切换至 "中华医学电子期刊资源库"
高级检索
中华临床实验室管理电子杂志

中华临床实验室管理电子杂志 ›› 2018, Vol. 06 ›› Issue (02) : 89 -98. doi: 10.3877/cma.j.issn.2095-5820.2018.02.006

所属专题： 文献；

实验研究

基于TCGA数据库构建肺腺癌预后相关的微小RNAs风险模型
林康1, 潘蓓2, 徐雪妮2, 孙慧玲2, 王书奎3,†()   
  1. 1. 210006　南京医科大学附属南京医院检验科
    2. 210006　南京医科大学附属南京医院中心实验室
    3. 210006　南京医科大学附属南京医院检验科；210006　南京医科大学附属南京医院中心实验室
  • 收稿日期:2018-03-14 出版日期:2018-05-28
  • 通信作者: 王书奎

Construction of risk model associated with prognosis of lung adenocarcinoma based on a set of microRNAs by analyzing TCGA database

Kang Lin1, Bei Pan2, Xueni Xu2, Huiling Sun2, Shukui Wang3,†()   

  1. 1. Clinical Laboratory, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China
    2. Central Laboratory, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China
    3. Clinical Laboratory, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China; Central Laboratory, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China
  • Received:2018-03-14 Published:2018-05-28
  • Corresponding author: Shukui Wang
  • About author:
    Corresponding author: Wang Shukui, Email:
全文 ( ) 下载PDF ( ) 导出引用
引用本文：

林康, 潘蓓, 徐雪妮, 孙慧玲, 王书奎. 基于TCGA数据库构建肺腺癌预后相关的微小RNAs风险模型[J/OL]. 中华临床实验室管理电子杂志, 2018, 06(02): 89-98.

Kang Lin, Bei Pan, Xueni Xu, Huiling Sun, Shukui Wang. Construction of risk model associated with prognosis of lung adenocarcinoma based on a set of microRNAs by analyzing TCGA database[J/OL]. Chinese Journal of Clinical Laboratory Management(Electronic Edition), 2018, 06(02): 89-98.

目的

寻找肺腺癌(lung adenocarcinoma，LUAD)特异性的预后相关微小RNAs(microRNAs, miRNAs)，为LUAD患者预后预测及个性化治疗方案制定提供依据。

方法

下载TCGA数据库中522例LUAD患者组织标本的miRNA-Seq数据和临床病理及生存时间数据，用R语言对LUAD与癌旁组织中差异miRNAs进行分析。采用LASSO & COX回归模型在训练集(245例LUAD)中进行LUAD预后相关miRNAs筛选，并构建基于7个miRNAs表达谱的线性风险模型。根据风险值的高低，以中位风险值为界将患者分为高、低风险组，并分别在测试集(245例LUAD)和总体标本(490例LUAD)中对风险模型预测患者预后的有效性进行验证。采用COX回归分析miRNAs风险模型是否是独立的预后因子。

结果

LUAD组织与癌旁组织中共有72个差异表达的miRNAs(上调45个、下调27个)。从训练集中确定miR-101-3p、miR-148a-3p、miR-192-5p、miR-193b-3p、miR-505-3p、miR-584-5p和miR-99a-5p 7个与总生存期相关的miRNAs构建预后风险模型。在训练集、测试集及总体标本中，高风险组患者与低风险组患者相比，总体生存时间均显著降低(P均<0.05)。经多因素COX回归分析，风险模型在训练集、测试集及总体样本中均是一个独立的预后因子(训练集HR=1.97，P=0.02；测试集HR=1.927，P=0.009；总体HR=1.909，P=0.001)。

结论

研究确定了7个与LUAD患者预后相关的miRNAs，基于7个miRNAs构建的风险模型是1个独立的预后因子。

关键词: 肺腺癌, 微小RNAs, 预后, TCGA
Objective

To explore the specific prognosis related microRNAs (miRNAs) of lung adenocarcinoma (LUAD), and provide basis for prognosis prediction and individualized treatment.

Methods

The miRNA-Seq data and clinical information of LUAD patients were downloaded from the TCGA database, and the differentially expressed miRNAs between LUAD and adjacent normal tissues were identified by R Language. The LASSO & COX regression was used to develop a miRNA-based model for predicting patients? survival in the training set (n=245) and to carry out LUAD prognostic related miRNAs screening, and to construct a linear risk model based on 7 miRNAs expression profiles. According to the risk values, the patients were divided into high and low risk groups with the median risk value as the boundary, and the effectiveness of the prognosis was verified by the risk model in the test set (n=245) and the total specimens (n=490) respectively. COX regression analysis was used to determine whether the miRNAs risk model was an independent prognostic factor.

