Comprehensive analysis of the long noncoding RNA HOXA11-AS gene interaction regulatory network in NSCLC cells
Zhang et al. Cancer Cell Int
Comprehensive analysis of the long noncoding RNA HOXA11-AS gene interaction regulatory network in NSCLC cells
Yu Zhang 1 4
Rong‑quan He 0 3
Yiw‑u Dang 1 4
Xiu‑ling Zhang 1 4
Xiao Wang 2
Su‑ning Huang 5
Wen‑ting Huang 1 4
Meng‑tong Jiang 1 4
Xiao‑ning Gan 1 4
You Xie 1 4
Ping Li 1 4
Dian‑zhong Luo 1 4
Gang Chen 1 4
Ting‑qing Gan 0 3
0 Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University , No. 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning 530021 , People's Republic of China
1 Department of Pathology, First Affiliated Hospital of Guangxi Medical University , No. 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning 530021 , People's Republic of China
2 Department of Orthopedics, China‐ Japan Union Hospital of Jilin University , 2 Sendai Street, Changchun 130033 , People's Republic of China
3 Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University , No. 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning 530021 , People's Republic of China
4 Department of Pathology, First Affiliated Hospital of Guangxi Medical Uni‐ versity , No. 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nan‐ ning 530021 , People's Republic of China
5 Department of Radiotherapy, First Affiliated Hospital of Guangxi Medical University , No. 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning 530021 , People's Republic of China
Background: Long noncoding RNAs (lncRNAs) are related to different biological processes in non‑ small cell lung cancer (NSCLC). However, the possible molecular mechanisms underlying the effects of the long noncoding RNA HOXA11‑ AS (HOXA11 antisense RNA) in NSCLC are unknown. Methods: HOXA11‑ AS was knocked down in the NSCLC A549 cell line and a high throughput microarray assay was applied to detect changes in the gene profiles of the A549 cells. Bioinformatics analyses (gene ontology (GO), pathway, Kyoto Encyclopedia of Genes and Genomes (KEGG), and network analyses) were performed to investigate the potential pathways and networks of the differentially expressed genes. The molecular signatures database (MSigDB) was used to display the expression profiles of these differentially expressed genes. Furthermore, the relationships between the HOXA11‑ AS, de‑ regulated genes and clinical NSCLC parameters were verified by using NSCLC patient information from The Cancer Genome Atlas (TCGA) database. In addition, the relationship between HOXA11‑ AS expression and clinical diagnostic value was analyzed by receiver operating characteristic (ROC) curve. Results: Among the differentially expressed genes, 277 and 80 genes were upregulated and downregulated in NSCLC, respectively (fold change ≥2.0, P < 0.05 and false discovery rate (FDR) < 0.05). According to the degree of the fold change, six upregulated and three downregulated genes were selected for further investigation. Only four genes (RSPO3, ADAMTS8, DMBT1, and DOCK8) were reported to be related with the development or progression of NSCLC based on a PubMed search. Among all possible pathways, three pathways (the PI3K‑ Akt, TGF‑ beta and Hippo signaling pathways) were the most likely to be involved in NSCLC development and progression. Furthermore, we found that HOXA11‑ AS was highly expressed in both lung adenocarcinoma and squamous cell carcinoma based on TCGA database. The ROC curve showed that the area under curve (AUC) of HOXA11‑ AS was 0.727 (95% CI 0.663-0.790) for lung adenocarcinoma and 0.933 (95% CI 0.906-0.960) for squamous cell carcinoma patients. Additionally, the original data from TCGA verified that ADAMTS8, DMBT1 and DOCK8 were downregulated in both lung adenocarcinoma and squamous cell carcinoma, whereas RSPO3 expression was upregulated in lung adenocarcinoma and downregulated in lung squamous cell carcinoma. For the other five genes (STMN2, SPINK6, TUSC3, LOC100128054, and C8orf22), we © The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
found that STMN2, TUSC3 and C8orf22 were upregulated in squamous cell carcinoma and that STMN2 and USC3
were upregulated in lung adenocarcinoma. Furthermore, we compared the correlation between HOXA11‑AS and
de‑regulated genes in NSCLC based on TCGA. The results showed that the HOXA11‑AS expression was negatively
correlated with DOCK8 in squamous cell carcinoma (r = −0.124, P = 0.048) and lung adenocarcinoma (r = −0.176,
P = 0.005). In addition, RSPO3, ADAMTS8 and DOCK8 were related to overall survival and disease‑free survival (all
P < 0.05) of lung adenocarcinoma patients in TCGA.
