癌睾丸基因的基因表达程序分析

doi:10.3969/j.issn.1674-8115.2023.08.001

摘要/Abstract

摘要：

目的·基于睾丸单细胞转录组数据，鉴定精子发生过程中癌睾丸基因（cancer-testis gene，CTG）的基因表达程序（gene expression program，GEP），并探究其与肿瘤患者预后的关系。方法·从GTEx数据库和TCGA数据库获取正常组织和肿瘤组织的表达谱，筛选CTG。基于睾丸单细胞转录组，使用leiden聚类算法鉴定出CTG在精子发生过程中的GEP。使用DecoupleR评估GEP的活跃程度，以确定每个GEP活跃的细胞类型和精子发生时期。利用DecoupleR评估GEP在肿瘤组织中的活跃程度，并分析GEP与肿瘤患者生存的相关性。结果·基于GTEx和TCGA数据库中正常组织和肿瘤组织的基因表达谱，筛选到917个CTG。利用CTG在睾丸单细胞转录组中的表达情况，通过聚类算法鉴定出7个GEP。GEP活性分析结果表明，GEP5活跃于精子发生前期，包括精原干细胞、分化中的精原细胞和早期初级精母细胞等细胞类型。统计其在染色体上的分布发现，GEP5包含的基因主要分布于X染色体上。生存分析结果表明GEP5在多种肿瘤类型中的活跃程度与患者的生存情况呈负相关。结论·在精子发生过程中，GEP5活跃于精子发生过程的前期，其包含的基因主要分布于X染色体上。在多种肿瘤类型中，GEP5的活跃程度与患者的预后密切相关。

关键词: 癌睾丸基因, 基因表达程序, 单细胞基因表达分析

Abstract:

Objective ·To identify the gene expression program (GEP) of cancer-testis genes (CTGs) during spermatogenesis based on single-cell transcriptome data from the testis and investigate their association with the prognosis of cancer patients. Methods ·Expression profiles of normal and tumor tissues were obtained from the GTEx and TCGA databases to screen CTGs. The GEP of CTGs during spermatogenesis was identified by applying the leiden clustering algorithm to testicular single-cell transcriptome data. DecoupleR was used to evaluate the activity levels of GEP and determine the cell types and stages of spermatogenesis where each GEP was active. Subsequently, DecoupleR was used to evaluate the activity levels of GEP in tumor tissues and analyze the correlation between GEP and cancer patient survival. Results ·Based on the expression profiles of normal and tumor tissues from the GTEx and TCGA databases, 917 CTGs were identified. By using the expression patterns of CTGs in the testicular single-cell transcriptome data, seven GEPs were identified through the clustering algorithm. Activity level analysis revealed that GEP5 was active in the early stages of spermatogenesis, including spermatogonia stem cells, differentiating spermatogonia, and early primary spermatocytes. The distribution of GEP5-associated genes was predominantly found on the X chromosome. Additionally, survival analysis demonstrated a statistically significant negative correlation between GEP5 activity levels and patient survival in various tumors. Conclusion ·During spermatogenesis, GEP5 is active in early stages, and its associated genes are primarily located on the X chromosome. In multiple tumor types, the activity level of GEP5 is closely related to patient prognosis.

Key words: cancer-testis genes, gene expression program, single-cell gene expression analysis

中图分类号:

R730.7

侯宗良, 杨琴, 李少白, 雷鸣. 癌睾丸基因的基因表达程序分析[J]. 上海交通大学学报（医学版）, 2023, 43(8): 945-954.

HOU Zongliang, YANG Qin, LI Shaobai, LEI Ming. Gene expression program analysis of cancer-testis genes[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(8): 945-954.

