Expression and Predictive Significance of FHL1 and SLIT3 in Surgically
Resected Lung Adenocarcinoma

Jinjing      Song; Kai      Liang; Tongtong      Wei; Li      Li; Zhiguang      Huang; Gang      Chen; Naiquan      Mao; Jie      Yang

doi:10.2174/1386207326666230208124028

Abstract

Objective: Lung adenocarcinoma (LUAD) is the most common type of lung cancer. However, predictive biomarkers for early efficacy and prognosis evaluation in patients with surgically resected LUAD are not completely explained.

Methods: Differentially expressed genes (DEGs), gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were identified by RNA sequencing (RNA-Seq) between thirteen LUAD tissues and five normal lung tissues. The expression of DEGs was confirmed by qRT-PCR and a validated cohort from GEPIA. Protein-protein interaction (PPI) network of the top 5% DEGs was constructed by STRING and visualized in Cytoscape. Immunofluorescence results were acquired from clinical specimens from LUAD patients. The expression of FHL1 was analyzed by ImageJ. Survival analysis was performed using the GEPIA dataset.

Results: Consistent with the RNA-Seq data, validation of DEGs expression by qRT-PCR and GEPIA cohort showed that FHL1 and SLIT3 were down-regulated in LUAD patient tissues compared with non-tumor tissues. Moreover, FHL1 was significantly reduced in LUAD cell lines compared to the bronchial epithelium cell line (P < 0.01). However, SLIT3 was elevated in A549 and H1299 cells (wide type EGFR) (P < 0.05) while decreased in HCC827 and PC9 cells (mutant EGFR) compared to BESA-2B cells (P < 0.01). PPI network revealed the most significant cluster with 10 nodes and 43 edges. Immunofluorescent staining also showed that the expression of FHL1 was lower in LUAD tissues compared with that in normal lung tissues (P < 0.01). The expressions of SLIT3 and FHL1 were positively correlated. Specifically, the higher expression level of SLIT3 and FHL1 independently predicted a better prognosis (P < 0.01 or P < 0.05).

Conclusion: Our findings provide two novel candidates, FHL1 and SLIT3, for prognostic evaluation and treatments after surgery.

« Previous Next »

Graphical Abstract

[1]
Bender, E. Epidemiology: The dominant malignancy. Nature,  2014, 513(7517), S2-S3.
 [http://dx.doi.org/10.1038/513S2a] [PMID:  25208070]

[2]
Ferlay, J.; Soerjomataram, I.; Dikshit, R.; Eser, S.; Mathers, C.; Rebelo, M.; Parkin, D.M.; Forman, D.; Bray, F. Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer,  2015, 136(5), E359-E386.
 [http://dx.doi.org/10.1002/ijc.29210] [PMID:  25220842]

[3]
Walters, S.; Maringe, C.; Coleman, M.P.; Peake, M.D.; Butler, J.; Young, N.; Bergström, S.; Hanna, L.; Jakobsen, E.; Kölbeck, K.; Sundstrøm, S.; Engholm, G.; Gavin, A.; Gjerstorff, M.L.; Hatcher, J.; Johannesen, T.B.; Linklater, K.M.; McGahan, C.E.; Steward, J.; Tracey, E.; Turner, D.; Richards, M.A.; Rachet, B. Lung cancer survival and stage at diagnosis in Australia, Canada, Denmark, Norway, Sweden and the UK: a population-based study, 2004-2007. Thorax,  2013, 68(6), 551-564.
 [http://dx.doi.org/10.1136/thoraxjnl-2012-202297] [PMID:  23399908]

[4]
Okami, J.; Shintani, Y.; Okumura, M.; Ito, H.; Ohtsuka, T.; Toyooka, S.; Mori, T.; Watanabe, S-I.; Date, H.; Yokoi, K.; Asamura, H.; Nagayasu, T.; Miyaoka, E.; Yoshino, I. Demographics, safety and quality, and prognostic information in both the Seventh and Eighth Editions of the TNM classification in 18,973 surgical cases of the japanese joint committee of lung cancer registry database in 2010. J. Thorac. Oncol.,  2019, 14, 212-222.

