Background::Artificial intelligence (AI) technology represented by deep learning has made remarkable achievements in digital pathology, enhancing the accuracy and reliability of diagnosis and prognosis evaluation. The spatial distribution of CD3 + and CD8 + T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer (CRC). This study aimed to investigate CD3 CT (CD3 + T cells density in the core of the tumor [CT]) prognostic ability in patients with CRC by using AI technology. Methods::The study involved the enrollment of 492 patients from two distinct medical centers, with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation cohort. To facilitate tissue segmentation and T-cells quantification in whole-slide images (WSIs), a fully automated workflow based on deep learning was devised. Upon the completion of tissue segmentation and subsequent cell segmentation, a comprehensive analysis was conducted.Results::The evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3 CT and CD3-CD8 (the combination of CD3 + and CD8 + T cells density within the CT and invasive margin) in predicting mortality (C-index in training cohort: 0.65 vs. 0.64; validation cohort: 0.69 vs. 0.69). The CD3 CT was confirmed as an independent prognostic factor, with high CD3 CT density associated with increased overall survival (OS) in the training cohort (hazard ratio [HR] = 0.22, 95% confidence interval [CI]: 0.12–0.38, P <0.001) and validation cohort (HR = 0.21, 95% CI: 0.05–0.92, P = 0.037). Conclusions::We quantify the spatial distribution of CD3 + and CD8 + T cells within tissue regions in WSIs using AI technology. The CD3 CT confirmed as a stage-independent predictor for OS in CRC patients. Moreover, CD3 CT shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.

[摘 要] 目的：探究钾钙激活通道亚家族N成员4（KCNN4）在胰腺癌组织中的表达及其对胰腺癌进展的影响，解析KCNN4在胰腺癌临床诊断及预后判断中的作用。方法：利用GEPIA2数据分析平台，结合TCGA和GTEx数据库的数据分析KCNN4在胰腺癌组织中的表达水平及其与患者预后的关系。收集24例海军军医大学长海医院手术切除的胰腺癌患者的癌及癌旁组织标本，通过qPCR、WB法和免疫组化染色技术验证KCNN4在胰腺癌组织中的表达水平。利用shRNA敲低人胰腺癌细胞中BXPC3和PANC-1中KCNN4的表达，通过CCK-8和克隆形成实验检测细胞增殖与生长情况。利用小鼠胰腺癌KPC细胞构建胰腺癌原位成瘤模型，观察敲低KCNN4对胰腺原位成瘤的影响，统计小鼠生存期（OS）。结果：整合TCGA和GTEx数据库数据分析结果发现，KCNN4在胰腺癌组织中高表达（P < 0.05），且与患者OS和DFS缩短相关（均P < 0.05）。胰腺癌组织中KCNN4 mRNA和蛋白表达量均显著高于癌旁组织（均P < 0.01）。KCNN4敲低后，胰腺癌细胞生长速率显著减慢、克隆形成数量显著减少（均P < 0.01）。小鼠胰腺原位荷瘤实验结果表明，KCNN4敲低可抑制肿瘤细胞在胰腺原位的生长并延长小鼠OS。结论：KCNN4在胰腺癌组织中高表达，其能促进胰腺癌细胞增殖和胰腺癌进展，与患者预后密切相关，有望作为胰腺癌临床诊断及预后评估的靶点。