Cancer cachexia is a multifactorial syndrome that leads to significant weight loss. Cachexia affects 50%–80% of cancer patients, depending on the tumor type, and is associated with 20%–40% of cancer patient deaths. Besides the efforts to identify molecular mechanisms of skeletal muscle atrophy—a key feature in cancer cachexia—no effective therapy for the syndrome is currently available. MicroRNAs are regulators of gene expression, with therapeutic potential in several muscle wasting disorders. We performed a meta-analysis of previously published gene expression data to reveal new potential microRNA–mRNA networks associated with muscle atrophy in cancer cachexia. We retrieved 52 differentially expressed genes in nine studies of muscle tissue from patients and rodent models of cancer cachexia. Next, we predicted microRNAs targeting these differentially expressed genes. We also include global microRNA expression data surveyed in atrophying skeletal muscles from previous studies as background information. We identified deregulated genes involved in the regulation of apoptosis, muscle hypertrophy, catabolism, and acute phase response. We further predicted new microRNA–mRNA interactions, such as miR-27a/Foxo1, miR-27a/Mef2c, miR-27b/Cxcl12, miR-27b/Mef2c, miR-140/Cxcl12, miR-199a/Cav1, and miR-199a/Junb, which may contribute to muscle wasting in cancer cachexia. Finally, we found drugs targeting MSTN, CXCL12, and CAMK2B, which may be considered for the development of novel therapeutic strategies for cancer cachexia. Our study has broadened the knowledge of microRNA-regulated networks that are likely associated with muscle atrophy in cancer cachexia, pointing to their involvement as potential targets for novel therapeutic strategies.

Pancreatic ductal adenocarcinoma (

Interleukin-6 (IL-6) is a pro-inflammatory cytokine associated with skeletal muscle wasting in cancer cachexia. The control of gene expression by microRNAs (miRNAs) in muscle wasting involves the regulation of thousands of target transcripts. However, the miRNA-target networks associated with IL6-induced muscle atrophy remain to be characterized. Here, we show that IL-6 promotes the atrophy of C2C12 myotubes and changes the expression of 20 miRNAs (5 up-regulated and 15 down-regulated). Gene Ontology analysis of predicted miRNAs targets revealed post-transcriptional regulation of genes involved in cell differentiation, apoptosis, migration, and catabolic processes. Next, we performed a meta-analysis of miRNA-published data that identified miR-497-5p, a down-regulated miRNAs induced by IL-6, also down-regulated in other muscle-wasting conditions. We used miR-497-5p mimics and inhibitors to explore the function of miR-497-5p in C2C12 myoblasts and myotubes. We found that miR-497-5p can regulate the expression of the cell cycle genes CcnD2 and CcnE1 without affecting the rate of myoblast cellular proliferation. Notably, miR-497-5p mimics induced myotube atrophy and reduced Insr expression. Treatment with miR-497-5p inhibitors did not change the diameter of the myotubes but increased the expression of its target genes Insr and Igf1r. These genes are known to regulate skeletal muscle regeneration and hypertrophy via insulin-like growth factor pathway and were up-regulated in cachectic muscle samples. Our miRNA-regulated network analysis revealed a potential role for miR-497-5p during IL6-induced muscle cell atrophy and suggests that miR-497-5p is likely involved in a compensatory mechanism of muscle atrophy in response to IL-6.

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Background Computed tomographies (CT) are useful for identifying muscle loss in non-small lung cancer (NSCLC) cachectic patients. However, we lack consensus on the best cutoff point for pectoralis muscle loss. We aimed to characterize NSCLC patients based on muscularity, clinical data, and the transcriptional profile from the tumor microenvironment to build a cachexia classification model. Methods We used machine learning to generate a muscle loss prediction model, and the tumor's cellular and transcriptional profile was characterized in patients with low muscularity. First, we measured the pectoralis muscle area (PMA) of 211 treatment-naive NSCLC patients using CT available in The Cancer Imaging Archive. The cutoffs were established using machine learning algorithms (CART and Cutoff Finder) on PMA, clinical, and survival data. We evaluated the prediction model in a validation set (36 NSCLC). Tumor RNA-Seq (GSE103584) was used to profile the transcriptome and cellular composition based on digital cytometry. Results CART demonstrated that a lower PMA was associated with a high risk of death (HR = 1.99). Cutoff Finder selected PMA cutoffs separating low-muscularity (LM) patients based on the risk of death (P-value = 0.003; discovery set). The cutoff presented 84% of success in classifying low muscle mass. The high risk of LM patients was also found in the validation set. Tumor RNA-Seq revealed 90 upregulated secretory genes in LM that potentially interact with muscle cell receptors. The LM upregulated genes enriched inflammatory biological processes. Digital cytometry revealed that LM patients presented high proportions of cytotoxic and exhausted CD8+ T cells. Conclusions Our prediction model identified cutoffs that distinguished patients with lower PMA and survival with an inflammatory and immunosuppressive TME enriched with inflammatory factors and CD8+ T cells.