[5]
Vansteenkiste, J.; Crinò, L.; Dooms, C.; Douillard, J.Y.; Faivre-Finn, C.; Lim, E.; Rocco, G.; Senan, S.; Van Schil, P.; Veronesi, G.; Stahel, R.; Peters, S.; Felip, E.; Stahel, R.; Felip, E.; Peters, S.; Kerr, K.; Besse, B.; Vansteenkiste, J.; Eberhardt, W.; Edelman, M.; Mok, T.; O’Byrne, K.; Novello, S.; Bubendorf, L.; Marchetti, A.; Baas, P.; Reck, M.; Syrigos, K.; Paz-Ares, L.; Smit, E.F.; Meldgaard, P.; Adjei, A.; Nicolson, M.; Crinò, L.; Van Schil, P.; Senan, S.; Faivre-Finn, C.; Rocco, G.; Veronesi, G.; Douillard, J-Y.; Lim, E.; Dooms, C.; Weder, W.; De Ruysscher, D.; Le Pechoux, C.; De Leyn, P.; Westeel, V. 2nd ESMO Consensus Conference on Lung Cancer: Early-stage non-small-cell lung cancer consensus on diagnosis, treatment and follow-up. Ann. Oncol.,  2014, 25(8), 1462-1474.
 [http://dx.doi.org/10.1093/annonc/mdu089] [PMID:  24562446]

[6]
Scafoglio, C.R.; Villegas, B.; Abdelhady, G.; Bailey, S.T.; Liu, J.; Shirali, A.S.; Wallace, W.D.; Magyar, C.E.; Grogan, T.R.; Elashoff, D.; Walser, T.; Yanagawa, J.; Aberle, D.R.; Barrio, J.R.; Dubinett, S.M.; Shackelford, D.B. Sodium-glucose transporter 2 is a diagnostic and therapeutic target for early-stage lung adenocarcinoma. Sci. Transl. Med.,  2018, 10(467), eaat5933.
 [http://dx.doi.org/10.1126/scitranslmed.aat5933] [PMID:  30429355]

[7]
Lissa, D.; Ishigame, T.; Noro, R.; Tucker, M.J.; Bliskovsky, V.; Shema, S.; Beck, J.A.; Bowman, E.D.; Harris, C.C.; Robles, A.I. HOXA9 methylation and blood vessel invasion in FFPE tissues for prognostic stratification of stage I lung adenocarcinoma patients. Lung Cancer,  2018, 122, 151-159.
 [http://dx.doi.org/10.1016/j.lungcan.2018.05.021] [PMID:  30032824]

[8]
Wang, J.; Dean, D.C.; Hornicek, F.J.; Shi, H.; Duan, Z. RNA sequencing (RNA-Seq) and its application in ovarian cancer. Gynecol. Oncol.,  2019, 152(1), 194-201.
 [http://dx.doi.org/10.1016/j.ygyno.2018.10.002] [PMID:  30297273]

