Open Access

Knockdown of TBRG4 affects tumorigenesis in human H1299 lung cancer cells by regulating DDIT3, CAV1 and RRM2

  • Authors:
    • Ansheng Wang
    • Chengling Zhao
    • Xuegang Liu
    • Wen Su
    • Guixin Duan
    • Zongyu Xie
    • Shanshan Chu
    • Yuan Gao
  • View Affiliations

  • Published online on: November 2, 2017
  • Pages: 121-128
  • Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: HTML 0 views | PDF 0 views     Cited By (CrossRef): 0 citations


The transforming growth factor β regulator 4 (TBRG4) gene, located on the 7p14‑p13 chromosomal region, is implicated in numerous types of cancer. However, the contribution(s) of TBRG4 in human lung cancer remains unknown. In the present study, the expression of TBRG4 mRNA was investigated in the H1299 lung cancer cell line using the quantitative polymerase chain reaction (qPCR) following the knockdown of TBRG4 by a lentivirus‑mediated small interfering RNA (siRNA). Results identified that the expression of TBRG4 within H1299 cells was significantly suppressed (P<0.01) by RNA interference, and 586 genes were differentially expressed following TBRG4 silencing. Ingenuity Pathway Analysis (IPA) revealed that these genes were often associated with infectious diseases, organismal injury, abnormalities and cancer functional networks. Further IPA of these networks revealed that TBRG4 knockdown in H1299 cells deregulated the expression of 21 downstream genes, including the upregulation of DNA damage‑inducible transcript 3 (DDIT3), also termed CCAAT/enhancer‑binding protein homologous protein, and downregulation of caveolin 1 (CAV1) and ribonucleotide reductase regulatory subunit M2 (RRM2). Results were validated using qPCR and western blotting. Furthermore, immunohistochemical staining of TBRG4 protein identified that expression was markedly increased in carcinoma compared with in normal tissue. In conclusion, TBRG4 serves a role in the tumorigenesis of lung cancer via deregulation of DDIT3, CAV1 and RRM2. The results of the present study may be important in contributing to our understanding of TBRG4 as a target for lung cancer treatment.


Lung cancer is the primary cause of cancer-associated mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for ~85% of all lung cancer cases in 2014 (1). The majority of patients present with advanced disease with a 5-year survival rate of <15% in 2014 (2,3). Although surgery remains the front-line choice of treatment for localized NSCLC, those with advanced forms may require chemotherapy (4). Targeted therapy constitutes a promising treatment strategy for prolonging the survival of a subset of patients with NSCLC (5). However, drug resistance conferred by complex recurrent genetic and epigenetic changes remains a crucial obstacle for targeted therapies (610). Consequently, there is urgent requirement to understand the molecular mechanism(s) underlying the development of NSCLC, and develop new therapies for treating NSCLC.

Transforming growth factor β (TGFβ) regulator 4 (TBRG4), also termed cell cycle progression restoration protein 2 or Fas-activated serine-threonine kinase domain-containing protein 4, encodes a regulator for TGFb (1114). TBRG4 (GenBank no. AAH14918.1) is located on chromosome 7p12.3–13, and is duplicated in patients with Sézary syndrome (15). TBRG4 has previously been demonstrated to stabilize the expression level of transcription of cyclin 1 and 2 (12). In 293 and Jurkat human cell lines, TBRG4 physically interacts with the virus protein U encoded by the human immunodeficiency virus (16). Furthermore, TBRG4 also interacts with pleiotrophin protein, and TBRG4 silencing affects the stability of certain mitochondrial mRNAs (17,18). However, the contribution of TBRG4 in the tumorigenesis of NSCLC remains unclear.

In the present study, the effect of TBRG4 silencing on genome-wide gene expression patterns within human H1299 lung cancer cells was investigated. The expression of TBRG4 in tumor and adjacent normal tissues was also evaluated.

