0.05 0.04 0.03 0.02 0.01 0
**
** **
**
**
ECM1 0.4
**
0.3 0.2
**
** **
0.1 0
IL-10 *
**
**
**
ABCA4 **
**
0.12 0.1 0.08 0.06 0.04 0.02 0
GABPB2 *
*
COL11A1 *
0.25
*
0.25 0.2 0.15 0.1 0.05 0
*
*
*
Fig. 5 The expression of stage-specific genes (ZY: stage-specific genes should be significantly expressed by stages) in patients with lung adenocarcinoma (ADC). *p
Global analysis of chromosome 1 genes among patients with lung adenocarcinoma, squamous carcinoma, large-cell carcinoma, small-cell carcinoma, or non-cancer Yong Zhang 1 & Haiyun Wang 2 & Jian Wang 1 & Lianming Bao 1 & Lingyan Wang 1 & Jiayuan Huo 1 & Xiangdong Wang 1
# Springer Science+Business Media New York 2015
Abstract The present study aimed at investigating genetic variations, specific signal pathways, or biological processes of chromosome 1 genes between subtypes and stages of lung cancer and prediction of selected targeting genes for patient survival rate. About 537 patients with lung adenocarcinoma (ADC), 140 with lung squamous carcinoma (SCC), 9 with lung large-cell carcinoma (LCC), 56 with small-cell lung cancer (SCLC), and 590 without caner were integrated from 16 databases and analyzed in the present study. Three (ASPM, CDC20, KIAA1799) or 28 genes significantly up- or downexpressed in four subtypes of lung cancer. The activated cell division and down-regulated immune responses were identified in patients with lung cancer. Keratinocyte development associated genes S100 and SPRR families dominantly upexpressed in SCC and AKT3 and NRAS in SCLC. Subtypespecific genes of ADC, SCC, LCC, or SCLC were also identified. C1orf106, CAPN8, CDC20, COL11A1, CRABP2, and NBPF9 up-expressed at four stages of ADC. Fifty six related with keratinocytes or potassium channels up-expressed in three stages of SCC. CDC20, IL10, ECM1, GABPB2, CRABP2, and COL11A1 significantly predicted the poor overall survival of ADC patients and S100A2 and TIMM17A in SCC patients. Our data indicate that a number of altered Electronic supplementary material The online version of this article (doi:10.1007/s10555-015-9558-0) contains supplementary material, which is available to authorized users. * Xiangdong Wang [email protected] 1
Zhongshan Hospital, Shanghai Institute of Clinical Bioinformatics, Fudan University Medical School, Shanghai, China
2
Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, China
chromosome 1 genes have the subtype and stage specificities of lung cancer and can be considered as diagnostic and prognosis biomarkers. Keywords Lung cancer . Genes . Bioinformatics . Data mining . Chromosome 1
1 Introduction Lung cancer becomes a leading one of diagnostic cancer cases, responsible for poor prognosis and the highest death rate in the world [1]. Lung cancer is mainly cataloged into lung adenocarcinoma (ADC), squamous-cell carcinoma (SCC), large-cell carcinoma (LCC), or small-cell lung carcinoma (SCLC). Lung cancer is a heterogeneous disease with a significant difference in the diagnosis and treatment in each subtype [2]. There is still a lack of knowledge of gene variations among lung cancer subtypes and stages for identification and validation of diagnosis and therapy, although few driven genes were proposed for lung cancer [3] Mapping and profiling of gene expression by microarray were used to detect specific genes expressed in lung cancer as diagnostic biomarkers and therapeutic targets [4–8]. It was indicated that the rationale and interaction of targeting oncogenic pathways and networks might be more important than a single oncogene [9]. The present study was to investigate and indentify genetic variations of chromosome 1 genes, specific signal pathways, and biological processes between subtypes and stages of lung cancer as potential candidates of biomarkers for future diagnosis, survival prediction, and therapy. The global analyses of different gene profile datasets and platforms [10] were performed with a special focus on chromosome 1 gene variations among ADC, SCC, LCC, SCLC, and with bioinformatics tools to systematically catalog the
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reference genes (ACTB, ALDOA, LDHA, NONO, and PGK1) for integration. Briefly, each gene expression data in a dataset was transformed to the ratio of expression data of itself with the average of reference genes. All data were integrated into one database, and a total of 3338 genes with official symbols in chromosome 1 were taken into study.
different expressions and functions of genes between lung cancer subtypes. Prediction of selected targeting genes in each subtype of lung cancer for prognosis of patients was furthermore evaluated in TCGA survival model.
