PreCanCell: An ensemble learning algorithm for predicting cancer and non-cancer cells from single-cell transcriptomes.

We propose PreCanCell, a novel algorithm for predicting malignant and non-malignant cells from single-cell transcriptomes. PreCanCell first identifies the differentially expressed genes (DEGs) between malignant and non-malignant cells commonly in five common cancer types-associated single-cell transcriptome datasets. The five common cancer types include renal cell carcinoma (RCC), head and neck squamous cell carcinoma (HNSCC), melanoma, lung adenocarcinoma (LUAD), and breast cancer (BC).

The Variation of Transcriptomic Perturbations is Associated with the Development and Progression of Various Diseases.

Background: Although transcriptomic data have been widely applied to explore various diseases, few studies have investigated the association between transcriptomic perturbations and disease development in a wide variety of diseases.

Methods: Based on a previously developed algorithm for quantifying intratumor heterogeneity at the transcriptomic level, we defined the variation of transcriptomic perturbations (VTP) of a disease relative to the health status. Based on publicly available transcriptome datasets, we compared VTP values between the disease and health status and analyzed correlations between VTP values and disease progression or severity in various diseases, including neurological disorders, infectious diseases, cardiovascular diseases, respiratory diseases, liver diseases, kidney diseases, digestive diseases, and endocrine diseases.

Identification of key molecules in COVID-19 patients significantly correlated with clinical outcomes by analyzing transcriptomic data.

Background: Although several key molecules have been identified to modulate SARS-CoV-2 invasion of human host cells, the molecules correlated with outcomes in COVID-19 caused by SARS-CoV-2 infection remain insufficiently explored.

Methods: This study analyzed three RNA-Seq gene expression profiling datasets for COVID-19 and identified differentially expressed genes (DEGs) between COVID-19 patients and normal people, commonly in the three datasets. Furthermore, this study explored the correlation between the expression of these genes and clinical features in COVID-19 patients.

Circular RNA circ‑CCT3 promotes hepatocellular carcinoma progression by regulating the miR‑1287‑5p/TEAD1/PTCH1/LOX axis.

Hepatocellular carcinoma (HCC) is characterized by a poor prognosis because of its insensitivity to radiation and chemotherapy. Recently, circular RNAs (circRNAs) have been found to serve important roles in hepatocellular carcinogenesis. circ‑CCT3, a novel circRNA, was screened from the differential tissue expression results of a circRNA microarray.