LncRNAs GIHCG and SPINT1-AS1 Are Crucial Factors for Pan-Cancer Cells Sensitivity to Lapatinib

Abstract

Lapatinib is a small molecule inhibitor of EGFR (HER1) and ERBB2 (HER2) receptors, which is used for treatment of advanced or metastatic breast cancer. To find the drug resistance mechanisms of treatment for EGFR/ERBB2 positive tumors, we analyzed the possible effects of lncRNAs. In this study, using CCLE (Cancer Cell Line Encyclopedia) database, we explored the relationship between the lncRNAs and Lapatinib sensitivity/resistance, and then validated those findings through in vitro experiments. We found that the expression of EGFR/ERBB2 and activation of ERBB pathway was significantly related to Lapatinib sensitivity. GO (Gene Oncology) analysis of top 10 pathways showed that the sensitivity of Lapatinib was positively correlated with cell keratin, epithelial differentiation, and cell-cell junction, while negatively correlated with signatures of extracellular matrix. Forty-four differentially expressed lncRNAs were found between the Lapatinib sensitive and resistant groups (fold-change > 1.5, P < 0.01). Gene set variation analysis (GSVA) was performed based on 44 lncRNAs and genes in the top 10 pathways. Five lncRNAs were identified as hub molecules. Co-expression network was constructed by more than five lncRNAs and 199 genes in the top 10 pathways, and three lncRNAs (GIHCG, SPINT1-AS1, and MAGI2-AS3) and 47 genes were identified as close-related molecules. The three lncRNAs in epithelium-derived cancers were differentially expressed between sensitive and resistant groups, but no significance was found in non-epithelium-derived cancer cells. Correlation analysis showed that SPINT1-AS1 (R = −0.715, P < 0.001) and GIHCG (R = 0.557, P = 0.013) were correlated with the IC50 of epithelium-derived cancer cells. In further experiments, GIHCG knockdown enhanced cancer cell susceptibility to Lapatinib, while high level of SPINT1-AS1 was a sensitive biomarker of NCI-N87 and MCF7 cancer cells to Lapatinib. In conclusions, lncRNAs GIHCG and SPINT1-AS1 were involved in regulating Lapatinib sensitivity. Up-regulation of GIHCG was a drug-resistant biomarker, while up-regulation of SPINT1-AS1 was a sensitive indicator.

Introduction

Lapatinib is a small molecular drug that has been shown to be a dual tyrosine kinase inhibitor, which is involved in the EGFR/HER1 and ERBB2/HER2 pathways and suppresses the autophosphorylation of these receptors. Clinically, it has been used in combination therapy with capecitabine in patients with advanced or metastatic breast cancer that overexpressed ERBB2/HER2 in the cases of previous treatment with anthracyclines, taxanes, or trastuzumab (Herceptin) (Geyer et al., 2006). In addition, a satisfactory response rate has also been found with Lapatinib treatment for ERBB2-positive progressive gastric cancer (Cetin et al., 2014; Satoh et al., 2014). However, in patients with head and neck squamous cell carcinoma, Lapatinib combined with radiotherapy did not show therapeutic effects (Harrington et al., 2015). Similarly, in ERBB2/EGFR positive metastatic bladder cancer patients who underwent first-line chemotherapy didn't get benefit from Lapatinib maintenance treatment (Powles et al., 2017). Therefore, uncovering the drug-resistant mechanism of Lapatinib will help improve the therapeutic effects of Lapatinib targeted therapy and find new sensitive biomarkers.

Long non-coding RNAs (lncRNAs) are a large class of transcribed RNA molecules that are longer than 200 nucleotides but do not encode proteins. In addition to the regulation of diverse cellular processes, such as epigenetics, cell cycle, and cell differentiation, they have been found to play important roles in carcinogenesis, tumor development, and treatment resistance (Heery et al., 2017; Peng et al., 2017; Hahne and Valeri, 2018; Wang et al., 2018; Wu et al., 2018). For instance, Ma et al. found that lncRNAs CASC9 and EWAST1 were two crucial molecules associated to EGFR-TKIs resistant in non-small cell lung cancer (Ma et al., 2017).