2 Application of data mining tools 2.1 GEO datasets mining
2.3 Comparisons
The search keywords of Blung cancer^ and organism as Bhuman^ were used to search GSE datasets in GEO microarray database. Among the 504 dataset results, there are 27 GSE datasets containing comparative gene expression profiling between lung cancer and non-cancer tissues. There are 21 GSE datasets with pathological definitions, including ADC, SCC, LCC, and SCLC. Eventually, 16 GSE datasets with the information of data normalizing methods were enrolled in the present study. There were totally 537 ADC, 140 SCC, 9 LCC, or 56 SCLC cases and 590 non-cancer cases (Table 1), where there were 148, 56, 55, or 7 cases at stages I, II, III, or IV in ADC, and 61, 17, or 13 cases at stages I, II, or III in SCC, respectively. The stages of LCC and SCLC were not taken into account in the present study due to the insufficient cases.
Gene expression profiling data of each lung cancer subtype was compared with non-cancer tissues, respectively, to indentify significantly differential genes with 2-folds and more than 2-folds up or down change. Differential genes identified from each lung cancer subtype were further compared with other lung cancer subtypes to indentify the specific differential genes. The gene expression data of ADC and SCC were sorted according to lung cancer stage, and the data at each stage was compared with non-cancer tissue.
2.4 Gene function analyses All differential genes were enrolled in further bioinformatics analyze. We used GenCLip 2.0 (http://ci.smu.edu.cn/ GenCLip/analysis.php)[11] for gene cluster analyze, Molecule Annotation System (MAS 3.0) (http://bioinfo. capitalbio.com/mas3/)to generate Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway [12] and Gene Ontology (GO) gene function annotation (Fig. 1).
2.2 Data normalization and integration Gene expression profiling data of each dataset were restored to original data according to the normalization rule of each. The original data of each dataset was re-normalized with
Table 1
The comparative gene expression datasets in GEO database (cases)
GEO series
Adenocarcinoma
Squamous carcinoma
Large-cell carcinoma
Small-cell carcinoma
Non-carcinoma
GSE10072 GSE12472
56 0
0 35
0 0
0 0
49 28
GSE18842 GSE19804 GSE1987 GSE29249 GSE31552 GSE32665 GSE3268 GSE32863 GSE33356 GSE40275 GSE43458 GSE4824 GSE6044 GSE7670 Total
14 60 7 3 35 87 0 58 60 8 80 26 16 27 537
32 0 17 3 25 0 5 0 0 4 0 4 15 0 140
0 0 0 0 0 0 0 0 0 2 0 6 0 1 9
0 0 0 0 0 0 0 0 0 25 0 22 9 0 56
45 60 7 6 66 92 5 58 60 43 30 8 5 28 590
Cancer Metastasis Rev Fig. 1 The study design and workflow to analyze genetic variations of chromosome 1 in lung cancer. Comparative gene expression profiles of lung cancer and non-cancer tissues were enrolled from global datasets. Data were mined, standardized, re-integrated, analyzed, and compared among subtypes of stages of lung cancer. Chromosome 1 genes were specially focused and analyzed for co-expressed, subtypespecific, or stage-specific genes of lung cancer which were then validated for patient survival prediction
Search GEO for gene expression profiling database on lung cancer 16 GSE datasets with gene expression profiling of 4 lung cancer subtype and non-cancer ssue
Data mining
Data re-integraon
Data standardizaon
Data analyses
3338 genes expression data in chromosome 1
GenClip 2.0
MAS system 3.0
Co-expressed genes of lung cancer
Gene cluster
Subtype-specific genes of lung cancer
Validaon and analyses of paent survival predicon
2.5 Survival analysis Selected target genes of ADC or SCC were further analyzed for survival prediction values. Univariate associations between expression profiles and survivals were assessed by Cox regression using the coxph function from the R statistical software package Bsurvival^ [13]. Differences between survival curves and log-rank pvalues were assessed using the survdiff function of the survival package. The normalized RNA-seq data from 528 ADC samples and 532 SCC samples were obtained from Broad GDAC FIREHOSE on 7th-Nov2013. In addition, the clinical survival data were pulled from the Cancer Genome Atlas (TCGA) FTP server (https://tcga-data.nci.nih.gov/tcgafiles/ftp_auth/distro_ ftpusers/anonymous/tumour) on 6 December 2013. These data were used to build survival models. Besides, another online survival prediction database including 1715 non-small-cell lung cancer samples of ten independent datasets [14] were also used to build survival models.
