Background: Colorectal cancer (CRC) is one of the most common tumors in the digestive system, and all its risk factors are not yet known. It is important to identify valuable clinical indicators to predict the risk of CRC. Methods: A total of 227 participants, comprising 162 healthy adults and 65 patients diagnosed with CRC at Tianjin Hospital from January 2017 to March 2022, were included in this study. Electrochemiluminescence was adopted to test the expression levels of carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA199). Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for CRC, and a joint prediction model was then constructed. A nomogram was prepared, and the model was later assessed using the receiver operating characteristic curve and calibration curve. Results: The univariate analysis showed that there were statistically significant differences between the two groups in terms of smoking (χ²=8.67), fecal occult blood (χ²=119.41), Helicobacter pylori (H. pylori) infection (χ²=30.87), a history of appendectomy (χ²=5.47), serum total bile acid levels (t=19.80), serum CEA levels (t=37.82), serum CA199 levels (t=6.82), and serum ferritin levels (t=54.31) (all P<0.05). The multiple logistic regression analysis showed that smoking, fecal occult blood, H. pylori infection, a history of appendectomy, serum CEA levels, and serum CA199 levels were independent risk factors for CRC (all P<0.05). Based on the above findings, a joint prediction model was constructed, and the area under the receiver operator characteristic (ROC) curve of the model was 0.842. A nomogram and calibration curve was drawn, and the internal validation results indicated that the model had good diagnostic value. Conclusions: Smoking, fecal occult blood, H. pylori infection, a history of appendectomy, serum CEA levels, and serum CA199 levels are independent risk factors for CRC, and the prediction model based on these factors had good predictive ability.

Objectives: Forkhead Box P4 (FOXP4) is a transcription factor that promotes tumor formation and progression. However, studies on its roles in colorectal adenocarcinoma (CRAC) and cell proliferation regulation are few to date. This work investigates the expression of FOXP4 in CRAC, explores the characteristic of FOXP4 in different clinicopathological features, and analyzes its regulation of cell proliferation. Methods: The GEPIA database was used to predict the trend of FOXP4 expression in colon cancer and normal mucosa. Tumor tissue and normal paracancerous mucosal tissue were sampled from 64 cases diagnosed with CRAC and who were receiving radical surgery at Tianjin Hospital from January 2017 and December 2022. FOXP4 and proliferating cell nuclear antigen (PCNA) were detected by the immunohistochemistry EnVision method. The colon cancer cell lines SW480, HCT15, and SW620 and the normal colon cell line NCM460 were selected, and expression of FOXP4 was detected by the Western blot method. The siRNA-FOXP4 plasmid was synthesized and transfected with SW480 and HCT15 cell lines, respectively, to establish si-FOXP4 groups, and empty vector transfection group (NC-FOXP4) and blank control group (NC) was set up. The expression levels of FOXP4 and PCNA were detected by the Western blot method, while the cell proliferation activity was assessed using CCK-8. Normally distributed quantitative data were compared between two and more groups with ANOVA (SNK-based pairwise comparison), while intergroup enumeration data comparisons were performed through χ ² test and assessed through linear correlation analysis. Results: GEPIA-based prediction shows a potential rise in FOXP4 expression in colon cancer. The rate of positive FOXP4 expression is significantly higher in CRAC tissue than in normal mucosa (p<0.05). The difference in FOXP4 is statistically significant in the comparison of maximum tumor diameter and depth of invasion in CRAC (p0.05). The expression levels of FOXP4 and PCNA in CRAC are positively correlated (p<0.05). FOXP4 expression is significantly higher in cell lines SW480, HCT15, and SW620 than in cell line NCM460. The cell proliferation activity and PCNA expression are significantly lower in si-FOXP4 group than in NC-FOXP4 and NC groups for cell lines SW480 and HCT15. Conclusions: FOXP4 is highly expressed and has a proliferative effect on tumor cells in CRAC.

Objectives: Eukaryotic translation initiation factors (eIFs) are genes related to tumor formation. They selectively translate some mRNAs, regulate protein synthesis, promote cell proliferation, and effectively control the progression of some tumors. As a member of this family, eukaryotic translation initiation factor 6 (eIF6) plays a crucial role in tumor formation and progression. Exploring the expression characteristics of eIF6 and proliferating cell nuclear antigen (PCNA), a proliferation-associated factor, in colorectal adenocarcinoma (CRAC) and analyzing their correlation and clinical significance can provide a theoretical basis for the study of CRAC and objective biological indicators for the clinical judgment of tumor formation and progression. Methods: The GEPIA database was used to predict the trend of eIF6 and PCNA in CRAC. Sixty-four patients diagnosed with CRAC and undergoing surgical treatment were selected from January 2017 to November 2022. CRAC tissues and normal mucous tissues (>3 cm away from the tumor margin) were retained. The expression of eIF6 and PCNA was detected by immunohistochemistry (IHC). Colon cancer cell lines SW480, HCT15, and SW620 and normal colon cell line NCM460 were selected, and the expression of eFI6 and PCNA in these cells was detected by Western blot. Results: A trend of elevated expression of eIF6 and PCNA was predicted in CRAC by the GEPIA database. The expression levels of eIF6 (65.63 % vs. 9.38 %, X²=43.2000, p<0.0001) and PCNA (84.38 % vs. 46.88 %, X²=19.9481, p<0.0001) were higher in CRAC tissues than that in normal mucosa as indicated by IHC. Significant difference in eIF6 and PCNA expression was found among different maxim tumor diameters and depths of infiltration in colon adenocarcinoma (p0.05). A positive correlation was found between eIF6 and PCNA in CRAC (X²=5.05, r=0.77, p=0.0283). Their expression was significantly higher in SW480, HCT15, and SW620 than in NCM460 (p<0.05). Conclusions: eIF6 is highly expressed in CRAC, participates in tumor formation and progression, and has a positive correlation with PCNA.

As a common benign bone tumor, the pathological diagnosis of chondrogenic tumors needs to accurately analyze the calcification pattern of the cartilage matrix, key spatial topology information, and other important indicators. However, chondrogenic tumors is a rare disease, doctors lack sufficient reference data and experience in diagnosis. At the same time, complex spatial structural information also increases the difficulty of diagnosis, which leads to inconsistencies in the diagnosis’s results of different doctors. In contrast, with the development of technology, artificial intelligence (AI) with its fast, accurate, and robust characteristics can effectively improve the efficiency and accuracy of diagnosis. However, the application of AI in this field is still unrecognized, so it is urgent to develop a model that can help doctors in diagnosis to improve accuracy and efficiency. In this study, we propose XChondNet, an explainable spatial-context-aware synergistic deep feature fusion model for WSI-based chondrogenic tumor classification. The model proposes a fusion mechanism of pathological and positional features so that the model can effectively perceive spatial structural information and a parallel classifier mechanism based on potential coding, which can effectively solve the problem of class imbalance in chondrogenic tumors data. We evaluated the XChondNet model on our chondrogenic tumors dataset and the experimental results verified its effectiveness in the classification of the chondrogenic tumors subtype. In the test phase, compared to the other advanced models, the XChondNet model achieved state-of-the-art accuracy (ACC), area under the curve (AUC), F1 score and Recall rate. And the XChondNet model explicitly reflects the consistency with pathologists’ concerns, which improves the interpretability of AI.