基于癌症基因组图谱计划多组学数据构建胶质母细胞瘤六基因预后模型

doi:10.16288/j.yczz.20-428

遗传 ›› 2021, Vol. 43 ›› Issue (7): 665-679.doi: 10.16288/j.yczz.20-428

基于癌症基因组图谱计划多组学数据构建胶质母细胞瘤六基因预后模型

雷常贵(), 贾学渊(), 孙文靖()

哈尔滨医科大学医学遗传学研究室,中国遗传资源保护与疾病防控教育部重点实验室(哈尔滨医科大学),哈尔滨 150081

收稿日期:2020-12-11 修回日期:2021-03-20 出版日期:2021-07-20 发布日期:2021-04-07
通讯作者: 孙文靖 E-mail:leichanggui@hrbmu.edu.cn;jiaxueyuan@hrbmu.edu.cn;sunwj@ems.hrbmu.edu.cn
作者简介:雷常贵,在读硕士研究生,专业方向：医学遗传学。E-mail: leichanggui@hrbmu.edu.cn|贾学渊,博士,副教授,研究方向：医学遗传学。E-mail: jiaxueyuan@hrbmu.edu.cn
基金资助:
教育部“创新团队发展计划”项目资助编号(IRT1230)

Establish six-gene prognostic model for glioblastoma based on multi-omics data of TCGA database

Changgui Lei(), Xueyuan Jia(), Wenjing Sun()

Laboratory of Medical Genetics, Harbin Medical University, Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin 150081, China

Received:2020-12-11 Revised:2021-03-20 Online:2021-07-20 Published:2021-04-07
Contact: Sun Wenjing E-mail:leichanggui@hrbmu.edu.cn;jiaxueyuan@hrbmu.edu.cn;sunwj@ems.hrbmu.edu.cn
Supported by:
Supported by Program for Changjiang Scholars and Innovative Research Team in University No(IRT1230)

摘要/Abstract

摘要：

胶质母细胞瘤(glioblastoma, GBM)是最常见的原发性颅内肿瘤,恶性程度极高,患者预后极差。为了识别GBM预后生物标记物,建立预后模型,本研究通过分析癌症基因组图谱计划(The Cancer Genome Atlas, TCGA)数据库中GBM的表达谱数据,筛选出不同生存期GBM患者差异基因。利用GISTIC软件和Kaplan-Meier (KM)生存分析方法分析TCGA数据库中的GBM拷贝数变异数据,识别影响生存的扩增基因(survival-associated amplified gene, SAG)。取短生存期组上调基因和SAG两者的交集基因,进行单因素Cox回归和迭代Lasso回归筛选重要候选基因并建立预后模型;计算预后评分,根据预后评分中位数将患者分为高风险组和低风险组。用ROC曲线判断模型的优良,KM生存分析高低风险组预后差异,并用GEO、CGGA和Rembrandt数据库3个外部数据集进行验证。多因素Cox回归分析判断预后评分的预后独立性。结果显示,GBM不同生存期差异分析得到上调基因426个,下调基因65个。短生存期组上调基因与SAG交集得到47个基因。经过筛选,最终确定六基因(EN2、PPBP、LRRC61、SEL1L3、CPA4、DDIT4L)预后模型。TCGA实验组和3个外部验证组模型的ROC曲线下面积均大于0.6,甚至达到0.912。KM分析显示高低风险组的预后都存在差异(P<0.05)。在多因素Cox回归分析中,六基因预后评分是GBM患者预后的独立影响因素(P<0.05)。通过一系列分析,本研究确立了六基因(EN2、PPBP、LRRC61、SEL1L3、CPA4、DDIT4L)的GBM预后模型,模型具有很好的预测能力,可作为预测GBM患者的独立预后标志物。

关键词: 胶质母细胞瘤, 多组学数据, 六基因组合, 预后模型, 癌症基因组图谱计划

Abstract:

Glioblastoma (GBM) is the most common primary intracranial tumor with extremely high malignancy and poor prognosis. In order to identify the GBM prognostic biomarkers and establish a prognostic model, we analyzed the expression profile data of GBM in The Cancer Genome Atlas (TCGA) database as the experimental group. First, we identified the differentially expressed genes of different survival periods among the GBM patients. The GISTIC software and Kaplan Meier (KM) survival curve were used to analyze the copy number variation of GBM to identify the survival-associated amplified gene (SAG). We selected the intersection genes of up-regulated ones in short survival group and SAG, performed univariate Cox regression and iterative Lasso regression with them to identify the important candidate genes and establish a prognostic model. Based on the model, the prognostic score was calculated. The patients were divided into high-risk and low-risk groups according to the median prognostic score. Meanwhile ROC curve was used to evaluate the validity of the model, applying the KM survival analysis of the high-risk and low-risk groups. Multivariate Cox regression analysis was used to determine the independence of the prognostic score. All the data were verified with three external datasets: GEO GSE16011, CGGA, and Rembrandt. The results showed that differential expression analysis of different survival periods of GBM identified 426 up-regulated genes and 65 down-regulated genes in the TCGA GBM dataset. The intersection of up-regulated genes in short survival group and SAG yielded 47 genes. After the screening, the six-gene combination (EN2,PPBP,LRRC61,SEL1L3,CPA4,DDIT4L) prognostic model was finally determined. The area under ROC curve of the model in TCGA experimental group and three external validation group were all greater than 0.6, even reaching 0.912. KM analysis showed that the prognosis of the high-risk and low-risk groups was significant different (P<0.05). In the multivariate Cox regression analysis, the six-gene prognostic score was an independent factor influencing the prognosis of GBM patients (P<0.05). In summary, this study established a prognostic model of six-gene (EN2,PPBP,LRRC61,SEL1L3,CPA4,DDIT4L) for GBM. This six-gene model has good predictive ability and could be used as an independent prognostic marker for GBM patients.

