Progress in research of the risk factors of lymph node metastasis in T1 stage colorectal cancer

doi:10.16139/j.1007-9610.2024.04.14

Abstract

Abstract:

Colorectal cancer is one of the common malignant tumors of the digestive tract. With the popularization of screening methods and advancement of endoscopic technology, an increasing number of T1 stage colorectal cancers can be discovered. Accurately predicting lymph node metastasis risk is significantly important for guiding clinical treatment decisions, reducing complications and mortality. Current research on risk factors for lymph node metastasis in T1 stage colorectal cancer covers multiple aspects including clinical pathological features, molecular phenotypes and genetic characteristics. Some studies have built prediction models by integrating these factors, which show higher sensitivity, specificity and accuracy compared to current clinical guidelines. These models provide valuable experience for clinical practice.

Key words: Colorectal cancer, Lymph node metastasis, Tumor microenvironment, Transcriptome, Genome, Multimodal model

CLC Number:

CHEN Fangqian, FENG Wenqing, ZHAO Jingkun, ZONG Yaping, LU Aiguo. Progress in research of the risk factors of lymph node metastasis in T1 stage colorectal cancer[J]. Journal of Surgery Concepts & Practice, 2024, 29(04): 358-364.

References 50

[1]	SUNG H, FERLAY J, SIEGEL R L, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3):209-249.
[2]	BERGER A C, SIGURDSON E R, LEVOYER T, et al. Colon cancer survival is associated with decreasing ratio of metastatic to examined lymph nodes[J]. J Clin Oncol, 2005, 23(34):8706-8712. doi: 10.1200/JCO.2005.02.8852 pmid: 16314630
[3]	中国抗癌协会, 中国抗癌协会大肠癌专业委员会. 中国恶性肿瘤整合诊治指南-直肠癌部分[J]. 中华结直肠疾病电子杂志, 2022, 11(2):89-103.
	China Anti-Cancer Association, Colorectal Cancer Professional Committee, Chinese Anti-Cancer Association. CACA guidelines for holistic integrative management of cancer-rectal cancer[J]. Chin J Colo Dis(Electronic Edition), 2022, 11(2):89-103.
[4]	BENSON A B, VENOOK A P, AL-HAWARY M M, et al. Colon cancer, version 2.2021, NCCN clinical practice guidelines in oncology[J]. J Natl Compr Canc Netw, 2021, 19(3):329-359.
[5]	BENSON A B, VENOOK A P, AL-HAWARY M M, et al. Rectal cancer, version 2.2022, NCCN clinical practice guidelines in oncology[J]. J Natl Compr Canc Netw, 2022, 20(10):1139-1167.
[6]	MACIAS-GARCIA F, CELEIRO-MUÑOZ C, LESQUEREUX-MARTINEZ L, et al. A clinical model for predicting lymph node metastasis in submucosal invasive (T1) colorectal cancer[J]. Int J Colorectal Dis, 2015, 30(6):761-768.
[7]	MORNEAU M, BOULANGER J, CHARLEBOIS P, et al. Laparoscopic versus open surgery for the treatment of colorectal cancer: a literature review and recommendations from the Comité de l'évolution des pratiques en oncologie[J]. Can J Surg, 2013, 56(5):297-310.
[8]	GIANDOMENICO F, GAVARUZZI T, LOTTO L, et al. Quality of life after surgery for rectal cancer: a systematic review of comparisons with the general population[J]. Expert Rev Gastroenterol Hepatol, 2015, 9(9):1227-1242.
[9]	BLAKELY A M, CHANSON D, WONG F L, et al. Health-related quality of life and oncologic outcomes after surgery in older adults with colorectal cancer[J]. Support Care Cancer, 2020, 28(6):2857-2865. doi: 10.1007/s00520-019-05087-3 pmid: 31729565
[10]	KUDO S E, ICHIMASA K, VILLARD B, et al. Artificial intelligence system to determine risk of T1 colorectal cancer metastasis to lymph node[J]. Gastroenterology, 2021, 160(4):1075-1084.
[11]	BURNETT-HARTMAN A N, POWERS J D, CHUBAK J, et al. Treatment patterns and survival differ between early-onset and late-onset colorectal cancer patients: the patient outcomes to advance learning network[J]. Cancer Causes Control, 2019, 30(7):747-755.
[12]	ZONG Z, LI H, HU C G, et al. Predictors of lymph-node metastasis in surgically resected T1 colorectal cancer in Western populations[J]. Gastroenterol Rep (Oxf), 2021, 9(5):470-474.
[13]	LI M, ZHANG J, DAN Y, et al. A clinical-radiomics nomogram for the preoperative prediction of lymph node metastasis in colorectal cancer[J]. J Transl Med, 2020, 18(1):46. doi: 10.1186/s12967-020-02215-0 pmid: 32000813
