Investigation into the Spatial Heterogeneity of Lung Composite Large-Cell Neuroendocrine Carcinoma Spatial Transcriptomic Analysis of Combined Large-Cell Neuroendocrine Carcinoma

小细胞肺癌 转录组 神经内分泌癌 神经内分泌肿瘤 肺癌 病理 肿瘤科 生物 小细胞癌 医学 癌症研究 内科学 基因 遗传学 基因表达
作者
Mingyu Ji,Daming Fan,Yaqi Yuan,Jing Wang,Xiaodong Feng,Weihua Yang,Xiaofei Dang,Yihui Xu,Jun Wang
出处
期刊:Cancer Biotherapy and Radiopharmaceuticals [Mary Ann Liebert, Inc.]
卷期号:40 (8): 551-566 被引量:1
标识
DOI:10.1089/cbr.2025.0043
摘要

Background: Lung combined large-cell neuroendocrine carcinoma (CoLCNEC) refers to lung regions exhibiting both the features of large-cell neuroendocrine carcinoma (LCNEC) and the defined components of nonsmall cell lung cancer (NSCLC), with a relatively high mitotic rate. Diagnosing and predicting the prognosis of CoLCNEC are challenging. This study explored spatial transcriptomic expression patterns and identified crucial genes. Methods: We utilized a sample from a CoLCNEC patient containing three distinct components, namely, LCNEC, adenocarcinoma, and squamous cell carcinoma, with the former being predominant. Spatial transcriptomics (ST) technology, which employs the 10× Genomics Visium formalin-fixed paraffin-embedded ST kit, was applied along with high-throughput sequencing to obtain gene expression information and spatial locations for each spot. Subsequent analysis included differentially gene expression and functional enrichment. Finally, immunohistochemistry was employed to validate the marker protein structural maintenance of chromosomes 1A (SMC1A). Then, SMC1A was overexpressed and silenced in NCI-H661 and LTEP-a-2 cells, and the migration and invasion ability of the cells were detected by scratch assay and Transwell, respectively. The role of SMC1A in cancer cell cycle was detected by Real-time Reverse Transcription-PCR(RT-qPCR), Western blot, and flow cytometry, the apoptosis was detected by flow cytometry. Results: The results revealed that tumor tissue regions had higher unique molecular identifiers and gene counts than nontumor regions did. Unsupervised clustering identified four clusters, revealing the uniform distribution of unique transcripts, which were mapped onto slices to display apparent spatial separation. Differentially gene expression analysis revealed genes highly expressed in cancer cells. Further analysis of different regions revealed distinct cellular subgroups enriched through differentially gene expression analysis in various pathways, such as the cell cycle and DNA replication. Finally, SMC1A was chosen as a candidate gene, and immunohistochemistry confirmed its elevated expression in tumor regions. In addition, compared with oe-NC, oe-SMC1A can significantly promote the migration, invasion and G1/S phase transition of lung cancer cells, and promote the inhibition of apoptosis of cancer cells, while sh-SMC1A is completely opposite. Conclusions: In the tumor region of CoLCNEC, SMC1A is significantly upregulated. Moreover, silencing SMC1A effectively inhibits lung cancer cell invasion, migration, and G1/S phase transition, while promoting apoptosis. These findings indicate that SMC1A has the potential to be a new therapeutic target for CoLCNEC treatment.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
不止到么完成签到,获得积分10
刚刚
知性的水杯完成签到 ,获得积分10
1秒前
满船清梦压星河完成签到 ,获得积分10
1秒前
carza发布了新的文献求助10
2秒前
waiting完成签到,获得积分10
2秒前
Dan完成签到,获得积分10
2秒前
bajie01发布了新的文献求助10
3秒前
稻米完成签到 ,获得积分10
4秒前
kenny完成签到,获得积分10
4秒前
壮观的海豚完成签到 ,获得积分10
5秒前
泡泡糖完成签到,获得积分10
6秒前
刘亦菲完成签到,获得积分10
7秒前
儒雅的寄翠完成签到,获得积分10
7秒前
xiaobuding完成签到,获得积分10
10秒前
about完成签到,获得积分10
11秒前
linliqing完成签到,获得积分10
12秒前
免疫小白完成签到 ,获得积分10
13秒前
huangxin完成签到,获得积分10
14秒前
LS完成签到,获得积分10
14秒前
Caiyuping完成签到 ,获得积分10
16秒前
852应助LS采纳,获得10
19秒前
体贴洋葱完成签到 ,获得积分10
19秒前
19秒前
科研通AI6.4应助xuan采纳,获得10
20秒前
狄淇儿完成签到,获得积分10
20秒前
典雅曼卉发布了新的文献求助10
22秒前
Cassiopiea19完成签到,获得积分10
23秒前
科研王子完成签到 ,获得积分10
23秒前
Sicecream完成签到,获得积分10
23秒前
盐焗双黄连完成签到,获得积分10
26秒前
摩天轮完成签到 ,获得积分10
28秒前
善良茗茗完成签到,获得积分10
28秒前
蛋花肉圆汤完成签到,获得积分0
29秒前
玩命的糖豆完成签到,获得积分10
31秒前
小胖子完成签到 ,获得积分10
32秒前
lw777完成签到,获得积分20
34秒前
李健应助典雅曼卉采纳,获得10
34秒前
dkclz完成签到 ,获得积分10
35秒前
gzslwddhjx完成签到,获得积分10
35秒前
顾顾完成签到 ,获得积分10
37秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
政治传播过程中的外交与说服——以中苏友好协会为例的历史考察 566
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7579583
求助须知:如何正确求助?哪些是违规求助? 9159072
关于积分的说明 19593462
捐赠科研通 7162215
什么是DOI,文献DOI怎么找? 3265716
关于科研通互助平台的介绍 2430726
邀请新用户注册赠送积分活动 2256503