Results

Seventy-two differentially expressed miRNAs were identified between LUAD and adjacent normal tissues. forty-five miRNAs were up-regulated and 27 were down-regulated in LUAD tissues. Seven survival-related miRNAs (miR-101-3p, miR-148a-3p, miR-192-5p, miR-193b-3p, miR-505-3p, miR-584-5p, and miR-99a-5p) were identified in the training set and a prognostic model based on the expression of the 7 miRNAs was developed and its coefficient was evaluated. It showed that the overall survival time of the high-risk group was significantly lower than that of the low risk group in the training set, test set and whole cohort (P<0.05). Multivariate cox regression analysis indicated that the risk model was an independent prognostic factor in the training set, test set and whole cohort (training set HR=1.97, P=0.02; test set HR=1.927, P=0.009; overall HR=1.909, P=0.001).

Conclusion

Seven miRNAs are identified to be significantly associated with the prognosis of LUAD patients and the risk model based on the 7 miRNAs could be an independent prognostic factor.

Key words: Lung adenocarcinoma, MicroRNAs, Prognosis, TCGA
表1 LUAD患者的基本临床病理资料情况
临床变量 总体(n=490) 训练集(n=245) 测试集(n=245) 检验值 P值
年龄[岁(±s)] 65±10 65±10 66±10 -1.144a 0.253
? <=65[岁,n(%)] ? 230(46.94) 121(49.39) 109(44.49) 1.402b 0.236
? >65[岁,n(%)] ? 250(51.02) 118(48.16) 132(53.88) ? ?
性别[n(%)] 男 230(46.94) 108(44.08) 122(49.80) 1.606b 0.205
? ? 女 260(53.06) 137(55.92) 123(50.20) ? ?
吸烟状态[n(%)] 否 69(14.08) 32(13.06) 37(15.10) 0.495b 0.781
? ? 戒烟 291(59.39) 145(59.18) 146(59.59) ? ?
? ? 是 116(23.67) 60(24.49) 56(22.86) ? ?
肿瘤分期[n(%)] I期 266(54.29) 140(57.14) 126(51.43) 2.388b 0.496
? ? II期 115(23.47) 56(22.86) 59(24.08) ? ?
? ? III期 79(16.12) 34(13.88) 45(18.37) ? ?
? ? IV期 23(4.69) 11(4.49) 12(4.90) ? ?
浸润深度[n(%)] T1 165(33.67) 83(33.88) 82(33.47) 1.554b 0.670
? ? T2 261(53.27) 134(54.69) 127(51.84) ? ?
? ? T3 43(8.78) 18(7.35) 25(10.20) ? ?
? ? T4 18(3.67) 8(3.27) 10(4.08) ? ?
淋巴结转移[n(%)] N0 317(64.69) 161(65.71) 156(63.67) 0.432b 0.962
? ? N1 92(18.78) 45(18.37) 47(19.18) ? ?
? ? N2 68(13.88) 33(13.47) 35(14.29) ? ?
? ? N3 2(0.41) 1(0.41) 1(0.41) ? ?
远处转移[n(%)] M0 326(66.53) 157(64.08) 167(68.16) 0.074b 0.785
? ? M1 22(4.49) 10(4.08) 12(4.90) ? ?
生存状态[n(%)] 存活 324(66.12) 161(65.71) 163(66.53) 0.036b 0.849
? ? 死亡 166(33.88) 84(34.29) 82(33.47) ? ?
生存时间[月(±s)] 27.08±17.22 27.26±18.32 26.90±16.08 0.231c 0.818
表2 LUAD患者490份癌组织和39份癌旁正常组织中差异表达的miRNAs分布
miRNA分类 log2FC FDR miRNA分类 log2FC FDR miRNA分类 log2FC FDR miRNA分类 log2FC FDR
miR-210-3p 6.00 2.2E-158 miR-21-3p 2.05 3.08E-47 miR-29b-3p 1.24 1.98E-17 let-7e-5p -1.41 1.20E-19
miR-9-5p 4.92 1.06E-50 miR-182-5p 2.00 5.03E-38 miR-200c-5p 1.23 4.30E-15 miR-101-3p -1.41 8.43E-33
miR-205-5p 3.09 3.26E-15 miR-200a-3p 1.83 3.31E-23 miR-429 1.21 8.53E-13 miR-126-5p -1.50 2.86E-32