Materials and methods

Cell culture

Human H1299 lung cancer cells were purchased from the Shanghai Cell Bank of the Chinese Academy of Sciences (Shanghai, China). Cells were cultured in Dulbecco's modified Eagle's medium (Corning Life Sciences, Shanghai, China) and supplemented with 10% fetal bovine serum (Sangon Biotech Co., Ltd., Shanghai, China) at 37°C in a humidified atmosphere containing 5% CO2.

siRNA transduction

The sequence of siRNA (5′-GTTCTTCAGCCTGGTACAT-3′) was designed by GeneChem Co., Ltd. (Shanghai, China) for targeting the TBRG4 sequence (GenBank no. NM_004749). The TBRG4 hairpin oligonucleotide was inserted into the pGV115-GFP lentiviral vector (GeneChem Co., Ltd.) to construct a pGV115-GFP-short hairpin TBRG4 (shTBRG4) TBRG4-knockdown vector. The negative control (shCtrl) sequence was previously reported (19,20), and when incorporated into the lentiviral vector was referred to as pGV115-GFP-shCtrl. The lentiviral particles were prepared as previously described (21). For cellular transduction of shTBRG4 lentiviral or shCtrl lentivirus, 1×105 cells/well were seeded into 6-well plates. The following day, cells were transduced with validated shTBRG4 lentivirus (5×105 TU/ml, 2 µl) or shCtrl lentivirus (8×105 TU/ml, 1.25 µl) using 4 µl Lipofectamine 2000 reagent (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA), according to the manufacturer's protocol. After evaluating infection efficiency using light and fluorescent microscopy at 72 h after infection. Cells were harvested and used for subsequent experiments.

RNA isolation and quantitative polymerase chain reaction (qPCR)

Total RNA was extracted from the aforementioned transduced cells using TRIzol reagent (Invitrogen; Thermo Fisher Scientific, Inc.), and then 2 µg total RNA was reverse-transcribed using a QuantiTect reverse transcription kit (Qiagen China Co., Ltd., Shanghai, China), according to the manufacturer's protocol. A 1 µg amount of cDNA was used as a template for qPCR using a SYBR® Green PCR Master mix (Applied Biosystems; Thermo Fisher Scientific, Inc., Waltham, MA, USA). The primer sequences used were as follows: TBRG4 forward, 5′-CAGCTCACCTGGTAAAGCGAT-3′ and reverse, 5′-GGGAGTAGATGCTCGTTCCTTC-3′; GAPDH forward, 5′-TGACTTCAACAGCGACACCCA-3′ and reverse, 5′-CACCCTGTTGCTGTAGCCAAA-3′. Results were normalized to GAPDH data as described previously (22). Data were analyzed using the method of Pfaffl (23). PCR primers used for validating the microarray data are listed in Table I.

Table I.

Primer sequences used for quantitative polymerase chain reaction.

Table I.

Primer sequences used for quantitative polymerase chain reaction.

GenePrimer sequence 5′-3′

[i] FP, forward primer; RP, reverse primer.

Gene microarray

The genome-wide effect of TBRG4 knockdown was studied using a GeneChip® PrimeView™ Human Gene Expression Array (Affymetrix; Thermo Fisher Scientific, Inc., Waltham, MA, USA) consisting of 20,000 genes. Three biological replicates of H1299 cells transduced with shTBRG4 or shCtrl lentiviruses (for 72 h) were microarrayed. RNA was initially isolated using TRIzol reagent, and quality was determined using a NanoDrop 2000 spectrophotometer (NanoDrop; Thermo Fisher Scientific, Inc., Wilmington, DE, USA) and Agilent Bioanalyzer 2100 (Agilent Technologies, Inc., Santa Clara, CA, USA). Individual microarrays were used for gene expression profiling of each sample. Briefly, 500 ng total RNA was reverse-transcribed and labeled with biotin using the GeneChip® 3′ IVT labeling kit, according to the manufacturer's protocol. Labeled cDNA was then hybridized onto the GeneChip® PrimeView™ Human Gene Expression Array at 60°C overnight. Arrays were performed with GeneChip® Hybridization Wash and Stain kit using GeneChip® Fluidics Station 450. All GeneChip® products were obtained from Affymetrix; Thermo Fisher Scientific, Inc., and all were used according to the manufacturer's protocol. The chip array was scanned directly post-hybridization using a GeneChip® Scanner 3000. Microarray data were analyzed with GeneSpring software (version 11; Agilent Technologies, Inc.). Data were normalized using the GeneSpring normalization algorithm. Finally, genes which were differentially expressed >1.5-fold, and had a differential score P≤0.05 among test samples, were identified from normalized data sets.