Conclusions: Our results showed that the gene profiles were significantly changed after HOXA11‑AS knock ‑ down
in NSCLC cells. We speculated that HOXA11‑AS may play an important role in NSCLC development and progression
by regulating the expression of various pathways and genes, especially DOCK8 and TGF‑beta pathway. However, the
exact mechanism should be verified by functional experiments.
Keywords: HOXA11‑AS, NSCLC, Microarray assay, GO, KEGG, Pathway
Lung cancer is the most common cancer worldwide and
the first leading cause of cancer death [1, 2]. More than
1.8 million lung cancer patients are diagnosed each year,
accounting for approximately 13% of newly diagnosed
cancer cases . Lung cancer can be divided into two
categories based on the histological type [small cell lung
cancer (SCLC) and non-small cell lung cancer (NSCLC)].
NSCLC accounts for 80–85% of new lung cancers.
NSCLC can be divided into different subgroups, such
as adenocarcinoma, squamous cell carcinoma,
adenosquamous carcinoma, undifferentiated carcinoma and
large cell carcinoma. More than 70% of NSCLC cases are
advanced disease and the 5-year survival rate for NSCLC
is only 16% . Hence, research into the etiology and
mechanism is of great significance for the diagnosis and
treatment of lung cancer.
Long non-coding RNAs (lncRNAs) represent RNAs
more than 200 nucleotides in length that lack a
proteincoding capacity. Many lncRNAs have been reported to
be associated with transcriptional regulation, disease
development or epigenetic gene regulation [5–7].
Additionally, lncRNAs are involved in numerous biological
functions, such as tumorigenesis, immune responses, cell
differentiation and other biological processes [8–11]. To
date, many lncRNAs have been reported to play
important roles in NSCLC, such as lncRNA-TATDN1, PVT1
and MALAT1, which may influence the NSCLC cell
proliferation, invasion and metastasis, respectively [12–14].
However, the biological and molecular mechanisms
underlying the actions of HOXA11-AS in NSCLC have
not been fully explored.
HOXA11-AS (also known as HOXA11S, HOXA-AS5,
HOXA11AS, HOXA11-AS1, and NCRNA00076) is
located on 7p15.2 (NCBI Gene ID: 221883).
HOXA11AS is a member of the homeobox (HOX) family of genes
with a length of 3885 nt. To date, only 2 studies have
reported a relationship between HOXA11-AS and
cancer. Richards et al.  demonstrated that HOXA11-AS
inhibited the oncogenic phenotype of epithelial ovarian
cancer by analyzing genome-wide association study data
and performing a series of functional experiments. Wang
et al.  confirmed that HOXA11-AS was a cell
cycleassociated lncRNA and could serve as a biomarker of
glioma progression using a high-throughput microarray
and gene set enrichment analysis. However, the
expression and function of HOXA11-AS in NSCLC tissues is
unknown. We designed this study to explore
expression profile changes after HOXA11-AS knock-down and
the possible molecular mechanisms of HOXA11-AS in
NSCLC development and progression. A flow chart of
this study was shown in Fig. 1.
Knock‑down of HOXA11A‑S in the NSCLC cell A549
and transfection with HOXA11A‑S‑siRNA
The human NSCLC A549 cell line was purchased from
the Type Culture Collection of the Chinese Academy
of Sciences (Shanghai, China). NSCLC A549 cells were
cultivated with 10% heat-inactivated fetal bovine serum
(Invitrogen Corp, Grand Island, NY, USA) in a
humidified 5% CO2 atmosphere with 2 mM glutamine and
gentamicin at 37 °C. Three DcR3-specific siRNAs were
synthesized by GenePharma (Shanghai, China) and
merged into one siRNA pool (Table 1). The NSCLC A549
cell line was transfected with the HOXA11-AS-siRNA.
The CombiMAG magnetofection reagent (OZ
BIOSCIENCES, Marseille, France) was used for the
transfection according to the manufacturer’s instructions.
Microarray analysis and computational analysis
The sample analysis and microarray hybridization were
performed by Kangchen Bio-tech (Shanghai, China).
