Semaphorin 5A suppresses the proliferation and migration of lung adenocarcinoma cells

The regulation of enteric neuron connectivity by semaphorin 5A is affected by the autism-associated S956G missense mutation

The neural network of the enteric nervous system (ENS) underlies gastrointestinal functions. However, the molecular mechanisms involved in enteric neuronal connectivity are poorly characterized. Here, we studied the role of semaphorin 5A (Sema5A), previously characterized in the central nervous system, on ENS neuronal connectivity. Sema5A is linked to autism spectrum disorder (ASD), a neurodevelopmental disorder frequently associated with gastrointestinal comorbidities, and potentially associated with ENS impairments. This study investigated in rat enteric neuron cultures and gut explants the role of Sema5A on enteric neuron connectivity and the impact of ASD-associated mutations on Sema5A activity. Our findings demonstrated that Sema5A promoted axonal complexity and reduced functional connectivity in enteric neurons. Strikingly, the ASD-associated mutation S956G in Sema5A strongly affected these activities. This study identifies a critical role of Sema5A in the ENS as a regulator of neuronal connectivity that might be compromised in ASD.

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.

A new immune signature for survival prediction and immune checkpoint molecules in non-small cell lung cancer

Background

Immune checkpoint blockade (ICB) therapy has brought remarkable clinical benefits to patients with advanced non-small cell lung carcinoma (NSCLC). However, the prognosis remains largely variable.

Methods

The profiles of immune-related genes for patients with NSCLC were extracted from TCGA database, ImmPort dataset, and IMGT/GENE-DB database. Coexpression modules were constructed using WGCNA and 4 modules were identified. The hub genes of the module with the highest correlations with tumor samples were identified. Then integrative bioinformatics analyses were performed to unveil the hub genes participating in tumor progression and cancer-associated immunology of NSCLC. Cox regression and Lasso regression analyses were conducted to screen prognostic signature and to develop a risk model.

Results

Functional analysis showed that immune-related hub genes were involved in the migration, activation, response, and cytokine-cytokine receptor interaction of immune cells. Most of the hub genes had a high frequency of gene amplifications. MASP1 and SEMA5A presented the highest mutation rate. The ratio of M2 macrophages and naïve B cells revealed a strong negative association while the ratio of CD8 T cells and activated CD4 memory T cells showed a strong positive association. Resting mast cells predicted superior overall survival. Interactions including protein-protein, lncRNA and transcription factor interactions were analyzed and 9 genes were selected by LASSO regression analysis to construct and verify a prognostic signature. Unsupervised hub genes clustering resulted in 2 distinct NSCLC subgroups. The TIDE score and the drug sensitivity of gemcitabine, cisplatin, docetaxel, erlotinib and paclitaxel were significantly different between the 2 immune-related hub gene subgroups.

Conclusions

These findings suggested that our immune-related genes can provide clinical guidance for the diagnosis and prognosis of different immunophenotypes and facilitate the management of immunotherapy in NSCLC.

A single N6-methyladenosine site regulates lncRNA HOTAIR function in breast cancer cells

N6-methyladenosine (m6A) modification of RNA regulates normal and cancer biology, but knowledge of its function on long noncoding RNAs (lncRNAs) remains limited. Here, we reveal that m6A regulates the breast cancer-associated human lncRNA HOTAIR. Mapping m6A in breast cancer cell lines, we identify multiple m6A sites on HOTAIR, with 1 single consistently methylated site (A783) that is critical for HOTAIR-driven proliferation and invasion of triple-negative breast cancer (TNBC) cells. Methylated A783 interacts with the m6A "reader" YTHDC1, promoting chromatin association of HOTAIR, proliferation and invasion of TNBC cells, and gene repression. A783U mutant HOTAIR induces a unique antitumor gene expression profile and displays loss-of-function and antimorph behaviors by impairing and, in some cases, causing opposite gene expression changes induced by wild-type (WT) HOTAIR. Our work demonstrates how modification of 1 base in an lncRNA can elicit a distinct gene regulation mechanism and drive cancer-associated phenotypes.

Semaphorin 6C Suppresses Proliferation of Pancreatic Cancer Cells via Inhibition of the AKT/GSK3/β-Catenin/Cyclin D1 Pathway

Semaphorins (SEMAs) are axon guidance factors that participate in axonal connections and nerve system development. However, the functional roles of SEMAs in tumorigenesis are still largely uncovered. By using in silico data analysis, we found that SEMA6C was downregulated in pancreatic cancer, and its reduction was correlated with worse survival rates. RNA sequencing revealed that cell cycle-related genes, especially cyclin D1, were significantly altered after blockage of SEMA6C by neutralizing antibodies or ectopic expressions of SEMA6C. Mechanistic investigation demonstrated that SEMA6C acts as a tumor suppressor in pancreatic cancer by inhibiting the AKT/GSK3 signaling axis, resulting in a decrease in cyclin D1 expression and cellular proliferation. The enhancement of cyclin D1 expression and cyclin-dependent kinase activation in SEMA6C-low cancer created a druggable target of CDK4/6 inhibitors. We also elucidated the mechanism underlying SEMA6C downregulation in pancreatic cancer and demonstrated a novel regulatory role of miR-124-3p in suppressing SEMA6C. This study provides new insights of SEMA6C-mediated anti-cancer action and suggests the treatment of SEMA6C-downregulated cancer by CDK4/6 inhibitors.

Semaphorins as emerging clinical biomarkers and therapeutic targets in cancer

Semaphorins are a large family of developmental regulatory signals, characterized by aberrant expression in human cancers. These molecules crucially control cell-cell communication, cell migration, invasion and metastasis, tumor angiogenesis, inflammatory and anti-cancer immune responses. Semaphorins comprise secreted and cell surface-exposed molecules and their receptors are mainly found in the Plexin and Neuropilin families, which are further implicated in a signaling network controlling the tumor microenvironment. Accumulating evidence indicates that semaphorins may be considered as novel clinical biomarkers for cancer, especially for the prediction of patient survival and responsiveness to therapy. Moreover, preclinical experimental studies have demonstrated that targeting semaphorin signaling can interfere with tumor growth and/or metastatic dissemination, suggesting their relevance as novel therapeutic targets in cancer; this has also prompted the development of semaphorin-interfering molecules for application in the clinic. Here we will survey, in diverse human cancers, the current knowledge about the relevance of semaphorin family members, and conceptualize potential lines of future research development in this field.