[9]
Zhang, W.; Yu, Y.; Hertwig, F.; Thierry-Mieg, J.; Zhang, W.; Thierry-Mieg, D.; Wang, J.; Furlanello, C.; Devanarayan, V.; Cheng, J.; Deng, Y.; Hero, B.; Hong, H.; Jia, M.; Li, L.; Lin, S.M.; Nikolsky, Y.; Oberthuer, A.; Qing, T.; Su, Z.; Volland, R.; Wang, C.; Wang, M.D.; Ai, J.; Albanese, D.; Asgharzadeh, S.; Avigad, S.; Bao, W.; Bessarabova, M.; Brilliant, M.H.; Brors, B.; Chierici, M.; Chu, T.M.; Zhang, J.; Grundy, R.G.; He, M.M.; Hebbring, S.; Kaufman, H.L.; Lababidi, S.; Lancashire, L.J.; Li, Y.; Lu, X.X.; Luo, H.; Ma, X.; Ning, B.; Noguera, R.; Peifer, M.; Phan, J.H.; Roels, F.; Rosswog, C.; Shao, S.; Shen, J.; Theissen, J.; Tonini, G.P.; Vandesompele, J.; Wu, P.Y.; Xiao, W.; Xu, J.; Xu, W.; Xuan, J.; Yang, Y.; Ye, Z.; Dong, Z.; Zhang, K.K.; Yin, Y.; Zhao, C.; Zheng, Y.; Wolfinger, R.D.; Shi, T.; Malkas, L.H.; Berthold, F.; Wang, J.; Tong, W.; Shi, L.; Peng, Z.; Fischer, M. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome Biol.,  2015, 16(1), 133.
 [http://dx.doi.org/10.1186/s13059-015-0694-1] [PMID:  26109056]

[10]
Li, S.; Xuan, Y.; Gao, B.; Sun, X.; Miao, S.; Lu, T.; Wang, Y.; Jiao, W. Identification of an eight-gene prognostic signature for lung adenocarcinoma. Cancer Manag. Res.,  2018, 10, 3383-3392.
 [http://dx.doi.org/10.2147/CMAR.S173941] [PMID:  30237740]

[11]
Kolodziejczyk, A.A.; Kim, J.K.; Svensson, V.; Marioni, J.C.; Teichmann, S.A. The technology and biology of single-cell RNA sequencing. Mol. Cell,  2015, 58(4), 610-620.
 [http://dx.doi.org/10.1016/j.molcel.2015.04.005] [PMID:  26000846]

[12]
Peng, J.; Sun, B.F.; Chen, C.Y.; Zhou, J.Y.; Chen, Y.S.; Chen, H.; Liu, L.; Huang, D.; Jiang, J.; Cui, G.S.; Yang, Y.; Wang, W.; Guo, D.; Dai, M.; Guo, J.; Zhang, T.; Liao, Q.; Liu, Y.; Zhao, Y.L.; Han, D.L.; Zhao, Y.; Yang, Y.G.; Wu, W. Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma. Cell Res.,  2019, 29(9), 725-738.
 [http://dx.doi.org/10.1038/s41422-019-0195-y] [PMID:  31273297]

[13]
Chen, Z.; Zhao, M.; Li, M.; Sui, Q.; Bian, Y.; Liang, J.; Hu, Z.; Zheng, Y.; Lu, T.; Huang, Y.; Zhan, C.; Jiang, W.; Wang, Q.; Tan, L. Identification of differentially expressed genes in lung adenocarcinoma cells using single-cell RNA sequencing not detected using traditional RNA sequencing and microarray. Lab. Invest.,  2020, 100(10), 1318-1329.
 [http://dx.doi.org/10.1038/s41374-020-0428-1] [PMID:  32327726]

[14]
Ma, B.; Geng, Y.; Meng, F.; Yan, G.; Song, F. Identification of a sixteen-gene prognostic biomarker for lung adenocarcinoma using a machine learning method. J. Cancer,  2020, 11(5), 1288-1298.
 [http://dx.doi.org/10.7150/jca.34585] [PMID:  31956375]

[15]
Kim, D.; Langmead, B.; Salzberg, S.L. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods,  2015, 12(4), 357-360.
 [http://dx.doi.org/10.1038/nmeth.3317] [PMID:  25751142]

[16]
Pertea, M.; Pertea, G.M.; Antonescu, C.M.; Chang, T.C.; Mendell, J.T.; Salzberg, S.L. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol.,  2015, 33(3), 290-295.
 [http://dx.doi.org/10.1038/nbt.3122] [PMID:  25690850]