Briefly, RNA was purified and extracted from 1 mg of
total RNA after removing the rRNA (mRNA-ONLY
Eukaryotic mRNA Isolation Kit, Epicentre
Biotechnologies, Madison, USA). Then, each sample was transcribed
and amplified into fluorescent cRNA using a random
Fig. 1 A flow chart of this study was shown
Table 1 The HOXA11-AS-siRNA sequences
CGTAATCGCCGGTGTAACT GC % 52.63 52.63
priming method. The cRNAs were labeled and hybridized
onto the Human MRNA Array v3.0 (8 × 60 K, Arraystar,
Rockville, MD, USA). After washing the slides, the arrays
were scanned with the Agilent Scanner G2505C. The
Agilent Feature Extraction software (version 184.108.40.206)
was used to analyze the acquired array images.
Quantile normalization and subsequent data processing were
implemented by the GeneSpring GX v11.5.1 software
package (Agilent Technologies). Differentially expressed
genes were identified based on fold change filtering (fold
change ≥2.0 or ≤0.5), a paired t test (p < 0.05) and
multiple hypothesis testing (FDR < 0.05). The P values and
FDR were calculated with Microsoft Excel and MATLAB,
respectively. Differentially expressed genes between the
RNAi and control samples were identified with an
absolute fold change >2 as the cut-off. The molecular
signatures database (MSigDB, http://www.broadinstitute.org/
msigdb) was applied to visualize the expression profiles
of these differentially expressed genes (Figs. 2, 3).
GO analysis and pathway analysis
To better understand the potential roles of the
differentially expressed genes, gene ontology (GO) analysis and
pathway analysis were performed as previously described
. In this process, we included the following three
independent categories derived from the GO Consortium
website (http://www.geneontology.org): biological
process (BP), cellular component (CC) and molecular
function (MF) . The enrichment of the upregulated and
downregulated coding genes was analyzed by uploading
the datasets to the database for annotation, visualization
and integrated discovery (DAVID,
http://david.abcc.ncifcrf.gov/). The Kyoto Encyclopedia of Genes and Genomes
(KEGG) database (http://www.genome.jp/kegg/) was used
to analyze the biological pathways where there was an
obvious enrichment of differentially expressed genes .
Additional analysis of 9 de‑regulated genes in NSCLC
TCGA is a collection of exome sequencing, DNA
methylation, SNP array, miRNA-seq, and RNA-seq data
. TCGA can be used to analyze complicated
clinical profiles and cancer genomics [20, 21]. In this study,
original expression data for HOXA11-AS and the 9
genes de-regulated in lung adenocarcinoma and
squamous cell carcinoma were extracted from TCGA and
analyzed. Additionally, original data for cancerous or
Fig. 2 Hierarchical clustering (heat map) of transcript expression for the 280 upregulated genes with the most differential expression between
non-cancerous lung tissues were downloaded and
analyzed. Also, the relationship between HOXA11-AS
expression and clinical diagnostic value was analyzed by
receiver operating characteristic (ROC) curve. Besides,
we extracted the co-genes of HOXA11-AS from TCGA
through R Project for Statistical Computing (https://
www.r-project.org/). Genes with a FDR < 0.05 was
considered for co-expressed relationship.
Fig. 3 Hierarchical clustering (heat map) of transcript expression for the 80 downregulated genes with the most differential expression between
SPSS 20.0 was applied for the statistical analysis. The
Mann–Whitney U test was used to compare the
expression of the four de-regulated genes in terms of different
clinical features (age, gender, TNM stage, tumor size,
distant metastasis and lymph node metastasis). P < 0.05 was
considered statistically significant (two-sides).
Gene expression profiles regulated by the HOXA11A‑S
A high throughput microarray assay was applied to
detect differential expression profiles between
HOXA11AS and HOXA11-AS RNAi in three paired A549 cell
cultures. Thirteen thousand three hundred and twenty-three
upregulated genes and 14,384 downregulated genes were
differentially expressed in the HOXA11-AS-control and
HOXA11-AS-RNAi groups. A summary of these
differentially expressed genes is presented in Fig. 4. The fold
changes (HOXA11-AS-control vs HOXA11-AS-RNAi)
and P values were calculated using the normalized
expression values. Using microarray analysis, 357 genes
were identified as significantly differentially expressed in
NSCLC compared with the RNAi control samples (fold
change ≥ 2.0, P < 0.05 and FDR < 0.05). Among them,
277 genes were upregulated in all three NSCLC groups
and 80 genes were downregulated. Furthermore, the
number of aberrantly expressed genes varied with the
different fold changes (Table 2). Among them, 16 genes
were upregulated by more than sixfold in the
HOXA11AS compared to the HOXA11-AS RNAi samples and
3 genes were downregulated by more than fourfold. 6
of the 15 upregulated genes were upregulated by more
than tenfold (Table 2). The top 6 upregulated and top 3
downregulated genes are shown in Table 3. Among these
9 aberrantly expressed genes, the expression of RSPO3
(NM_032784, fold change = 41.610487, P = 8.0502E−09)
was dramatically upregulated and the expression of
LOC100128054 (NR_033969, fold change = 4.6652225,
P = 4.45517E−05) was significantly downregulated.