The Cancer Cell Line Encyclopedia (CCLE) database (https://portals.broadinstitute.org/ccle) is an open access resource with the most completely integrated datasets of cancer cells genomes and drug effectiveness. It includes the experimental datasets of drug treatment of 24 kinds of chemical compounds in almost 1,000 cancer cell lines of various human cancers (Barretina et al., 2012). Kim et al. used CCLE database in their recent publication. They found that high levels of FGFR and integrin β3 are resistant to crizotinib treatment, suggesting that FGFR, and integrin β3 could be predictive markers for Met-targeted therapy (Kim et al., 2015). To date, there is a limited number of studies (Jiang et al., 2014; Niknafs et al., 2016; Bester et al., 2018; Li D. et al., 2018; Sun et al., 2018) to explore lncRNAs by CCLE database. In this study, we analyzed the lncRNAs of whole-genome datasets of CCLE after treatment with Lapatinib on pan-cancer cell lines, and proposed crucial lncRNAs GIHCG and SPINT1-AS1 involved in regulating Lapatinib sensitivity.

Materials and Methods

Data Extraction From CCLE

There are 5,344 lncRNA probes and 49,331 non-lncRNA probes in the whole-genome gene expression profile chip used in CCLE (Barretina et al., 2012). There are 1,037 cell lines of various cancer types in the database. Among those, 504 cell lines had been treated with Lapatinib and got IC50 (half maximal inhibitory concentration) data and 501 cell lines were examined by microarrays. Since the study focused on solid tumors, we deleted cell lines of hematopoietic and lymphoid cell lines. Finally, 420 solid tumor cell lines were enrolled in the study (Table 1).

Cancer Cell Lines and Cell Culture

Nineteen cancer cell lines were used for validating experiments in vitro. Four of those were gastric cancer cell lines (NCI-N87, SGC-7901, AGS, and MKN-45), three were melanoma cell lines (MuM-2C, MV3, and A-375), three were hepatocarcinoma cell lines (LM3, 97L, and Huh7), three were thyroid cancer cell lines (KHM-5M, CAL-62, and C643), two were breast cancer cell lines (MCF7 and SK-BR-3), two were pancreatic cancer cell lines (TCC-PAN2 and BxPC3), and two were colorectal cancer lines (DLD-1 and NCIH-747). Cell lines NCI-N87, MuM-2C, LM3, MV3, Huh7, SGC-7901, CAL-62, AGS, MCF7, C643, 97L, SK-BR-3, KHM-5M, A-375, TCC-PAN2, MKN-45, and BxPC3 were purchased from the Cell Bank of Type Culture Collection of Chinese Academy of Sciences (Shanghai, China). Cell lines DLD-1 and NCIH-747 were purchased from The Global Bioresource Center ATCC (Maryland, USA). The cell lines were cultured in RPMI-1640 supplemented with 10% fetal bovine serum in a humidified incubator at 37°C with 95% air and 5% CO2.

Transient Transfection of siRNAs

SPINT1-AS1 and GIHCG siRNAs were transfected into cancer cells by Lipofectamine 2000 (Invitrogen, Carlsbad, California, USA) according to the manufacturer's instructions. The siRNA sequences are shown in Table S1.

RNA Extraction and Quantitative Real-Time PCR Analysis

Total RNA was isolated using the TRIzol solution (Invitrogen, California, USA). The cDNA was synthesized using Reverse Transcription kit (TOYOBO, Japan). Real-time PCR was performed in 10 μl reaction mixtures with the HT 7900 (Applied Biosystems, Foster City, USA) using SYBR™ Select Master Mix (Applied Biosystems, Foster City, USA). The sequences of primers were designed and synthesized by Sunny Biotech (Shanghai, China): The primer sequences are shown in Table S1.

Cell Viability Assay

Five thousand cells of different cancers were placed in each well of 96-well plates (100 μl/well). Different concentrations of Lapatinib (Selleck, Houston, USA) were incubated for 48 h. After adding 10 μl CCK-8 for 2 h, OD value was measured at 450 nm by spectrophotometry (BioTek, Vermont, USA).