[17]
Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol.,  2014, 15(12), 550.
 [http://dx.doi.org/10.1186/s13059-014-0550-8] [PMID:  25516281]

[18]
Young, M.D.; Wakefield, M.J.; Smyth, G.K.; Oshlack, A. Gene ontology analysis for RNA-seq: Accounting for selection bias. Genome Biol.,  2010, 11(2), R14.
 [http://dx.doi.org/10.1186/gb-2010-11-2-r14] [PMID:  20132535]

[19]
Mao, X.; Cai, T.; Olyarchuk, J.G.; Wei, L. Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics,  2005, 21(19), 3787-3793.
 [http://dx.doi.org/10.1093/bioinformatics/bti430] [PMID:  15817693]

[20]
Bader, G.D.; Hogue, C.W.V. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics,  2003, 4(1), 2.
 [http://dx.doi.org/10.1186/1471-2105-4-2] [PMID:  12525261]

[21]
Li, X.; Hong, G.; Zhao, G.; Pei, H.; Qu, J.; Chun, C.; Huang, Z.; Lu, Z. Red blood cell membrane-camouflaged PLGA nanoparticles loaded with basic fibroblast growth factor for attenuating sepsis-induced cardiac injury. Front. Pharmacol.,  2022, 13, 881320.
 [http://dx.doi.org/10.3389/fphar.2022.881320] [PMID:  35656291]

[22]
Cai, J.X.; Liu, J.H.; Wu, J.Y.; Li, Y.J.; Qiu, X.H.; Xu, W.J.; Xu, P.; Xiang, D.X. Hybrid cell membrane-functionalized biomimetic nanoparticles for targeted therapy of osteosarcoma. Int. J. Nanomedicine,  2022, 17, 837-854.
 [http://dx.doi.org/10.2147/IJN.S346685] [PMID:  35228800]

[23]
Tang, Z.; Li, C.; Kang, B.; Gao, G.; Li, C.; Zhang, Z. GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res.,  2017, 45(W1), W98-W102.
 [http://dx.doi.org/10.1093/nar/gkx247] [PMID:  28407145]

[24]
Zhang, W.; Fan, J.; Chen, Q.; Lei, C.; Qiao, B.; Liu, Q. SPP1 and AGER as potential prognostic biomarkers for lung adenocarcinoma. Oncol. Lett.,  2018, 15(5), 7028-7036.
 [http://dx.doi.org/10.3892/ol.2018.8235] [PMID:  29849788]

[25]
Qiu, X.; Zhu, X.; Zhang, L.; Mao, Y.; Zhang, J.; Hao, P.; Li, G.; Lv, P.; Li, Z.; Sun, X.; Wu, L.; Zheng, J.; Deng, Y.; Hou, C.; Tang, P.; Zhang, S.; Zhang, Y. Human epithelial cancers secrete immunoglobulin g with unidentified specificity to promote growth and survival of tumor cells. Cancer Res.,  2003, 63(19), 6488-6495.
 [PMID:  14559841]

[26]
Chen, Z.; Gu, J.; Immunoglobulin, G. Immunoglobulin G expression in carcinomas and cancer cell lines. FASEB J.,  2007, 21(11), 2931-2938.
 [http://dx.doi.org/10.1096/fj.07-8073com] [PMID:  17475920]

[27]
Chu, J.; Li, Y.; Deng, Z.; Zhang, Z.; Xie, Q.; Zhang, H.; Zhong, W.; Pan, B. IGHG1 regulates prostate cancer growth via the MEK/ERK/c-Myc pathway. BioMed Res. Int.,  2019, 2019, 1-10.
 [http://dx.doi.org/10.1155/2019/7201562] [PMID:  31355278]

[28]
Pan, B.; Zheng, S.; Liu, C.; Xu, Y. Suppression of IGHG1 gene expression by siRNA leads to growth inhibition and apoptosis induction in human prostate cancer cell. Mol. Biol. Rep.,  2013, 40(1), 27-33.
 [http://dx.doi.org/10.1007/s11033-012-1944-x] [PMID:  23117283]