[14]	LEE Y J, HUH J W, SHIN J K, et al. Risk factors for lymph node metastasis in early colon cancer[J]. Int J Colorectal Dis, 2020, 35(8):1607-1613. doi: 10.1007/s00384-020-03618-7 pmid: 32447479
[15]	MIYACHI H, KUDO S, MOCHIZUKI K, et al. Tumor location and patient sex are novel risk factors of lymph node metastasis in T1 colorectal cancer[J]. J Gastroenterol Hepatol, 2020, 35(12):2292.
[16]	ABUSAL F, ALADWAN M, ALOMARI Y, et al. Oral contraceptives and colorectal cancer risk - a meta-analysis and systematic review[J]. Ann Med Surg (Lond), 2022,83:104254.
[17]	AHN J H, KWAK M S, LEE H H, et al. Development of a novel prognostic model for predicting lymph node metastasis in early colorectal cancer: analysis based on the surveillance, epidemiology, and end results database[J]. Front Oncol, 2021,11:614398.
[18]	OUCHI A, TORIYAMA K, KINOSHITA T, et al. Variations in clinical features and oncologic behaviors of T1 colorectal cancer according to tumor location[J]. Int J Clin Oncol, 2020, 25(6):1130-1136. doi: 10.1007/s10147-020-01642-9 pmid: 32124095
[19]	CHANG L C, SHUN C T, LIN B R, et al. Recurrence outcomes less favorable in T1 rectal cancer than in T1 colon cancer[J]. Oncologist, 2021, 26(9):e1548-e1554.
[20]	BELDERBOS T D, VAN ERNING F N, DE HINGH I H, et al. Long-term recurrence-free survival after standard endoscopic resection versus surgical resection of submucosal invasive colorectal cancer: a population-based study[J]. Clin Gastroenterol Hepatol, 2017, 15(3):403-411.
[21]	ZHOU C, LIU H S, LIU X H, et al. Preoperative assessment of lymph node metastasis in clinically node-negative rectal cancer patients based on a nomogram consisting of five clinical factors[J]. Ann Transl Med, 2019, 7(20):543. doi: 10.21037/atm.2019.09.127 pmid: 31807525
[22]	张鑫东, 葛晓蕾, 刘省存, 等. 血清CA199和CEA对结直肠癌转移和预后预测的价值[J]. 中华疾病控制杂志, 2018, 22(1):57-61.
	ZHANG X D, GE X L, LIU S C, et al. The prognostic and predictive value of serum CA199 and CEA in colorectal cancer metastasis[J]. Chin J Dis Control Prev, 2018, 22(1):57-61.
[23]	YE H, ZHENG B, ZHENG Q, et al. Influence of old age on risk of lymph node metastasis and survival in patients with T1 colorectal cancer: a population-based analysis[J]. Front Oncol, 2021,11:706488.
[24]	谭杨波, 甘嘉亮, 陈龙. 肿瘤标志物、临床病理因素与结直肠癌淋巴结转移的关系[J]. 中国现代医学杂志, 2018, 28(17):59-64.
	TAN Y B, GAN J L, CHEN L. Correlations of lymphatic metastasis with clinicopathological factors and tumor markers in colorectal cancer[J]. China J Mod Med, 2018, 28(17):59-64.
[25]	BARTON M K. False elevations of carcinoembryonic antigen levels are common in patients under surveillance for colorectal cancer recurrence[J]. CA Cancer J Clin, 2014, 64(6):365-366.
[26]	BROWN SR, MATHEW R, KEDING A, et al. The impact of postoperative complications on long-term quality of life after curative colorectal cancer surgery[J]. Ann Surg, 2014, 259(5):916-923. doi: 10.1097/SLA.0000000000000407 pmid: 24374539
[27]	PIESSEN G, CABRAL C, BENOIST S, et al. Previous transanal full-thickness excision increases the morbidity of radical resection for rectal cancer[J]. Colorectal Dis, 2012, 14(4):445-452. doi: 10.1111/j.1463-1318.2011.02671.x pmid: 21689342
[28]	MORENO C C, SULLIVAN P S, MITTAL P K. MRI evaluation of rectal cancer: staging and restaging[J]. Curr Probl Diagn Radiol, 2017, 46(3):234-241. doi: S0363-0188(16)30146-3 pmid: 28089690
[29]	BROWN G, RICHARDS C J, BOURNE M W, et al. Morphologic predictors of lymph node status in rectal cancer with use of high-spatial-resolution MR imaging with histopathologic comparison[J]. Radiology, 2003, 227(2):371-377. doi: 10.1148/radiol.2272011747 pmid: 12732695
[30]	刘媛, 万丽娟, 张红梅. T1-2期直肠癌淋巴结转移的MRI相关因素分析[J]. 中华肿瘤杂志, 2021, 43(2):207-212.
	LIU Y, WAN L J, ZHANG H M. MRI associated biomarker analysis for diagnosis of lymph node metastasis in T1-2 stage rectal cancer[J]. Chin J Oncol, 2021, 43(2):207-212.
