Apolipoprotein L1 is a tumor suppressor in clear cell renal cell carcinoma metastasis.

The 5-year survival rate of kidney cancer drops dramatically from 93% to 15% when it is metastatic. Metastasis constitutes for 30% of kidney cancer cases, in which clear cell renal cell carcinoma (ccRCC) is the most prominent subtype. By sequencing mRNA of ccRCC patient samples, we found that apolipoprotein L1 (APOL1) was highly expressed in tumors compared to their adjacent normal tissues. This gene has been previously identified in a large body of kidney disease research and was reported as a potential prognosis marker in many types of cancers. However, the molecular function of APOL1 in ccRCC, especially in metastasis, remained unknown. In this study, we modulated the expression of APOL1 in various renal cancer cell lines and analyzed their proliferative, migratory, and invasive properties. Strikingly, APOL1 overexpression suppressed ccRCC metastasis both in vitro and in vivo. We then explored the mechanism by which APOL1 alleviated ccRCC malignant progression by investigating its downstream pathways. APOL1 overexpression diminished the activity of focal adhesive molecules, Akt signaling pathways, and EMT processes. Furthermore, in the upstream, we discovered that miR-30a-3p could inhibit APOL1 expression. In conclusion, our study revealed that APOL1 play a role as a tumor suppressor in ccRCC and inhibit metastasis, which may provide novel potential therapeutic approaches for ccRCC patients.

EGFR is a master switch between immunosuppressive and immunoactive tumor microenvironment in inflammatory breast cancer.

Inflammatory breast cancer (IBC), the most aggressive breast cancer subtype, is driven by an immunosuppressive tumor microenvironment (TME). Current treatments for IBC have limited efficacy. In a clinical trial (NCT01036087), an anti-EGFR antibody combined with neoadjuvant chemotherapy produced the highest pathological complete response rate ever reported in patients with IBC having triple-negative receptor status. We determined the molecular and immunological mechanisms behind this superior clinical outcome. Using novel humanized IBC mouse models, we discovered that EGFR-targeted therapy remodels the IBC TME by increasing cytotoxic T cells and reducing immunosuppressive regulatory T cells and M2 macrophages. These changes were due to diminishing immunosuppressive chemokine expression regulated by transcription factor EGR1. We also showed that induction of an immunoactive IBC TME by an anti-EGFR antibody improved the antitumor efficacy of an anti-PD-L1 antibody. Our findings lay the foundation for clinical trials evaluating EGFR-targeted therapy combined with immune checkpoint inhibitors in patients with cancer.

Using k-mer embeddings learned from a Skip-gram based neural network for building a cross-species DNA N6-methyladenine site prediction model.

KEY MESSAGE: This study used k-mer embeddings as effective feature to identify DNA N6-Methyladenine sites in plant genomes and obtained improved performance without substantial effort in feature extraction, combination and selection. Identification of DNA N6-methyladenine sites has been a very active topic of computational biology due to the unavailability of suitable methods to identify them accurately, especially in plants. Substantial results were obtained with a great effort put in extracting, heuristic searching, or fusing a diverse types of features, not to mention a feature selection step. In this study, we regarded DNA sequences as textual information and employed natural language processing techniques to decipher hidden biological meanings from those sequences. In other words, we considered DNA, the human life book, as a book corpus for training DNA language models. K-mer embeddings then were generated from these language models to be used in machine learning prediction models. Skip-gram neural networks were the base of the language models and ensemble tree-based algorithms were the machine learning algorithms for prediction models. We trained the prediction model on Rosaceae genome dataset and performed a comprehensive test on 3 plant genome datasets. Our proposed method shows promising performance with AUC performance approaching an ideal value on Rosaceae dataset (0.99), a high score on Rice dataset (0.95) and improved performance on Rice dataset while enjoying an elegant, yet efficient feature extraction process.