Data Analysis

The “corrplot” and “pheatmap” package in R software were utilized for visualizing pearson correlation analysis and cluster analysis by “euclidean” method. The Benjamini and Hochberg method was used to calculate P. adjust value. By means of “clusterProfiler” package in R, GSEA (Gene Set Enrichment Analysis) and GO (Gene Ontology) analyses were carried out to explore involved gene clusters. GSEA is a computational method based on previous publication by Subramanian et al. (2005). GO analysis is a kind of gene enrichment analysis to classify gene set on three aspects: molecular function, cellular component and biological process (Ashburner et al., 2000). Differentially expressed lncRNAs and genes with difference larger than 1.5-fold were obtained by “limma” package, which is often used to explore differentially expressed genes between two phenotypes (Ritchie et al., 2015). The top 10 gene clusters of all cancer cell lines were scored using “GSVA” package (Gene Set Variation Analysis,) in R language, which utilizes non-parametric unsupervised method for evaluating gene set enrichment (GSE) in transcriptomic data (Hanzelmann et al., 2013). Cytoscape software was applied to establish co-expression network and determine hub lncRNAs. The inhibiting ratio and Lapatinib IC50 were calculated according to OD value by GraphPad Prism 6.0 (Inc., La Jolla, CA, USA). The relative RNA levels were calculated by 2^−ΔΔCT (ΔCT = LncRNA^CTvalue − GAPDH^CTvalue, ΔΔCT = ΔCT−ΔCT^min, ΔCT^min: minimum ΔCT of expression levels of lncRNA GIHCG or SPINT1-AS1 in cell line). Student's t-tests were performed by GraphPad Prism 6.0. P < 0.05 was considered statistically significant.

Results

Lapatinib IC50 From Pan-Cancer Cell Lines Analysis

The CCLE data of Lapatinib IC50 of the selected 420 cell lines was shown in Table 2. The upper limit of IC50 was originally determined as 8 μM for those cancer cell lines in the database. There were 302 cancer cell lines with IC50 higher than 8 μM, which were insensitive to Lapatinib drug. There were 118 cancer cell lines with IC50 lower than 8 μM, which were relatively sensitive to Lapatinib drug. Taking 8 μM of IC50 as a threshold, we categorized 420 cancer cell lines into two groups, high_IC50 (n = 302) and low_IC50 (n = 118). Since EGFR and ERBB2 are the targets of the Lapatinib drug, the expression levels of EGFR, and ERBB2 in high_IC50 and low_IC50 groups were analyzed. The expression levels of EGFR and ERBB2 were significantly higher in low-IC50 group than in high_IC50 (Figure 1A, P = 0.006 and P < 0.001, respectively). The distribution tendency of 22 types of solid cancer cell lines in high-IC50 (up to 8 μM) and low_IC50 (lower than 8 μM) groups is presented in Figure 1B. GSEA analysis showed that ERBB pathway-related genes were enriched in low_IC50 group (Figure 1C, ERBB signaling pathway NES = −1.81, P < 0.002, p. adjust = 0.064; regulation of ERBB signaling pathway NES = −1.69, P < 0.002, p. adjust = 0.064).

Figure 1

Pathway Analysis Involved in Lapatinib Sensitivity

To illustrate the mechanism of Lapatinib resistance, we selected genes with fold-change >1.5 times to perform GO analysis (Table S2). In the top 10 involved pathways, Lapatinib sensitivity was positively associated with cell keratin, epithelial differentiation, and cell-cell junction, while negatively related to signatures of extracellular matrix (Figure 2, P < 0.001, P. adjust < 0.001).

Figure 2

Analysis of LncRNAs Involved in Lapatinib Sensitivity

We further screened the differentially expressed lncRNAs, and 44 lncRNAs were identified between the high_IC50 group and low_IC50 group (Figure 3A and Table 3, fold-change >1.5, P < 0.01). Then, we selected genes in the top 10 pathways and 44 differential lncRNAs for the construction of the co-expression network. The enrichment scores of the top 10 pathway genes in every cancer cell lines were calculated and determined by GSVA analysis. Five lncRNAs were highlighted as the hub factors in the top 10 regulating pathways (Figure 3B). The association of the 5 lncRANs with 199 genes in the top 10 pathways was further analyzed, and a molecular network of co-expression was established, which included top 50 key molecules closely associated to Lapatinib sensitivity. Three crucial lncRNAs, GIHCG, SPINT1-AS1, and MAGI2-AS3, still remained in the co-expression network (Figure 3C).

Figure 3

Differential Expressing Analysis of Three LncRNAs Between Epithelial and Non-epithelial Cancer Groups

We divided the 420 cancer cell lines into epithelium derived group (n = 278) and non-epithelium derived group (n = 142; including nervous system, bone, cartilage, and pleura). The differential expression levels of the three lncRNAs between the two groups are presented in Figure 4A. In the epithelium-derived group, the differential expression levels of the three lncRNAs between Lapatinib high_IC50 and low_IC50 groups were significantly different (Figure 4B, P < 0.05). In the non-epithelium groups, there was no significant difference of the three lncRNAs between Lapatinib high_IC50 and low_IC50 groups. Higher expressing level of SPINT1-AS1 was found in epithelium-derived cancer cells, and higher expressing levels of MAGI2-AS3 and GIHCG were observed in the non-epithelium group.