Key words: glioblastoma, multi-omics data, six-gene combination, prognostic model, The Cancer Genome Atlas

雷常贵, 贾学渊, 孙文靖. 基于癌症基因组图谱计划多组学数据构建胶质母细胞瘤六基因预后模型[J]. 遗传, 2021, 43(7): 665-679.

Changgui Lei, Xueyuan Jia, Wenjing Sun. Establish six-gene prognostic model for glioblastoma based on multi-omics data of TCGA database[J]. Hereditas(Beijing), 2021, 43(7): 665-679.

图/表 21

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图3

Table 1

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图5

表2

图6

图7

附图1

附图2

附图3

附图4

附表1

续附表1

基因	Log₂(fold change)	P值	Diff type	基因	Log₂(fold change)	P值	Diff type
HOXC6	2.15	0.000187	UP	AQP5	1.99	0.000759	UP
MMP7	3.17	0.00021	UP	TFAP2C	2.58	0.00081	UP
PTX3	2.37	0.000219	UP	CCL20	2.77	0.000822	UP
RARRES1	2.23	0.000223	UP	HOXC13	2.97	0.000842	UP
COL1A1	2.72	0.000228	UP	CCL18	3.11	0.000873	UP
SPNS3	-1.26	0.000231	DOWN	MICAL2	1.29	0.000933	UP
AREG	2.93	0.000265	UP	FBLN1	1.80	0.000938	UP
PLA2G2A	3.15	0.000294	UP	CA9	2.12	0.00101	UP
TREM1	2.07	0.000299	UP	LIN7A	1.16	0.00102	UP
ULBP1	2.35	0.000299	UP	SFRP5	2.24	0.00104	UP
LRRC61	1.85	0.000317	UP	LOXL1	1.68	0.00106	UP
FLJ16779	-2.32	0.000327	DOWN	VWC2L	1.99	0.0011	UP
STC1	1.74	0.000332	UP	NKX3-1	1.44	0.00111	UP
CHODL	2.77	0.000334	UP	C4orf48	-1.17	0.00111	DOWN
GADD45G	-1.44	0.000349	DOWN	PPBP	2.44	0.00116	UP
DLX5	2.96	0.000365	UP	FCN3	2.02	0.00117	UP
HOXB9	2.69	0.000366	UP	RHOD	2.25	0.00118	UP
NNAT	2.88	0.000373	UP	ISLR2	2.03	0.00118	UP
CCL2	1.79	0.000374	UP	ZNF560	-2.88	0.00122	DOWN
EN2	1.84	0.000382	UP	COL3A1	2.22	0.00123	UP
BIRC3	1.80	0.000397	UP	EMP2	1.32	0.00126	UP
HOXC8	2.31	0.000398	UP	DCTN3	-1.01	0.00131	DOWN
PTPRN	2.04	0.000437	UP	DCT	-1.77	0.00131	DOWN
MAB21L2	3.93	0.00044	UP	EEF1A2	1.48	0.00134	UP
OLFM1	1.58	0.000456	UP	TWIST2	2.26	0.00134	UP
EBF3	2.67	0.000462	UP	CD70	2.56	0.00135	UP
CXCL13	3.44	0.000463	UP	SLC18A3	3.56	0.00136	UP
COL6A3	2.73	0.000469	UP	IL1R1	1.79	0.00137	UP
GATA3	2.50	0.000474	UP	NOG	-1.33	0.00138	DOWN
ULBP2	1.30	0.000511	UP	FAM81B	2.98	0.00143	UP
ZFR2	2.16	0.000527	UP	IL6	2.02	0.00147	UP
SHISA6	2.23	0.000584	UP	TSHR	-2.53	0.00148	DOWN
ANK1	1.49	0.000589	UP	NPR3	1.94	0.00148	UP
ALDH1A3	2.59	0.000651	UP	PLIN2	1.30	0.00149	UP
EREG	3.09	0.000683	UP	CYP2E1	-1.39	0.00151	DOWN
IGFBP6	2.15	0.000683	UP	CHI3L2	1.82	0.00151	UP
SOCS3	1.48	0.000688	UP	BMP2	-1.29	0.00153	DOWN
NFKBIZ	1.56	0.000712	UP	ITGA5	1.09	0.00156	UP
ADAMTS2	1.83	0.000717	UP	FAM20A	1.66	0.00159	UP
SFRP2	2.51	0.000734	UP	SEMA3F	1.04	0.00161	UP

