TCF19 promotes cell proliferation and tumor formation in lung cancer by activating the Raf/MEK/ERK signaling pathway.

OBJECTIVE: This study aimed to investigate TCF19's role in lung cancer development, specifically its involvement in the RAF/MEK/ERK signaling pathway. METHODS: Lung cancer tissue analysis revealed significant TCF19 overexpression. In vitro experiments using A549 and Hop62 cells with TCF19 overexpression demonstrated enhanced cell growth. Transgenic mouse models confirmed TCF19's role in primary tumor development. Transcriptome sequencing identified altered gene expression profiles, linking TCF19 to RAF/MEK/ERK pathway activation. Functional assays elucidated underlying mechanisms, revealing increased phosphorylation of Raf1, MEK1/2, and ERK1/2. Inhibiting RAF1 or ERK through shRaf1 or ERK inhibitor reduced cell cycle-related proteins and inhibited TCF19-overexpressing cell growth. RESULTS: TCF19 was identified as an oncogene in lung carcinoma, specifically impacting the RAF/MEK/ERK pathway. Elevated TCF19 levels in lung cancer suggest targeting TCF19 or its associated pathways as a promising strategy for disease management. CONCLUSION: This study unveils TCF19's oncogenic role in lung cancer, emphasizing its modulation of the RAF/MEK/ERK pathway and presenting a potential therapeutic target for TCF19-overexpressing lung cancers.

Segment alignment based cross-subject motor imagery classification under fading data.

Motor imagery (MI) aims to use brain imagination without actual body activities to support motor learning, and machine learning algorithms such as common spatial patterns (CSP) are proven effective in the analysis of MI signals. In the conventional machine learning-based approaches, there are two main difficulties in feature extraction and recognition of MI signals: high personalization and data fading. The high personalization problem is due to the multi-subject nature when collecting MI signals, and the data fading problem as a recurring issue in MI signal quality is first raised by us but is not widely discussed at present. Aiming to solve the above two mentioned problems, a cross-subject fading data classification approach with segment alignment is proposed to classify the fading data of one single target with the model trained with the normal data of multiple sources in this paper. he effectiveness of proposed method is verified via two experiments: a dataset-based experiment with the dataset from BCI Competition and a lab-based experiment designed and conducted by us. The experimental results obtained from both experiments show that the proposed method can obtain optimal classification performance effectively under different fading levels with data from different subjects.