Figure 4

Differentially expressed genes (1.5-fold change) between the Lapatinib high_IC50 and low_IC50 groups in epithelial group (Table S3) were utilized to perform GO analysis. Enhanced signatures of cell keratin, epithelial differentiation, and cell-cell junction were observed in Lapatinib low_IC50 group, and decreased signature of extracellular matrix were observed in Lapatinib low_IC50 group (Figure 5, P < 0.001, P. adjust < 0.001).

Figure 5

Correlation of LncRNAs SPINT1-AS1, GIHCG, or MAGI2-AS3 and Lapatinib Sensitivity in Epithelial Group

Correlation analysis revealed that Lapatinib IC50 of the non-epithelial group was higher than that of the epithelial group (Figure 6A). Of the three critical lncRNAs, SPINT1-AS1, and GIHCG were the lncRNAs most correlated to Lapatinib sensitivity (Figure 6B). SPINT1-AS1 and GIHCG were selected as key factors of affecting Lapatinib sensitivity of epithelial cancers. The up-regulation of SPINT1-AS1 was found in low_IC50 group and increased GIHCG was found in high_IC50 group (Figure 6C).

Figure 6

Validating Study of GIHCG and SPINT1-AS1 on Regulating Lapatinib Sensitivity in vitro

In validating experiments, we examined expression levels of GIHCG and SPINT1-AS1 in seven types of cancer cell lines (thyroid cancer, pancreatic cancer, liver cancer, melanoma, gastric cancer, breast cancer, and colorectal cancer) and Lapatinib IC50 of the same cancer cell lines. Correlation analysis showed that higher expression levels of SPINT1-AS1 were significantly associated with lower Lapatinib IC50 (Figure 7A, R = −0.715, P < 0.001), while higher expression levels of GIHCG were significantly related to higher Lapatinib IC50 (Figure 7A, R = 0.557, P = 0.013).

Figure 7

The sensitive cancer cell lines of NCI-N87 (gastric cancer) and MCF7 (breast cancer), as well as the resistant cancer cell lines of NCIH-747(colon cancer) and BxPC3 (pancreatic cancer) were selected for a subsequent validating study. After knocking-down expression levels of GIHCG and SPINT1-AS1 by small interfering RNAs, Lapatinib IC50, and inhibitory rate of cancer cells were detected. Among three small interference sequences of GIHCG and SPINT1-AS1 mRNAs, siRNA sequence 3 of GIHCG (Si3, Figure 7B), and siRNA sequence 1 of SPINT1-AS1 (Si1, Figure 7C) were identified as effective siRNAs for further experiments.

Knocking-down of GIHCG could significantly enhance the sensitivity to Lapatinib in MCF7 and BxPC3 cancer cell lines (Figure 7D), while down-regulation of SPINT1-AS1 could promote resistance to Lapatinib in NCI-N87 and MCF7 cancer cell lines (Figure 7E). To clarify whether there is a mutual regulatory relationship between GIHCG and SPINT1-AS1, we detected the expression level of SPINT1-AS1 after GIHCG knockdown and vice versa. As shown in Figures 7F,G, suppression of GIHCG in Lapatinib resistant cancer cell lines NCIH-747 and BxPC3 could induce up-regulation of SPINT1-AS1 (P < 0.05), while knockdown of SPINT1-AS1 did not change the expression level of GIHCG (P > 0.05).

Discussion

LncRNA is an important regulatory molecule in drug resistance during chemotherapy or gene targeted therapy (Li et al., 2016; Dong et al., 2018; Wu et al., 2018; Zhou et al., 2018). In this study, we analyzed Lapatinib sensitivity to EGFR and ERBB2 targeted therapy pan-cancer cell line wide. We noticed that Lapatinib sensitivity was not only positively correlated to the activation of EGFR and ERBB2 signaling pathways, but also positively associated to cell keratin, epithelial differentiation, and cell-cell junction. The Lapatinib sensitivity of cancer cell lines was negatively associated to extracellular matrix signature. By screening differentially expressed lncRNAs and establishing co-expression network between Lapatinib high_IC50 and low_IC50 groups, three key lncRNAs, SPINT1-AS1, GIHCG, and MAGI2-AS3, were found. Of those, GIHCG and SPINT1-AS1 were only differentially expressed in epithelial derived cancers. SPINT1-AS1 was negatively related to Lapatinib IC50, whereas GIHCG was positively associated to Lapatinib IC50. By siRNAs treatment, downregulation of SPINTA-AS1 could promote Lapatinib resistance, while downregulation of GIHCG promoted Lapatinib sensitivity. The combination of bioinformatical approach and experimental study confirmed that lncRNAs were involved in regulating sensitivity to Lapatinib targeted therapy.