续附表1

续附表1

基因	Log₂(fold change)	P值	Diff type	基因	Log₂(fold change)	P值	Diff type
IBSP	2.26	0.00162	UP	CTHRC1	2.00	0.00263	UP
MRC1	2.17	0.00165	UP	CD300E	2.17	0.00265	UP
BARX1	2.56	0.00166	UP	MXRA5	1.69	0.0027	UP
VDR	1.81	0.00168	UP	GJB2	1.87	0.00274	UP
CNPY1	2.14	0.00168	UP	KCTD4	-2.07	0.00277	DOWN
TCTEX1D1	1.83	0.00183	UP	RGS4	1.73	0.00282	UP
P4HA2	1.10	0.00184	UP	EMILIN2	1.15	0.00285	UP
SDCBP2	-1.01	0.00187	DOWN	CPAMD8	1.70	0.00292	UP
LOX	1.64	0.00188	UP	PLD4	-1.28	0.00297	DOWN
FSTL3	1.06	0.00192	UP	TPBG	1.95	0.00301	UP
COCH	1.88	0.00194	UP	ADM2	1.81	0.00305	UP
MYBPHL	-1.32	0.002	DOWN	TSPAN31	1.86	0.00308	UP
C9orf24	2.01	0.00201	UP	S100A3	-1.32	0.00313	DOWN
SPAG17	2.08	0.00204	UP	GRIN2A	1.87	0.00314	UP
TMEM100	-1.45	0.00207	DOWN	ADAMTS14	1.49	0.00317	UP
COL12A1	1.94	0.0021	UP	SVEP1	1.47	0.00323	UP
RASL10A	-1.24	0.00211	DOWN	CXCL6	2.91	0.00334	UP
FAM110C	1.61	0.00211	UP	COL10A1	2.47	0.00337	UP
MOCOS	1.66	0.00214	UP	RCAN2	1.60	0.00339	UP
PDE4C	1.65	0.00217	UP	PLAUR	1.12	0.00343	UP
CSDC2	-1.59	0.0022	DOWN	SLC2A3	1.19	0.00347	UP
KLK14	-1.00	0.0022	DOWN	ANGPTL4	1.53	0.00349	UP
SLC6A6	1.06	0.0022	UP	SH2D5	1.37	0.00359	UP
LANCL2	2.00	0.00225	UP	LTBP2	1.30	0.00361	UP
CYP1B1	2.01	0.00225	UP	FLNC	1.34	0.00367	UP
BMP5	3.07	0.00225	UP	C5orf49	1.75	0.00371	UP
FCGR2B	1.67	0.00225	UP	HS3ST3A1	1.89	0.00376	UP
BTC	2.54	0.00227	UP	PTGES	1.69	0.00378	UP
CR1	2.31	0.00229	UP	MMP9	1.97	0.00386	UP
PCDHB4	1.62	0.00232	UP	MYO1G	1.24	0.00388	UP
COL13A1	1.82	0.00233	UP	CDH15	-1.92	0.00393	DOWN
TMEM233	1.74	0.00234	UP	BICC1	1.40	0.00396	UP
WIF1	2.79	0.00237	UP	FBLN2	1.75	0.00397	UP
PRRG3	2.14	0.00242	UP	FGF17	-1.86	0.00402	DOWN
SLC16A10	1.70	0.00244	UP	COL6A2	1.75	0.00409	UP
IL7R	1.74	0.00247	UP	IGF2	1.79	0.00412	UP
STMN2	2.26	0.0025	UP	DKK1	2.74	0.00415	UP
DLK1	2.99	0.00256	UP	MMEL1	-1.32	0.00415	DOWN
S100A8	1.81	0.00256	UP	CHST9	-1.54	0.00419	DOWN
THBS1	1.87	0.00261	UP	TNFSF18	-1.15	0.00421	DOWN

续附表1

续附表1

基因	Log₂(fold change)	P值	Diff type	基因	Log₂(fold change)	P值	Diff type
LGR6	2.22	0.00428	UP	THBS4	1.54	0.00678	UP
COL1A2	1.69	0.00438	UP	CAMK4	1.10	0.00686	UP
ELF3	1.53	0.00439	UP	RIT2	1.99	0.00689	UP
RANBP17	-1.56	0.00443	DOWN	TPPP3	1.41	0.00709	UP
NRXN3	1.33	0.00443	UP	BDKRB2	1.46	0.00714	UP
ACY3	-1.53	0.00446	DOWN	PCDHGB3	1.78	0.00719	UP
RPRM	-1.76	0.00452	DOWN	PDLIM1	1.00	0.00732	UP
IL1R2	1.86	0.00462	UP	LILRA5	1.71	0.00739	UP
MXRA8	1.11	0.00466	UP	CFH	1.29	0.00744	UP
GPX3	1.38	0.00477	UP	CDK4	1.85	0.00746	UP
RDH10	1.19	0.00478	UP	LAMB1	1.23	0.00774	UP
SLAMF8	1.36	0.00485	UP	XKR7	2.11	0.00783	UP
RPH3A	2.01	0.00493	UP	FERMT1	-1.61	0.00784	DOWN
ROR2	1.94	0.00497	UP	MDM2	1.77	0.00793	UP
TNFSF14	1.74	0.005	UP	DHRS9	-1.62	0.00816	DOWN
ACTG2	1.32	0.00515	UP	HSPG2	1.10	0.00818	UP
CAPN6	2.88	0.00517	UP	SLIT2	1.54	0.0082	UP
F13A1	2.19	0.0052	UP	DCHS2	1.32	0.00824	UP
GALNT9	1.80	0.00527	UP	CA14	-1.20	0.00847	DOWN
MAP3K9	1.19	0.00537	UP	PRAME	1.89	0.00857	UP
CLEC9A	-1.53	0.00543	DOWN	PDZK1IP1	1.53	0.00866	UP
BHMT2	1.63	0.00549	UP	MYL9	1.09	0.0087	UP
MYBPH	1.87	0.00552	UP	TDO2	1.57	0.00879	UP
EGFLAM	1.38	0.00555	UP	CHRDL2	1.69	0.00881	UP
VGF	1.88	0.00566	UP	TRNP1	1.12	0.00888	UP
VASN	1.05	0.00571	UP	CLEC3B	-1.27	0.00892	DOWN
ICAM1	1.23	0.00574	UP	MMP13	2.96	0.00894	UP
FN1	1.04	0.00579	UP	BNC2	1.47	0.00911	UP
CD163	1.47	0.00588	UP	SEZ6	1.67	0.00922	UP
MAPK15	2.02	0.0059	UP	GPC3	1.63	0.00923	UP
TYW1B	1.00	0.00602	UP	SYT13	1.85	0.00931	UP
CD209	2.09	0.00609	UP	FAM183A	1.92	0.00939	UP
RIMBP2	1.55	0.00617	UP	TBR1	1.69	0.00942	UP
IGF2BP1	1.82	0.00621	UP	IL2RA	2.07	0.00944	UP
DAPL1	-2.34	0.00622	DOWN	RNF128	1.88	0.00945	UP
GPR68	1.30	0.00635	UP	HTRA3	1.14	0.00947	UP
HECW1	1.63	0.00651	UP	SLC4A10	1.21	0.00994	UP
NEFL	2.14	0.0066	UP	DCDC2	1.25	0.00996	UP
GABRA4	1.99	0.00667	UP	EN1	1.54	0.01	UP
BCL11B	1.26	0.00672	UP	CX3CR1	-1.21	0.01	DOWN

