Glypican-1 is a novel immunohistochemical marker to differentiate poorly differentiated squamous cell carcinoma from solid predominant adenocarcinoma of the lung

OrgXenomics: an integrated proteomic knowledge base for patient-derived organoid and xenograft

Patient-derived models (PDMs, particularly organoids and xenografts) are irreplaceable tools for precision medicine, from target development to lead identification, then to preclinical evaluation, and finally to clinical decision-making. So far, PDM-based proteomics has emerged to be one of the cutting-edge directions and massive data have been accumulated. However, such PDM-based proteomic data have not been provided by any of the available databases, and proteomics profiles of all proteins in proteomic study are also completely absent from existing databases. Herein, an integrated database named 'OrgXenomics' was thus developed to provide the proteomic data for PDMs, which was unique in (a) explicitly describing the establishment detail for a wide array of models, (b) systematically providing the proteomic profiles (expression/function/interaction) for all proteins in studied proteomic analysis and (c) comprehensively giving the raw data for diverse organoid/xenograft-based proteomic studies of various diseases. Our OrgXenomics was expected to server as one good complement to existing proteomic databases, and had great implication for the practice of precision medicine, which could be accessed at: https://idrblab.org/orgxenomics/.

ClearF++: Improved Supervised Feature Scoring Using Feature Clustering in Class-Wise Embedding and Reconstruction

Feature selection methods are essential for accurate disease classification and identifying informative biomarkers. While information-theoretic methods have been widely used, they often exhibit limitations such as high computational costs. Our previously proposed method, ClearF, addresses these issues by using reconstruction error from low-dimensional embeddings as a proxy for the entropy term in the mutual information. However, ClearF still has limitations, including a nontransparent bottleneck layer selection process, which can result in unstable feature selection. To address these limitations, we propose ClearF++, which simplifies the bottleneck layer selection and incorporates feature-wise clustering to enhance biomarker detection. We compare its performance with other commonly used methods such as MultiSURF and IFS, as well as ClearF, across multiple benchmark datasets. Our results demonstrate that ClearF++ consistently outperforms these methods in terms of prediction accuracy and stability, even with limited samples. We also observe that employing the Deep Embedded Clustering (DEC) algorithm for feature-wise clustering improves performance, indicating its suitability for handling complex data structures with limited samples. ClearF++ offers an improved biomarker prioritization approach with enhanced prediction performance and faster execution. Its stability and effectiveness with limited samples make it particularly valuable for biomedical data analysis.

Clinicopathological features of cytokeratin 5-positive pulmonary adenocarcinoma

Cytokeratin 5 (CK5) is a marker for pulmonary squamous cell carcinoma; however, CK5 is sometimes present in pulmonary adenocarcinoma (ADC), and there is insufficient information regarding the clinicopathological features of CK5-positive ADC. We aimed to explore the clinicopathological characteristics of CK5-positive ADC using immunohistochemistry. We prepared the following two cohorts: a resected cohort containing 220 resected tumours for primarily studying the detailed morphological characteristics, and a tissue microarray (TMA) cohort containing 337 samples for investigating the associations of CK5 expression with other protein expressions, genetic and prognostic findings. CK5-positive ADC was defined to have ≥ 10% tumour cells and presence of CK5-positive tumour cells in the resected and TMA cohorts, respectively. CK5-positive ADCs were identified in 91 (16.3%) patients in the combined cohort. CK5-positive ADCs had male predominance (P = 0.012), smoking history (P = 0.001), higher stage (P < 0.001), histological high-grade components (P < 0.001), vascular invasion (P < 0.001), mucinous differentiation (P < 0.001), spread through airspaces (P < 0.001), EGFR wild-type (P < 0.001), KRAS mutations (P < 0.001), ALK rearrangement (P < 0.001) and ROS1 rearrangement (P = 0.002). In the resected cohort, more than half the CK5-positive ADCs (19 cases, 65.5%) showed mucinous differentiation; the remaining cases harboured high-grade components. In the TMA cohort, CK5-positive ADCs correlated with TTF-1 negativity (P = 0.002) and MUC5B, MUC5AC and HNF4alpha positivity (P < 0.001, 0.048, < 0.001). Further, CK5-positive ADCs had significantly lower disease-free and overall survival rates than CK5-negative ADCs (P < 0.001 for each). Additionally, multivariate analysis revealed that CK5 expression was an independent poor prognostic factor. CK5-positive ADCs showed aggressive clinical behaviour, with high-grade morphology and mucinous differentiation.