2.6 Statistical analysis All values are expressed as the mean±standard error. Statistical analyses were performed using SPSS software (SPSS 18.0, SPSS Inc., Chicago, IL, USA). Values between lung cancer subtypes or between stages were measured and statistically compared using Student’s t test and Mann–Whitney U test. The statistical significance was expressed as p value less than 0.05.
KEGG pathway and GO annotaon
Stage-specific genes of lung ADC and SCC
Selected biomarkers
3 Stage-specific, type-specific, and co-expressed genes 3.1 Differential genes The number of up-expressed genes more than 2-folds in patients with ADC, SCC, LCC, or SCLC, as compared with non-cancer were 176, 379, 69, or 145, respectively. The number of down-expressed genes less than 2-folds of patients with ADC, SCC, LCC, or SCLC were 120, 253, 635, or 629, respectively, as shown in Fig. 2. More details of the information on genes are listed in Supplementary Table 1. Three genes: ASPM, CDC20, and KIAA1799, significantly up-expressed over 2-fold, while 28 genes significantly down-expressed over 2-fold in four subtypes of lung cancer (Supplementary Table S2) 3.2 Gene clusters Gene clusters associated with chromosome segregation or mitosis significantly up-expressed in four subtypes of lung cancer (Fig. 3); spindle pole in ADC (Fig. 3a), SCC (Fig. 3b), and LCC (Fig. 3c); M phase in ADC, LCC, and SCLC (Fig. 3d); cell division in SCC, LCC, and SCLC; or RNA processing in ADC and LCC. Up-expressed genes were also associated with ligase in ADC; channel activity (e.g., ion, metal, or potassium channels), cornified envelope, epidermal differentiation complex, gap junction, or keratinocyte differentiation and growth in SCC; C terminal domain, centrosomes, kinetochore, or spindle-related (assembly, checkpoint, microtubule) in LCC; as well as cell cycle arrest, DNA replication, or S phase in SCLC. Significantly down-expressed gene clusters were
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A
ADC Non-ca SCC
LCC
SCLC
B
SCC Non-ca ADC LCC
SCLC
C
LCC
Non-ca ADC SCC
SCLC
D
SCLC Non-ca ADC SCC
LCC
Fig. 2 Gene expression more than up- or down-expressed 2-folds in patients with lung adenocarcinoma (ADC), lung squamous carcinoma (SCC), lung large-cell carcinoma (LCC), or small-cell lung cancer (SCLC), as compared with non-tumor tissue. a Up- or down-expressed
genes in ADC expressed in others, b up- or down-expressed genes in SCC expressed in others, c up- or down-expressed genes in LCC expressed in others, or d up- or down-expressed genes in SCLC expressed in others
immune system and cell surface in four subtypes of lung cancer; tumor necrosis factor, inflammatory response, cell adhesion in ADC (Fig. 3e), LCC (Fig. 3g), and SCLC (Fig. 3h); extracellular matrix in SCC (Fig. 3f), LCC, and SCLC; immune response and cell activation in LCC and SCLC; cell differentiation in ADC and LCC; or cell growth in ADC and SCC. Mitogen-activated protein, wound healing, or endothelial growth factor were only indentified in ADC; focal adhesion, receptor tyrosine kinase, kinase inhibitor, or phospholipase C in SCC; or plasma membrane and transforming growth factor in LCC.
