GUCA2A dysregulation as a promising biomarker for accurate diagnosis and prognosis of colorectal cancer.
CeRNA network
Colorectal cancer
Diagnostic biomarker
GUCA2A
Integrated bioinformatics analysis
Prognostic biomarker
Journal
Clinical and experimental medicine
ISSN: 1591-9528
Titre abrégé: Clin Exp Med
Pays: Italy
ID NLM: 100973405
Informations de publication
Date de publication:
01 Nov 2024
01 Nov 2024
Historique:
received:
05
06
2024
accepted:
21
10
2024
medline:
1
11
2024
pubmed:
1
11
2024
entrez:
1
11
2024
Statut:
epublish
Résumé
Colorectal cancer is a leading cause of global mortality and presents a significant barrier to improving life expectancy. The primary objective of this study was to discern a unique differentially expressed gene (DEG) that exhibits a strong association with colorectal cancer. By achieving this goal, the research aims to contribute valuable insights to the field of translational medicine. We performed analysis of colorectal cancer microarray and the TCGA colon adenoma carcinoma (COAD) datasets to identify DEGs associated with COAD and common DEGs were selected. Furthermore, a pan-cancer analysis encompassing 33 different cancer types was performed to identify differential genes significantly expressed only in COAD. Then, comprehensively in-silico analysis including gene set enrichment analysis, constructing Protein-Protein interaction, co-expression, and competing endogenous RNA (ceRNA) networks, investigating the correlation between tumor-immune signatures in distinct tumor microenvironment and also the potential interactions between the identified gene and various drugs was executed. Further, the candidate gene was experimentally validated in tumoral colorectal tissues and colorectal adenomatous polyps by qRael-Time PCR. GUCA2A emerged as a significant DEG specific to colorectal cancer (|log2FC|> 1 and adjusted q-value < 0.05). Importantly, GUCA2A exhibited excellent diagnostic performance for COAD, with a 99.6% and 78% area under the curve (AUC) based on TCGA-COAD and colon cancer patients. In addition, GUCA2A expression in adenomatous polyps equal to or larger than 5 mm was significantly lower compared to smaller than 5 mm. Moreover, low expression of GUCA2A significantly impacted overall patient survival. Significant correlations were observed between tumor-immune signatures and GUCA2A expression. The ceRNA constructed included GUCA2A, 8 shared miRNAs, and 61 circRNAs. This study identifies GUCA2A as a promising prognostic and diagnostic biomarker for colorectal cancer. Further investigations are warranted to explore the potential of GUCA2A as a therapeutic biomarker.
Identifiants
pubmed: 39485546
doi: 10.1007/s10238-024-01512-y
pii: 10.1007/s10238-024-01512-y
doi:
Substances chimiques
Biomarkers, Tumor
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
251Informations de copyright
© 2024. The Author(s).
Références
Organization, W.H., World health statistics 2018: monitoring health for the SDGs, sustainable development goals. 2018: World Health Organization.
Bray F, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.
pubmed: 30207593
doi: 10.3322/caac.21492
Siegel RL, et al. Colorectal cancer statistics, 2020. CA Cancer J Clin. 2020;70(3):145–64.
pubmed: 32133645
doi: 10.3322/caac.21601
Vogelstein B, Kinzler KW. Cancer genes and the pathways they control. Nat Med. 2004;10(8):789–99.
pubmed: 15286780
doi: 10.1038/nm1087
Wang X, et al. Development and validation of a DNA repair gene signature for prognosis prediction in colon cancer. J Cancer. 2020;11(20):5918.
pubmed: 32922534
pmcid: 7477412
doi: 10.7150/jca.46328
Chen J, et al. ZMYND8 expression combined with pN and pM classification as a novel prognostic prediction model for colorectal cancer: based on TCGA and GEO database analysis. Cancer Biomark. 2020;28(2):201–11.
pubmed: 32224527
doi: 10.3233/CBM-191261
Zou J, et al. Mining the potential prognostic value of synaptosomal-associated protein 25 (SNAP25) in colon cancer based on stromal-immune score. PeerJ. 2020;8: e10142.
pubmed: 33150073
pmcid: 7583623
doi: 10.7717/peerj.10142
Zheng W, et al. Transcriptional information underlying the generation of CSCs and the construction of a nine-mRNA signature to improve prognosis prediction in colorectal cancer. Cancer Biol Ther. 2020;21(8):688–97.