图/表 6

表1 睾丸细胞类型、缩写以及其对应的标志基因和细胞数

Tab 1 Testicular cell types， abbreviations， and their corresponding marker genes and cell numbers

Cell type	Abbreviation	Marker gene	Cell number/n
Spermatogonia stem cell	SSC	UTF1, DMRT1	5 822
Differentiating spermatogonia	Differentiating SPG	DMRT1	2 006
Early primary spermatocyte	Early primary SPC	DMC1	2 085
Late primary spermatocyte	Late primary SPC	ZPBP, SPAG6	4 484
Round spermatid	Round ST	ZPBP, SPAG6, ACR	5 653
Elongating spermatid	Elongating ST	TNP1	3 541
Elongated spermatid	Elongated ST	TNP2	2 511
Sertoli cell	SC	SOX9	1 368
Peritubular cell	PC	MYH11	3 740
Leydig cell	LC	DLK1	4 485
Smooth muscle cell	SMC	MYH11, NOTCH3	672
Epithelial cell	EC	VWF	1 792
Testis macrophage	TM	CD14	1 442

图1 CTG的筛选Note： A. Comparison between CTGs identified in this study and previous studies. B. Distribution of CTGs and non-CTGs in testis. C. The proportion of CTGs identified in different chromosomes. D. Odds ratio of CTGs on chromosomes. ①P=0.000, compared with other chromosomes.

Fig 1 Screening of CTGs

图2 睾丸单细胞转录组聚类分析和细胞类型标注Note： A. Relationship between resolution parameters in cell clustering and the silhouette score. The red dotted line in the panel indicates the optimal resolution parameter in clustering. B. Single-cell transcriptome map of the testis. C. Expression of marker genes of somatic cell types (SOX9, CD14, VWF, DLK1, NOTCH3 and MYH11) in testis. D. Expression of marker genes of germline cell types (UTF1, DMRT1, DMC1, ZPBP, SPAG6, ACR, TNP1 and PRM2) in testis. The intensity of color in the C/D panels represents the level of gene expression after normalization.

Fig 2 Cluster analysis of testicular single-cell transcriptome and cell types labeling

图3 鉴定CTG包含的精子发生过程中的GEPNote：A. Expression of CTGs in the entire testicular single-cell transcriptome. B. The relationship between resolution and silhouette score in CTGs clustering. The red dotted line in the panel indicates the optimal position of resolution. C. Expression of CTGs in testicular germ line cells after clustering. D. The proportion of CTGs contained in each GEP on the chromosomes. The values in the heat map of A/B panels were converted to z-scores by the gene. The closer the color is to yellow, the higher the gene expression is; the closer the color is to purple, the lower the gene expression is.

Fig 3 Identification of GEP contained in CTGs during spermatogenesis

图4 GEP的活跃情况以及GEP5与肿瘤患者生存的关系Note：A. Single-cell map of testis germ line cells. B. The activity of GEP in testis germ line cells. The colors in the panel represent the degree of activity of each GEP. The closer the GEP is to red, the more active it is, and the closer the GEP is to blue, the more inhibited it is. C. Relationship between GEP5 activity and prognosis in different cancer types. OS—overall survival; PFI—progression free interval; DFI—disease free interval. The colors represent the hazard ratio (HR) of Kaplan-Meier (KM) survival analysis and Cox regression analysis. White means HR=1; pink means HR=2; light blue means HR=0; gray indicates missing data. ①P<0.001; ②P<0.01; ③P<0.05; ④P<0.10.

Fig 4 Activity of GEP and the relationship between GEP5 and the survival of cancer patients

图5 GEP5在不同癌症类型中的活跃程度与DSS曲线Note：A. DSS curve of BRCA.B. DSS curve of KIRC. C. DSS curve of KIRP. D. DSS curve of UCEC. The upper panel shows Kaplan-Meier survival curves; the lower panel presents the number of people at risk (disease) at different time points.