interaction, calcium signaling pathway, ECM-receptor interaction, small-cell lung cancer, cytokine-cytokine receptor interaction, synthesis and degradation of ketone bodies in SCC; starch and sucrose metabolism, olfactory transduction in LCC; or thyroid cancer, tight junction, renal cell carcinoma, pyrimidine metabolism, gap junction, MAPK signaling pathway in SCLC (Table 2). Down-regulated KEGG pathways indentified included drug metabolism cytochrome P450 and systemic lupus erythematosus (please check this with the above) in four subtypes of lung cancer; cytokine-cytokine receptor interaction, and hematopoietic cell lineage in ADC, LCC, and SCLC; complement and coagulation cascades in SCC, LCC, and SCLC; arachidonic acid metabolism, axon guidance, and MAPK signaling pathway in LCC and SCLC; or cell adhesion molecules in ADC and SCLC. The unique down-regulated KEGG pathways included hedgehog signaling pathway, TGF-beta signaling pathway, Jak-STAT signaling pathway, neuroactive ligand-receptor interaction in ADC; asthma, Fc epsilon RI signaling pathway, glycerolipid metabolism, natural killer cell-mediated cytotoxicity, pathogenic Escherichia coli
3.3 KEGG pathways Up-regulated KEGG pathways were indentified in the MAS system, including systemic lupus erythematosus in ADC, SCC, and SCLC, or cell cycle in ADC and SCLC. Other pathways included adipocytokine signaling pathway, Parkinson’s disease, ubiquitin-mediated proteolysis, focal adhesion, regulation of actin cytoskeleton in ADC; complement and coagulation cascades, neuroactive ligand-receptor
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Fig. 3 The alterations of gene clusters in patients with lung adenocarcinoma (ADC), lung squamous carcinoma (SCC), lung largecell carcinoma (LCC), or small-cell lung cancer (SCLC). The up-
expression gene cluster key word of ADC (a), SCC (b), LCC (c), or SCLC (d), and the down-expression gene cluster key word of ADC (e), SCC (f), LCC (g), or SCLC (h)
infection–EHEC and EPEC, pyrimidine metabolism, starch and sucrose metabolism in SCC; calcium signaling pathway and linoleic acid metabolism in LCC; or olfactory transduction in SCLC (Table 3).
translation initiation factor activity, or ligase activity in ADC; calcium ion binding in SCC; or alpha-amylase activity, transcription factor activity, olfactory receptor activity, magnesium ion binding, or chloride ion binding in LCC. The GO biological processes of up-expressed genes mainly included cell division, DNA-dependent regulation of transcription, or mitosis in four subtypes of lung cancer (Fig. 4b); cell cycle in ADC, LCC, and SCLC; response to drug in ADC and SCLC; signal transduction in SCC and LCC; or transcription in SCC and SCLC. Up-expressed genes in the GO biological processes were also associated with RNA splicing, regulation of mRNA processing, tRNA processing, phospholipid transfer to membrane, or nuclear pore distribution in ADC; keratinization, keratinocyte differentiation, epidermis development, cell adhesion, or protein amino acid phosphorylation in SCC; G-protein-coupled receptor protein signaling pathway, sensory perception of smell, carbohydrate metabolism, microtubule-based movement, or DNA protection in LCC;
