基于单细胞RNA测序的结直肠癌预后预测模型的建立和验证

doi:10.3969/j.issn.1674-8115.2021.02.006

上海交通大学学报(医学版) ›› 2021, Vol. 41 ›› Issue (2): 159-165.doi: 10.3969/j.issn.1674-8115.2021.02.006

基于单细胞RNA测序的结直肠癌预后预测模型的建立和验证

马燕如¹(), 季林华², 童天颖¹, 严宇青¹, 沈超琴¹, 张昕雨¹, 曹颖颖¹, 洪洁¹, 陈豪燕¹()

^1.上海交通大学医学院附属仁济医院消化科，上海 200001
^2.上海交通大学医学院附属仁济医院胃肠外科，上海 200001

收稿日期:2020-05-21 出版日期:2021-02-28 发布日期:2021-02-28
通讯作者: 陈豪燕 E-mail:mayanru0213@163.com;haoyanchen@shsmu.edu.cn
作者简介:马燕如（1995—），女，硕士生；电子信箱：mayanru0213@163.com。
基金资助:
上海市教育委员会高峰高原学科建设计划(20161309);上海交通大学医学院高水平地方高校创新团队(SSMU-ZLCX20180200)

Establishment and validation of prognostic prediction model of colorectal cancer based on single-cell RNA sequencing

Yan-ru MA¹(), Lin-hua JI², Tian-ying TONG¹, Yu-qing YAN¹, Chao-qin SHEN¹, Xin-yu ZHANG¹, Ying-ying CAO¹, Jie HONG¹, Hao-yan CHEN¹()

^1.Department of Gastroenterology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200001, China
^2.Department of Gastrointestinal Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200001, China

Received:2020-05-21 Online:2021-02-28 Published:2021-02-28
Contact: Hao-yan CHEN E-mail:mayanru0213@163.com;haoyanchen@shsmu.edu.cn
Supported by:
Shanghai Municipal Education Commission—Gaofeng Clinical Medicine Grant Support(20161309);Innovative Research Team of High-Level Local Universities in Shanghai(SSMU-ZLCX20180200)

摘要/Abstract

摘要：

目的·基于单细胞RNA测序（single cell RNA sequence，scRNA-seq）技术构建结直肠癌预后预测模型。方法·利用GEO（Gene Expression Omnibus）数据库获取结直肠癌样本的scRNA-seq数据集，筛选与结直肠癌转移相关的差异基因作为预测模型的候选基因，运用套索回归算法（LASSO）、Logistic回归和Kaplan-Meier生存分析进一步在癌症基因组图谱（The Cancer Genome Atlas，TCGA）数据库中筛选及验证与结直肠癌预后相关的基因集，并建立结直肠癌预后预测模型。通过决策曲线分析和受试者工作特征（receiver operating characteristic，ROC）曲线评估预测模型在临床应用中的价值。结果·利用GEO数据库获取的scRNA-seq数据筛选出30个差异表达基因，进一步在TCGA数据库中利用LASSO回归得到9个关键基因，并以此对每例患者的关键基因表达进行评分。分别在训练集和验证集中对复发和未复发患者的评分进行比较，差异均有统计学意义（P<0.05）。采用 Logistic回归分析将肿瘤原发灶分级（T stage）和是否发生远处转移（M stage）2个独立的临床变量纳入评分-临床变量整合模型。对评分-临床变量整合模型的实际预测价值进行评估，ROC曲线在训练集和验证集的曲线下面积分别为0.775和0.705。结论·基于scRNA-seq结果，构建了较为稳定的结直肠癌预后预测模型，可供临床评估患者预后参考。

关键词: 单细胞RNA测序, 结直肠癌, 预后, 生物信息学

Abstract:

Objective·To establish a model for predicting the prognosis in patients with colorectal cancer (CRC) using single cell RNA sequencing (scRNA-seq).

Methods·scRNA-seq data of patients with CRC from Gene Expression Omnibus (GEO) database was used to filter out candidate genes, which were related to metastatic CRC. The least absolute shrinkage and selection operator (LASSO) regression, Logistic regression and Kaplan-Meier analysis were used to select and evaluate the significance of the hub gene filtered out in The Cancer Genome Atlas (TCGA) database, and to develop the prognostic prediction model of CRC. Decision curve analysis and receiver operating characteristic (ROC) curve were used to assess the clinical use of the prediction model.

Results·Thirty candidate genes were filtered out from the scRNA-seq data which was downloaded in GEO database, and then 9 hub genes were selected by LASSO regression in the TCGA database. The hub-gene expression was scored for each patient. The scores had significant difference between the groups with and without recurrence both in the training set and the validation set (P<0.05). In addition, Logistic regression analysis was carried out to incorporate the two independent clinical variables of primary tumor grade (T stage) and metastasis status (M stage) into the score-clinical variable integration model. Area under curve of the ROC curve in the training set and validation set were 0.775 and 0.705, respectively.