When we searched PubMed (http://www.ncbi.nlm.nih.
gov/pubmed) to identify reported functions for these
differentially expressed genes, we found that only 4
genes (RSPO3, ADAMTS8, DMBT1, and DOCK8) were
reported to be associated with NSCLC. RSPO3 was
reported to promote tumor aggressiveness in
Keap1-deficient lung adenocarcinomas . ADAMTS8 was related
to promoter hyper methylation in early-stage NSCLCs
[23, 24]. DMBT1 was a candidate tumor suppressor gene;
DMBT1 expression is often lost in lung cancer, indicating
that DMBT1 inactivation may have a significant influence
on lung tumorigenesis . DOCK8 was suggested to be
involved in the development and/or progression of lung
cancer . Thus, these genes may play essential roles in
the occurrence and development of NSCLC.
GO and pathway analysis
The GO analysis identified biological processes,
molecular functions and cellular components in which the
differentially expressed genes may be involved. The top five
most enriched GO terms are shown in Table 4. The GO
analysis results clarified the most significant functional
Fig. 4 Gene clip after HOXA11‑AS knock ‑ down in NSCLC. a Volcano plot; b box‑scatter plot
groups, such as single-organism process, cellular
response to stimulus, biological regulation, and cellular
component organization (Figs. 5, 6). To better
understand the relevant functions of these genes, a function
network was constructed based on the GO analysis
(Figs. 7, 8). We constructed only the BP ontology for the
downregulated genes because only 80 genes were
identified (Fig. 8).
The KEGG analysis showed that the aberrantly
expressed genes might be related to different pathways.
A total of 21 upregulated pathways and only 1
downregulated pathway were available through the pathway
analysis. The most important enriched pathway terms
are shown in Table 5 (Pupregulated < 0.01). Three
pathways (PI3K-Akt signaling pathway, TGF-beta signaling
pathway and Hippo signaling pathway) were previously
reported to be involved in NSCLC development and
progression. As reported, the PI3K-Akt signaling
pathway was related to NSCLC cell proliferation, apoptosis
and autophagy [27–29]. The TGF-beta signaling
pathway could be associated with the NSCLC cell DNA
damage response, radiation sensitivity, viability and
invasion capacity [30, 31]. The Hippo signaling pathway was
involved in NSCLC cell migration and invasion .
A gene network of these 357 genes was constructed
in the present study (Fig. 9). The relationships between
HOXA11-AS and the differentially expressed genes were
easily observed from this network analysis.
Supplementary information from the TCGA
In order to explore the relationship between
HOXA11AS expression and NSCLC, we performed a clinical study
with the original data in TCGA. We found that
HOXA11AS was upregulated in both lung adenocarcinoma and
Table 4 Top 5 enriched GO terms (BP, CC, and MF) from the microarray data
squamous cell carcinoma compared to
non-cancerous lung tissues (both P < 0.0001, Fig. 10a, b). And the
ROC curve revealed that the area under curve (AUC) of
HOXA11-AS was 0.727 (95% CI 0.663–0.790) for lung
adenocarcinoma patients and 0.933 (95% CI 0.906–0.960)
for squamous cell carcinoma patients (both P < 0.0001),
which could gain high diagnostic value of HOXA11-AS
level in NSCLC (Fig. 10c, d).
To elucidate the relationships between the 9
de-regulated genes and NSCLC, we searched the original data
from 514 adenocarcinoma cases and 501 squamous
cell carcinoma cases in TCGA. We also compared gene
expression between adenocarcinoma, squamous cell
carcinoma and non-cancerous lung tissues. We found that
RSPO3, ADAMTS8, DMBT1 and DOCK8 were all
downregulated in squamous cell carcinoma tissues compared
to non-cancerous lung tissues, whereas STMN2, TUSC3
and C8orf22 were upregulated in squamous cell
carcinoma (all P < 0.001, Fig. 11). Additionally, ADAMTS8,
DMBT1 and DOCK8 were down-regulated in
adenocarcinoma and STMN2 and TUSC3 were up-regulated in
lung adenocarcinoma (all P < 0.01, Fig. 12). RSPO3 was
overexpressed in adenocarcinoma but not squamous cell
carcinoma (P = 0.023).
