Four-protein model for predicting prognostic risk of lung cancer

doi:10.1007/s11684-021-0867-0

Frontiers of Medicine

2022, Vol. 16

Issue (4): 618-626 https://doi.org/10.1007/s11684-021-0867-0

本期目录

Four-protein model for predicting prognostic risk of lung cancer

Xiang Wang¹, Minghui Wang¹, Lin Feng¹, Jie Song¹, Xin Dong²(

), Ting Xiao¹(

), Shujun Cheng¹(

)

¹. State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
². Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China

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Abstract：

Patients with lung cancer at the same stage may have markedly different overall outcome and a lack of specific biomarker to predict lung cancer outcome. Heat-shock protein 90 β (HSP90β) is overexpressed in various tumor cells. In this study, the ELISA results of HSP90β combined with CEA, CA125, and CYFRA21-1 were used to construct a recursive partitioning decision tree model to establish a four-protein diagnostic model and predict the survival of patients with lung cancer. Survival analysis showed that the recursive partitioning decision tree could distinguish the prognosis between high- and low-risk groups. Results suggested that the joint detection of HSP90β, CEA, CA125, and CYFRA21-1 in the peripheral blood of patients with lung cancer is plausible for early diagnosis and prognosis prediction of lung cancer.

Key words： lung cancer HSP90β decision tree model prognosis

收稿日期: 2021-01-20 出版日期: 2022-09-02

Corresponding Author(s): Xin Dong,Ting Xiao,Shujun Cheng

引用本文:

. [J]. Frontiers of Medicine, 2022, 16(4): 618-626.
Xiang Wang, Minghui Wang, Lin Feng, Jie Song, Xin Dong, Ting Xiao, Shujun Cheng. Four-protein model for predicting prognostic risk of lung cancer. Front. Med., 2022, 16(4): 618-626.

链接本文:

https://academic.hep.com.cn/fmd/CN/10.1007/s11684-021-0867-0
https://academic.hep.com.cn/fmd/CN/Y2022/V16/I4/618

Group	Number	HSP90β levels (ng/mL)			P value
Group	Number	Mean±SD	Range	Median	P value
Healthy group	282	52.15±50.61	14.22–293.61	30.41	<0.001^a
?Age (year)					<0.001
??>60	93	71.47±58.19	14.37–293.61	50.17
??≤60	189	42.65±43.55	14.22–277.25	23.17
?Gender					0.394
??Male	182	53.07±50.30	14.37–293.61	32.09
??Female	100	50.82±51.55	14.22–277.25	29.57
Lung cancer	1162	196.65±127.72	15.96–686.28	162.02
?SCLC	80	247.11±123.38	40.71–661.23	221.80	<0.001^b
??Age					0.528
???>60	26	227.25±98.73	70.80–505.89	207.00
???≤60	54	256.68±133.44	40.71–661.23	231.15
??Gender					0.158
???Male	58	237.63±126.53	40.71–661.23	214.62
???Female	22	272.10±113.60	134.26–530.86	231.37
?NSCLC ??Age	1082	192.91±127.30	14.93–686.28	157.06	0.973
???>60	553	191.35±124.61	14.93–681.09	158.00
???≤60	529	194.55±130.15	15.54–686.28	155.01
??Gender
???Male	681	195.51±128.19	14.93–686.28	156.99	0.295
???Female	401	188.50±125.81	16.83–681.09	157.37
??Stage					0.130
???I	510	187.81±132.76	15.96–686.28	147.22
???II	204	195.67±118.11	16.66–663.66	169.90
???III–IV	350	196.85±124.20	14.93–673.27	159.59
???NA	18
??Lymph node metastasis					0.118
???Yes	459	197.63±123.92	15.54–673.27	161.57
???No	565	190.54±131.51	15.96–686.28	150.21
???NA	58
??Pathologic types					0.086
???LUSC	371	201.22±130.39	15.54–686.28	157.12
???LUAD	705	188.34±125.66	14.93–681.09	155.91
???NA	6
??Differentiation					0.137
???High	124	171.14±109.59	31.78–498.61	137.50
???Middle	550	196.88±129.22	15.96–681.09	164.41
???Low	378	197.34±130.44	14.93–686.28	156.26
???NA	30
??Smoking history					0.170
???Yes	576	196.41±126.28	15.96–686.28	157.69
???No	499	188.39±127.84	14.93–681.09	155.01
???NA	7
??Family history					0.143
???Yes	166	181.69±127.28	16.66–673.49	133.43
???No	904	194.63±126.87	14.93–686.28	160.56
???NA	12

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