Prediction of recurrence free survival for esophageal cancer patients using a protein signature based risk model.

Background: Biomarkers to predict the risk of disease recurrence in Esophageal squamous cell carcinoma (ESCC) patients are urgently needed to improve treatment. We developed proteins expression-based risk model to predict recurrence free survival for ESCC patients.

Methods: Alterations in Wnt pathway components expression and subcellular localization were analyzed by immunohistochemistry in 80 ESCCs, 61 esophageal dysplastic and 47 normal tissues; correlated with clinicopathological parameters and clinical outcome over 86 months by survival analysis.

Role of S100 A7 as a diagnostic biomarker in oral potentially malignant disorders and oral cancer.

Background: S100 proteins have been implicated in the tumorigenesis of different human cancers and in oral dysplasia, as they are keratinocytes.

Materials And Methods: In the present study, we have attempted to compare the expression of S100-A7 within young-onset (age ≤45 years, Group 1) oral squamous cell carcinoma (OSCC), OSCC in older age groups (age >45 years Group 2), oral potentially malignant disorders (OPMDs, Group 3) and inflammatory lesions (Group 4). The tissue sections were scored based on the percentage of immunostained cells and staining intensity.

Dual blockade of EGFR and CDK4/6 delays head and neck squamous cell carcinoma progression by inducing metabolic rewiring.

Despite preclinical success, monotherapies targeting EGFR or cyclin D1-CDK4/6 in Head and Neck squamous cell carcinoma (HNSCC) have shown a limited clinical outcome. Here, we aimed to determine the combined effect of palbociclib (CDK4/6) and afatinib (panEGFR) inhibitors as an effective strategy to target HNSCC. Using TCGA-HNSCC co-expression analysis, we found that patients with high EGFR and cyclin D1 expression showed enrichment of gene clusters associated with cell-growth, glycolysis, and epithelial to mesenchymal transition processes.

The Hemolyzed Sample: To Analyse Or Not To Analyse.

Preanalytical errors constitute about 40-65% of laboratory errors, of which 60% are due to hemolysis. This leads to imprecise reporting and misinterpretation of the actual concentration of analytes. Hence the aim of this study was to estimate the extent of different degrees of interference by visible hemolysis.

Programmed death-ligand 1 expression by digital image analysis advances thyroid cancer diagnosis among encapsulated follicular lesions.

Recognition of noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) that distinguishes them from invasive malignant encapsulated follicular variant of papillary thyroid carcinoma (EFVPTC) can prevent overtreatment of NIFTP patients. We and others have previously reported that programmed death-ligand 1 (PD-L1) is a useful biomarker in thyroid tumors; however, all reports to date have relied on manual scoring that is time consuming as well as subject to individual bias. Consequently, we developed a digital image analysis (DIA) protocol for cytoplasmic and membranous stain quantitation (ThyApp) and evaluated three tumor sampling methods [Systemic Uniform Random Sampling, hotspot nucleus, and hotspot nucleus/3,3'-Diaminobenzidine (DAB)].