miR-708-5p 2.81 3.14E-47 miR-331-3p 1.82 2.25E-36 miR-625-3p 1.19 5.71E-17 miR-374a-5p -1.69 3.49E-59
miR-192-5p 2.78 1.93E-16 miR-141-5p 1.74 4.48E-29 miR-199a-5p 1.17 1.51E-16 miR-378a-3p -1.76 9.37E-31
miR-135b-5p 2.70 1.41E-34 miR-339-5p 1.68 1.57E-29 miR-17-5p 1.11 7.45E-14 miR-145-3p -1.76 1.17E-48
let-7g-3p 2.63 1.03E-57 miR-1307-3p 1.67 1.84E-33 miR-328-3p 1.10 1.06E-14 let-7a-5p -1.80 9.89E-39
miR-708-3p 2.62 3.36E-53 miR-424-5p 1.67 9.25E-19 miR-501-3p 1.06 1.20E-12 let-7c-5p -1.86 3.09E-33
miR-193b-3p 2.59 5.56E-36 miR-21-5p 1.62 2.87E-45 miR-191-5p 1.04 2.49E-21 miR-30a-5p -1.98 9.77E-31
miR-455-3p 2.51 5.30E-47 miR-141-3p 1.48 4.05E-21 miR-584-5p -1.03 6.11E-06 miR-218-5p -1.99 1.02E-53
miR-127-5p 2.51 2.62E-26 miR-134-5p 1.45 1.35E-08 miR-99a-5p -1.06 3.95E-14 let-7f-5p -2.18 6.85E-36
miR-143-5p 2.40 5.74E-33 miR-532-3p 1.38 9.24E-21 miR-139-5p -1.09 1.34E-10 miR-195-5p -2.42 6.51E-68
miR-183-5p 2.39 3.18E-42 miR-93-5p 1.34 1.32E-21 let-7a-3p -1.10 2.22E-23 miR-30a-3p -2.72 2.80E-54
miR-375 2.38 1.73E-17 miR-505-3p 1.33 3.40E-28 miR-223-3p -1.19 9.09E-11 miR-143-3p -2.82 2.05E-56
miR-200a-5p 2.21 3.12E-41 miR-151a-5p 1.30 8.34E-34 miR-374b-5p -1.20 2.27E-41 miR-30c-2-3p -2.89 1.96E-59
miR-194-5p 2.21 1.36E-11 miR-425-5p 1.28 2.05E-19 miR-26a-5p -1.30 1.40E-33 miR-451a -2.98 7.90E-37
miR-224-5p 2.19 2.90E-18 miR-423-3p 1.27 1.09E-35 let-7g-5p -1.32 3.03E-34 miR-486-5p -3.11 1.58E-39
miR-142-3p 2.09 6.51E-29 miR-148a-3p 1.25 1.23E-17 miR-140-3p -1.35 4.62E-38 miR-144-5p -3.42 4.03E-50
图1 LUAD患者生存相关的miRNAs筛选图
图2 训练集中7个miRNAs的预后风险评分模型评价LUAD患者生存时间图
图3 测试集中7个miRNAs的预后风险评分模型评价LUAD患者生存时间图
图4 总体LUAD患者中7个miRNAs的预后风险评分模型评价LUAD患者生存时间图
表3 LUAD患者训练集、测试集、总体生存率的单因素和多因素分析比较
临床变量 变量比较类别 单因素分析 多因素分析
HR(95%CI) P值 HR(95%CI) P值
训练集(n=245)
? 年龄 >65岁比≤65岁 1.121(0.727-1.729) 0.606 0.902(0.517-1.573) 0.717
? 性别 男比女 1.101(0.715-1.694) 0.662 0.808(0.436-1.496) 0.498
? 浸润深度 T4＋T3比T2＋T1 2.749(1.536-4.920) 0.001 2.624(1.318-5.221) 0.006
? 淋巴结转移 N3＋N2＋N1比N0 2.343(1.514-3.625) <0.001 1.817(0.999-3.303) 0.050
? 远处转移 M1比M0 2.678(1.212-5.918) 0.015 1.455(0.516-4.106) 0.479
? 肿瘤分期 IV＋III比II＋I 2.632(1.671-4.148) <0.001 1.227(0.497-3.026) 0.657
? 风险评估模型 高风险比低风险 2.233(1.436-3.471) <0.001 1.970(1.112-3.490) 0.020
测试集(n=245)
? 年龄 >65岁比. ≤65岁 1.083(0.701-1.673) 0.720 1.203(0.717-2.018) 0.483
? 性别 男比女 1.005(0.651-1.551) 0.984 0.997(0.617-1.609) 0.989
? 肿瘤浸润深度 T4＋T3比T2＋T1 2.058(1.200-3.528) 0.009 1.652(0.867-3.146) 0.127
? 淋巴结转移 N3＋N2＋N1比N0 2.749(1.772-4.264) <0.001 1.876(1.084-3.247) 0.025
? 远处转移 M1比M0 2.111(0.961-4.641) 0.063 1.664(0.624-4.434) 0.309
? 肿瘤分期 IV＋III比II＋I 2.867(1.817-4.523) <0.001 1.977(1.124-3.478) 0.018
? 风险评估模型 高风险比低风险 1.686(1.082-2.626) 0.021 1.927(1.181-3.145) 0.009
总体(n=490)