续附表1

续附表1

基因	Log₂(fold change)	P值	Diff type	基因	Log₂(fold change)	P值	Diff type
CXCL1	1.67	0.0101	UP	C1orf158	2.20	0.0141	UP
CCDC8	1.27	0.0101	UP	GALNT14	-1.10	0.0142	DOWN
GOLT1A	1.85	0.0101	UP	HAS1	1.47	0.0142	UP
PPEF1	1.16	0.0104	UP	CDCP1	1.09	0.0146	UP
TNNT1	1.52	0.0105	UP	MMP11	1.08	0.0147	UP
HK3	1.30	0.0105	UP	STAT4	1.18	0.0149	UP
VSTM2A	1.71	0.0106	UP	EXPH5	-1.17	0.0152	DOWN
C1QL2	1.89	0.0107	UP	FPR2	1.72	0.0153	UP
TMEM26	1.07	0.0107	UP	ME1	1.32	0.0154	UP
PLA2R1	1.35	0.0107	UP	ADAM8	1.25	0.0155	UP
METTL1	1.36	0.0107	UP	FNDC1	1.76	0.0155	UP
PTPRU	1.27	0.011	UP	TNFAIP2	1.13	0.016	UP
PCSK1	1.42	0.011	UP	SP6	1.09	0.016	UP
ANPEP	1.40	0.0111	UP	CD163L1	1.22	0.0162	UP
DPT	2.34	0.0113	UP	HS3ST2	1.55	0.0163	UP
PTGS2	1.48	0.0113	UP	FAP	1.38	0.0164	UP
DOK2	1.29	0.0114	UP	CHGA	1.83	0.0166	UP
EFCAB1	2.01	0.0117	UP	FOSL1	1.16	0.0168	UP
GSTM1	2.32	0.0118	UP	ADAMTS15	-1.10	0.0169	DOWN
CABP1	1.34	0.012	UP	PXDN	1.11	0.017	UP
FAM83G	1.26	0.012	UP	SOX3	-1.54	0.0171	DOWN
TIMP1	1.15	0.0121	UP	ESM1	1.29	0.0171	UP
MMP19	1.56	0.0122	UP	GSX2	1.70	0.0173	UP
LYVE1	1.99	0.0123	UP	FBLIM1	1.04	0.0175	UP
PNMT	1.73	0.0124	UP	SYN1	1.38	0.0175	UP
TMPRSS9	-1.27	0.0124	DOWN	TNF	-1.23	0.0176	DOWN
CPLX2	1.82	0.0124	UP	GPC5	1.35	0.0176	UP
AGAP2	1.69	0.0125	UP	ADCYAP1R1	-1.02	0.0177	DOWN
RCN3	1.13	0.0125	UP	TFPI2	1.66	0.0178	UP
MLPH	1.41	0.0125	UP	MYT1L	1.69	0.0178	UP
ATP13A5	-1.41	0.0125	DOWN	C7	1.69	0.018	UP
HYDIN	1.43	0.0127	UP	LRRC15	1.79	0.0182	UP
CPS1	-1.15	0.0128	DOWN	MMP24	1.04	0.0182	UP
MSTN	-1.72	0.013	DOWN	OS9	1.07	0.0183	UP
SOD3	1.05	0.0131	UP	G0S2	1.11	0.0183	UP
P2RY12	-1.25	0.0132	DOWN	DKK2	1.49	0.0186	UP
C8orf34	-1.48	0.0133	DOWN	CSF3	2.28	0.0187	UP
FOXJ1	1.46	0.0135	UP	ZNF488	-1.77	0.0187	DOWN
C1QL1	-1.24	0.0135	DOWN	ELAVL2	1.56	0.0189	UP
NTSR1	1.76	0.0137	UP	MT1H	1.43	0.0189	UP