[29]
Hsu, H.M.; Chu, C.M.; Chang, Y.J.; Yu, J.C.; Chen, C.T.; Jian, C.E.; Lee, C.Y.; Chiang, Y.T.; Chang, C.W.; Chang, Y.T. Six novel immunoglobulin genes as biomarkers for better prognosis in triple-negative breast cancer by gene co-expression network analysis. Sci. Rep.,  2019, 9(1), 4484.
 [http://dx.doi.org/10.1038/s41598-019-40826-w] [PMID:  30872752]

[30]
Asada, K.; Ando, T.; Niwa, T.; Nanjo, S.; Watanabe, N.; Okochi-Takada, E.; Yoshida, T.; Miyamoto, K.; Enomoto, S.; Ichinose, M.; Tsukamoto, T.; Ito, S.; Tatematsu, M.; Sugiyama, T.; Ushijima, T. FHL1 on chromosome X is a single-hit gastrointestinal tumor-suppressor gene and contributes to the formation of an epigenetic field defect. Oncogene,  2013, 32(17), 2140-2149.
 [http://dx.doi.org/10.1038/onc.2012.228] [PMID:  22689052]

[31]
Hemming, S.; Cakouros, D.; Vandyke, K.; Davis, M.J.; Zannettino, A.C.W.; Gronthos, S. Identification of novel EZH2 targets regulating osteogenic differentiation in mesenchymal stem cells. Stem Cells Dev.,  2016, 25(12), 909-921.
 [http://dx.doi.org/10.1089/scd.2015.0384] [PMID:  27168161]

[32]
Wang, J.; Huang, F.; Huang, J.; Kong, J.; Liu, S.; Jin, J. Epigenetic analysis of FHL1 tumor suppressor gene in human liver cancer. Oncol. Lett.,  2017, 14(5), 6109-6116.
 [http://dx.doi.org/10.3892/ol.2017.6950] [PMID:  29113254]

[33]
Wang, Y.; Fu, J.; Jiang, M.; Zhang, X.; Cheng, L.; Xu, X.; Fan, Z.; Zhang, J.; Ye, Q.; Song, H. MiR-410 is overexpressed in liver and colorectal tumors and enhances tumor cell growth by silencing FHL1 via a direct/indirect mechanism. PLoS One,  2014, 9(10), e108708.
 [http://dx.doi.org/10.1371/journal.pone.0108708] [PMID:  25272045]

[34]
Zhang, F.; Feng, F.; Yang, P.; Li, Z.; You, J.; Xie, W.; Gao, X.; Yang, J. Four-and-a-half-LIM protein 1 down-regulates estrogen receptor α activity through repression of AKT phosphorylation in human breast cancer cell. Int. J. Biochem. Cell Biol.,  2012, 44(2), 320-326.
 [http://dx.doi.org/10.1016/j.biocel.2011.11.002] [PMID:  22094188]

[35]
Wang, X.; Wei, X.; Yuan, Y.; Sun, Q.; Zhan, J.; Zhang, J.; Tang, Y.; Li, F.; Ding, L.; Ye, Q.; Zhang, H. Src-mediated phosphorylation converts FHL1 from tumor suppressor to tumor promoter. J. Cell Biol.,  2018, 217(4), 1335-1351.
 [http://dx.doi.org/10.1083/jcb.201708064] [PMID:  29434030]

[36]
Niu, C.; Liang, C.; Guo, J.; Cheng, L.; Zhang, H.; Qin, X.; Zhang, Q.; Ding, L.; Yuan, B.; Xu, X.; Li, J.; Lin, J.; Ye, Q. Downregulation and growth inhibitory role of FHL1 in lung cancer. Int. J. Cancer,  2012, 130(11), 2549-2556.
 [http://dx.doi.org/10.1002/ijc.26259] [PMID:  21702045]