Furthermore, we compared the correlation between
HOXA11-AS and de-regulated genes in NSCLC based
on TCGA. The results showed that the HOXA11-AS
expression was negatively correlated with DOCK8 in
squamous cell carcinoma (r = −0.124, P = 0.048) and
lung adenocarcinoma (r = −0.176, P = 0.005). No
obviously correlation was found between HOXA11-AS and
other de-regulated genes (Table 6). Besides, the co-genes
Fig. 5 Distribution of gene ontology (GO) terms for the upregulated genes in NSCLC. The pie plot showing the gene ontology classification for
the upregulated genes in NSCLC. The graph does not contain all upregulated genes because the majority do not have assigned GOs. a Biological
process (BP). b Cellular component (CC). c Molecular function (MF)
Fig. 6 Distribution of gene ontology (GO) terms for the downregulated genes in NSCLC. The pie plot showing the gene ontology classification
for the downregulated genes in NSCLC. The graph does not contain all downregulated genes because the majority do not have assigned GOs. a
Biological process (BP). b Cellular component (CC). c Molecular function (MF)
Fig. 7 A function network of gene ontology (GO) terms for the upregulated genes in NSCLC. a Biological process (BP). b Cellular component (CC). c
Molecular function (MF)
Fig. 8 A function network (BP) of Gene Ontology (GO) terms for the downregulated genes in NSCLC. BP biological process
Table 5 The most important enriched pathway terms from the microarray data
Mineral absorption—Homo sapiens (human)
Complement and coagulation cascades—Homo sapiens (human)
Pathways in cancer—Homo sapiens (human)
Prion diseases—Homo sapiens (human)
Malaria—Homo sapiens (human)
Basal cell carcinoma—Homo sapiens (human)
Rheumatoid arthritis—Homo sapiens (human)
Cytokine‑ cytokine receptor interaction—Homo sapiens (human)
Transcriptional misregulation in cancer—Homo sapiens (human)
PI3K‑Akt signaling pathway—Homo sapiens (human)
TGF‑beta signaling pathway—Homo sapiens (human)
of HOXA11-AS in TCGA was extracted through R
Project for Statistical Computing. We found that RSPO3,
ADAMTS8, DMBT1, DOCK8, STMN2, SPINK6 and
TUSC3 were the co-genes of HOXA11-AS in lung
adenocarcinoma whereas RSPO3, ADAMTS8, DMBT1,
DOCK8, STMN2, SPINK6, TUSC3 and C8orf22 were the
co-genes of HOXA11-AS in squamous cell carcinoma.
In addition, we also investigated the relationship
between the expression levels of the de-regulated genes
and clinicopathological parameters or patient
survival. Only ADAMTS8 was related to the TNM stage
(t = 0.041, P = 0.032) in squamous cell carcinoma. In
lung adenocarcinoma tissues, RSPO3 was obviously
more highly expressed in the advanced stages (III and IV)
than the early stages (I–II, t = −2.462, P = 0.015). When
lymph node metastasis was analyzed, higher RSPO3
expression was found in cases with lymph node
metastasis than in cases without (t = −2.346, P = 0.020). We
also found that higher ADAMTS8 expression was more
common in females (t = −2.924, P = 0.004) and cases
with distant metastasis (P = 0.045). Higher DMBT1 and
DOCK8 expression was also more common in females
than males (all P < 0.05). DOCK8 was significantly more
highly expressed in the advanced stages (III and IV,
t = 3.482, P = 0.001) and cases with lymph node
metastasis (t = 2.087, P = 0.037). Additionally, TUSC3 was
related to age (P = 0.037). The upregulated expression
of RSPO3, ADAMTS8 and DOCK8 was associated with
the overall survival (all P < 0.05) and disease-free survival
of adenocarcinoma patients (all P < 0.05, Fig. 13), which
indicated that RSPO3, ADAMTS8 and DOCK8 might
influence the prognosis of adenocarcinoma. Based on the
aforementioned results, we speculated that HOXA11-AS
may play an important role in NSCLC development and
progression by regulating the expression of various
pathways and genes, especially DOCK8 and TGF-beta
pathway. However, the exact mechanism should be verified by
Lung cancer is the most common malignancy in humans
and accounts for approximately 13% of newly diagnosed
cancer cases per year [1–3]. NSCLC accounts for 80–85%
of all lung cancers. Over the past decades, the
possible molecular mechanism underlying NSCLC has been
extensively explored. However, the particular
pathogenesis of NSCLC is still vague. Growing evidence indicates
that lncRNAs may play important roles in regulating gene
expression in NSCLCs. For example, lncRNA-TATDN1
is associated with NSCLC invasion and metastasis by
influencing E-cadherin, HER2, β-catenin and Ezrin
expression , lncRNA-PVT1 promotes NSCLC cell
proliferation by epigenetically regulating LATS2
expression  and lncRNA-MALAT1 influences tumor
invasion in NSCLC by regulating DNA methylation .