pubmed: 32453965
pmcid: 7515529
doi: 10.1080/15384047.2020.1762419
Zhang R, et al. Mining featured biomarkers associated with vascular invasion in HCC by bioinformatics analysis with TCGA RNA sequencing data. Biomed Pharmacother. 2019;118: 109274.
pubmed: 31545220
doi: 10.1016/j.biopha.2019.109274
Al-Sheikh YA, et al. Screening for differentially-expressed microRNA biomarkers in Saudi colorectal cancer patients by small RNA deep sequencing. Int J Mol Med. 2019;44(6):2027–36.
pubmed: 31638163
pmcid: 6844639
Yamada A, et al. A RNA-Sequencing approach for the identification of novel long non-coding RNA biomarkers in colorectal cancer. Sci Rep. 2018;8(1):575.
pubmed: 29330370
pmcid: 5766599
doi: 10.1038/s41598-017-18407-6
O’Connell MJ, et al. Relationship between tumor gene expression and recurrence in four independent studies of patients with stage II/III colon cancer treated with surgery alone or surgery plus adjuvant fluorouracil plus leucovorin. J Clin Oncol. 2010;28(25):3937.
pubmed: 20679606
pmcid: 2940392
doi: 10.1200/JCO.2010.28.9538
Barrier A, et al. Stage II colon cancer prognosis prediction by tumor gene expression profiling. J Clin Oncol. 2006;24(29):4685–91.
pubmed: 16966692
doi: 10.1200/JCO.2005.05.0229
Liu X, et al. Identification of crucial genes and pathways associated with colorectal cancer by bioinformatics analysis. Oncol Lett. 2020;19(3):1881–9.
pubmed: 32194683
pmcid: 7039150
Shangguan H, Tan S, Zhang J. Bioinformatics analysis of gene expression profiles in hepatocellular carcinoma. Eur Rev Med Pharmacol Sci. 2015;19(11):2054–61.
pubmed: 26125269
Kosti A, et al. Microarray profile of human kidney from diabetes, renal cell carcinoma and renal cell carcinoma with diabetes. Genes Cancer. 2015;6(1–2):62.
pubmed: 25821562
pmcid: 4362485
doi: 10.18632/genesandcancer.51
Christgen M, et al. IPH-926 lobular breast cancer cells are triple-negative but their microarray profile uncovers a luminal subtype. Cancer Sci. 2013;104(12):1726–30.
pubmed: 24344720
pmcid: 7654255
doi: 10.1111/cas.12276
Hu Y, et al. Colorectal cancer susceptibility loci as predictive markers of rectal cancer prognosis after surgery. Genes Chromosom Cancer. 2018;57(3):140–9.
pubmed: 29119627
doi: 10.1002/gcc.22512
Kagawa Y, et al. Cell cycle-dependent Rho GTPase activity dynamically regulates cancer cell motility and invasion in vivo. PLoS ONE. 2013;8(12): e83629.
pubmed: 24386239
pmcid: 3875446
doi: 10.1371/journal.pone.0083629
Sveen A, et al. Transcriptome instability in colorectal cancer identified by exon microarray analyses: associations with splicing factor expression levels and patient survival. Genome medicine. 2011;3:1–13.
doi: 10.1186/gm248
Ågesen TH, et al. ColoGuideEx: a robust gene classifier specific for stage II colorectal cancer prognosis. Gut. 2012;61(11):1560–7.
pubmed: 22213796
doi: 10.1136/gutjnl-2011-301179
Bian Q, et al. Four targeted genes for predicting the prognosis of colorectal cancer: a bioinformatics analysis case. Oncol Lett. 2019;18(5):5043–54.
pubmed: 31612015
pmcid: 6781647
Ji F, Sadreyev RI. RNA-seq: basic bioinformatics analysis. Curr Protoc Mol Biol. 2018;124(1): e68.
pubmed: 30222249
pmcid: 6168365
doi: 10.1002/cpmb.68
Barrett T, et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 2012;41(D1):D991–5.
pubmed: 23193258
pmcid: 3531084
doi: 10.1093/nar/gks1193
Irizarry RA, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003;4(2):249–64.
pubmed: 12925520
doi: 10.1093/biostatistics/4.2.249
Ritchie ME, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47–e47.
