Lu Guomin, Wang Hui, Xu Rui, Xu Junying, An Fangmei, Xu Haoran, Nie He, Mei Jie, Zhan Qiang, Zhang Qinglin
Departments of Gastroenterology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu 214023, China.
The First College of Clinical Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
J Cancer. 2023 Sep 11;14(16):2978-2989. doi: 10.7150/jca.86965. eCollection 2023.
Increased studies on the basis of bulk RNA-sequencing (RNA-seq) data of cancer identify numbers of immune-related genes which may play potential regulatory roles in the tumor microenvironment (TME) without in-depth validation. In the current study, the immunological correlation and cell subpopulation expression pattern of FMNL1 were analyzed using public data. In addition, the cell subpopulation expression pattern of FMNL1 was also deeply validated using single-cell RNA-sequencing (scRNA-seq) and multiplexed quantitative immunofluorescence (mQIF). Bulk mRNA was related to better prognosis in hepatocellular carcinoma (HCC) and was able to identify immuno-hot tumor in not only HCC but also multiple cancer types. Bulk mRNA also predicted the response to immunotherapy in multiple cancers. Further validation using scRNA-seq and mQIF revealed that FMNL1 was a biomarker for immune cells. FMNL1 is a biomarker for immune cells in not only hepatocellular carcinoma, but also multiple cancer types. Moreover, immune infiltration analysis using the bulk RNA-seq data would be further validated using scRNA-seq and/or mQIF to describe the cell subpopulation expression pattern in tumor tissues for more in-depth and appropriate understanding.
基于癌症的大量RNA测序(RNA-seq)数据开展的研究日益增多,这些研究识别出了许多免疫相关基因,它们可能在肿瘤微环境(TME)中发挥潜在的调节作用,但缺乏深入验证。在本研究中,利用公开数据对FMNL1的免疫相关性和细胞亚群表达模式进行了分析。此外,还使用单细胞RNA测序(scRNA-seq)和多重定量免疫荧光(mQIF)对FMNL1的细胞亚群表达模式进行了深入验证。大量mRNA与肝细胞癌(HCC)的较好预后相关,并且不仅能够在HCC中,还能在多种癌症类型中识别免疫热肿瘤。大量mRNA还能预测多种癌症对免疫治疗的反应。使用scRNA-seq和mQIF进行的进一步验证表明,FMNL1是免疫细胞的生物标志物。FMNL1不仅是肝细胞癌,也是多种癌症类型中免疫细胞的生物标志物。此外,将使用scRNA-seq和/或mQIF对基于大量RNA-seq数据的免疫浸润分析进行进一步验证,以描述肿瘤组织中的细胞亚群表达模式,从而获得更深入和恰当的理解。