In this study, we explored the possible biological and
molecular mechanisms of HOXA11-AS in NSCLC. A
microarray assay, various bioinformatics analyses (GO,
pathway, KEGG, and network analyses) and the original
data in TCGA were used to study differentially expressed
genes and their relationships with NSCLC. After
analyzing the original data from TCGA database, we found
that HOXA11-AS was upregulated in both lung
adenocarcinoma and squamous cell carcinoma. Also, ROC
curve showed that HOXA11-AS expression might have
an important value in diagnosis of lung cancer.
Moreover, we searched Oncomine (https://www.oncomine.
org/resource/login.html) and gene expression omnibus
Fig. 9 Network analysis between HOXA11‑AS and the differentially expressed genes. Yellow indicates activation and green indicates inhibition
(GEO; http://www.ncbi.nlm.nih.gov/geo/) database, but
no positively relationship was found. In addition, through
the above-mentioned bioinformatics analyses, 4 genes
(RSPO3, ADAMTS8, DMBT1, and DOCK8) and 3
pathways (PI3K-Akt signaling pathway, TGF-beta signaling
pathway and Hippo signaling pathway) were identified as
related to NSCLC. The original data from TCGA verified
that ADAMTS8, DMBT1 and DOCK8 were
down-regulated in adenocarcinoma and squamous cell carcinoma,
whereas RSPO3 was overexpressed in adenocarcinoma
and down-regulated in squamous cell carcinoma.
Furthermore, RSPO3, ADAMTS8 and DOCK8 were also
related to the overall survival and disease-free survival
of lung adenocarcinoma patients in the TCGA data.
Besides, we found that the HOXA11-AS expression was
negatively correlated with DOCK8 both in squamous
cell carcinoma and lung adenocarcinoma. Therefore, we
hypothesized that HOXA11-AS might play an essential
role in NSCLC development and progression by
regulating DOCK8 expression through TGF-beta pathway.
Fig. 10 Differential expression and ROC curve of HOXA11‑AS in lung adenocarcinoma and squamous cell carcinoma based on The Cancer Genome
Atlas (TCGA) database. a Differential expression of HOXA11‑AS in lung adenocarcinoma. b Differential expression of HOXA11‑AS in squamous cell
carcinoma. c ROC curve of HOXA11‑AS in lung adenocarcinoma. d ROC curve of HOXA11‑AS in squamous cell carcinoma
However, the real mechanism should be verified by
During the process of researching the relationship
between these 4 de-regulated genes and 3 pathways, we
found that DOCK8 and the TGF-beta signaling pathway
played significant roles in the metastasis of lung
adenocarcinoma . Yu et al.  used RNA and protein
analysis, Rac1 activity, imaging, cellular assays, public data
set analysis and xenograft mouse models to show that
DOCK4 played an important role in mediating
TGF-betadriven lung adenocarcinoma cell extravasation and
metastasis. Thus, DOCK4 may act as a key component of the
TGF-beta pathway. Additionally, we found that DOCK8
and the Hippo signaling pathway could play a role in
neuroblastoma relapse . DOCK8 mutations and YAP
activation were reported to be associated with neuroblastoma
relapse in one study. YAP is a member of the Hippo
signaling pathway ; however, whether the expression of
DOCK8 plays a role in NSCLC through the Hippo
signaling pathway is unknown. DOCK8 (also known as MRD2,
ZIR8 and HEL-205) is located on 9p24.3 (NCBI Gene ID:
81704). DOCK family proteins have been confirmed to
play roles in the regulation of cell morphology, adhesion,
migration and growth [36–39]. DOCK8 was reported to
Fig. 11 Differential expression of genes between squamous cell carcinoma and normal lung tissues based on The Cancer Genome Atlas (TCGA)
database. a RSPO3; b ADAMTS8; c DMBT1; d DOCK8; e SPINK6; f TUSC3; g C8orf22
Fig. 12 Differential expression of RSPO3, ADAMTS8, DMBT1 and DOCK8 between lung adenocarcinoma and normal lung tissues based on The
Cancer Genome Atlas (TCGA) database. a RSPO3; b ADAMTS8; c DMBT1; d DOCK8; e SPINK6; f TUSC3
Table 6 The correlation between HOXA11-AS
and de-regulated genes in NSCLC based on TCGA
expressed in different cancers, such as hepatocellular
carcinoma and some epithelial cancers [40, 41]. However, to
date only 2 papers have reported roles for DOCK8 in lung
cancer. Kang et al.  analyzed 22 lung squamous cell
carcinoma cases and found that the loss of chromosome
9 p was specific for lung squamous cell carcinoma; thus,
the DOCK8 gene might be a potential target for
therapeutic measures against lung squamous cell carcinoma.