[31]	BEETS-TAN R G H, LAMBREGTS D M J, MAAS M, et al. Magnetic resonance imaging for clinical management of rectal cancer: updated recommendations from the 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus meeting[J]. Eur Radiol, 2018, 28(4):1465-1475.
[32]	许梅海, 炜申. MRI定量分析及影像组学在直肠癌淋巴结转移评估中的研究进展[J]. 肿瘤影像学, 2021, 30(3):209-213.
	XU M H, SHEN W. Research progress of quantitative assessment of MRI and radiomics for lymph node metastasis of rectal cancer[J]. Oncoradiology, 2021, 30(3):209-213.
[33]	DETERING R, VAN OOSTENDORP S E, MEYER V M, et al. MRI cT1-2 rectal cancer staging accuracy: a population-based study[J]. Br J Surg, 2020, 107(10):1372-1382.
[34]	PATEL R K, SAYERS A E, KUMAR P, et al. The role of endorectal ultrasound and magnetic resonance imaging in the management of early rectal lesions in a tertiary center[J]. Clin Colorectal Cancer, 2014, 13(4):245-250.
[35]	BRUNNER W, WIDMANN B, MARTI L, et al. Predictors for regional lymph node metastasis in T1 rectal cancer: a population-based SEER analysis[J]. Surg Endosc, 2016, 30(10):4405-4415. doi: 10.1007/s00464-016-4759-3 pmid: 26895892
[36]	CHENG P, LU Z, ZHANG M, et al. Is Additional surgery necessary after non-curative endoscopic submucosal dissection for early colorectal cancer?[J]. J Invest Surg, 2021, 34(8):889-894.
[37]	KANG J, CHOI Y J, KIM I K, et al. LASSO-Based machine learning algorithm for prediction of lymph node metastasis in T1 colorectal cancer[J]. Cancer Res Treat, 2021, 53(3):773-783. doi: 10.4143/crt.2020.974 pmid: 33421980
[38]	REICHLING C, TAIEB J, DERANGERE V, et al. Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage Ⅲ colon cancer outcomes in PETACC08 study[J]. Gut, 2020, 69(4):681-690.
[39]	CAVALLERI T, BIANCHI P, BASSO G, et al. Combined low densities of FoxP3(+) and CD3(+) tumor-infiltrating lymphocytes identify stage Ⅱ colorectal cancer at high risk of progression[J]. Cancer Immunol Res, 2019, 7(5):751-758.
[40]	JANSEN R C, NAP J P. Genetical genomics: the added value from segregation[J]. Trends Genet, 2001, 17(7):388-391. doi: 10.1016/s0168-9525(01)02310-1 pmid: 11418218
[41]	CHEN X, BA Y, MA L, et al. Characterization of micro- RNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases[J]. Cell Res, 2008, 18(10):997-1006.
[42]	OZAWA T, KANDIMALLA R, GAO F, et al. A MicroRNA signature associated with metastasis of T1 colorectal cancers to lymph nodes[J]. Gastroenterology, 2018, 154(4):844-848. doi: S0016-5085(17)36664-7 pmid: 29199088
[43]	WADA Y, SHIMADA M, MURANO T, et al. A liquid biopsy assay for noninvasive identification of lymph node metastases in T1 colorectal cancer[J]. Gastroenterology, 2021, 161(1):151-162. doi: 10.1053/j.gastro.2021.03.062 pmid: 33819484
[44]	HUANG D, SUN W, ZHOU Y, et al. Mutations of key driver genes in colorectal cancer progression and metastasis[J]. Cancer Metastasis Rev, 2018, 37(1):173-187.
[45]	TSUKUI Y, YAMAGUCHI T, MAEKAWA S, et al. Dickkopf-4 gene expression is associated with differentiation and lymph node metastasis in colorectal cancer[J]. JGH Open, 2019, 3(5):409-416. doi: 10.1002/jgh3.12177 pmid: 31633047
[46]	ANDO J, SAITO M, IMAI J I, et al. TBX19 is overexpressed in colorectal cancer and associated with lymph node metastasis[J]. Fukushima J Med Sci, 2017, 63(3):141-151. doi: 10.5387/fms.2017-08 pmid: 29199261
[47]	DE MAAT M F, VAN DE VELDE C J, VAN DER WERFF M P, et al. Quantitative analysis of methylation of genomic loci in early-stage rectal cancer predicts distant recurrence[J]. J Clin Oncol, 2008, 26(14):2327-2335. doi: 10.1200/JCO.2007.14.0723 pmid: 18467724
[48]	LEONG K J, BEGGS A, JAMES J, et al. Biomarker-based treatment selection in early-stage rectal cancer to promote organ preservation[J]. Br J Surg, 2014, 101(10):1299-1309. doi: 10.1002/bjs.9571 pmid: 25052224
[49]	刘蓬然, 霍彤彤, 陆林, 等. 人工智能在医学中的应用现状与展望[J]. 中华医学杂志, 2021, 101(44):3677-3683.
	LIU P R, HUO T T, LU L, et al. Current status and prospects of artificial intelligence in medicine[J]. Natl Med J China, 2021, 101(44):3677-3683.
[50]	KIEHL L, KUNTZ S, HÖHN J, et al. Deep learning can predict lymph node status directly from histology in colorectal cancer[J]. Eur J Cancer, 2021,157:464-473.