Journal of Computational Biology

Downregulations of miR-449a and miR-145-5p Act as Prognostic Biomarkers for Endometrial Cancer

This investigation aimed to explore the underlying prognosis-associated microRNA (miRNA) biomarkers in endometrial cancer. Homo sapiens miRNA data set GSE35794 and miRNA data in TGGA database were downloaded and applied to screen the differentially expressed miRNAs (DE-miRNAs) using unpaired t -test in limma package in R. Basing on Venn analysis, the overlapped DE-miRNAs were screened and their potential targets were predicted according to miRWalk followed by target functional enrichment analyses and protein–protein interaction network visualized using Cytoscape. Finally, according to the information provided by the The Cancer Genome Atlas (TCGA) database, correlations between miRNAs or targets and patient prognosis were analyzed by survival package in R. A total of 24 overlapped DE-miRNAs were identified between endometrioid endometrial cancer samples and normal samples. Then, the miRNA-target regulatory network was constructed, including 11 upregulated miRNAs (e.g., miR-200a, miR-200b, and miR-200c) and five downregulated miRNAs (e.g., miR-449a, miR-145-5p, and miR-145-3p). Lymphocyte enhancer factor-1 ( LEF1 ) was predicted to be a target of miR-449a and SOX11 was a target of miR-145-5p. Functional enrichment analyses of these targets were significantly related to the biological process of “negative regulation of transcription from RNA polymerase II promoter” and “positive regulation of transcription from RNA polymerase II promoter” (e.g., NOTCH1 , LEF1 , and SOX11 ). In addition, survival analysis showed that miR-449a, miR-145-5p, and LEF1 were approximately correlated with the overall survival prognosis of endometrial cancer patients. Downregulations of miR-449a and miR-145-5p might be involved in the pathogenesis of endometrial cancer and could act as prognostic biomarkers for endometrial cancer patients.

Screening of Critical Genes Involved in Metastasis and Prognosis of High-Grade Serous Ovarian Cancer by Gene Expression Profile Data

We aimed to identify critical genes for metastasis and prognosis of high-grade serous ovarian cancer (HGSOC). The mRNA data of GSE73064 including 15 HGSOC samples (5 primary tumor cells, 5 ascites tumor cells, and 5 metastasis tumor cells) were applied for the present analysis. The data were first preprocessed using robust multiarray average (RMA) method, and time-series gene expression analyses were performed using short time-series expression miner (version 1.3.11). Then, functional analysis for clustered genes was conducted, and protein-protein interaction (PPI) network was constructed. Subnetwork module was also analyzed. After that, we analyzed the miRNA regulatory network. Finally, survival analysis was performed. In this study,

CerviNet: A Novel Approach for Cervical Cancer Classification Using Pap-Smear Images

Cervical cancer is the fourth most common disease among women worldwide, and pap smear images are used as a primary diagnostic technique to detect precancerous and cancerous abnormalities in the cervix, vagina, and vulva. Deep learning algorithms have gained popularity in developing automated computer-aided diagnostic systems to solve the difficulties associated with manual assessment. This article introduces an innovative hybrid approach to effectively and accurately categorizing cervical cells. The proposed model employs advanced data enhancement techniques, including resampling to address class imbalance and augmentation (e.g., random horizontal flips and rotations) to increase dataset diversity and improve generalization. These strategies help the model handle different types of data more effectively, making it more adaptable and reliable in real-world scenarios. We use Vision Transformer’s (ViT) linear projection and position embedding to change the input images into patches that can be sent to a transformer encoder. A fusion architecture is established by incorporating supplementary convolutional layers, followed by a fully connected layer, to improve the features extracted by the model. The ViT-based model is developed using pretrained weights and allows fine-tuning to address problems with cervical cancer classification efficiently. To enhance the quality of these cell images, we employ median smoothing and Gaussian filtering as preprocessing techniques. The experiment results demonstrate the proposed methodology’s potential for improving the precision of cervical cancer classification. Notably, our model exhibited outstanding accuracy on the 2-state classification on the Herlev dataset and the 3-state classification on the SIPaKMeD dataset, at 98.07% and 98.08%, respectively. The model’s ability to effectively categorize cervical cancer images across various datasets is evidenced by the accuracy rates specific to each dataset. This indicates the model’s robustness and promise for practical clinical use.