PI3K/Akt, Ras/Raf/MEK/ERK1/2, and PLCγ pathways are downstream pathways of EGFR and ERBB2 and play important roles in cell proliferation and survival of multiple cancers (Roskoski, 2014). The expression levels of EGFR and ERBB2 are positively correlated to Lapatinib sensitivity (Rusnak et al., 2007; Xiang et al., 2018). Trastuzumab (Herceptin) is a molecular targeted drug of ERBB2-positive metastatic/advanced breast cancer and gastric cancer (Bang et al., 2010; Loibl and Gianni, 2017). Lapatinib is a small molecule chemical, which proved effective for ERBB2-positive advanced or metastatic breast cancer when combined with capecitabine after previous treatment with anthracyclines, paclitaxel, or trastuzumab (Geyer et al., 2006). In gastric cancer, treatment with Lapatinib plus capecitabine and oxaliplatin also revealed anti-cancer effects on HER2-amplified gastroesophageal adenocarcinoma, especially in Asian and younger patients (Hecht et al., 2016). LncRNAs emerged as one of the new resistance mechanisms to chemotherapy or molecule targeted therapy. By bioinformatics analysis, Lapatinib sensitive cancer cells exhibited enrichment of genes related to cell keratin, epithelial differentiation, and cell-cell junction. The ERBB family plays an important role in regulating cell differentiation (Pellat et al., 2017). We noticed that Lapatinib sensitivity is positively correlated to ERBB pathway activation. It means that cancer cells sensitive to Lapatinib drug often showed enrichment of cell differentiation-related genes, while Lapatinib-resistant cancer cells are often accompanied by enrichment of extracellular matrix pathway (D'Amato et al., 2015; Khan et al., 2016; Lin et al., 2017; Watson et al., 2018). Furthermore, increases of extracellular matrix could further induce epithelial-mesenchymal transition of cancer cells (Tzanakakis et al., 2018).

Although the role of lncRNAs in cancer progression and Lapatinib resistance have been reported in other studies (Russell et al., 2015; Li et al., 2016; Liang et al., 2018; Ma et al., 2018), this is the first study that proved that lncRNAs GIHCG and SPINT1-AS1 are involved in regulating therapeutic sensitivity to Lapatinib. Based on pan-cancer cell lines analysis, Lapatinib IC50 is significantly different between non-epithelial cancer cell lines, and epithelial cancer cell Lines. As the inhibitor of miR-200b/200a/429, LncRNA GIHCG was shown effectively promoting the progression of liver cancer through inducing methylation of miR-200b/200a/429 promoter (Sui et al., 2016). GIHCG is also involved in promoting cancer proliferation and migration in tongue and renal cancers (D'Aniello et al., 2018; Ma et al., 2018). However, there is no study $om whether or not GIHCG could regulate Lapatinib drug sensitivity in cancers. LncRNA SPINT1-AS1 is a Kunitz type 1 antisense RNA1, belonging to serine peptidase inhibitor. An increased expression of SPINT1-AS1 has been observed in colorectal cancer (Li C. et al., 2018). It is also the first time that lncRNA SPINT1-AS1 has been found regulating Lapatinib drug sensitivity on multiple cancer cells. In validating experiments, the knockdown of SPINT1-AS1 did not result in the up-regulation of GIHCG. We speculated that GIHCG may regulate SPINT1-AS1 expression through regulating promoter methylation or by manner of competitive endogenous RNA (ceRNA) (Zhang G. et al., 2018; Zhang L. et al., 2018). However, the mutual regulatory mechanisms of lncRNA GIHCG and SPINT1-AS1 remain to be studied in the future.

Conclusion

In conclusion, the current study proposed a group of lncRNAs related to Lapatinib sensitivity based on pan-cancer cell lines analysis. By subsequent experimental study, lncRNAs GIHCG and SPINT1-AS1 were firstly identified as crucial lncRNAs in regulating Lapatinib resistance or sensitivity in epithelium-derived cancer cell lines. SPINT1-AS1 is a Lapatinib sensitivity predictor, while GIHCG is a predictive molecule for Lapatinib resistance.