Ingenuity pathway analysis (IPA)

Datasets representing differentially expressed genes derived from microarray analyses were imported into the IPA tool (; Ingenuity® Systems, Redwood City, CA, USA). The ‘core analysis’ function of the IPA software in the present study was used to interpret the differentially expressed data, which included ‘Disease and Functions’, and ‘Molecular Network’. Differentially expressed genes were mapped onto genetic networks available in the Ingenuity database and then ranked on the basis of the overlap P-value to measure enrichment of network-regulated genes in the present dataset and the activation z-score algorithms computed by IPA software (24). Analyses performed within the IPA program include the identification of biological networks of a particular interesting dataset according to the purpose of the present study, and its global functions, functional signaling pathways and downstream target genes.

Patients and tissue samples

A total of 75 patients diagnosed with lung cancer and who received surgery at The First Affiliated Hospital of Bengbu Medical College (Anhui, China) between December 2011 and June 2015 were randomly enrolled into the present study. All samples were obtained following provision of written informed consent and used with approval from the Review Board of The First Affiliated Hospital of Bengbu Medical College. The age range of diagnosed patients was between 35 and 77 years, with a median age of 60 years. Tissue samples were classified as tumor or adjacent carcinomatous tissue. Baseline characteristics of enrolled patients included age at time of diagnosis, tissue type, histological grade, distant metastasis and tumor-node-metastasis (TNM) stage (Table II). All patients were clinically staged according to the criteria of the American Joint Committee on Cancer staging system (25). Histological grades of primary tumors were based on the World Health Organization recommendations (26).

Table II.

Association of TBRG4 expression and clinicopathological characteristics.

Table II.

Association of TBRG4 expression and clinicopathological characteristics.


Sex 0.808
  Male3931  8
  Female3527  8
Age, years 0.348
  ≤603428  6
Tumor size, cm 0.698
  >42520  5
Distant metastasis 0.269
  M1  4  4  0
TNM stage 0.014a
  TNM21816  2
  TNM31614  2
  TNM4  4  4  0
Tumor grade
  III2218  4

a P-value for the TNM stage was corrected for multiple comparison by the Bonferroni correction method. P=0.014 was not considered to indicate a statistically significant difference according to the principle of multiple testing of Bonferroni correction. TNM, tumor-node-metastasis.


Tissues were fixed in 4% neutral-buffered formalin at room temperature overnight and then embedded in paraffin. Sections of formalin-fixed paraffin-embedded sections (5 µm thick) were dehydrated and deparaffinized in xylene 2–3 times at room temperature and rehydrated with a gradient alcohol series. Antigen retrieval was performed using 0.1 mol/l citrate buffer (pH 6.0) at 95°C for 30 min. Endogenous peroxidase was quenched for 10 min with 3% (v/v) H2O2 at room temperature. Slides were then washed three times with PBS and blocked for 30 min with 10% (w/v) normal goat serum (Sangon Biotech Co., Ltd., Shanghai, China) in 1% (w/v) bovine serum albumin (Sangon Biotech Co., Ltd.) in PBS. Slides were incubated with a 1:1,000 dilution of anti-TBRG4 monoclonal antibody (cat. no. D154006; Sangon Biotech Co., Ltd.) at 37°C for 30 min. Following incubation for 1 h at room temperature with horseradish peroxidase-labeled streptavidin secondary antibody (dilution 1:1,000; cat. no. D111054; Sangon Biotech Co., Ltd.), the development reaction was detected by exposure to 3,3′-diaminobenzidine (Sangon Biotech Co., Ltd.) for 5 min at room temperature. Immunostained slides were digitized using the ScanScope XT (Aperio, Cista, CA, USA) and scored independently by two pathologists according to the Allred scoring system (27). The scoring was based on staining intensity: Negative, 0; weak, 1; intermediate, 2; and strong, 3. A proportionate score was assigned to represent the estimated percentage of positively stained cells (0, <1%; 1, 1–25%; 2, 26–50%; 3, 51–75%; 4, ≥76%). The multiplication of the two parameters was performed to obtain a total score ranging from negative (0–1) to positive (>2).