3.4 GO gene function annotation Ten most frequent GO terms were selected for GO molecular function and biological process analysis in the present study. In GO molecular function, genes related with protein binding, nucleotide binding, ATP binding, and transferase activity were up-expressed in four subtypes of lung cancer (Fig. 4a). Genes with DNA binding were up-expressed in ADC, SCC, and SCLC; zinc ion binding, receptor activity, metal ion binding, hydrolase activity in SCC and SCLC; or microtubule motor activity in LCC and SCLC. Up-expressed genes in GO molecular function (Fig. 4a) also included RNA binding, transporter activity, cyclin-dependent protein kinase activity,
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Fig. 3 (continued)
or DNA replication, response to DNA damage stimulus, negative regulation of cell growth, or mitotic spindle checkpoint in SCLC. GO molecular functions of mainly down-expressed genes included nucleotide binding, protein binding, or receptor activity in four subtypes of lung cancer (Fig. 4c); ATP binding or calcium ion binding in ADC, LCC, and SCLC; sugar binding or dimethylaniline monooxygenase (N-oxide-forming) activity in ADC and SCC; or zinc ion binding or metal ion binding in SCC, LCC, and SCLC. Down-expressed genes were associated with monooxygenase activity, NADP binding, or receptor binding in ADC; IgE receptor activity, alpha-amylase activity, or GTPase activator activity in SCC; transcription factor activity, transferase activity, or electron carrier activity in LCC; or olfactory receptor activity, GTP binding, or oxidoreductase activity in SCLC. The GO biological processes of down-expressed genes included signal transduction, oxidation reduction, DNA-dependent regulation of transcription, or development in four subtypes of lung cancer (Fig. 4d);
keratinization in ADC, LCC, and SCLC; G-protein-coupled receptor protein signaling pathway in SCC and SCLC; or cell adhesion or immune response in LCC and SCLC. Downexpressed genes were associated negative regulation of angiogenesis, xenobiotic metabolism, protein amino acid phosphorylation, or neuron differentiation in ADC; complement activation, classical pathway, positive regulation of type I hypersensitivity, innate immune response, positive regulation of mast cell degranulation, activation of JNK activity, or negative regulation of signal transduction in SCC; cell differentiation, immune response, or ion transport in LCC; or sensory perception of smell, or complement activation-classical pathway in SCLC. 3.5 Subtype-specific genes Subtype-specific genes were defined when the expression of genes in a subtype of lung cancer different from non-cancer and other lung cancer subtypes more than 1.5-fold changes
Cancer Metastasis Rev Table 2
Activated pathway of lung cancer subtype in KEGG
Pathway ADC Adipocytokine signaling pathway Cell cycle Parkinson’s disease Ubiquitin-mediated proteolysis Systemic lupus erythematosus Focal adhesion Regulation of actin cytoskeleton SCC Complement and coagulation cascades Neuroactive ligand-receptor interaction Systemic lupus erythematosus Calcium signaling pathway ECM-receptor interaction Small-cell lung cancer Cytokine-cytokine receptor interaction Synthesis and degradation of ketone bodies LCC Starch and sucrose metabolism Olfactory transduction SCLC Cell cycle Thyroid cancer Tight junction Systemic lupus erythematosus Renal cell carcinoma Pyrimidine metabolism Gap junction MAPK signaling pathway