续附表1

续附表1

基因	Log₂(fold change)	P值	Diff type	基因	Log₂(fold change)	P值	Diff type
TSFM	1.30	0.0191	UP	SLC17A6	1.98	0.0283	UP
ST6GALNAC1	-1.12	0.0194	DOWN	CTSK	1.03	0.0283	UP
SNX31	-1.47	0.0195	DOWN	GLRA2	1.54	0.029	UP
CENPV	1.21	0.0197	UP	IL11	1.24	0.0291	UP
CHIT1	1.49	0.0198	UP	NPPA	1.71	0.0295	UP
CLIC6	1.74	0.0199	UP	CHD5	1.37	0.0303	UP
PCDHGB2	1.47	0.0208	UP	TMEM130	1.28	0.0304	UP
SNCB	1.50	0.021	UP	GABRD	1.12	0.0308	UP
ALPK2	1.50	0.0213	UP	LUZP2	-1.53	0.0309	DOWN
NELL1	1.69	0.0214	UP	PCDHB11	1.40	0.031	UP
ENHO	-1.06	0.0215	DOWN	ATP8A2	1.43	0.031	UP
TMIGD2	-1.40	0.0216	DOWN	ITK	1.17	0.0313	UP
LRRC36	-1.05	0.0219	DOWN	NOS1	1.49	0.0314	UP
SECTM1	1.05	0.0221	UP	NEU4	-1.24	0.0314	DOWN
TAC3	1.51	0.0221	UP	MME	1.43	0.0315	UP
CFTR	-1.16	0.0222	DOWN	LOXL2	1.04	0.0316	UP
HSPA7	1.14	0.0222	UP	SDK1	1.09	0.0318	UP
RSPO2	1.59	0.0224	UP	PTPRR	1.30	0.0319	UP
EMILIN1	1.01	0.0225	UP	UBE2QL1	1.15	0.0319	UP
SH2D2A	1.13	0.0229	UP	GLRA3	2.04	0.032	UP
SULT4A1	1.61	0.0231	UP	TSHZ2	1.15	0.0326	UP
DNAH2	1.24	0.0231	UP	SERPINA5	1.10	0.0327	UP
KRT17	1.41	0.0234	UP	ARMC3	2.01	0.0328	UP
SLC1A6	1.91	0.0234	UP	GLT1D1	1.20	0.0328	UP
PCDHB12	1.06	0.0238	UP	OLFML1	1.07	0.033	UP
SPON2	1.23	0.024	UP	CPNE7	1.32	0.0332	UP
TMEM200B	1.21	0.0245	UP	HPD	1.23	0.0333	UP
CLEC2B	1.12	0.0247	UP	WDR38	1.50	0.0335	UP
ENTPD3	1.28	0.0254	UP	DDIT4L	1.27	0.0339	UP
NPTX2	1.49	0.0259	UP	MSX2	1.31	0.034	UP
CCR2	1.45	0.0259	UP	CPNE8	1.09	0.034	UP
CNTN4	1.27	0.0267	UP	HOTAIR	1.77	0.034	UP
NGFR	1.10	0.0267	UP	NDST3	1.79	0.0341	UP
LRRIQ1	1.53	0.0268	UP	NPTX1	1.37	0.0341	UP
CPA4	1.24	0.0268	UP	SLC6A15	1.32	0.0345	UP
NEFM	1.80	0.0268	UP	ADAMDEC1	1.40	0.0349	UP
AK7	1.36	0.0272	UP	VSNL1	1.49	0.035	UP
RGS7	1.12	0.0276	UP	FABP5	1.08	0.035	UP
MMP12	2.35	0.0279	UP	LUM	1.39	0.035	UP
PCDHGA7	1.22	0.028	UP	HTR1D	1.38	0.0351	UP