? 年龄 >65岁比≤65岁 1.103(0.812-1.500) 0.530 1.033(0.717-1.489) 0.862
? 性别 男比女 1.055(0.778-1.432) 0.729 0.920(0.641-1.320) 0.651
? 肿瘤浸润深度 T4＋T3比T2＋T1 2.351(1.583-3.490) <0.001 1.794(1.103-2.918) 0.019
? 淋巴结转移 N3＋N2＋N1比N0 2.526(1.854-3.440) <0.001 1.753(1.139-2.699) 0.011
? 远处转移 M1比.M0 2.359(1.350-4.120) 0.003 1.374(0.692-2.728) 0.364
? 肿瘤分期 IV＋III比II＋I 2.733(1.983-3.767) <0.001 1.605(0.989-2.604) 0.055
? 风险评估模型 高风险比低风险 1.941(1.420-2.653) <0.001 1.909(1.320-2.760) 0.001
1
Iorio MV, Croce CM. microRNA involvement in human cancer[J]. Carcinogenesis, 2012,33(6):1126-1133.
2
Joshi P, Middleton J, Jeon YJ, et al. MicroRNAs in lung cancer [J]. World J Methodol, 2014,4(2):59-72.
3
Hou J, Meng F, Chan LW, et al. Circulating Plasma MicroRNAs As Diagnostic Markers for NSCLC [J]. Front Genet, 2016,7:193.
4
Hashemi ZS, Khalili S, Forouzandeh Moghadam M, et al. Lung cancer and miRNAs: a possible remedy for anti-metastatic, therapeutic and diagnostic applications[J]. Expert Rev Respirat Med, 2017,11(2): 147-157.
5
Inamura K, Ishikawa Y. MicroRNA In Lung Cancer: Novel Biomarkers and Potential Tools for Treatment[J]. J Clin Med, 2016,5(3):36.
6
Wang C, Ding M, Xia M, et al. A Five-miRNA Panel Identified From a Multicentric Case-control Study Serves as a Novel Diagnostic Tool for Ethnically Diverse Non-small-cell Lung Cancer Patients[J]. EBioMedicine, 2015,2(10):1377-1385.
7
Wozniak MB, Scelo G, Muller DC, et al. Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer[J]. PloS One, 2015,10(5):e0125026.
8
Shi SB, Wang M, Tian J, et al. MicroRNA 25, microRNA 145, and microRNA 210 as biomarkers for predicting the efficacy of maintenance treatment with pemetrexed in lung adenocarcinoma patients who are negative for epidermal growth factor receptor mutations or anaplastic lymphoma kinase translocations[J]. Transl Res J Lab Clin Med, 2016,170:1-7.
9
Zhu J, Qi Y, Wu J, et al. Evaluation of plasma microRNA levels to predict insensitivity of patients with advanced lung adenocarcinomas to pemetrexed and platinum[J]. Oncol Lett, 2016,12(6): 4829-4837.
10
Liu Q, Yu Z, Yuan S, et al. Circulating exosomal microRNAs as prognostic biomarkers for non-small-cell lung cancer[J]. Oncotarget, 2017,8(8):13048-13058.
11
Petriella D, De Summa S, Lacalamita R, et al. miRNA profiling in serum and tissue samples to assess noninvasive biomarkers for NSCLC clinical outcome[J]. Tumour biol, 2016,37(4):5503-5513.
12
Tomczak K, Czerwinska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge[J]. Contemp Oncol(Pozn), 2015,19(1A):A68-77.
13
Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours[J]. Nature, 2012,490(7418):61-70.
14
Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers[J]. Nature, 2012,489(7417):519-525.
15
Tibshirani R. The lasso method for variable selection in the Cox model[J]. Stat Med, 1997,16(4):385-395.