Gene count
Gene symbol
2 2 1 1 2 2 2
FRAP1; SLC2A1 ORC1L; ORC1L; CDC20 hCG_25371 CDC20 HIST2H4B; HIST2H4A COL11A1; LOC646821 LOC646821; IQGAP3
6 9 7 7 5 5 7 2
CR2; SERPINC1; MASP2; F5; C8A; C4BPB HTR6; AVPR1B; ADORA1; PTGER3; HTR1D; TSHB; PTAFR; PTGFR; S1PR1 ACTN2; FCGR1A; HIST3H3; C8A; FCGR2B; FCGR2C; HIST2H2BE HTR6; AVPR1B; CACNA1E; PTGER3; PTAFR; PTGFR; CACNA1A LAMB3; SV2A; COL11A1; LAMC2; SDC3 CKS1B; LAMB3; PIK3R3; LAMC2; TRAF5 TNFSF4; XCL2; IL24; IL6R; TNFSF13B; XCL1; TNFRSF25 HMGCL; HMGCS2
2 2
AMY2A; AMY2B OR2T8; OR2T6
5 2 3 3 2 2 2 3
CDC20; CDKN2C; MAD2L2; ORC1L; CDC7; ORC1L NTRK1; NRAS RAB3B; NRAS; ASH1L HIST2H2AB; HIST2H2AC; HIST3H2A SLC2A1; NRAS CTPS; UCK2 NRAS; GJD2 STMN1; NTRK1; NRAS
with statistical significance. The up-expression numbers of subtype-specific genes of ADC, SCC, LCC, or SCLC were 6, 422, 1, or 35, respectively. ADC-specific up-expression genes were CTSE, RAB25, PLA2G4A, PTCH2, C1orf106, and SLC50A1. SCC-specific up-expression genes included SPRR family (SPRR1A, SPRR1B, SPRR2B, SPRR2C, and SPRR3), and S100 family (S100A1, S100A11, S100A13, S100A2, S100A7, S100A8, S100A9). GLRX2 was the only one LCC-specific up-expression gene indentified. SCLCspecific up-expression genes include PRAMEF12, STMN1, MLLT11, DLEU2L, SYT11, TRIT1, PROX1, GNG4, BEND5 NKAIN1, and others, as listed in Supplementary Table 3. Subtype-specific down-expression gene numbers of SCC, LCC, or SCLC were 7, 166, or 14, respectively. SCC-specific down-expression genes were TSNAX-DISC1, SLC35E2B, LINC00115, NBPF10, CROCCP2, or ATP5G2P1. LCCspecific down-expression genes include MMP23A/B, GJC2,
FCGR3B/FCGR3A, CELF3, SLC35E2, CLCNKB/CLCNKA, or HIST2H2AA3. SCLC-specific down-expression genes included S100 family (S100A10, S100A11, S100A4, S100A6), BCAR3, GLTPD1, GNG12, LMNA, MT1HL1, NOTCH2NL, RAB13, RHOC, or RPS14P3 (Supplementary Table 3). 3.6 Differential genes of lung cancer stages About 157, 10, 21, and 28, or 429, 519, 521, and 642 genes significantly up-expressed or down-expressed at stages I, II, III, and IVof ADC, respectively. Of those, C1orf106, CAPN8, CDC20, COL11A1, CRABP2, or NBPF9 significantly upexpressed at all four stages of ADC, ABCA4 at stages I, II, and III, or ECM1, GABPB2, IL10, or PLA2G4A at two stages (Fig. 5). There were 154 significantly up-expressed genes at stage I of ADC as the early diagnostic markers for ADC. Ten genes, e.g., FOXO6, RAB42, AMY1C, NBPF1, GBAP1,
Cancer Metastasis Rev Table 3
Inactivated pathways of lung cancer subtype in KEGG
Pathway
Gene count
Gene symbol
ADC Hedgehog signaling pathway
2
BMP8A; WNT3A
Drug metabolism—cytochrome P450
2
FMO2; FMO4
TGF-beta signaling pathway
2
BMP8A; ID3
Hematopoietic cell lineage
2
CD34; CSF3R
Cell adhesion molecules (CAMs)
2
SELE; CD34
Systemic lupus erythematosus
2
HIST3H3; FCGR3B
Jak-STAT signaling pathway
2
LEPR; CSF3R
Neuroactive ligand-receptor interaction
2
S1PR1; LEPR; S1PR1
Cytokine-cytokine receptor interaction
2
LEPR; CSF3R
SCC Complement and coagulation cascades
4
C1QA; C1QC; C4BPA; C1QB
Systemic lupus erythematosus
4
HIST2H3D; C1QA; C1QC; C1QB
Asthma
2
FCER1G; FCER1A
Glycerolipid metabolism
2
ALDH9A1; PPAP2B
Starch and sucrose metabolism
2
AMY1C; AMY2A
Pathogenic Escherichia coli infection—EHEC
2
ARHGEF2; ARPC5
Pathogenic Escherichia coli infection—EPEC
2
ARHGEF2; ARPC5
Drug metabolism—cytochrome P450