Statistical analysis

Numerical data were expressed as the mean ± standard deviation, and analyzed using Student's t-test. All categorical data were expressed as a frequency. The Mann-Whitney U test was used to analyze the clinicopathological and TBRG4 gene expression data. Analysis was performed with SPSS (version 16.0; SPSS, Inc., Chicago, IL, USA). P<0.05 was considered to indicate a statistically significant difference.


Lentivirus-mediated TBRG4 knockdown

Efficacy of TBRG4-specific siRNA in downregulating the expression of TBRG4 in H1299 lung cancer cells was evaluated with RNA expression data generated by qPCR. The proportions of infected H1299 cells transduced with either shCtrl or shTBRG4 lentiviruses were >70% at 72 h post-infection (Fig. 1A). The level of TBRG4 mRNA expression in cells transduced with shTBRG4 was significantly decreased at 72 h post infection compared with those transduced with a shCtrl lentivirus (P<0.01; Fig. 1B). Thus, shTBRG4 lentivirus was specific and effective in knocking down TBRG4.

Genome-wide effects of TBRG4 knockdown

In total, 20,000 genes were microarrayed to determine the influence that TBRG4 knockdown has on downstream gene expression. The gene expression profiles of H1299 cells transduced with shTBRG4 or shCtrl-payload lentiviruses were determined using a GeneChip® PrimeView™ Human Gene Expression Array using three biological replicates. A total of 586 differentially expressed genes were identified, of which 357 were downregulated and 229 were upregulated (Fig. 2A).

Elucidation of pathways and interactions among differentially expressed genes

Biological interactions were identified in the 586 differently regulated genes using the IPA program. This output highlighted 25 significant networks (data not shown), of which IPA identified a list of the top 10 networks (Fig. 2B). Of these networks, those associated with infectious diseases, cancer, organismal injury and abnormalities were the highest ranked networks, with 27 focus molecules and a significance score of 42 (Fig. 2C). In particular, Fig. 2C presents that TBRG4 is located upstream of all focus genes.

TBRG4 knockdown markedly upregulates DDIT3 and downregulates CAV1 and RRM2

Expression of mRNA for 14 downstream genes of TBRG4 identified using IPA was analyzed using qPCR, including AURKA, CAV1, IGF2, MYBL2, RAP1A, CDC20, DDIT3, FOXM1, IDI1, PGK1, RRM3, SCD, SERPINE1 and SESN2 (Fig. 3A). Results demonstrated that IGF2, RAP1A, DDIT3 and SESN2 were upregulated following TBRG4 knockdown, whereas the remaining 10 genes were downregulated (Fig. 3B). Western blotting also demonstrated that knockdown of TBRG4 increased the level of DDIT3 expression and decreased the levels of CAV1 and RRM2 (Fig. 3C and D).

Association of TBRG4 expression in lung cancer tissues and clinicopathological characteristics

The present study immunostained the TBRG4 protein expressed within lung cancer and adjacent normal tissues. The immunohistochemical data revealed that the expression of TBRG4 was markedly increased within lung cancer tissues compared with normal tissues retrieved from the adjacent epithelium (Fig. 4). There was no significant difference identified in TBRG4 expression between the sexes, neither was it significantly associated with age, tumor size, distant metastasis, TNM stage or tumor grade (P>0.05; Table II).