续附表1

续附表2

参考文献 34

[1]	Schwartzbaum JA, Fisher JL, Aldape KD, Wrensch M. Epidemiology and molecular pathology of glioma. Nat Clin Pract Neurol, 2006, 2(9):494-503. pmid: 16932614
[2]	Su CL, Li L, Chen XW, Zhang J, Shen NX, Wang ZX, Yang SQ, Li J, Zhu WZ, Wang CY. Summary of classification of central nervous system tumors in WHO 2016. Radiol Prac, 2016, 31(7):570-579.
	苏昌亮, 李丽, 陈小伟, 张巨, 申楠茜, 王振熊, 杨时骐, 李娟, 朱文珍, 王承缘. 2016年WHO中枢神经系统肿瘤分类总结. 放射学实践, 2016, 31(7):570-579.
[3]	Parsons DW, Jones S, Zhang XS, Lin JCH, Leary RJ, Angenendt P, Mankoo P, Carter H, Siu IM, Gallia GL, Olivi A, McLendon R, Rasheed BA, Keir S, Nikolskaya T, Nikolsky Y, Busam DA, Tekleab H, Diaz LA, Hartigan J, Smith DR, Strausberg RL, Marie SKN, Shinjo SMO, Yan H, Riggins GJ, Bigner DD, Karchin R, Papadopoulos N, Parmigiani G, Vogelstein B, Velculescu VE, Kinzler KW. An integrated genomic analysis of human glioblastoma multiforme. Science, 2008, 321(5897):1807-1812. doi: 10.1126/science.1164382
[4]	Krex D, Klink B, Hartmann C, von Deimling A, Pietsch T, Simon M, Sabel M, Steinbach JP, Heese O, Reifenberger G, Weller M, Schackert G, German Glioma Network. Long-term survival with glioblastoma multiforme. Brain, 2007, 130:2596-2606. doi: 10.1093/brain/awm204
[5]	Gerber NK, Goenka A, Turcan S, Reyngold M, Makarov V, Kannan K, Beal K, Omuro A, Yamada Y, Gutin P, Brennan CW, Huse JT, Chan TA. Transcriptional diversity of long-term glioblastoma survivors. Neuro Oncol, 2014, 16(9):1186-1195. doi: 10.1093/neuonc/nou043
[6]	Asiedu MK, Thomas CF, Dong J, Schulte SC, Khadka P, Sun ZF, Kosari F, Jen J, Molina J, Vasmatzis G, Kuang R, Aubry MC, Yang P, Wigle DA. Pathways impacted by genomic alterations in pulmonary carcinoid tumors. Clin Cancer Res, 2018, 24(7):1691-1704. doi: 10.1158/1078-0432.CCR-17-0252
[7]	Chang J, Tan WL, Ling ZQ, Xi RB, Shao MM, Chen MJ, Luo YY, Zhao YJ, Liu Y, Huang XC, Xia YC, Hu JL, Parker JS, Marron D, Cui QH, Peng LN, Chu JH, Li HM, Du ZL, Han YL, Tan W, Liu ZH, Zhan QM, Li Y, Mao WM, Wu C, Lin DX. Genomic analysis of oesophageal squamous-cell carcinoma identifies alcohol drinking- related mutation signature and genomic alterations. Nat Commun, 2017, 8:15290. doi: 10.1038/ncomms15290
[8]	Liang L, Fang JY, Xu J. Gastric cancer and gene copy number variation: emerging cancer drivers for targeted therapy. Oncogene, 2016, 35(12):1475-1482. doi: 10.1038/onc.2015.209 pmid: 26073079
[9]	Wang Z, Wang LX. Gene amplification in lung cancer. Int J Genet, 2010, 33(3):155-159.
	王箴, 王良旭. 基因扩增在肺癌中的研究进展. 国际遗传学杂志, 2010, 33(3):155-159.
[10]	Ohgaki H, Kleihues P. Genetic alterations and signaling pathways in the evolution of gliomas. Cancer Sci, 2009, 100(12):2235-2241. doi: 10.1111/cas.2009.100.issue-12
[11]	Jin WX, Yang TM, Dong XY, Hua ZC, Xu XX. P53 mutation in human brain gliomas: Comparison of loss of heterozygosity with mutation frequency. Hereditas (Beijing), 2000, 22(6):357-360.
	金卫新, 杨天明, 董雪吟, 华子春, 徐贤秀. 脑胶质瘤中P53基因突变及其与染色体17p杂合性丢失的比较. 遗传, 2000, 22(6):357-360.
[12]	Wang YL, Zhao WJ, Xiao Z, Guan GF, Liu X, Zhuang MH. A risk signature with four autophagy-related genes for predicting survival of glioblastoma multiforme. J Cell Mol Med, 2020, 24(7):3807-3821. doi: 10.1111/jcmm.v24.7
[13]	Lin WZ, Wu SH, Chen XC, Ye YL, Weng YL, Pan YH, Chen ZJ, Chen L, Qiu XX, Qiu SF. Characterization of hypoxia signature to evaluate the tumor immune microenvironment and predict prognosis in glioma groups. Front Oncol, 2020, 10:796. doi: 10.3389/fonc.2020.00796
[14]	Wang JJ, Wang H, Zhu BL, Wang X, Qian YH, Xie L, Wang WJ, Zhu J, Chen XY, Wang JM, Ding ZL. Development of a prognostic model of glioma based on immune-related genes. Oncol Lett, 2021, 21(2):116. doi: 10.3892/ol
[15]	Hermansen SK, Sørensen MD, Hansen A, Knudsen S, Alvarado AG, Lathia JD, Kristensen BW. A 4-miRNA signature to predict survival in glioblastomas. PLoS One, 2017, 12(11):e0188090. doi: 10.1371/journal.pone.0188090
[16]	Jia DY, Lin W, Tang HL, Cheng YF, Xu KW, He YS, Geng WJ, Dai QX. Integrative analysis of DNA methylation and gene expression to identify key epigenetic genes in glioblastoma. Aging (Albany NY), 2019, 11(15):5579-5592.
[17]	Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, Sabedot TS, Malta TM, Pagnotta SM, Castiglioni I, Ceccarelli M, Bontempi G, Noushmehr H. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res, 2016, 44(8):e71. doi: 10.1093/nar/gkv1507
[18]	Li X, Li MW, Zhang YN, Xu HM. Common cancer genetic analysis methods and application study based on TCGA database. Hereditas(Beijing), 2019, 41(3):234-242.
	李鑫, 李梦玮, 张依楠, 徐寒梅. 常用肿瘤基因分析方法及基于TCGA数据库的分析应用. 遗传, 2019, 41(3):234-242.
[19]	Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol, 2014, 15(12):550. doi: 10.1186/s13059-014-0550-8
[20]	Beroukhim R, Getz G, Nghiemphu L, Barretina J, Hsueh T, Linhart D, Vivanco I, Lee JC, Huang JH, Alexander S, Du JY, Kau T, Thomas RK, Shah K, Soto H, Perner S, Prensner J, Debiasi RM, Demichelis F, Hatton C, Rubin MA, Garraway LA, Nelson SF, Liau L, Mischel PS, Cloughesy TF, Meyerson M, Golub TA, Lander ES, Mellinghoff I, Sellers WR. Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proc Natl Acad Sci USA, 2007, 104(50):20007-20012. doi: 10.1073/pnas.0710052104
[21]	Stalpers LJA, Kaplan EL. Edward L. Kaplan and the Kaplan-Meier survival curve. BSHM Bulletin, 2018, 33(2):109-135. doi: 10.1080/17498430.2018.1450055
[22]	Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw, 2010, 33(1):1-22. pmid: 20808728
[23]	Sveen A, Ågesen TH, Nesbakken A, Meling GI, Rognum TO, Liestøl K, Skotheim RI, Lothe RA. ColoGuidePro: a prognostic 7-gene expression signature for stage III colorectal cancer patients. Clin Cancer Res, 2012, 18(21):6001-6010. doi: 10.1158/1078-0432.CCR-11-3302
[24]	Blanche P, Dartigues JF, Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med, 2013, 32(30):5381-5397. doi: 10.1002/sim.5958 pmid: 24027076
[25]	Yu GC, Wang LG, Han YY, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS, 2012, 16(5):284-287. doi: 10.1089/omi.2011.0118
[26]	Korotkevich G, Sukhov V, Budin N, Shpak B, Artyomov M, Sergushichev A. Fast gene set enrichment analysis. bioRxiv, 2019: 060012.
[27]	Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 2008, 9:559. doi: 10.1186/1471-2105-9-559 pmid: 19114008
[28]	Zeng Y, Xiao HL, Guo QN. Research progress on the mechanism of epithelial mesenchymal transition in glioblastoma. Chin J Diagn Pathol, 2019, 26(8):533-538.
	曾英, 肖华亮, 郭乔楠. 胶质母细胞瘤上皮间叶转化机制研究进展. 诊断病理学杂志, 2019, 26(8):533-538.
[29]	Zeng S, Zhou C, Yang DH, Xu LS, Yang HJ, Xu MH, Wang H. LEF1-AS1 is implicated in the malignant development of glioblastoma via sponging miR-543 to upregulate EN2. Brain Res, 2020, 1736:146781. doi: S0006-8993(20)30137-2 pmid: 32184164
[30]	Achyut BR, Shankar A, Iskander ASM, Ara R, Angara K, Zeng P, Knight RA, Scicli AG, Arbab AS. Bone marrow derived myeloid cells orchestrate antiangiogenic resistance in glioblastoma through coordinated molecular networks. Cancer Lett, 2015, 369(2):416-426. doi: 10.1016/j.canlet.2015.09.004 pmid: 26404753
[31]	Du Q, Li EC, Liu YE, Xie WL, Huang C, Song JQ, Zhang W, Zheng YJ, Wang HL, Wang Q. CTAPIII/CXCL7: a novel biomarker for early diagnosis of lung cancer. Cancer Med, 2018, 7(2):325-335. doi: 10.1002/cam4.2018.7.issue-2
[32]	Ross PL, Cheng I, Liu X, Cicek MS, Carroll PR, Casey G, Witte JS. Carboxypeptidase 4 gene variants and early-onset intermediate-to-high risk prostate cancer. BMC Cancer, 2009, 9:69. doi: 10.1186/1471-2407-9-69
[33]	Pan HD, Pan JX, Ji L, Song SB, Lv H, Yang ZR, Guo YB. Carboxypeptidase A4 promotes cell growth via activating STAT3 and ERK signaling pathways and predicts a poor prognosis in colorectal cancer. Int J Biol Macromol, 2019, 138:125-134. doi: 10.1016/j.ijbiomac.2019.07.028
[34]	Simonson B, Subramanya V, Chan MC, Zhang AF, Franchino H, Ottaviano F, Mishra MK, Knight AC, Hunt D, Ghiran I, Khurana TS, Kontaridis MI, Rosenzweig A, Das S. DDiT4L promotes autophagy and inhibits pathological cardiac hypertrophy in response to stress. Sci Signal , 2017, 10(468): eaaf5967.

