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Background: Our study aimed to develop a nomogram incorporating cytokeratin fragment antigen 21-1 (CYFRA21-1) to assist in differentiating between patients with intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC)., Methods: A total of 487 patients who were diagnosed with ICC and HCC at Qilu Hospital of Shandong University were included in this study. The patients were divided into a training cohort and a validation cohort based on whether the data collection was retrospective or prospective. Univariate and multivariate analyses were employed to select variables for the nomogram. The discrimination and calibration of the nomogram were evaluated using the area under the receiver operating characteristic curve (AUC) and calibration plots. Decision curve analysis (DCA) was used to assess the nomogram's net benefits at various threshold probabilities., Results: Six variables, including CYFRA21-1, were incorporated to establish the nomogram. Its satisfactory discriminative ability was indicated by the AUC (0.972 for the training cohort, 0.994 for the validation cohort), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) values. The Hosmer-Lemeshow test and the calibration plots demonstrated favorable consistency between the nomogram predictions and the actual observations. Moreover, DCA revealed the clinical utility and superior discriminative ability of the nomogram compared to the model without CYFRA21-1 and the model consisting of the logarithm of alpha-fetoprotein (Log AFP) and the logarithm of carbohydrate antigen 19-9 (Log CA19-9). Additionally, the AUC values suggested that the discriminative ability of Log CYFRA21-1 was greater than that of the other variables used as diagnostic biomarkers., Conclusions: This study developed and validated a nomogram including CYFRA21-1, which can aid clinicians in the differential diagnosis of ICC and HCC patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Liu, Li, Liu, Zhang and Gao.)

Purpose: To systematically analyze the overall m 6 A modification pattern in hyperplastic scars (HS). Methods: The m 6 A modification patterns in HS and normal skin (NS) tissues were described by m 6 A sequencing and RNA sequencing, and subsequently bioinformatics analysis was performed. The m 6 A-related RNA was immunoprecipitated and verified by real-time quantitative PCR. Results: The appearance of 14,791 new m 6 A peaks in the HS sample was accompanied by the disappearance of 7,835 peaks. The unique m 6 A-related genes in HS were thus associated with fibrosis-related pathways. We identified the differentially expressed mRNA transcripts in HS samples with hyper-methylated or hypo-methylated m 6 A peaks. Conclusion: This study is the first to map the m 6 A transcriptome of human HS, which may help clarify the possible mechanism of m 6 A-mediated gene expression regulation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Liu, Wu, Chen, Zou, Teng, Zhang, Li, Guo and Yuan.)

Objective: To observe the surface ultrastructure of different tumor cells in vivo using atomic force microscope (AFM) and analyze their common characteristics., Methods: We selected 60 specimens of each of normal liver cells, liver cancer, cervical squamous cells, cervical cancer cells, ductal epithelial cells and breast cancer cells for scanning using AFM. The cell surface scan images were analyzed using image analysis software to identify their common morphological features., Results: From normal cervical squamous epithelial cells, intermediate cells, and basal cells to HPV-infected cells, CIN2-3 cells and cervical cancer cells, the membrane surface roughness became gradually increased (P<0.05). Similarly, the surface roughness increased significantly in the order of normal liver cells, hepatitis B cirrhosis liver cells, and hepatocellular carcinoma cells (P<0.05). The average surface roughness also tended to increase from normal mammary gland cells to mammary gland hyperplasia cells and breast cancer cells (P<0.05)., Conclusion: Normal cells and tumor cells show different cell membrane morphologies, and such morphological features provide a reliable basis for clinical pathological diagnosis and differential diagnosis of malignancies.