Fig 5 GEP5 activity in different cancer types and DSS curves

参考文献 19

1	SCANLAN M J, GORDON C M, WILLIAMSON B, et al. Identification of cancer/testis genes by database mining and mRNA expression analysis[J]. Int J Cancer, 2002, 98(4): 485-492.
2	HUBBARD J M, AHN D H, JONES J C, et al. Trial in progress: a phase Ⅱ, multicenter, open-label study of PolyPEPI1018 in combination with atezolizumab in participants with relapsed or refractory microsatellite-stable metastatic colorectal (MSS mCRC) cancer (Oberto-301)[J]. J Clin Oncol, 2023, 41(4 suppl): TPS283-TPS283.
3	ALMEIDA L G, SAKABE N J, DEOLIVEIRA A R, et al. CTdatabase: a knowledge-base of high-throughput and curated data on cancer-testis antigens[J]. Nucleic Acids Res., 2009, 37(suppl_1): D816-D819.
4	WANG C, GU Y Y, ZHANG K, et al. Systematic identification of genes with a cancer-testis expression pattern in 19 cancer types[J]. Nat Commun, 2016, 7: 10499.
5	GORDEEVA O. Cancer-testis antigens: unique cancer stem cell biomarkers and targets for cancer therapy[J]. Semin Cancer Biol, 2018, 53: 75-89.
6	MENG X Y, SUN X Q, LIU Z L, et al. A novel era of cancer/testis antigen in cancer immunotherapy[J]. Int Immunopharmacol, 2021, 98: 107889.
7	SIMPSON A J G, CABALLERO O L, JUNGBLUTH A, et al. Cancer/testis antigens, gametogenesis and cancer[J]. Nat Rev Cancer, 2005, 5(8): 615-625.
8	MOUNIR M, LUCCHETTA M, SILVA T C, et al. New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx[J]. PLoS Comput Biol, 2019, 15(3): e1006701.
9	GUO J T, GROW E J, MLCOCHOVA H, et al. The adult human testis transcriptional cell atlas[J]. Cell Res, 2018, 28(12): 1141-1157.
10	HERMANN B P, CHENG K R, SINGH A, et al. The mammalian spermatogenesis single-cell transcriptome, from spermatogonial stem cells to spermatids[J]. Cell Rep, 2018, 25(6): 1650-1667.e8.
11	SOHNI A, TAN K, SONG H W, et al. The neonatal and adult human testis defined at the single-cell level[J]. Cell Rep, 2019, 26(6): 1501-1517.e4.
12	ALEXANDER WOLF F, ANGERER P, THEIS F J. SCANPY: large-scale single-cell gene expression data analysis[J]. Genome Biol, 2018, 19(1): 15.
13	LOPEZ R, REGIER J, COLE M B, et al. Deep generative modeling for single-cell transcriptomics[J]. Nat Methods, 2018, 15(12): 1053-1058.
14	BERNSTEIN N J, FONG N L, LAM I, et al. Solo: doublet identification in single-cell RNA-seq via semi-supervised deep learning[J]. Cell Syst, 2020, 11(1): 95-101.e5.
15	TRAAG V A, WALTMAN L, VAN ECK N J. From Louvain to Leiden: guaranteeing well-connected communities[J]. Sci Rep, 2019, 9(1): 5233.
16	WANG M, LIU X X, CHANG G, et al. Single-cell RNA sequencing analysis reveals sequential cell fate transition during human spermatogenesis[J]. Cell Stem Cell, 2018, 23(4): 599-614.e4.
17	FAYOMI A P, ORWIG K E. Spermatogonial stem cells and spermatogenesis in mice, monkeys and men[J]. Stem Cell Res, 2018, 29: 207-214.
18	BADIA-I-MOMPEL P, VÉLEZ SANTIAGO J, BRAUNGER J, et al. decoupleR: ensemble of computational methods to infer biological activities from omics data[J]. Bioinformatics Adv, 2022, 2(1): vbac016.
19	LIU W S. Mammalian sex chromosome structure, gene content, and function in male fertility[J]. Annu Rev Anim Biosci, 2019, 7(1): 103-124.