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基因	HR	95%CI	P值	Log₂(fold change)
COL1A2	1.209	0.972~1.502	0.088	1.686
CPA4	1.268	1.018~1.579	0.034	1.238
CXCL5	1.343	1.070~1.686	0.011	3.125
CXCL6	1.498	1.006~2.231	0.047	2.908
DDIT4L	1.416	1.137~1.764	0.002	1.274
EN2	1.716	1.290~2.282	0	1.840
FLNC	1.282	1.062~1.546	0.010	1.339
LAMB1	1.227	0.981~1.534	0.073	1.232
LRRC61	1.432	1.168~1.756	0.001	1.852
MXRA8	1.298	1.047~1.608	0.017	1.113
MYO1G	1.236	0.995~1.534	0.055	1.242
NPTX2	1.174	0.976~1.412	0.088	1.492
PDLIM1	1.246	1.002~1.548	0.048	1.003
PPBP	1.284	0.968~1.702	0.083	2.439
SDK1	1.178	0.971~1.428	0.096	1.088
SEL1L3	1.317	1.098~1.580	0.003	1.560
SOD3	1.234	1.021~1.490	0.029	1.049
VGF	1.322	1.074~1.627	0.008	1.884

数据集	变量	HR	95%CI	P值
TCGA GBM实验组	低风险组	1(ref)	1(ref)
	高风险组	2.39	1.582~3.617	0.0004
	年龄	1.02	1.005~1.04	0.01056
	性别(女)	1(ref)	1(ref)
	性别(男)	0.83	0.557~1.256	0.38845
	IDH野生型	1(ref)	1(ref)
	IDH突变型	0.69	0.202~2.357	0.55348
GEO GSE16011验证组	低风险组	1(ref)	1(ref)
	高风险组	1.46	1.012~2.125	0.04285
	年龄	1.03	1.02~1.052	0.0001
CGGA验证组	低风险组	1(ref)	1(ref)
	高风险组	1.92	1.189~3.102	0.00766
	年龄	1.01	0.989~1.028	0.38646
Rembrandt验证组	低风险组	1(ref)	1(ref)
	高风险组	1.76	1.18~2.61	0.005
	性别(女)	1(ref)	1(ref)
	性别(男)	1.0058	0.68~1.49	0.977

基因	Log₂(fold change)	P值	Diff type	基因	Log₂(fold change)	P值	Diff type
PCDHGB4	-4.56	2.46e-21	DOWN	CXCL14	2.53	6.87e-05	UP
SAA1	4.24	1.99e-06	UP	MCTP2	3.03	6.89e-05	UP
SAA2	4.36	2.32e-06	UP	COL5A1	2.39	7.29e-05	UP
PI3	4.49	3.95e-06	UP	CXCL5	3.13	7.71e-05	UP
MARCO	3.84	7.41e-06	UP	SPAG4	1.75	8.98e-05	UP
RPL39L	2.30	1.07e-05	UP	THBD	1.80	0.000109	UP
OSMR	1.51	3.67e-05	UP	NXPH4	1.57	0.000113	UP
ZIC3	-1.74	4.21e-05	DOWN	RASSF9	2.96	0.000128	UP
HOXB13	4.22	4.43e-05	UP	SEL1L3	1.56	0.000132	UP
KCNK5	2.51	5.12e-05	UP	MMP1	2.97	0.000153	UP
H19	3.70	5.28e-05	UP	AQP9	2.49	0.000165	UP
HOXC9	2.69	5.57e-05	UP	REM1	-2.67	0.000167	DOWN
GPR1	2.80	6.26e-05	UP	TMPRSS3	2.88	0.000185	UP
CYGB	1.86	6.39e-05	UP	CLEC10A	2.93	0.000185	UP

基于癌症基因组图谱计划多组学数据构建胶质母细胞瘤六基因预后模型

Establish six-gene prognostic model for glioblastoma based on multi-omics data of TCGA database