16
Lossos IS, Czerwinski DK, Alizadeh AA, et al. Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes[J]. N Engl J Med, 2004,350(18):1828-1837.
17
Goodgame B, Viswanathan A, Miller CR, et al. A clinical model to estimate recurrence risk in resected stage I non-small cell lung cancer[J]. Am J Clin Oncol, 2008,31(1):22-28.
18
Yoshizawa A, Motoi N, Riely GJ, et al. Impact of proposed IASLC/ATS/ERS classification of lung adenocarcinoma: prognostic subgroups and implications for further revision of staging based on analysis of 514 stage I cases[J]. Mod Pathol, 2011,24(5):653-664.
19
Lu Y, Govindan R, Wang L, et al. MicroRNA profiling and prediction of recurrence/relapse- free survival in stage I lung cancer[J]. Carcinogenesis, 2012,33(5):1046-1054.
20
Yu SL, Chen HY, Chang GC, et al. MicroRNA signature predicts survival and relapse in lung cancer[J]. Cancer cell, 2008,13(1):48-57.
21
Hu Z, Chen X, Zhao Y, et al. Serum microRNA signatures identified in a genome-wide serum microRNA expression profiling predict survival of non-small-cell lung cancer[J]. J Clin Oncol, 2010,28(10): 1721-1726.
22
Chen W, Yu Q, Chen B, et al. The prognostic value of a seven-microRNA classifier as a novel biomarker for the prediction and detection of recurrence in glioma patients[J]. Oncotarget, 2016,7(33): 53392-53413.
23
Xu J, Zhao J, Zhang R. Four microRNAs Signature for Survival Prognosis in Colon Cancer using TCGA Data[J]. Sci Rep, 2016,6:38306.
24
Lu M, Kong X, Wang H, et al. A novel microRNAs expression signature for hepatocellular carcinoma diagnosis and prognosis[J]. Oncotarget, 2017,8(5):8775-8784.
25
Zhou H, Tang K, Xiao H, et al. A panel of eight-miRNA signature as a potential biomarker for predicting survival in bladder cancer[J]. J Exp Clin Cancer Res, 2015,34:53.
26
Zhou X, Huang Z, Xu L, et al. A panel of 13-miRNA signature as a potential biomarker for predicting survival in pancreatic cancer[J]. Oncotarget, 2016,7(43):69616-69624.
27
Gao X, Wu Y, Yu W, et al. Identification of a seven-miRNA signature as prognostic biomarker for lung squamous cell carcinoma[J]. Oncotarget, 2016,7(49):81670-81679.
28
Li X, Shi Y, Yin Z, et al. An eight-miRNA signature as a potential biomarker for predicting survival in lung adenocarcinoma[J]. J Transl Med, 2014,12:159.
29
Zhou X, Wang X, Huang Z, et al. An ER-associated miRNA signature predicts prognosis in ER-positive breast cancer[J]. J Exp Clin Cancer Res, 2014,33:94.
30
Hou Y, Li L, Xiang X. MiR-101-3p Regulates the Viability of Lung Squamous Carcinoma Cells via Targeting EZH2[J]. J Cellul Biochem, 2017,118(10):3142-3149.
31
Gu W, Fang S, Gao L, et al. Clinic significance of microRNA-99a expression in human lung adenocarcinoma[J]. J Surg Oncol, 2013,108(4):248-255.
