Nuclear factor kappa B expression in non-small cell lung cancer

A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data

Interpreting transcriptome data is an important yet challenging aspect of bioinformatic analysis. While gene set enrichment analysis is a standard tool for interpreting regulatory changes, we utilize deep learning techniques, specifically autoencoder architectures, to learn latent variables that drive transcriptome signals. We investigate whether simple, variational autoencoder (VAE), and beta-weighted VAE are capable of learning reduced representations of transcriptomes that retain critical biological information. We propose a novel VAE that utilizes priors from biological data to direct the network to learn a representation of the transcriptome that is based on understandable biological concepts. After benchmarking five different autoencoder architectures, we found that each succeeded in reducing the transcriptomes to 50 latent dimensions, which captured enough variation for accurate reconstruction. The simple, fully connected autoencoder, performs best across the benchmarks, but lacks the characteristic of having directly interpretable latent dimensions. The beta-weighted, prior-informed VAE implementation is able to solve the benchmarking tasks, and provide semantically accurate latent features equating to biological pathways. This study opens a new direction for differential pathway analysis in transcriptomics with increased transparency and interpretability.

Extracellular vesicles isolated from curcumin-medium weakened RKO cell proliferation and migration

Background

Curcumin (Cur) is a natural phytochemical that is expected to become an indispensable drug for the treatment of colorectal cancer. A comprehensive understanding of the anti-tumor mechanism of Cur will provide a better reference for its clinical application. This study aimed to examine the effects of extracellular vesicles (EVs) isolated from Cur-medium on RKO colorectal cancer cell proliferation, apoptosis, and migration.

Methods

RKO cells were cultured in various concentrations of Cur-medium, and the EVs were isolated from the Cur-medium. The EVs were identified by transmission electron microscopy and western blotting. The effects of the EVs on RKO cell proliferation, apoptosis, and migration were analyzed, as was the expression of proliferating cell nuclear antigen (PCNA), Bax, vimentin, and E-cadherin. The expression of nuclear factor κB (NF-κB) p65 in the EVs was also detected.

Results

Our results showed that the EVs isolated from the Cur-medium weakened RKO cell proliferation and migration but had no effect on cell apoptosis. Cur suppressed the expression of NF-κB p65 in the EVs. Overall, this study revealed that Cur exerts anti-tumor effects by suppressing NF-κB p65 in EVs to weaken RKO cell proliferation and migration.

Conclusions

In conclusion, the packaging of Cur into EVs is expected to become an indispensable treatment of colorectal cancer in the future.

In search of the ratio of miRNA expression as robust biomarkers for constructing stable diagnostic models among multi-center data

MicroRNAs (miRNAs) are promising biomarkers for the early detection of disease, and many miRNA-based diagnostic models have been constructed to distinguish patients and healthy individuals. To thoroughly utilize the miRNA-profiling data across different sequencing platforms or multiple centers, the models accounting the batch effects were demanded for the generalization of medical application. We conducted transcription factor (TF)-mediated miRNA-miRNA interaction network analysis and adopted the within-sample expression ratios of miRNA pairs as predictive markers. The ratio of the expression values between each miRNA pair turned out to be stable across multiple data sources. A genetic algorithm-based classifier was constructed to quantify risk scores of the probability of disease and discriminate disease states from normal states in discovery, with a validation dataset for COVID-19, renal cell carcinoma, and lung adenocarcinoma. The predictive models based on the expression ratio of interacting miRNA pairs demonstrated good performances in the discovery and validation datasets, and the classifier may be used accurately for the early detection of disease.

Role of MARK2 in the nervous system and cancer

Microtubule-Affinity Regulating Kinase 2 (MARK2), a member of the serine/threonine protein kinase family, phosphorylates microtubule-associated proteins, playing a crucial role in cancer and neurodegenerative diseases. This kinase regulates multiple signaling pathways, including the WNT, PI3K/AKT/mTOR (PAM), and NF-κB pathways, potentially linking it to cancer and the nervous system. As a crucial regulator of the PI3K/AKT/mTOR pathway, the loss of MARK2 inhibits the growth and metastasis of cancer cells. MARK2 is involved in the excessive phosphorylation of tau, thus influencing neurodegeneration. Therefore, MARK2 emerges as a promising drug target for the treatment of cancer and neurodegenerative diseases. Despite its significance, the development of inhibitors for MARK2 remains limited. In this review, we aim to present detailed information on the structural features of MARK2 and its role in various signaling pathways associated with cancer and neurodegenerative diseases. Additionally, we further characterize the therapeutic potential of MARK2 in neurodegenerative diseases and cancer, and hope to facilitate basic research on MARK2 and the development of inhibitors targeting MARK2.