[37]
Ren, J.; Zhou, L.; Ding, L.; Liu, J.; Zhang, Y.; Luo, X.; Zhao, L. Four-and-a-half LIM protein 1 promotes paclitaxel resistance in hepatic carcinoma cells through the regulation of caspase-3 activation. J. Cancer Res. Ther.,  2018, 14(Suppl. 10), 767.
 [http://dx.doi.org/10.4103/0973-1482.187304] [PMID:  30249901]

[38]
Cao, W.; Liu, J.; Xia, R.; Lin, L.; Wang, X.; Xiao, M.; Zhang, C.; Li, J.; Ji, T.; Chen, W. X-linked FHL1 as a novel therapeutic target for head and neck squamous cell carcinoma. Oncotarget,  2016, 7(12), 14537-14550.
 [http://dx.doi.org/10.18632/oncotarget.7478] [PMID:  26908444]

[39]
Fu, Y.; Xu, M.; Cui, Z.; Yang, Z.; Zhang, Z.; Yin, X.; Huang, X.; Zhou, M.; Wang, X.; Chen, C. Genome-wide identification of FHL1 as a powerful prognostic candidate and potential therapeutic target in acute myeloid leukaemia. EBioMedicine,  2020, 52, 102664.
 [http://dx.doi.org/10.1016/j.ebiom.2020.102664] [PMID:  32062360]

[40]
Ji, C.; Liu, H.; Xiang, M.; Liu, J.; Yue, F.; Wang, W.; Chu, X. Deregulation of decorin and FHL1 are associated with esophageal squamous cell carcinoma progression and poor prognosis. Int. J. Clin. Exp. Med.,  2015, 8(11), 20965-20970.
 [PMID:  26885026]

[41]
Yang, W.; Huan, S.; Jihong, Z. Down-regulation of FHL1 is associated with a poor prognosis of patients with oral cancer. Int. J. Clin. Exp. Med.,  2016, 9, 21794-21800.

[42]
Gazdar, A.F. Personalized medicine and inhibition of EGFR signaling in lung cancer. N. Engl. J. Med.,  2009, 361(10), 1018-1020.
 [http://dx.doi.org/10.1056/NEJMe0905763] [PMID:  19692681]

[43]
Denk, A.E.; Braig, S.; Schubert, T.; Bosserhoff, A.K. Slit3 inhibits activator protein 1-mediated migration of malignant melanoma cells. Int. J. Mol. Med.,  2011, 28(5), 721-726.
 [PMID:  21743955]

[44]
Guan, H.; Wei, G.; Wu, J.; Fang, D.; Liao, Z.; Xiao, H.; Li, M.; Li, Y. Down-regulation of miR-218-2 and its host gene SLIT3 cooperate to promote invasion and progression of thyroid cancer. J. Clin. Endocrinol. Metab.,  2013, 98(8), E1334-E1344.
 [http://dx.doi.org/10.1210/jc.2013-1053] [PMID:  23720784]

[45]
Marlow, R.; Strickland, P.; Lee, J.S.; Wu, X.; PeBenito, M.; Binnewies, M.; Le, E.K.; Moran, A.; Macias, H.; Cardiff, R.D.; Sukumar, S.; Hinck, L. SLITs suppress tumor growth in vivo by silencing Sdf1/Cxcr4 within breast epithelium. Cancer Res.,  2008, 68(19), 7819-7827.
 [http://dx.doi.org/10.1158/0008-5472.CAN-08-1357] [PMID:  18829537]

[46]
Zhang, C.; Guo, H.; Li, B.; Sui, C.; Zhang, Y.; Xia, X.; Qin, Y.; Ye, L.; Xie, F.; Wang, H.; Yuan, M.; Yuan, L.; Ye, J. Effects of Slit3 silencing on the invasive ability of lung carcinoma A549 cells. Oncol. Rep.,  2015, 34(2), 952-960.
 [http://dx.doi.org/10.3892/or.2015.4031] [PMID:  26045181]