Takahashi et al.  found that genetic and epigenetic
inactivation of DOCK8 was related to the development
and/or progression of lung cancer using an array-CGH
analysis. The original data from TCGA verified that higher
DOCK8 expression was related to gender, TNM stage,
lymph node metastasis and survival, which indicated
that DOCK8 might play a significant role in NSCLC. We
found that the TGF-beta signaling pathway was related
to radiation sensitivity, extravasation, metastasis and
apoptosis [30, 33, 43]. Additionally, the deregulation of
the Hippo signaling pathway induced tumors in model
organisms and occurred in different human carcinomas,
including lung, ovarian, colorectal and liver cancers .
The Hippo signaling pathway controls organ size by
regulating the cell cycle, proliferation, and apoptosis [45, 46].
However, numerous in vivo and in vitro experiments need
to be performed to verify whether HOXA11-AS plays a
role in NSCLC development and progression by
regulating DOCK8 expression through the TGF-beta or Hippo
Other differentially expressed genes and pathways
were investigated. Several studies have reported the
functions of these genes and pathways. Gong et al. 
found that RSPO3 was aberrantly overexpressed in
half of Keap1-deficient lung adenocarcinomas and that
RSPO3 overexpression resulted in much poorer
survival. In vitro experiments verified that RSPO3
overexpression was related to cell proliferation and migration.
The findings of these authors suggest that RSPO3
overexpression may potentially act as a driving
mechanism behind the aggressiveness of Keap1-deficient
lung adenocarcinomas. Dunn et al.  performed a
microarray analysis combined with comparative
multiplex RT-PCR, immunohistochemical studies and DNA
methylation analysis and found that ADAMTS8 was
down-regulated in primary NSCLC. ADAMTS8
downregulation was related to promoter hypermethylation,
which might be associated with NSCLC development.
Mollenhauer et al.  explored DMBT1 expression
in normal and lung cancer tissues using
reverse-transcription PCR and immunohistochemical studies and
found DMBT1 down-regulation in the lung cancer cell
lines. However, this finding was controversial because
up-regulated expression was detected in the
tumorflanking epithelium and upon respiratory
inflammation. The authors found that a switch took place during
lung carcinogenesis. Finally, they hypothesized that the
sequential changes in DMBT1 expression in different
locations reflected a time course that might indicate a
possible mechanism in epithelial cancer. In addition, we
also further researched the relationships between the
other 5 de-regulated genes (STMN2, SPINK6, TUSC3,
LOC100128054, and C8orf22 and disease progression.
As reported, STMN2 could be a novel
developmentallyassociated marker and STMN2 could contribute to
regulating the adipocyte/osteoblast balance . Also
STMN2 could be a novel target of
beta-catenin/TCFmediated carcinogenesis in hepatoma cells . SPINK6
(See figure on next page.)