pubmed: 25605792
pmcid: 4402510
doi: 10.1093/nar/gkv007
Tang Z, et al. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019;47(W1):W556–60.
pubmed: 31114875
pmcid: 6602440
doi: 10.1093/nar/gkz430
Tang G, Cho M, Wang X. OncoDB: an interactive online database for analysis of gene expression and viral infection in cancer. Nucleic Acids Res. 2022;50(D1):D1334–9.
pubmed: 34718715
doi: 10.1093/nar/gkab970
Chandrashekar DS, et al. UALCAN: an update to the integrated cancer data analysis platform. Neoplasia. 2022;25:18–27.
pubmed: 35078134
pmcid: 8788199
doi: 10.1016/j.neo.2022.01.001
Vasaikar SV, et al. LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res. 2018;46(D1):D956–63.
pubmed: 29136207
doi: 10.1093/nar/gkx1090
Kuleshov MV, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44(W1):W90–7.
pubmed: 27141961
pmcid: 4987924
doi: 10.1093/nar/gkw377
Chen Y, Wang X. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res. 2020;48(D1):D127–31.
pubmed: 31504780
doi: 10.1093/nar/gkz757
Dweep H, Gretz N, Sticht C. miRWalk database for miRNA–target interactions. RNA Mapp Method Protoc. 2014. https://doi.org/10.1007/978-1-4939-1062-5_25 .
doi: 10.1007/978-1-4939-1062-5_25
Agarwal V, et al., Predicting effective microRNA target sites in mammalian mRNAs. elife, 2015. 4 e05005.
Huang H-Y, et al. miRTarBase update 2022: an informative resource for experimentally validated miRNA–target interactions. Nucleic Acids Res. 2022;50(D1):D222–30.
pubmed: 34850920
doi: 10.1093/nar/gkab1079
Liu M, et al. Circbank: a comprehensive database for circRNA with standard nomenclature. RNA Biol. 2019;16(7):899–905.
pubmed: 31023147
pmcid: 6546381
doi: 10.1080/15476286.2019.1600395
Shannon P, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.
pubmed: 14597658
pmcid: 403769
doi: 10.1101/gr.1239303
Cerami E, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4.
pubmed: 22588877
doi: 10.1158/2159-8290.CD-12-0095
Thul PJ, Lindskog C. The human protein atlas: a spatial map of the human proteome. Protein Sci. 2018;27(1):233–44.
pubmed: 28940711
doi: 10.1002/pro.3307
Ru B, et al. TISIDB: an integrated repository portal for tumor–immune system interactions. Bioinformatics. 2019;35(20):4200–2.
pubmed: 30903160
doi: 10.1093/bioinformatics/btz210
Freshour SL, et al. Integration of the drug-gene interaction database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res. 2021;49(D1):D1144–51.
pubmed: 33237278
doi: 10.1093/nar/gkaa1084
Abbott M, Ustoyev Y. Cancer and the immune system: the history and background of immunotherapy. In seminars in oncology nursing. Amsterdam: Elsevier; 2019.
Roma-Rodrigues C, et al. Targeting tumor microenvironment for cancer therapy. Int J Mol Sci. 2019;20(4):840.
pubmed: 30781344
pmcid: 6413095
doi: 10.3390/ijms20040840
Xia J et al., Single‐cell landscape and clinical outcomes of infiltrating B cells in colorectal cancer. Immunology, 2023
Yang W, et al. Integrated analysis of necroptosis-related genes for evaluating immune infiltration and colon cancer prognosis. Front Immunol. 2022. https://doi.org/10.3389/fimmu.2022.1085038 .
doi: 10.3389/fimmu.2022.1085038
pubmed: 36999166
pmcid: 9832027
Giannakis M, et al. Genomic correlates of immune-cell infiltrates in colorectal carcinoma. Cell Rep. 2016;15(4):857–65.
pubmed: 27149842
pmcid: 4850357
doi: 10.1016/j.celrep.2016.03.075
Seshagiri S, et al. Recurrent R-spondin fusions in colon cancer. Nature. 2012;488(7413):660–4.
pubmed: 22895193
pmcid: 3690621
doi: 10.1038/nature11282
Weinstein JN, et al. The cancer genome atlas pan-cancer analysis project. Nat Genet. 2013;45(10):1113–20.