References

• 1

  AshburnerM.BallC. A.BlakeJ. A.BotsteinD.ButlerH.CherryJ. M.et al. (2000). Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat. Genet. 25, 25–29. 10.1038/75556

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 2

  BangY. J.Van CutsemE.FeyereislovaA.ChungH. C.ShenL.SawakiA.et al. (2010). Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA): a phase 3, open-label, randomised controlled trial. Lancet376, 687–697. 10.1016/S0140-6736(10)61121-X

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 3

  BarretinaJ.CaponigroG.StranskyN.VenkatesanK.MargolinA. A.KimS.et al. (2012). The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature483, 603–607. 10.1038/nature11003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 4

  BesterA. C.LeeJ. D.ChavezA.LeeY. R.NachmaniD.VoraS.et al. (2018). An integrated genome-wide CRISPRa approach to functionalize lncRNAs in drug resistance. Cell173, 649–664 e620. 10.1016/j.cell.2018.03.052

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 5

  CetinB.BenekliM.TurkerI.KoralL.UlasA.DaneF.et al. (2014). Lapatinib plus capecitabine for HER2-positive advanced breast cancer: a multicentre study of anatolian society of medical oncology (ASMO). J. Chemother. 26, 300–305. 10.1179/1973947813Y.0000000147

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 6

  D'AmatoV.RaimondoL.FormisanoL.GiulianoM.De PlacidoS.RosaR.et al. (2015). Mechanisms of lapatinib resistance in HER2-driven breast cancer. Cancer Treat. Rev. 41, 877–883. 10.1016/j.ctrv.2015.08.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 7

  D'AnielloC.PiscontiS.FacchiniS.ImbimboC.CavaliereC. (2018). Long noncoding RNA GIHCG is a potential diagnostic and prognostic biomarker and therapeutic target for renal cell carcinoma. Eur. Rev. Med. Pharmacol. Sci. 22, 1169–1170. 10.26355/eurrev_201803_14452

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 8

  DongH.WangW.MoS.ChenR.ZouK.HanJ.et al. (2018). SP1-induced lncRNA AGAP2-AS1 expression promotes chemoresistance of breast cancer by epigenetic regulation of MyD88. J. Exp. Clin. Cancer Res. 37:202. 10.1186/s13046-018-0875-3

  • CrossRef
  • Google Scholar

• 9

  GeyerC. E.ForsterJ.LindquistD.ChanS.RomieuC. G.PienkowskiT.et al. (2006). Lapatinib plus capecitabine for HER2-positive advanced breast cancer. N. Engl. J. Med. 355, 2733–2743. 10.1056/NEJMoa064320

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 10

  HahneJ. C.ValeriN. (2018). Non-coding RNAs and resistance to anticancer drugs in gastrointestinal tumors. Front. Oncol. 8:226. 10.3389/fonc.2018.00226

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 11

  HanzelmannS.CasteloR.GuinneyJ. (2013). GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics14:7. 10.1186/1471-2105-14-7

  • CrossRef
  • Google Scholar
  • View reference in article

• 12

  HarringtonK.TemamS.MehannaH.D'CruzA.JainM.D'OnofrioI.et al. (2015). Postoperative adjuvant lapatinib and concurrent chemoradiotherapy followed by maintenance lapatinib monotherapy in high-risk patients with resected squamous cell carcinoma of the head and neck: a phase III, randomized, double-blind, placebo-controlled study. J. Clin. Oncol. 33, 4202–4209. 10.1200/JCO.2015.61.4370

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 13

  HechtJ. R.BangY. J.QinS. K.ChungH. C.XuJ. M.ParkJ. O.et al. (2016). Lapatinib in combination with capecitabine plus oxaliplatin in human epidermal growth factor receptor 2-positive advanced or metastatic gastric, esophageal, or gastroesophageal adenocarcinoma: TRIO-013/LOGiC–a randomized phase III trial. J. Clin. Oncol. 34, 443–451. 10.1200/JCO.2015.62.6598

  • CrossRef
  • Google Scholar
  • View reference in article

• 14

  HeeryR.FinnS. P.CuffeS.GrayS. G. (2017). Long non-coding RNAs: key regulators of epithelial-mesenchymal transition, tumour drug resistance and cancer stem cells. Cancers9:E38. 10.3390/cancers9040038

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 15

  JiangM.HuangO.XieZ.WuS.ZhangX.ShenA.et al. (2014). A novel long non-coding RNA-ARA: adriamycin resistance-associated. Biochem. Pharmacol. 87, 254–283. 10.1016/j.bcp.2013.10.020

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 16

  KhanI. A.YooB. H.MassonO.BaronS.CorkeryD.DellaireG.et al. (2016). ErbB2-dependent downregulation of a pro-apoptotic protein Perp is required for oncogenic transformation of breast epithelial cells. Oncogene35, 5759–5769. 10.1038/onc.2016.109