TBRG4 is implicated in a number of human malignancies; however, its contribution to lung cancer has not yet been evaluated. To the best of our knowledge, the present study is the first to investigate the expression of TBRG4 within lung carcinomas. The results of the present study demonstrate that there is a markedly increased expression of TBRG4 in lung cancer in vitro and ex vivo. As previously identified, TBRG4 is a regulator of TGFb located on chromosome 7p12.7–13 (15). Chromosomal abnormity, including breakpoint and duplication at the 7p13 locus, has previously been reported in a range of hematological malignancies (28). Previously, it was reported that this region where TBRG4 is located is disrupted in patients with Sézary syndrome, a leukemic form of cutaneous T-cell lymphoma, with chromosomal abnormalities and mutations in certain genes that are relevant to this disease (14,29). The region is also amplified in 30% of cell lines derived from patients with head and neck squamous cell carcinoma (30). Furthermore, recent research has demonstrated that TBRG4 exhibits significant changes in extramedullary relapse of multiple myelomas (14). This evidence suggests that TBRG4 serves an important role in several types of human cancer.

In order to assess the contribution of TBRG4 to lung cancer, a shTBRG4 lentiviral vector was initially constructed, which specifically silenced the expression of TBRG4 in H1299 lung cancer cells. The shTBRG4-infected cells demonstrated a decrease in expression of TBRG4 compared with those transduced with a shCtrl control lentivirus. In order to obtain an understanding of the downstream biological alterations, the transcriptome of H1299 cells transduced with either shTBRG4 or shCtrl lentiviruses was investigated with an Affymetrix microarray of 20,000 genes. Gene expression profiling and network analysis revealed a set of 586 differentially expressed genes, a number of which were categorized into infectious diseases, cancer, organismal injury and abnormalities. A key observation in the present study was the identification of the set of genes, which were altered downstream due to the knockdown of TBRG4. Results of the present study demonstrated a significant increase in the level of DDIT3, and a decrease in the levels of CAV1 and RRM2 genes at the transcriptional (qPCR) and translational (western blotting) levels. DDIT3 is a transcription factor which is considered as a marker of commitment of endoplasmic reticulum stress-mediated apoptosis via induction of B-cell lymphoma 2 downregulation and death receptor 5 activation (31,32). Caveolin-1 is a multifunctional molecule typically expressed in membranous structures. The wild-type form acts as a tumor suppressor protein through contact inhibition of signaling molecules; however, its loss in cancer cells promotes tumor growth, motility, vascularization and metastasis, and decreases survival outcomes (3336). The ribonucleotide reductase subunit M2 (RRM2) is reported to regulate several oncogenes that control malignant potential, and therefore may serve a major role in tumor progression (37,38). RRM2 serves as a prognostic biomarker for several types of cancer (3941), and knockdown is reported to lead to apoptosis in NSCLC (38). Immunohistochemistry results validated the increased levels of TBRG4 in tumor tissue; therefore, TBRG4 knockdown complicated the tumorigenesis of H1299 cells by upregulating DDIT3 and downregulating CAV1 and RRM2. Further study is currently ongoing in order to validate the precise role of TBRG4 knockdown.

In conclusion, the results of the present study highlighted the tumorigenic roles of TBRG4 by silencing its expression in human H1299 lung cancer cells. This present study has provided evidence to further the understanding of the precise role of TBRG4 in the tumorigenesis of human lung cancer. However, the exact underlying molecular mechanisms by which TBRG4 influences the biological behaviors of lung cancer cells are yet to be determined.



Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J and Jemal A: Global cancer statistics, 2012. CA Cancer J Clin. 65:87–108. 2015. View Article : Google Scholar : PubMed/NCBI


Reungwetwattana T and Dy GK: Targeted therapies in development for non-small cell lung cancer. J Carcinog. 12:222013. View Article : Google Scholar : PubMed/NCBI


Kumarakulasinghe NB, van Zanwijk N and Soo RA: Molecular targeted therapy in the treatment of advanced stage non-small cell lung cancer (NSCLC). Respirology. 20:370–378. 2015. View Article : Google Scholar : PubMed/NCBI


Chang JW, Wei NC, Su HJ, Huang JL, Chen TC, Wu YC, Yu CT, Hou MM, Hsieh CH, Hsieh JJ, et al: Comparison of genomic signatures of non-small cell lung cancer recurrence between two microarray platforms. Anticancer Res. 32:1259–1265. 2012.PubMed/NCBI


Gridelli C and Felip E: Targeted therapies development in the treatment of advanced nonsmall cell lung cancer. J Biomed Biotechnol. 2011:4156412011. View Article : Google Scholar : PubMed/NCBI


Bergethon K, Shaw AT, Ou SH, Katayama R, Lovly CM, McDonald NT, Massion PP, Siwak-Tapp C, Gonzalez A, Fang R, et al: ROS1 rearrangements define a unique molecular class of lung cancers. J Clin Oncol. 30:863–870. 2012. View Article : Google Scholar : PubMed/NCBI


Hammerman PS, Sos ML, Ramos AH, Xu C, Dutt A, Zhou W, Brace LE, Woods BA, Lin W, Zhang J, et al: Mutations in the DDR2 kinase gene identify a novel therapeutic target in squamous cell lung cancer. Cancer Discov. 1:78–89. 2011. View Article : Google Scholar : PubMed/NCBI


Ding L, Getz G, Wheeler DA, Mardis ER, McLellan MD, Cibulskis K, Sougnez C, Greulich H, Muzny DM, Morgan MB, et al: Somatic mutations affect key pathways in lung adenocarcinoma. Nature. 455:1069–1075. 2008. View Article : Google Scholar : PubMed/NCBI


Peifer M, Fernandez-Cuesta L, Sos ML, George J, Seidel D, Kasper LH, Plenker D, Leenders F, Sun R, Zander T, et al: Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nat Genet. 44:1104–1110. 2012. View Article : Google Scholar : PubMed/NCBI


Wangari-Talbot J and Hopper-Borge E: Drug resistance mechanisms in non-small cell lung carcinoma. J Can Res Updates. 2:265–282. 2013.PubMed/NCBI


Strausberg RL, Feingold EA, Grouse LH, Derge JG, Klausner RD, Collins FS, Wagner L, Shenmen CM, Schuler GD, Altschul SF, et al: Generation and initial analysis of more than 15,000 full-length human and mouse cDNA sequences. Proc Natl Acad Sci USA. 99:pp. 16899–16903. 2002; PubMed/NCBI


Edwards MC, Liegeois N, Horecka J, DePinho RA, Sprague GF Jr, Tyers M and Elledge SJ: Human CPR (cell cycle progression restoration) genes impart a Far-phenotype on yeast cells. Genetics. 147:1063–1076. 1997.PubMed/NCBI


Simarro M, Gimenez-Cassina A, Kedersha N, Lazaro JB, Adelmant GO, Marto JA, Rhee K, Tisdale S, Danial N, Benarafa C, et al: Fast kinase domain-containing protein 3 is a mitochondrial protein essential for cellular respiration. Biochem Biophys Res Commun. 401:440–446. 2010. View Article : Google Scholar : PubMed/NCBI


Sevcikova S, Paszekova H, Besse L, Sedlarikova L, Kubaczkova V, Almasi M, Pour L and Hajek R: Extramedullary relapse of multiple myeloma defined as the highest risk group based on deregulated gene expression data. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 159:288–293. 2015.PubMed/NCBI


Prasad A, Rabionet R, Espinet B, Zapata L, Puiggros A, Melero C, Puig A, Sarria-Trujillo Y, Ossowski S, Garcia-Muret MP, et al: Identification of gene mutations and fusion genes in patients with sezary syndrome. J Invest Dermatol. 136:1490–1499. 2016. View Article : Google Scholar : PubMed/NCBI


Jager S, Cimermancic P, Gulbahce N, Johnson JR, McGovern KE, Clarke SC, Shales M, Mercenne G, Pache L, Li K, et al: Global landscape of HIV-human protein complexes. Nature. 481:365–370. 2012.


