The present results suggest that decreased Gal-9 expression is inversely associated with malignant potential or differentiation of cervical CIN and SCC as a differentiation biomarker.

The CXC chemokines belong to a family which includes 17 different CXC members. Accumulating evidence suggests that CXC chemokines regulate tumor cell proliferation, invasion, and metastasis in various types of cancers by influencing the tumor microenvironment. The different expression profiles and specific function of each CXC chemokine in head and neck squamous cell carcinoma (HNSCC) are not yet clarified. In our work, we analyzed the altered expression, interaction network, and clinical data of CXC chemokines in patients with HNSCC by using the following: the Oncomine dataset, cBioPortal, Metascape, String analysis, GEPIA, and the Kaplan–Meier plotter. The transcriptional level analysis suggested that the mRNA levels of CXCL1, CXCL2, CXCL3, CXCL5, CXCL6, CXCL8, CXCL9, CXCL10, CXCL11, and CXCL13 increased in HNSCC tissue samples when compared to the control tissue samples. The expression levels of CXCL9, CXCL10, CXCL11, CXCL12, and CXCL14 were associated with various tumor stages in HNSCC. Clinical data analysis showed that high transcription levels of CXCL2, CXCL3, and CXCL12, were linked with low relapse-free survival (RFS) in HNSCC patients. On the other hand, high CXCL14 levels predicted high RFS outcomes in HNSCC patients. Meanwhile, increased gene transcription levels of CXCL9, CXCL10, CXCL13, CXCL14, and CXCL17 were associated with a higher overall survival (OS) advantage in HNSCC patients, while high levels of CXCL1, and CXCL8 were associated with poor OS in all HNSCC patients. This study implied that CXCL1, CXCL2, CXCL3, CXCL8, and CXCL12 could be used as prognosis markers to identify low survival rate subgroups of patients with HNSCC as well as be potential suitable therapeutic targets for HNSCC patients. Additionally, CXCL9, CXCL10, CXCL13, CXCL14, and CXCL17 could be used as functional prognosis biomarkers to identify better survival rate subgroups of patients with HNSCC.

Colon cancer identification is of great significance in medical diagnosis. Real-time, objective and accurate inspection results will facilitate medical professionals to explore symptomatic treatment promptly. However, the existing methods depend on hand-crafted features which require extensive professional expertise and long inspection period. Therefore, we propose a multi-scale feature fusion convolutional neural network (MFF-CNN) based on shearlet transform to identify histopathological image of colon cancer. The characteristic of the framework is the shearlet coefficients of histopathological image in multiple decomposition scales were extracted as supplementary feature which were also fed to the network with the original pathological image. After feature learning and feature fusion, the MFF-CNN based on shearlet transform can achieve the identification accuracy of 96% and average F-1 score of 0.9594 for colorectal adenocarcinoma epithelium (TUM) and normal colon mucosa (NORM). The false negative rate and false positive rate can be reduced to 5.5% and 2.5%, respectively. The superior performance of the network opens a new perspectives for real-time, objective and accurate diagnosis of cancer.

No abstract