pubmed: 24071849
pmcid: 3919969
doi: 10.1038/ng.2764
Network CGAR, Weinstein JN, Collisson EA, Mills GB, Shaw KRM, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM. The cancer genome atlas pan-cancer analysis project. Nat Genet. 2013;45(10):1113–20.
doi: 10.1038/ng.2764
Steinbrecher KA, et al. Increases in guanylin and uroguanylin in a mouse model of osmotic diarrhea are guanylate cyclase C—independent. Gastroenterology. 2001;121(5):1191–202.
pubmed: 11677212
doi: 10.1053/gast.2001.28680
Furuya S, Naruse S, Hayakawa T. Intravenous injection of guanylin induces mucus secretion from goblet cells in rat duodenal crypts. Anat Embryol. 1998;197:359–67.
doi: 10.1007/s004290050146
Kita T, et al. Marked increase of guanylin secretion in response to salt loading in the rat small intestine. Am J Physiol Gastrointest Liver Physiol. 1999;277(5):960–6. https://doi.org/10.1152/ajpgi.1999.277.5.G960 .
doi: 10.1152/ajpgi.1999.277.5.G960
Xi Y, Xu P. Global colorectal cancer burden in 2020 and projections to 2040. Trans Oncol. 2021;14(10): 101174.
doi: 10.1016/j.tranon.2021.101174
Zhang H, et al. Integrated analysis of oncogenic networks in colorectal cancer identifies GUCA2A as a molecular marker. Biochem Res Int. 2019. https://doi.org/10.1155/2019/6469420 .
doi: 10.1155/2019/6469420
pubmed: 31467713
pmcid: 6701329
Piroozkhah M, et al. Guanylate cyclase-C signaling axis as a theragnostic target in colorectal cancer: a systematic review of literature. Front Oncol. 2023;13:1277265.
pubmed: 37927469
pmcid: 10623427
doi: 10.3389/fonc.2023.1277265
Xie Y-H, Chen Y-X, Fang J-Y. Comprehensive review of targeted therapy for colorectal cancer. Signal Transduct Target Ther. 2020;5(1):22.
pubmed: 32296018
pmcid: 7082344
doi: 10.1038/s41392-020-0116-z
Biller LH, Schrag D. Diagnosis and treatment of metastatic colorectal cancer: a review. JAMA. 2021;325(7):669–85.
pubmed: 33591350
doi: 10.1001/jama.2021.0106
Cardoso R, et al. Colorectal cancer incidence, mortality, and stage distribution in European countries in the colorectal cancer screening era: an international population-based study. Lancet Oncol. 2021;22(7):1002–13.
pubmed: 34048685
doi: 10.1016/S1470-2045(21)00199-6
Liu Y, et al. Identification of hub genes in colorectal adenocarcinoma by integrated bioinformatics. Front Cell Develop Biol. 2022. https://doi.org/10.3389/fcell.2022.897568 .
doi: 10.3389/fcell.2022.897568
Morson B. President’s address. The polyp-cancer sequence in the large bowel. Proc R Soc Med. 1974;67(61):451–7.
pubmed: 4853754
pmcid: 1645739
Stryker SJ, et al. Natural history of untreated colonic polyps. Gastroenterology. 1987;93(5):1009–13.
pubmed: 3653628
doi: 10.1016/0016-5085(87)90563-4
Kuhn M. Molecular physiology of membrane guanylyl cyclase receptors. Physiol Rev. 2016;96(2):751–804.
pubmed: 27030537
doi: 10.1152/physrev.00022.2015
Camilleri M. Guanylate cyclase C agonists: emerging gastrointestinal therapies and actions. Gastroenterology. 2015;148(3):483–7.
pubmed: 25576859
doi: 10.1053/j.gastro.2015.01.003
Steinbrecher KA, et al. Murine guanylate cyclase C regulates colonic injury and inflammation. J Immunol. 2011;186(12):7205–14.
pubmed: 21555532
doi: 10.4049/jimmunol.1002469
Brenna Ø, et al. The guanylate cyclase-C signaling pathway is down-regulated in inflammatory bowel disease. Scand J Gastroenterol. 2015;50(10):1241–52.