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参考文献 34

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Metrics

基因	Log₂(fold change)	P值	Diff type	基因	Log₂(fold change)	P值	Diff type
GABRA2	1.30	0.0353	UP	NRGN	1.15	0.0406	UP
TCF23	1.58	0.0354	UP	CHRNB2	1.05	0.041	UP
ZBBX	-1.37	0.036	DOWN	CLVS1	1.43	0.0412	UP
CAMK2A	1.37	0.0363	UP	RAB3C	1.35	0.0417	UP
TP63	1.58	0.0364	UP	OR51E1	1.13	0.0418	UP
UBD	1.43	0.0364	UP	SCN3B	1.02	0.0419	UP
GATA4	1.81	0.0365	UP	TFAP2A	1.35	0.042	UP
SYNGR3	1.11	0.0367	UP	DACH1	-1.01	0.0421	DOWN
GNG3	1.32	0.0368	UP	PODNL1	1.23	0.043	UP
CELF4	1.34	0.0372	UP	IGFN1	-1.77	0.043	DOWN
RYR3	1.19	0.0374	UP	STEAP4	1.30	0.0432	UP
BCL2A1	1.05	0.0375	UP	CYP3A5	1.06	0.0437	UP
ECEL1	-1.89	0.0377	DOWN	PCDHGA10	1.53	0.0438	UP
OLFML2B	1.04	0.0378	UP	ISLR	1.04	0.0439	UP
SNAP25	1.40	0.0379	UP	CLUL1	1.24	0.0441	UP
CAMK1G	1.42	0.0379	UP	ST8SIA3	1.44	0.0448	UP
IL18R1	1.17	0.038	UP	SVOP	1.44	0.045	UP
FCN1	1.53	0.0381	UP	IQSEC3	1.17	0.045	UP
SERTAD4	1.27	0.0383	UP	C4BPA	2.52	0.0455	UP
SHANK1	1.09	0.0383	UP	ENPP5	1.27	0.0463	UP
POPDC3	1.21	0.0385	UP	CREG2	1.28	0.0464	UP
SYT1	1.42	0.0386	UP	C2CD4A	1.47	0.0466	UP
SLAMF6	1.08	0.0388	UP	NKAIN4	-1.11	0.0466	DOWN
PCDHGB5	1.15	0.039	UP	SDS	1.02	0.0469	UP
SP8	1.71	0.039	UP	CHGB	1.09	0.0474	UP
GABRB2	1.47	0.0391	UP	PRRX2	1.50	0.0481	UP
IDO1	1.53	0.0394	UP	LIF	1.27	0.0491	UP
WNT16	1.46	0.0397	UP	FOSB	1.54	0.0491	UP
ICAM5	1.11	0.0402	UP	MFAP2	1.04	0.0494	UP
VGLL3	1.16	0.0402	UP	KRT75	1.51	0.0497	UP
PCOLCE2	1.14	0.0404	UP	LYZ	1.08	0.0497	UP
HP	1.46	0.0405	UP

基因	KM_P值	Log₂(fold change)	P值	Diff type	基因	KM_P值	Log₂(fold change)	P值	Diff type
AREG	0.03	2.93	0.000265	UP	LANCL2	0.01	2	0.00225	UP
ARMC3	0.04	2.01	0.0328	UP	LRRC61	0	1.85	0.000317	UP
BTC	0.03	2.54	0.00227	UP	MAB21L2	0.03	3.93	0.00044	UP
CNPY1	0.04	2.14	0.00168	UP	MRC1	0.01	2.17	0.00165	UP
COL1A2	0.02	1.69	0.00438	UP	MXRA8	0.05	1.11	0.00466	UP
CPA4	0.03	1.24	0.0268	UP	MYO1G	0.01	1.24	0.00388	UP
CXCL1	0.02	1.67	0.0101	UP	NDST3	0.01	1.79	0.0341	UP
CXCL13	0.03	3.44	0.000463	UP	NPTX2	0.02	1.49	0.0259	UP
CXCL5	0.02	3.13	7.71e-05	UP	PDLIM1	0.03	1	0.00732	UP
CXCL6	0.02	2.91	0.00334	UP	PPBP	0.02	2.44	0.00116	UP
CYP3A5	0.02	1.06	0.0437	UP	SDK1	0	1.09	0.0318	UP
DCHS2	0.03	1.32	0.00824	UP	SEL1L3	0.05	1.56	0.000132	UP
DDIT4L	0.01	1.27	0.0339	UP	SFRP2	0.03	2.51	0.000734	UP
DLX5	0.03	2.96	0.000365	UP	SLIT2	0	1.54	0.0082	UP
EN2	0.04	1.84	0.000382	UP	SOD3	0.01	1.05	0.0131	UP
EREG	0.03	3.09	0.000683	UP	SPON2	0.03	1.23	0.024	UP
FLNC	0.04	1.34	0.00367	UP	STEAP4	0.01	1.3	0.0432	UP
GABRA2	0	1.3	0.0353	UP	TDO2	0	1.57	0.00879	UP
GABRA4	0	1.99	0.00667	UP	TFPI2	0	1.66	0.0178	UP
GABRD	0	1.12	0.0308	UP	TMEM130	0.02	1.28	0.0304	UP
HECW1	0.01	1.63	0.00651	UP	TYW1B	0	1	0.00602	UP
IL6	0.01	2.02	0.00147	UP	VGF	0.02	1.88	0.00566	UP
IQSEC3	0.01	1.17	0.045	UP	VSTM2A	0	1.71	0.0106	UP
LAMB1	0.04	1.23	0.00774	UP

[1]	程敏, 张静, 曹鹏博, 周钢桥. 缺氧相关长链非编码RNA作为肝癌预后预测标志物的潜在价值[J]. 遗传, 2022, 44(2): 153-167.
[2]	陈凤珍, 游丽金, 杨帆, 王丽娜, 郭学芹, 高飞, 华聪, 谈聪, 方林, 单日强, 曾文君, 王博, 王韧, 徐讯, 魏晓锋. CNGBdb：国家基因库生命大数据平台[J]. 遗传, 2020, 42(8): 799-809.
[3]	骆红波, 曹鹏博, 周钢桥. DNA甲基化驱动的转录表达特征作为肝癌预后预测标志物的价值[J]. 遗传, 2020, 42(8): 775-787.