Rolland T, Tasan M, Charloteaux B, Pevzner SJ, Zhong Q, Sahni N, Yi S, Lemmens I, Fontanillo C, Mosca R, et al: A proteome-scale map of the human interactome network. Cell. 159:1212–1226. 2014. View Article : Google Scholar : PubMed/NCBI


Wolf AR and Mootha VK: Functional genomic analysis of human mitochondrial RNA processing. Cell Rep. 7:918–931. 2014. View Article : Google Scholar : PubMed/NCBI


Liu H, Liang S, Yang X, Ji Z, Zhao W, Ye X and Rui J: RNAi-mediated RPL34 knockdown suppresses the growth of human gastric cancer cells. Oncol Rep. 34:2267–2272. 2015. View Article : Google Scholar : PubMed/NCBI


Li LH, He J, Hua D, Guo ZJ and Gao Q: Lentivirus-mediated inhibition of Med19 suppresses growth of breast cancer cells in vitro. Cancer Chemother Pharmacol. 68:207–215. 2011. View Article : Google Scholar : PubMed/NCBI


Lois C, Hong EJ, Pease S, Brown EJ and Baltimore D: Germline transmission and tissue-specific expression of transgenes delivered by lentiviral vectors. Science. 295:868–872. 2002. View Article : Google Scholar : PubMed/NCBI


Pfaffl MW, Horgan GW and Dempfle L: Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res. 30:e362002. View Article : Google Scholar : PubMed/NCBI


Pfaffl MW: A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 29:e452001. View Article : Google Scholar : PubMed/NCBI


Dubey R, Chhabra R and Saini N: Small interfering RNA against transcription factor STAT6 leads to increased cholesterol synthesis in lung cancer cell lines. PLoS One. 6:e285092011. View Article : Google Scholar : PubMed/NCBI


Edge SB and Compton CC: The American Joint Committee on Cancer: The 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol. 17:1471–1474. 2010. View Article : Google Scholar : PubMed/NCBI


Cho KR and Shih IeM: Ovarian cancer. Annu Rev Pathol. 4:287–313. 2009. View Article : Google Scholar : PubMed/NCBI


Harvey JM, Clark GM, Osborne CK and Allred DC: Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer. J Clin Oncol. 17:1474–1481. 1999. View Article : Google Scholar : PubMed/NCBI


Dyer MJ, Nacheva E, Fischer P, Heward JM, Labastide W and Karpas A: A new human T-cell lymphoma cell line (Karpas 384) of the T-cell receptor gamma/delta lineage with translocation t(7:14) (p13;q11.2). Leukemia. 7:1047–1053. 1993.PubMed/NCBI


Liu H, Krizek J and Bretscher A: Construction of a GAL1-regulated yeast cDNA expression library and its application to the identification of genes whose overexpression causes lethality in yeast. Genetics. 132:665–673. 1992.PubMed/NCBI


Carey TE, Van Dyke DL and Worsham MJ: Nonrandom chromosome aberrations and clonal populations in head and neck cancer. Anticancer Res. 13:2561–2567. 1993.PubMed/NCBI


Han J, Murthy R, Wood B, Song B, Wang S, Sun B, Malhi H and Kaufman RJ: ER stress signalling through eIF2α and CHOP, but not IRE1α, attenuates adipogenesis in mice. Diabetologia. 56:911–924. 2013. View Article : Google Scholar : PubMed/NCBI


Flocke LS, Trondl R, Jakupec MA and Keppler BK: Molecular mode of action of NKP-1339-a clinically investigated ruthenium-based drug-involves ER- and ROS-related effects in colon carcinoma cell lines. Invest New Drugs. 34:261–268. 2016. View Article : Google Scholar : PubMed/NCBI