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 17

  KimB.WangS.LeeJ. M.JeongY.AhnT.SonD. S.et al. (2015). Synthetic lethal screening reveals FGFR as one of the combinatorial targets to overcome resistance to Met-targeted therapy. Oncogene34, 1083–1093. 10.1038/onc.2014.51

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 18

  LiC.LiW.ZhangY.ZhangX.LiuT.YangY.et al. (2018). Increased expression of antisense lncRNA SPINT1-AS1 predicts a poor prognosis in colorectal cancer and is negatively correlated with its sense transcript. OncoTargets Ther. 11, 3969–3978. 10.2147/OTT.S163883

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 19

  LiD.ZhangJ.WangM.LiX.GongH.TangH.et al. (2018). Activity dependent LoNA regulates translation by coordinating rRNA transcription and methylation. Nat. Commun. 9:1726. 10.1038/s41467-018-04072-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 20

  LiW.ZhaiL.WangH.LiuC.ZhangJ.ChenW.et al. (2016). Downregulation of LncRNA GAS5 causes trastuzumab resistance in breast cancer. Oncotarget7, 27778–27786. 10.18632/oncotarget.8413

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 21

  LiangY.ChenX.WuY.LiJ.ZhangS.WangK.et al. (2018). LncRNA CASC9 promotes esophageal squamous cell carcinoma metastasis through upregulating LAMC2 expression by interacting with the CREB-binding protein. Cell Death Differ25, 1980–1995. 10.1038/s41418-018-0084-9

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 22

  LinC. H.JokelaT.GrayJ.LaBargeM. A. (2017). Combinatorial microenvironments impose a continuum of cellular responses to a single pathway-targeted anti-cancer compound. Cell Rep. 21, 533–545. 10.1016/j.celrep.2017.09.058

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 23

  LoiblS.GianniL. (2017). HER2-positive breast cancer. Lancet389, 2415–2429. 10.1016/S0140-6736(16)32417-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 24

  MaL.WangQ.GongZ.XueL.ZuoZ. (2018). Long noncoding RNA GIHCG enhanced tongue squamous cell carcinoma progression through regulating miR-429. J. Cell. Biochem. 119, 9064–9071. 10.1002/jcb.27164

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 25

  MaP.ZhangM.NieF.HuangZ.HeJ.LiW.et al. (2017). Transcriptome analysis of EGFR tyrosine kinase inhibitors resistance associated long noncoding RNA in non-small cell lung cancer. Biomed. Pharmacother. 87, 20–26. 10.1016/j.biopha.2016.12.079

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 26

  NiknafsY. S.HanS.MaT.SpeersC.ZhangC.Wilder-RomansK.et al. (2016). The lncRNA landscape of breast cancer reveals a role for DSCAM-AS1 in breast cancer progression. Nat. Commun. 7:12791. 10.1038/ncomms12791

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 27

  PellatA.VaqueroJ.FouassierL. (2017). Role of ErbB/HER family of receptor tyrosine kinases in cholangiocyte biology. Hepatology67, 762–773. 10.1002/hep.29350

  • CrossRef
  • Google Scholar
  • View reference in article

• 28

  PengW. X.KoiralaP.MoY. Y. (2017). LncRNA-mediated regulation of cell signaling in cancer. Oncogene36, 5661–5667. 10.1038/onc.2017.184

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 29

  PowlesT.HuddartR. A.ElliottT.SarkerS. J.AckermanC.JonesR.et al. (2017). Phase III, double-blind, randomized trial that compared maintenance lapatinib versus placebo after first-line chemotherapy in patients with human epidermal growth factor receptor 1/2-positive metastatic bladder cancer. J. Clin. Oncol. 35, 48–55. 10.1200/JCO.2015.66.3468

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 30

  RitchieM. E.PhipsonB.WuD.HuY.LawC. W.ShiW.et al. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43:e47. 10.1093/nar/gkv007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 31

  RoskoskiR.Jr. (2014). The ErbB/HER family of protein-tyrosine kinases and cancer. Pharmacol. Res. 79, 34–74. 10.1016/j.phrs.2013.11.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 32

  RusnakD. W.AlligoodK. J.MullinR. J.SpeharG. M.Arenas-ElliottC.MartinA. M.et al. (2007). Assessment of epidermal growth factor receptor (EGFR, ErbB1) and HER2 (ErbB2) protein expression levels and response to lapatinib (Tykerb, GW572016) in an expanded panel of human normal and tumour cell lines. Cell Prolif. 40, 580–594. 10.1111/j.1365-2184.2007.00455.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 33