pubmed: 25979109
pmcid: 4673555
doi: 10.3109/00365521.2015.1038849
Pattison AM, et al. Guanylyl cyclase C signaling axis and colon cancer prevention. World J Gastroenterol. 2016;22(36):8070.
pubmed: 27688649
pmcid: 5037076
doi: 10.3748/wjg.v22.i36.8070
Blomain ES, et al. Translating colorectal cancer prevention through the guanylyl cyclase C signaling axis. Expert Rev Clin Pharmacol. 2013;6(5):557–64.
pubmed: 23971873
pmcid: 4048542
doi: 10.1586/17512433.2013.827406
Basu N, Arshad N, Visweswariah SS. Receptor guanylyl cyclase C (GC-C): regulation and signal transduction. Mol Cell Biochem. 2010;334:67–80.
pubmed: 19960363
doi: 10.1007/s11010-009-0324-x
Li P, et al. Guanylyl cyclase C suppresses intestinal tumorigenesis by restricting proliferation and maintaining genomic integrity. Gastroenterology. 2007;133(2):599–607.
pubmed: 17681179
doi: 10.1053/j.gastro.2007.05.052
Lin JE, et al. The hormone receptor GUCY2C suppresses intestinal tumor formation by inhibiting AKT signaling. Gastroenterology. 2010;138(1):241–54.
pubmed: 19737566
doi: 10.1053/j.gastro.2009.08.064
Bashir B, et al. Silencing the GUCA2A-GUCY2C tumor suppressor axis in CIN, serrated, and MSI colorectal neoplasia. Hum Pathol. 2019;87:103–14.
pubmed: 30716341
pmcid: 6988773
doi: 10.1016/j.humpath.2018.11.032
De La Cena KO, et al. Transmembrane and immunoglobulin domain containing 1, a putative tumor suppressor, induces G2/M cell cycle checkpoint arrest in colon cancer cells. Am J Pathol. 2021;191(1):157–67.
pubmed: 33129760
doi: 10.1016/j.ajpath.2020.09.015
Mu L, et al. The role of TMIGD1 as a Tumor suppressor in colorectal cancer. Genet Test Mol Biomarkers. 2022;26(4):174–83.
pubmed: 35481970
doi: 10.1089/gtmb.2021.0169
Ding X, et al. SLC26A3 (DRA) prevents TNF-alpha-induced barrier dysfunction and dextran sulfate sodium-induced acute colitis. Lab Invest. 2018;98(4):462–76.
pubmed: 29330471
doi: 10.1038/s41374-017-0005-4
Zhang M, et al. Physiological and pathophysiological role of ion channels and transporters in the colorectum and colorectal cancer. J Cell Mol Med. 2020;24(17):9486–94.
pubmed: 32662230
pmcid: 7520301
doi: 10.1111/jcmm.15600
Scott RO, Thelin WR, Milgram SL. A novel PDZ protein regulates the activity of guanylyl cyclase C, the heat-stable enterotoxin receptor. J Biol Chem. 2002;277(25):22934–41.
pubmed: 11950846
doi: 10.1074/jbc.M202434200
Zachos NC, et al. Elevated intracellular calcium stimulates NHE3 activity by an IKEPP (NHERF4) dependent mechanism. Cell Physiol Biochem. 2008;22(5–6):693–704.
pubmed: 19088451
pmcid: 3740532
doi: 10.1159/000185553
Gu Y, et al. NHERF1 regulates the progression of colorectal cancer through the interplay with VEGFR2 pathway. Oncotarget. 2017;8(5):7753.
pubmed: 27999191
doi: 10.18632/oncotarget.13949
Leiphrakpam PD, et al. Prognostic and therapeutic implications of NHERF1 expression and regulation in colorectal cancer. J Surg Oncol. 2020;121(3):547–60.
pubmed: 31867736
doi: 10.1002/jso.25805
Chen X, et al. Long non-coding RNAs and complex diseases: from experimental results to computational models. Brief Bioinform. 2017;18(4):558–76.
pubmed: 27345524
Chen X, et al. MicroRNAs and complex diseases: from experimental results to computational models. Brief Bioinform. 2019;20(2):515–39.
pubmed: 29045685
doi: 10.1093/bib/bbx130
Chen X, et al. Predicting miRNA–disease association based on inductive matrix completion. Bioinformatics. 2018;34(24):4256–65.
