CD44 is a nexus between prognosis and therapeutics for brain cancer management.

Background: Brain tumors, both primary and metastatic, are the main cause of cancer-associated mortality leading to patients' poor quality-of-life that remain as a global challenge. This study aims to identify the hub molecular controlling brain cancer progression, and establish corresponding prognostic index and therapeutic modality for improved brain cancer management.

Method: We performed pan-cancer analysis unifying 9 publicly available datasets of cancers metastasized to the brain from different organs to identify the hub gene feasible for brain cancer prognosis.

SNRPD1 conveys prognostic value on breast cancer survival and is required for anthracycline sensitivity.

Background: Cancers harboring spliceosome mutations are highly sensitive to additional perturbations on the spliceosome that leads to the development of onco-therapeutics targeting the spliceosome and opens novel opportunities for managing aggressive tumors lacking effective treatment options such as triple negative breast cancers. Being the core spliceosome associated proteins, SNRPD1 and SNRPE have been both proposed as therapeutic targets for breast cancer management. Yet, their differences regarding their prognostic and therapeutic use as well as roles during carcinogenesis are largely unreported.

Cold Atmospheric Plasma Conveys Selectivity Against Hepatocellular Carcinoma Cells Triggering EGFR(Tyr1068)-Mediated Autophagy.

Hepatocellular carcinomas remain as a global health threat given its high mortality rate. We have previously identified the selectivity of cold atmospheric plasma (CAP) against multiple types of malignant tumors and proposed it as a promising onco-therapeutic strategy. Here, we investigated its roles in controlling hepatocellular carcinoma malignancy and one possible driving molecular mechanism.

Single-cell sequencing: expansion, integration and translation.

With the rapid advancement in sequencing technologies, the concept of omics has revolutionized our understanding of cellular behaviors. Conventional omics investigation approaches measure the averaged behaviors of multiple cells, which may easily hide signals represented by a small-cell cohort, urging for the development of techniques with enhanced resolution. Single-cell RNA sequencing, investigating cell transcriptomics at the resolution of a single cell, has been rapidly expanded to investigate other omics such as genomics, proteomics and metabolomics since its invention.