Identification and Validation of DEPDC1B as an Independent Early Diagnostic and Prognostic Biomarker in Liver Hepatocellular Carcinoma

Unveiling the hub genes associated with aflatoxin B1-induced hepatotoxicity in chicken

Aflatoxin B1 (AFB1), a ubiquitous and toxic mycotoxin in human food and animal feedstuff, can impair the function and health of some organs, especially the liver. However, the knowledge about the potential mechanisms of AFB1-induced hepatotoxicity in chickens is limited. Therefore, we analyzed the gene expression data of chicken embryo primary hepatocytes (CEPHs) treated with and without AFB1 at the dose of 0.1 μg/mL which were cultured at 37 °C in Medium 199 (Life Technologies, Shanghai, China) with 5.0% CO2 for 48 h. Totally 1,711 differentially expressed genes (DEGs) were identified, in which 1,170 and 541 genes were up- and down-regulated in AFB1-administrated CEPHs compared to the control, respectively. Biological process analysis suggested that these DEGs might take part in angiogenesis, cell adhesion, immune response, cell differentiation, inflammatory response, cell migration regulation, and blood coagulation. Signaling pathways analysis revealed that these DEGs were mainly linked to metabolic pathways, MAPK, TLR2, and actin cytoskeleton regulation pathways. Moreover, the hub genes, including GYS2, NR1H4, ALDH8A1, and ANGPTL3, might participate in AFB1-induced hepatotoxicity. Taken together, our study offers a new insight into the mechanisms of the AFB1-induced hepatotoxicity.

Assessment of emerging pretraining strategies in interpretable multimodal deep learning for cancer prognostication

BACKGROUND

Deep learning models can infer cancer patient prognosis from molecular and anatomic pathology information. Recent studies that leveraged information from complementary multimodal data improved prognostication, further illustrating the potential utility of such methods. However, current approaches: 1) do not comprehensively leverage biological and histomorphological relationships and 2) make use of emerging strategies to "pretrain" models (i.e., train models on a slightly orthogonal dataset/modeling objective) which may aid prognostication by reducing the amount of information required for achieving optimal performance. In addition, model interpretation is crucial for facilitating the clinical adoption of deep learning methods by fostering practitioner understanding and trust in the technology.

METHODS

Here, we develop an interpretable multimodal modeling framework that combines DNA methylation, gene expression, and histopathology (i.e., tissue slides) data, and we compare performance of crossmodal pretraining, contrastive learning, and transfer learning versus the standard procedure.

RESULTS

Our models outperform the existing state-of-the-art method (average 11.54% C-index increase), and baseline clinically driven models (average 11.7% C-index increase). Model interpretations elucidate consideration of biologically meaningful factors in making prognosis predictions.

DISCUSSION

Our results demonstrate that the selection of pretraining strategies is crucial for obtaining highly accurate prognostication models, even more so than devising an innovative model architecture, and further emphasize the all-important role of the tumor microenvironment on disease progression.

DEPDC1B collaborates with GABRD to regulate ESCC progression

Background

Esophageal squamous cell carcinoma (ESCC) is the leading cause of cancer-related death worldwide with a poor prognosis. Given that DEPDC1B plays a key role in multiple cancers, the role of this molecule in ESCC was explored to identify potential targets for ESCC patients.

Method

The expression level of DEPDC1B in ESCC was revealed based on the TCGA database and immunohistochemical experiments on clinical tissues. The correlation between DEPDC1B and survival of ESCC patients was analyzed by Kaplan–Meier method. Small hairpin RNA (shRNA)-mediated silencing of DEPDC1B expression in ESCC cells and performed a series of in vitro and in vivo functional validations.

Result

DEPDC1B was overexpressed in ESCC. High expression of DEPDC1B was significantly negatively correlated with overall survival in patients with ESCC. Moreover, knockdown of DEPDC1B inhibited ESCC cell proliferation, clone formation, migration, tumor formation and promoted apoptosis. Furthermore, knockdown of DEPDC1B leaded to significant downregulation of GABRD in ESCC cells. Meanwhile, GABRD expression was upregulated in ESCC, and its silencing can inhibit the proliferation and migration of the tumor cells. Interestingly, there was a protein interaction between DEPDC1B and GABRD. Functionally, GABRD knockdown partially reversed the contribution of DEPDC1B to ESCC progression. In addition, GABRD regulated ESCC progression may depend on PI3K/AKT/mTOR signaling pathway.

Conclusion

DEPDC1B collaborated with GABRD to regulate ESCC progression, and inhibition of this signaling axis may be a potential therapeutic target for ESCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02593-z.