Fig. 13 Kaplan‑Meyer curves of RSPO3, ADAMTS8 and DOCK8 expression in lung adenocarcinoma based on The Cancer Genome Atlas (TCGA)
database. a Overall survival of RSPO3 in lung adenocarcinoma. Patients with high RSPO3 expression had a significantly poorer prognosis
(46.749 ± 7.528 months) than those with low expression (90.101 ± 8.759 months, P < 0.0001). b Disease‑free survival of RSPO3 in lung adeno ‑
carcinoma. Patients with high RSPO3 expression had a significantly poorer prognosis (56.254 ± 10.462 months) than those with low expression
(127.159 ± 13.180, P < 0.0001). c Overall survival of ADAMTS8 in lung adenocarcinoma. Patients with low ADAMTS8 expression had a significantly
poorer prognosis (80.869 ± 8.989 months) than those with high expression (92.497 ± 8.863 months, P = 0.007). d Disease‑free survival of ADAMTS8
in lung adenocarcinoma. Patients with high ADAMTS8 expression had a significantly poorer prognosis (107.704 ± 10.239 months) than those with
low expression (121.080 ± 14.027 months, P = 0.009). e Overall survival of DOCK8 in lung adenocarcinoma. Patients with low DOCK8 expression
had a significantly poorer prognosis (80.028 ± 9.108 months) than those with high expression (81.730 ± 8.029 months, P = 0.024). f Disease‑free
survival of DOCK8 in lung adenocarcinoma. Patients with high DOCK8 expression had a significantly poorer prognosis (107.246 ± 8.779 months)
than those with high expression (114.254 ± 13.518 months, P = 0.024)
could be a prognostic indicator in nasopharyngeal
carcinoma patients, and SPINK6 could play a critical role
in promoting metastasis of nasopharyngeal carcinoma
patients . Moreover, TUSC3 was reported to related
to the development of different cancers, such as
glioblastoma, colorectal cancer, pancreatic cancer, and so
on [51–53]. No items of LOC100128054 and C8orf22
were found from pubmed.
In addition, many studies have researched the
different mechanisms of the PI3K-Akt signaling pathway. The
PI3K/AKT/mTOR signaling pathway is well-known to
play essential roles in cell proliferation, invasion,
apoptosis, and angiogenesis in lung cancer [54–56].
However, numerous experiments are required to identify the
real mechanisms underlying the roles of HOXA11-AS
and its corresponding differentially expressed genes in
In summary, because HOXA11-AS may be an important
factor in different biological processes of lung cancer, we
performed bioinformatics analyses (GO, pathway, KEGG,
and network analyses) to identify differentially expressed
genes and potential pathways. In this work, we
systematically analyzed HOXA11-AS-related genes and their
functional categorization, pathways and networks.
Original data from TCGA was used to verify the relationships
between the expression levels of HOXA11-AS and the
de-regulated genes and clinicopathological parameters
or patient survival. Based on the results, we speculated
that HOXA11-AS may play an important role in NSCLC
development and progression by regulating the
expression of various pathways and genes, especially DOCK8
and TGF-beta pathway. However, the exact mechanism
should be verified by functional experiments.
LncRNAs: long noncoding RNAs; NSCLC: non‑small cell lung cancer; GO: gene
ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; MSigDB: molec‑
ular signatures database; TCGA: the cancer genome atlas; FDR: false discovery
rate; BP: biological process; CC: cellular component; MF: molecular function;
DAVID: database for annotation, visualization and integrated discovery.
YZ and RH participated in clinical data analysis and drafted the manuscript. YD
and XW participated in the statistical analysis and corrected the manuscript.
XZ and SH prepared for the specimens, carried out the siRNA isolation. WH, MJ
and NG performed the statistical analysis, prepared for the figures and revised
the manuscript., YX, PL, DL, GC and TG conceived of the study, participated in
design and coordination and corrected the manuscript. All authors read and
approved the final manuscript.
The study was supported by a Fund of the Guangxi Provincial Health Bureau
Scientific Research Project (Z2013201, Z2014055), a Fund of the National
Natural Science Foundation of China (NSFC81360327, NSFC81560469), the
Natural Science Foundation of Guangxi, China (2015GXNSFCA139009) and
the Scientific Research Project of the Basic Ability Promoting for Middle Age
and Youth Teachers of Guangxi Universities (KY2016YB077). The funders had
no role in the study design, the data collection and analysis, the decision to
publish, or the preparation of the manuscript.
The authors declare that they have no competing interests.
Availability of data and materials
Data sharing not applicable to this article as no datasets were generated or
analysed during the current study.
Fund of the Guangxi Provincial Health Bureau Scientific Research Project
(Z2013201, Z2014055) was used for the design of the study and collection.
Fund of the National Natural Science Foundation of China (NSFC81360327,
NSFC81560469) was used for data analysis, and the Natural Science Founda‑
tion of Guangxi, China (2015GXNSFCA139009) and the Scientific Research
Project of the Basic Ability Promoting for Middle Age and Youth Teachers of
Guangxi Universities (KY2016YB077) was used for interpretation of data and in
writing the manuscript.
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