  RussellM. R.PenikisA.OldridgeD. A.Alvarez-DominguezJ. R.McDanielL.DiamondM.et al. (2015). CASC15-S is a tumor suppressor lncRNA at the 6p22 neuroblastoma susceptibility locus. Cancer Res. 75, 3155–3166. 10.1158/0008-5472.CAN-14-3613

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 34

  SatohT.XuR. H.ChungH. C.SunG. P.DoiT.XuJ. M.et al. (2014). Lapatinib plus paclitaxel versus paclitaxel alone in the second-line treatment of HER2-amplified advanced gastric cancer in Asian populations: TyTAN–a randomized, phase III study. J. Clin. Oncol. 32, 2039–2049. 10.1200/JCO.2013.53.6136

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 35

  SubramanianA.TamayoP.MoothaV. K.MukherjeeS.EbertB. L.GilletteM. A.et al. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U.S.A. 102, 15545–15550. 10.1073/pnas.0506580102

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 36

  SuiC. J.ZhouY. M.ShenW. F.DaiB. H.LuJ. J.ZhangM. F.et al. (2016). Long noncoding RNA GIHCG promotes hepatocellular carcinoma progression through epigenetically regulating miR-200b/a/429. J. Mol. Med. 94, 1281–1296. 10.1007/s00109-016-1442-z

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 37

  SunQ.TripathiV.YoonJ. H.SinghD. K.HaoQ.MinK. W.et al. (2018). MIR100 host gene-encoded lncRNAs regulate cell cycle by modulating the interaction between HuR and its target mRNAs. Nucleic Acids Res. 46, 10405–10416. 10.1093/nar/gky696

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 38

  TzanakakisG.KavasiR. M.VoudouriK.BerdiakiA.SpyridakiI.TsatsakisA.et al. (2018). Role of the extracellular matrix in cancer-associated epithelial to mesenchymal transition phenomenon. Dev. Dyn. 247, 368–381. 10.1002/dvdy.24557

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 39

  WangQ.LiC.TangP.JiR.ChenS.WenJ. (2018). A minimal lncRNA-mRNA signature predicts sensitivity to neoadjuvant chemotherapy in triple-negative breast cancer. Cell. Physiol. Biochem. 48, 2539–2548. 10.1159/000492698

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 40

  WatsonS. S.DaneM.ChinK.TatarovaZ.LiuM.LibyT.et al. (2018). Microenvironment-mediated mechanisms of resistance to HER2 inhibitors differ between HER2+ breast cancer subtypes. Cell Syst. 6, 329–342 e326. 10.1016/j.cels.2018.02.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 41

  WuL.PanC.WeiX.ShiY.ZhengJ.LinX.et al. (2018). lncRNA KRAL reverses 5-fluorouracil resistance in hepatocellular carcinoma cells by acting as a ceRNA against miR-141. Cell Commun. Signal. 16:47. 10.1186/s12964-018-0260-z

  • CrossRef
  • Google Scholar
  • View reference in article

• 42

  XiangZ.HuangX.WangJ.ZhangJ.JiJ.YanR.et al. (2018). Cross-database analysis reveals sensitive biomarkers for combined therapy for ERBB2+ gastric cancer. Front. Pharmacol. 9:861. 10.3389/fphar.2018.00861

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 43

  ZhangG.LiS.LuJ.GeY.WangQ.MaG.et al. (2018). LncRNA MT1JP functions as a ceRNA in regulating FBXW7 through competitively binding to miR-92a-3p in gastric cancer. Mol. Cancer17:87. 10.1186/s12943-018-0829-6

  • CrossRef
  • Google Scholar
  • View reference in article

• 44

  ZhangL.KangW.LuX.MaS.DongL.ZouB. (2018). LncRNA CASC11 promoted gastric cancer cell proliferation, migration and invasion in vitro by regulating cell cycle pathway. Cell Cycle17, 1886–1900. 10.1080/15384101.2018.1502574

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 45

  ZhouZ.LinZ.HeY.PangX.WangY.PonnusamyM.et al. (2018). The long non-coding RNA D63785 regulates chemotherapy sensitivity in human gastric cancer by targeting miR-422a. Mol. Ther. Nucleic Acids. 12, 405–419. 10.1016/j.omtn.2018.05.024

  • CrossRef
  • Google Scholar
  • View reference in article