2
FMO2; FMO4
Fc epsilon RI signaling pathway
2
FCER1G; FCER1A
Pyrimidine metabolism
2
POLR3GL; CMPK1
Natural killer cell-mediated cytotoxicity
2
FCER1G; SH2D1B
LCC Complement and coagulation cascades
10
C1QA; C1QB; SERPINC1; C4BPA; CFH; C8B; CR2; F13B; CD55; C8A
Hematopoietic cell lineage
10
CD1D; CSF3R; IL6R; CD1C; CSF1; CD2; FCGR1A; CD1B; CR2; CD55
Systemic lupus erythematosus
11
C1QA; HIST3H3; HIST2H2AA3; C1QB; FCGR3B; IL10; FCGR2C; ACTN2; FCGR1A; C8B; C8A
Axon guidance
10
EFNA1; SEMA4A; PLXNA2; SRGAP3; SRGAP2; SEMA6C; CDC42; EFNA3; NTNG1; EFNA4
MAPK signaling pathway
13
PLA2G2A; JUN; MAP3K6; NGF; RPS6KA1; PLA2G2D; PLA2G5; HSPA6; CDC42; CACNA1E; MAPKAPK2; CACNA1A; PTPN7
Arachidonic acid metabolism
6
PLA2G2A; CYP2J2; PLA2G2D; PLA2G5; CYP4A22; CYP4A11
Cytokine-cytokine receptor interaction
11
TNFRSF14; TNFRSF1B; IL10; XCL2; TNFSF4; CSF3R; IL6R; CSF1; XCL1; MPL; TNFRSF25
Calcium signaling pathway
9
RYR2; AVPR1B; ITPKB; ATP2B4; HTR6; CACNA1E; CHRM3; PTAFR; CACNA1A
Drug metabolism—cytochrome P450
6
FMO3; FMO2; FMO4; FMO1; GSTM5; FMO5
Linoleic acid metabolism
4
PLA2G2A; CYP2J2; PLA2G2D; PLA2G5
26
OR11L1; OR6Y1; OR6N2; OR2M5; OR2L3; OR10Z1; OR10J3; OR2T10; OR2B11; OR6K2; OR2T4; OR2T33; OR2G6; OR10K2; OR2M2; OR6F1; OR6K6; OR10J5; OR2L8; OR2G2; OR2W3; OR6N1; OR10T2; CLCA2; OR2G3; OR2T12
SCLC Olfactory transduction
Complement and coagulation cascades
11
C4BPA; CD55; C1QA; CFH; C1QB; C1QC; F3; SERPINC1; C8B; F5; CD46
Systemic lupus erythematosus
12
HIST3H3; C1QA; C1QB; FCGR2A; FCGR2C; HIST2H2AA3; IL10; C1QC; FCGR3B; C8B; FCGR1A; HIST2H2BF
Hematopoietic cell lineage
8
CD55; CSF3R; IL6R; CD1D; CD2; CSF1; CD1C; FCGR1A
Drug metabolism—cytochrome P450
7
FMO2; FMO4; FMO3; FMO5; GSTM1; GSTM5; GSTM2
Cytokine-cytokine receptor interaction
11
XCL2; TNFRSF14; CSF3R; TNFRSF1B; IL6R; IL10; TNFSF4; TGFB2; CSF1; IL24; IL28RA
Cell adhesion molecules (CAMs)
7
SELE; PTPRC; CD58; SELL; CNTN1; CD2; SELP
MAPK signaling pathway
9
GNG12; MAP3K6; TGFB2; CDC42; RPS6KA1; HSPA6; PLA2G2A; PLA2G5; NGF
Axon guidance
6
EFNA1; EPHA2; CDC42; SEMA4A; SRGAP3; SRGAP2
Arachidonic acid metabolism
4
PTGS2; CYP2J2; PLA2G2A; PLA2G5
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Fig. 4 Molecular function of chromosome 1 genes in patients with lung adenocarcinoma (ADC), lung squamous carcinoma (SCC), lung largecell carcinoma (LCC), or small-cell lung cancer (SCLC), including up-
expressed genes in GO molecular function (a) or biological processes (b) and down-expressed genes in GO molecular function (c) or biological processes (d)
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Fig. 4 (continued)
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C1orf106
CAPN8
0.25 0.2
CDC20
0.2 **
**
**
**
**
0.15 **
0.15
0.15
0.1
**
0.1 0.05
0.05
0
0
0
CRABP2 ** **
0.25 0.2 0.15 0.1 0.05 0
**
PLA2G4A 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0
**
NBPF9
** **
**
0.1
0.05
0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0
*
0.2
**
*
**
**
1 0.8 0.6 0.4 0.2 0
0.07 0.06 0.05 0.04 0.03 0.02 0.01 0
**
** **
**
**
ECM1 0.4
**
0.3 0.2
**
** **
0.1 0
IL-10 *
**
**
**
ABCA4 **
**
0.12 0.1 0.08 0.06 0.04 0.02 0
GABPB2 *
*
COL11A1 *
0.25
*
0.25 0.2 0.15 0.1 0.05 0
*
*
*
Fig. 5 The expression of stage-specific genes (ZY: stage-specific genes should be significantly expressed by stages) in patients with lung adenocarcinoma (ADC). *p