pubmed: 29939227
doi: 10.1093/bioinformatics/bty503
Chen X, et al. BNPMDA: bipartite network projection for MiRNA–disease association prediction. Bioinformatics. 2018;34(18):3178–86.
pubmed: 29701758
doi: 10.1093/bioinformatics/bty333
Chen X, et al. MDHGI: matrix decomposition and heterogeneous graph inference for miRNA-disease association prediction. PLoS Comput Biol. 2018;14(8): e1006418.
pubmed: 30142158
pmcid: 6126877
doi: 10.1371/journal.pcbi.1006418
Chen X, Huang L. LRSSLMDA: laplacian regularized sparse subspace learning for MiRNA-disease association prediction. PLoS Comput Biol. 2017;13(12): e1005912.
pubmed: 29253885
pmcid: 5749861
doi: 10.1371/journal.pcbi.1005912
Salmena L, et al. A ceRNA hypothesis: the rosetta stone of a hidden RNA language? Cell. 2011;146(3):353–8.
pubmed: 21802130
pmcid: 3235919
doi: 10.1016/j.cell.2011.07.014
Yan C, et al. PVT 1-derived miR-1207-5p promotes breast cancer cell growth by targeting STAT 6. Cancer Sci. 2017;108(5):868–76.
pubmed: 28235236
pmcid: 5448618
doi: 10.1111/cas.13212
Wang X, et al. Plasma microRNA-1207–5p as a potential biomarker for diagnosis and prognosis of colorectal cancer. Clin Lab. 2020. https://doi.org/10.7754/Clin.Lab.2020.191269 .
doi: 10.7754/Clin.Lab.2020.191269
pubmed: 33337840
Ng L, et al. High Levels of Tumor miR-187-3p—a potential tumor-suppressor microRNA—Are correlated with poor prognosis in colorectal cancer. Cells. 2022;11(15):2421.
pubmed: 35954265
pmcid: 9367907
doi: 10.3390/cells11152421
Sun J, et al. Tumor exosome promotes Th17 cell differentiation by transmitting the lncRNA CRNDE-h in colorectal cancer. Cell Death Dis. 2021;12(1):123.
pubmed: 33495437
pmcid: 7835218
doi: 10.1038/s41419-020-03376-y
Hansen FJ, et al. Tumor infiltration with CD20+ CD73+ B cells correlates with better outcome in colorectal cancer. Int J Mol Sci. 2022;23(9):5163.
pubmed: 35563553
pmcid: 9101418
doi: 10.3390/ijms23095163
Nersesian S, et al. NK cell infiltration is associated with improved overall survival in solid cancers: a systematic review and meta-analysis. Trans Oncol. 2021;14(1): 100930.
doi: 10.1016/j.tranon.2020.100930
Forssell J, et al. High macrophage infiltration along the tumor front correlates with improved survival in colon cancer. Clin Cancer Res. 2007;13(5):1472–9.
pubmed: 17332291
doi: 10.1158/1078-0432.CCR-06-2073
Oosterling SJ, et al. Macrophages direct tumour histology and clinical outcome in a colon cancer model. J Pathol A J Pathol Soc Great Britain Ireland. 2005;207(2):147–55.
Andrzej P, et al. Influence of lactose intolerance on colorectal cancer incidence in the Polish population. Hered Cancer Clin Pract. 2015. https://doi.org/10.1186/1897-4287-13-S1-A7 .
doi: 10.1186/1897-4287-13-S1-A7
pmcid: 4565464
Kurniali PC, Hrinczenko B, Al-Janadi A. Management of locally advanced and metastatic colon cancer in elderly patients. World J Gastroenterol: WJG. 2014;20(8):1910.
pubmed: 24616568
pmcid: 3934461
doi: 10.3748/wjg.v20.i8.1910
Esan O, Wierzbicki AS. Volanesorsen in the treatment of familial chylomicronemia syndrome or hypertriglyceridaemia: design, development and place in therapy. Drug Design Develop Ther. 2020;14:2623–36.
doi: 10.2147/DDDT.S224771
Hsu S-H, et al. The association between hypertriglyceridemia and colorectal cancer: a long-term community cohort study in Taiwan. Int J Environ Res Public Health. 2022;19(13):7804.
pubmed: 35805464
pmcid: 9265720
doi: 10.3390/ijerph19137804