[47]
Egeblad, M.; Nakasone, E.S.; Werb, Z. Tumors as organs: Complex tissues that interface with the entire organism. Dev. Cell,  2010, 18(6), 884-901.
 [http://dx.doi.org/10.1016/j.devcel.2010.05.012] [PMID:  20627072]

[48]
Liu, T.; Wu, H.; Qi, J.; Qin, C.; Zhu, Q. Seven immune-related genes prognostic power and correlation with tumor-infiltrating immune cells in hepatocellular carcinoma. Cancer Med.,  2020, 9(20), 7440-7452.
 [http://dx.doi.org/10.1002/cam4.3406] [PMID:  32815653]

[49]
Wei, T.; Bi, G.; Bian, Y.; Ruan, S.; Yuan, G.; Xie, H.; Zhao, M.; Shen, R.; Zhu, Y.; Wang, Q.; Yang, Y.; Zhu, D. The significance of secreted phosphoprotein 1 in multiple human cancers. Front. Mol. Biosci.,  2020, 7, 565383.
 [http://dx.doi.org/10.3389/fmolb.2020.565383] [PMID:  33324676]

[50]

Secreted Phosphoprotein 1 as a Potential Prognostic and Immunotherapy Biomarker in Multiple Human Cancers; United States, 2022, 13, . 

[51]
Wang, G.; Li, H.; Pan, J.; Yan, T.; Zhou, H.; Han, X.; Su, L.; Hou, L.; Xue, X. Upregulated expression of cancer-derived immunoglobulin G is associated with progression in glioma. Front. Oncol.,  2021, 11, 758856.
 [http://dx.doi.org/10.3389/fonc.2021.758856] [PMID:  34760705]

[52]
Li, X.; Ni, R.; Chen, J.; Liu, Z.; Xiao, M.; Jiang, F.; Lu, C. The presence of IGHG1 in human pancreatic carcinomas is associated with immune evasion mechanisms. Pancreas,  2011, 40(5), 753-761.
 [http://dx.doi.org/10.1097/MPA.0b013e318213d51b] [PMID:  21654544]

[53]
Xu, Y.; Wei, Z.; Feng, M.; Zhu, D.; Mei, S.; Wu, Z.; Feng, Q.; Chang, W.; Ji, M.; Liu, C.; Zhu, Y.; Shen, L.; Yang, F.; Chen, Y.; Feng, Y.; Xu, J.; Zhu, D. Tumor-infiltrated activated B cells suppress liver metastasis of colorectal cancers. Cell Rep.,  2022, 40(9), 111295.
 [http://dx.doi.org/10.1016/j.celrep.2022.111295] [PMID:  36044847]

[54]
Jabbari, K.; Cheng, Q.; Winkelmaier, G.; Furuta, S.; Parvin, B. CD36+ fibroblasts secrete protein ligands that growth-suppress triple-negative breast cancer cells while elevating adipogenic markers for a model of cancer-associated fibroblast. Int. J. Mol. Sci.,  2022, 23(21), 12744.
 [http://dx.doi.org/10.3390/ijms232112744] [PMID:  36361532]

Rights & Permissions Print Cite

Journal Information

For Authors

For Editors

For Reviewers

Explore Articles

Open Access

Open Access Articles

For Visitors

DOI https://dx.doi.org/10.2174/1386207326666230208124028	Print ISSN 1386-2073
Publisher Name Bentham Science Publisher	Online ISSN 1875-5402

Combinatorial Chemistry & High Throughput Screening

Expression and Predictive Significance of FHL1 and SLIT3 in Surgically Resected Lung Adenocarcinoma

Abstract Play Pause

Graphical Abstract

Related Journals

Abstract