32
Yang JS, Li BJ, Lu HW, et al. Serum miR-152, miR-148a, miR-148b, and miR-21 as novel biomarkers in non-small cell lung cancer screening[J]. Tumour biol, 2015,36(4):3035-3042.
33
Xu X, Zhang Y, Jasper J, et al. MiR-148a functions to suppress metastasis and serves as a prognostic indicator in triple-negative breast cancer[J]. Oncotarget, 2016,7(15):20381-20394.
34
Mancuso G, Bovio E, Rena O, et al. Prognostic impact of a 3-MicroRNA signature in cytological samples of small cell lung cancer[J]. Cancer cytopathol, 2016,124(9):621-629.
35
Cao J, He Y, Liu HQ, et al. MicroRNA 192 regulates chemo-resistance of lung adenocarcinoma for gemcitabine and cisplatin combined therapy by targeting Bcl-2[J]. Int J Clin Exp Med, 2015,8(8): 12397-12403.
36
Nadal E, Truini A, Nakata A, et al. A Novel Serum 4-microRNA Signature for Lung Cancer Detection[J]. Sci Rep, 2015,5:12464.
37
Hu H, Li S, Liu J, et al. MicroRNA-193b modulates proliferation, migration, and invasion of non-small cell lung cancer cells[J]. Acta Biochimica Biophys Sin(Shnghai), 2012,44(5):424-430.
[1] 张晓宇, 殷雨来, 张银旭. 阿帕替尼联合新辅助化疗对三阴性乳腺癌的疗效及预后分析[J/OL]. 中华乳腺病杂志(电子版), 2024, 18(06): 346-352.
[2] 许杰, 李亚俊, 韩军伟. 两种入路下腹腔镜根治性全胃切除术治疗超重胃癌的效果比较[J/OL]. 中华普外科手术学杂志(电子版), 2025, 19(01): 19-22.
[3] 高杰红, 黎平平, 齐婧, 代引海. ETFA和CD34在乳腺癌中的表达及与临床病理参数和预后的关系研究[J/OL]. 中华普外科手术学杂志(电子版), 2025, 19(01): 64-67.
[4] 李代勤, 刘佩杰. 动态增强磁共振评估中晚期低位直肠癌同步放化疗后疗效及预后的价值[J/OL]. 中华普外科手术学杂志(电子版), 2025, 19(01): 100-103.
[5] 屈翔宇, 张懿刚, 李浩令, 邱天, 谈燚. USP24及其共表达肿瘤代谢基因在肝细胞癌中的诊断和预后预测作用[J/OL]. 中华普外科手术学杂志(电子版), 2024, 18(06): 659-662.
[6] 顾雯, 凌守鑫, 唐海利, 甘雪梅. 两种不同手术入路在甲状腺乳头状癌患者开放性根治性术中的应用比较[J/OL]. 中华普外科手术学杂志(电子版), 2024, 18(06): 687-690.
[7] 付成旺, 杨大刚, 王榕, 李福堂. 营养与炎症指标在可切除胰腺癌中的研究进展[J/OL]. 中华普外科手术学杂志(电子版), 2024, 18(06): 704-708.
[8] 韩加刚, 王振军. 梗阻性左半结肠癌的治疗策略[J/OL]. 中华结直肠疾病电子杂志, 2024, 13(06): 450-458.
[9] 刘郁, 段绍斌, 丁志翔, 史志涛. miR-34a-5p 在结肠癌患者的表达及其与临床特征及预后的相关性研究[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(06): 485-490.
[10] 陈倩倩, 袁晨, 刘基, 尹婷婷. 多层螺旋CT 参数、癌胚抗原、错配修复基因及病理指标对结直肠癌预后的影响[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(06): 507-511.
[11] 曾明芬, 王艳. 急性胰腺炎合并脂肪肝患者CT 与彩色多普勒超声诊断参数与其病情和预后的关联性研究[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(06): 531-535.
[12] 沈炎, 张俊峰, 唐春芳. 预后营养指数结合血清降钙素原、胱抑素C及视黄醇结合蛋白对急性胰腺炎并发急性肾损伤的预测价值[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(06): 536-540.
[13] 董佳, 王坤, 张莉. 预后营养指数结合免疫球蛋白、血糖及甲胎蛋白对HBV 相关慢加急性肝衰竭患者治疗后预后不良的预测价值[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(06): 555-559.
[14] 王景明, 王磊, 许小多, 邢文强, 张兆岩, 黄伟敏. 腰椎椎旁肌的研究进展[J/OL]. 中华临床医师杂志(电子版), 2024, 18(09): 846-852.
[15] 郭曌蓉, 王歆光, 刘毅强, 何英剑, 王立泽, 杨飏, 汪星, 曹威, 谷重山, 范铁, 李金锋, 范照青. 不同亚型乳腺叶状肿瘤的临床病理特征及预后危险因素分析[J/OL]. 中华临床医师杂志(电子版), 2024, 18(06): 524-532.
阅读次数
全文


摘要

版权所有 中华医学会 中华医学电子音像出版社有限责任公司  京ICP备14006079号-1  网络出版服务许可证：(署)网出证(京)字第075号