Conclusion·A relatively stable model for predicting prognosis in CRC was constructed based on the results of scRNA-seq, which has certain guiding significance for treatment decision and prognostic prediction.

Key words: single cell RNA sequencing (scRNA-seq), colorectal cancer, prognosis, bioinformatics

中图分类号:

R735.3

马燕如, 季林华, 童天颖, 严宇青, 沈超琴, 张昕雨, 曹颖颖, 洪洁, 陈豪燕. 基于单细胞RNA测序的结直肠癌预后预测模型的建立和验证[J]. 上海交通大学学报(医学版), 2021, 41(2): 159-165.

Yan-ru MA, Lin-hua JI, Tian-ying TONG, Yu-qing YAN, Chao-qin SHEN, Xin-yu ZHANG, Ying-ying CAO, Jie HONG, Hao-yan CHEN. Establishment and validation of prognostic prediction model of colorectal cancer based on single-cell RNA sequencing[J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2021, 41(2): 159-165.

图/表 7

图1 单细胞水平筛选CRC预后相关基因Note:A. The distribution of cell population in primary tumor and lymph node metastasis of CRC patients was analyzed by the UMAP algorithm. B/C. The distribution of CDH1 in the cell population (B), and based on this, the cells were divided in to 0 and 1 groups (C). D. Based on the function of CDH1, the cells were divided into metastatic and non-metastatic groups. E. The heat map shows significant differences between the metastasis group and the non-metastasis group (P<0.05), and the darker the color is, the greater the difference is.

Fig 1 Screening genes associated with prognosis of CRC from single-cell level

图2 采用LASSO回归选择关键基因Note:The adjustment parameter selection in the LASSO model passes the minimum standard using 5-fold cross validation. The solid gray line represents the local likelihood. A dashed vertical line is drawn with the best value by using the minimum criteria and the 1 standard error of the minimum criteria (the 1-SE criteria). The optimal λ value of 0.029 06 with log (λ)=-3.538 4 was selected.

Fig 2 Texture feature selection using LASSO binary Logistic regression model

表1 CRC患者的临床特征

Tab 1 Clinical characteristics of patients with CRC

Item	Training set (n=293)	Validation set (n=295)	P value^①
Gender/n (%)			0.406
Male	160 (54.61)	145 (49.20)
Female	133 (45.39)	150 (50.80)
Age/year	66.86±12.77	65.43±12.96	0.178
T stage/n (%)			0.478
T1	9 (3.07)	13 (4.41)
T2	56 (19.11)	45 (15.25)
T3	198 (67.58)	201 (68.14)
T4	30 (10.24)	36 (12.20)
N stage/n (%)			0.592
N0^②	173 (59.05)	165 (44.93)
N1^③	119 (40.61)	129 (43.73)
NA^④	1 (0.34)	1 (0.34)
M stage/n (%)			0.342
M0	220 (75.09)	217 (73.56)
M1	36 (12.29)	46 (15.59)
NA^④	37 (12.62)	32 (10.85)
Score	-0.02±0.16	0.04±0.17	0.222

图3 关键基因评分验证Note:A/B. Violin plot of the score for prognosis in the training (A) and validation (B) set, respectively. C?E. Kaplan-Meier analysis shows that patients with high score have significantly poorer prognosis in the training set (C, P=0.002), validation set (D, P=0.017) and total TCGA set (E, P=0.000), respectively.

Fig 3 Hub gene score validation

表2 训练集中评分和临床候选预测变量的单因素Logistic回归分析

Tab 2 Univariate Logistic regression analysis of scores and clinical candidate predictors in training set

Variable	OR(95%CI)	P value
Age	1.01 (0.99?1.03)	0.406
Gender	0.93 (0.54?1.57)	0.779
T stage	2.82 (1.73?4.82)	0.000
N stage	2.15 (1.55?3.00)	0.000
M stage	6.47 (3.09?13.95)	0.000
Score	35.35 (6.27?241.99)	0.000

图4 基于评分和部分临床数据预测复发的列线图Note:A. Nomogram for predicting prognosis. B/C. The ROC curves of the training set (B) and the validation set (C) are shown respectively.

Fig 4 Nomogram for predicting prognosis based on the score and some clinical variables

图5 列线图和关键基因评分的DCANote:The Y-axis is the standardized net benefit, and the X-axis is the ratio of high-risk threshold probability and risk benefit. The net benefit is the proportion of patients who were true positive minus the proportion of patients who were false positive and then weighted according to the relative harm of continuing treatment versus the adverse consequences of unnecessary treatment. The figure shows a prognostic model of hub gene scores with (blue line) and without (red line) clinical variables, the solid gray line represents progression in all patients and the solid black line represents progression in none.

Fig 5 DCA of the nomogram and the hub gene scores

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基于单细胞RNA测序的结直肠癌预后预测模型的建立和验证

Establishment and validation of prognostic prediction model of colorectal cancer based on single-cell RNA sequencing

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