Engelman JA, Chu C, Lin A, Jo H, Ikezu T, Okamoto T, Kohtz DS and Lisanti MP: Caveolin-mediated regulation of signaling along the p42/44 MAP kinase cascade in vivo. A role for the caveolin-scaffolding domain. FEBS Lett. 428:205–211. 1998. View Article : Google Scholar : PubMed/NCBI


Podar K, Tai YT, Cole CE, Hideshima T, Sattler M, Hamblin A, Mitsiades N, Schlossman RL, Davies FE, Morgan GJ, et al: Essential role of caveolae in interleukin-6- and insulin-like growth factor I-triggered Akt-1-mediated survival of multiple myeloma cells. J Biol Chem. 278:5794–5801. 2003. View Article : Google Scholar : PubMed/NCBI


Quest AF, Gutierrez-Pajares JL and Torres VA: Caveolin-1: An ambiguous partner in cell signalling and cancer. J Cell Mol Med. 12:1130–1150. 2008. View Article : Google Scholar : PubMed/NCBI


Li M, Chen H, Diao L, Zhang Y, Xia C and Yang F: Caveolin-1 and VEGF-C promote lymph node metastasis in the absence of intratumoral lymphangiogenesis in non-small cell lung cancer. Tumori. 96:734–743. 2010.PubMed/NCBI


Fan H, Villegas C and Wright JA: Ribonucleotide reductase R2 component is a novel malignancy determinant that cooperates with activated oncogenes to determine transformation and malignant potential. Proc Natl Acad Sci USA. 93:pp. 14036–14040. 1996; View Article : Google Scholar : PubMed/NCBI


Rahman MA, Amin AR, Wang D, Koenig L, Nannapaneni S, Chen Z, Wang Z, Sica G, Deng X, Chen ZG and Shin DM: RRM2 regulates Bcl-2 in head and neck and lung cancers: A potential target for cancer therapy. Clin Cancer Res. 19:3416–3428. 2013. View Article : Google Scholar : PubMed/NCBI


Mah V, Alavi M, Márquez-Garbán DC, Maresh EL, Kim SR, Horvath S, Bagryanova L, Huerta-Yepez S, Chia D, Pietras R and Goodglick L: Ribonucleotide reductase subunit M2 predicts survival in subgroups of patients with non-small cell lung carcinoma: Effects of gender and smoking status. PLoS One. 10:e01276002015. View Article : Google Scholar : PubMed/NCBI


Zhang H, Liu X, Warden CD, Huang Y, Loera S, Xue L, Zhang S, Chu P, Zheng S and Yen Y: Prognostic and therapeutic significance of ribonucleotide reductase small subunit M2 in estrogen-negative breast cancers. BMC Cancer. 14:6642014. View Article : Google Scholar : PubMed/NCBI


Liu X, Zhang H, Lai L, Wang X, Loera S, Xue L, He H, Zhang K, Hu S, Huang Y, et al: Ribonucleotide reductase small subunit M2 serves as a prognostic biomarker and predicts poor survival of colorectal cancers. Clin Sci (Lond). 124:567–578. 2013. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

January 2018
Volume 15 Issue 1

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
Wang, A., Zhao, C., Liu, X., Su, W., Duan, G., Xie, Z. ... Gao, Y. (2018). Knockdown of TBRG4 affects tumorigenesis in human H1299 lung cancer cells by regulating DDIT3, CAV1 and RRM2. Oncology Letters, 15, 121-128.
Wang, A., Zhao, C., Liu, X., Su, W., Duan, G., Xie, Z., Chu, S., Gao, Y."Knockdown of TBRG4 affects tumorigenesis in human H1299 lung cancer cells by regulating DDIT3, CAV1 and RRM2". Oncology Letters 15.1 (2018): 121-128.
Wang, A., Zhao, C., Liu, X., Su, W., Duan, G., Xie, Z., Chu, S., Gao, Y."Knockdown of TBRG4 affects tumorigenesis in human H1299 lung cancer cells by regulating DDIT3, CAV1 and RRM2". Oncology Letters 15, no. 1 (2018): 121-128.