Department of Pathomorphology, Faculty of Medicine, Jagiellonian University Medical College, Grzegorzecka 16, 31-531 Krakow, Poland; Department of Chemical Physics, Faculty of Chemistry, Jagiellonian University in Kraków, Gronostajowa 2, 30-387 Krakow, Poland.
Department of Analytical Chemistry, University of Valencia, 50 Dr Moliner Street, Research Building, 46100 Burjassot, Valencia, Spain.
Biochim Biophys Acta Mol Basis Dis. 2023 Dec;1869(8):166840. doi: 10.1016/j.bbadis.2023.166840. Epub 2023 Aug 7.
The process of metastasis is complex and often impossible to be recognized in conventional clinical diagnosis. Lymph node metastasis (LNM) of bladder carcinoma (BC) is often associated with muscle-invasive tumors. To prevent and recognize LNM, the standard treatment includes radical cystectomy with lymph node dissection and histological examination. Here, we propose infrared (IR) microscopy as a tool for the prediction of LNM. For this purpose, IR images of bladder biopsies from patients with diagnosed non-metastatic early (E BC) and advanced (A BC), as well as metastatic advanced (M BC) bladder cancer were first collected. Furthermore, this dataset was complemented with images of the secondary tumors from the lymph nodes (M LN) of the M BC patients. Unsupervised clustering was used to extract tissue structures from IR images to create a data set comprising 382 IR spectra of non-metastatic bladder tumors and 241 metastatic ones. Based on that, we next established discrimination models using PLS-DA with repeated random sampling double cross-validation, and permutation test to perform the classification. The accuracy of BC metastasis prediction from IR bladder biopsies was 83 % and 78 % for early and advanced BC, respectively, herein demonstrating a proof-of-concept IR detection of BC metastasis. The analysis of spectral profiles additionally showed molecular composition similarity between metastatic bladder and lymph node tumors. We also determined spectral biomarkers of LNM that are associated with sugar metabolism, remodeling of extracellular matrix, and morphological features of cancer cells. Our approach can improve clinical decision-making in urological oncology.
转移的过程非常复杂，在常规临床诊断中往往无法识别。膀胱癌（BC）的淋巴结转移（LNM）通常与肌浸润性肿瘤有关。为了预防和识别 LNM，标准治疗包括根治性膀胱切除术伴淋巴结清扫和组织学检查。在这里，我们提出红外（IR）显微镜作为预测 LNM 的工具。为此，我们首先收集了来自诊断为非转移性早期（EBC）和晚期（ABC）以及转移性晚期（MBC）膀胱癌患者的膀胱活检的 IR 图像。此外，该数据集还补充了来自 MBC 患者淋巴结（MLN）的继发性肿瘤的图像。我们使用无监督聚类从 IR 图像中提取组织结构，创建了一个包含 382 个非转移性膀胱肿瘤和 241 个转移性肿瘤的 IR 光谱数据集。在此基础上，我们接下来使用 PLS-DA 建立了判别模型，并使用重复随机抽样双交叉验证、置换检验进行分类。从 IR 膀胱活检中预测 BC 转移的准确率分别为早期和晚期 BC 的 83%和 78%，这证明了 IR 检测 BC 转移的概念验证。光谱谱图的分析还显示了转移性膀胱和淋巴结肿瘤之间的分子组成相似性。我们还确定了与糖代谢、细胞外基质重塑和癌细胞形态特征相关的 LNM 的光谱生物标志物。我们的方法可以改善泌尿科肿瘤学的临床决策。
