Background: Cachexia is a multifactorial syndrome highly associated with specific tumour types, but the causes of variation in cachexia prevalence and severity are unknown. While circulating plasma mediators (soluble cachectic factors) derived from tumours have been implicated with the pathogenesis of the syndrome, these associations were generally based on plasma concentration rather than tissue‐specific gene expression levels. Here, we hypothesized that tumour gene expression profiling of cachexia‐inducing factors (CIFs) in human cancers with different prevalence of cachexia could reveal potential cancer‐specific cachexia mediators and biomarkers of clinical outcome.Methods: First, we combined uniformly processed RNA sequencing data from The Cancer Genome Atlas and Genotype‐Tissue Expression databases to characterize the expression profile of secretome genes in 12 cancer types (4651 samples) compared with their matched normal tissues (2737 samples). We systematically investigated the transcriptomic data to assess the tumour expression profile of 25 known CIFs and their predictive values for patient survival. We used the Xena Functional Genomics tool to analyse the gene expression of CIFs according to neoplastic cellularity in pancreatic adenocarcinoma, which is known to present the highest prevalence of cachexia.Results: A comprehensive characterization of the expression profiling of secreted genes in different human cancers revealed pathways and mediators with a potential role in cachexia within the tumour microenvironment. Cytokine‐related and chemokine‐related pathways were enriched in tumour types frequently associated with the syndrome. CIFs presented a tumour‐specific expression profile, in which the number of upregulated genes was correlated with the cachexia prevalence (r²: 0.80; P value: 0.002) and weight loss (r²: 0.81; P value: 0.002). The distinct gene expression profile, according to tumour type, was significantly associated with prognosis (P value ≤ 1.96 E‐06). In pancreatic adenocarcinoma, the upregulated CIF genes were associated with tumours presenting low neoplastic cellularity and high leucocyte fraction and not with tumour grade.Conclusions: Our results present a biological dimension of tumour‐secreted elements that are potentially useful to explain why specific cancer types are more likely to develop cachexia. The tumour‐specific profile of CIFs may help the future development of better targeted therapies to treat cancer types highly associated with the syndrome.

Amino acids (AA) and IGF1 have been demonstrated to play essential roles in protein synthesis and fish muscle growth. The myoblast cell culture is useful for studying muscle regulation, and omics data have contributed enormously to understanding its molecular biology. However, to our knowledge, no study has performed the large-scale sequencing of fish-cultured muscle cells stimulated with pro-growth signals. In this work, we obtained the transcriptome and microRNAome of pacu (Piaractus mesopotamicus)-cultured myotubes treated with AA or IGF1. We identified 1228 and 534 genes differentially expressed by AA and IGF1. An enrichment analysis showed that AA treatment induced chromosomal changes, mitosis, and muscle differentiation, while IGF1 modulated IGF/PI3K signaling, metabolic alteration, and matrix structure. In addition, potential molecular markers were similarly modulated by both treatments. Muscle-miRNAs (miR-1, -133, -206 and -499) were up-regulated, especially in AA samples, and we identified molecular networks with omics integration. Two pairs of genes and miRNAs demonstrated a high-level relationship, and involvement in myogenesis and muscle growth: marcksb and miR-29b in AA, and mmp14b and miR-338-5p in IGF1. Our work helps to elucidate fish muscle physiology and metabolism, highlights potential molecular markers, and creates a perspective for improvements in aquaculture and in in vitro meat production.

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.

Cachexia is a syndrome characterized by an ongoing loss of skeletal muscle mass associated with poor patient prognosis in non-small cell lung cancer (NSCLC). However, prognostic cachexia biomarkers in NSCLC are unknown. Here, we analyzed computed tomography (CT) images and tumor transcriptome data to identify potentially secreted cachexia biomarkers (PSCB) in NSCLC patients with low-muscularity. We integrated radiomics features (pectoralis muscle, sternum, and tenth thoracic (T10) vertebra) from CT of 89 NSCLC patients, which allowed us to identify an index for screening muscularity. Next, a tumor transcriptomic-based secretome analysis from these patients (discovery set) was evaluated to identify potential cachexia biomarkers in patients with low-muscularity. The prognostic value of these biomarkers for predicting recurrence and survival outcome was confirmed using expression data from eight lung cancer datasets (validation set). Finally, C2C12 myoblasts differentiated into myotubes were used to evaluate the ability of the selected biomarker, interleukin (IL)-8, in inducing muscle cell atrophy. We identified 75 over-expressed transcripts in patients with low-muscularity, which included IL-6, CSF3, and IL-8. Also, we identified NCAM1, CNTN1, SCG2, CADM1, IL-8, NPTX1, and APOD as PSCB in the tumor secretome. These PSCB were capable of distinguishing worse and better prognosis (recurrence and survival) in NSCLC patients. IL-8 was confirmed as a predictor of worse prognosis in all validation sets. In vitro assays revealed that IL-8 promoted C2C12 myotube atrophy. Tumors from low-muscularity patients presented a set of upregulated genes encoding for secreted proteins, including pro-inflammatory cytokines that predict worse overall survival in NSCLC. Among these upregulated genes, IL-8 expression in NSCLC tissues was associated with worse prognosis, and the recombinant IL-8 was capable of triggering atrophy in C2C12 myotubes.

Pancreatic ductal adenocarcinoma (PDAC) is extremely aggressive, has an unfavorable prognosis, and there are no biomarkers for early detection of the disease or identification of individuals at high risk for morbidity or mortality. The cellular and molecular complexity of PDAC leads to inconsistences in clinical validations of many proteins that have been evaluated as prognostic biomarkers of the disease. The tumor secretome, a potential source of biomarkers in PDAC, plays a crucial role in cell proliferation and metastasis, as well as in resistance to treatments, which together contribute to a worse clinical outcome. The massive amount of proteomic data from pancreatic cancer that has been generated from previous studies can be integrated and explored to uncover secreted proteins relevant to the diagnosis and prognosis of the disease. The present study aimed to perform an integrated meta-analysis of PDAC proteome and secretome public data to identify potential biomarkers of the disease. Our meta-analysis combined mass spectrometry data obtained from two systematic reviews of the pancreatic cancer literature, which independently selected 20 studies of the secretome and 35 of the proteome. Next, we predicted the secreted proteins using seven in silico tools or databases, which identified 39 secreted proteins shared between the secretome and proteome data. Notably, the expression of 31 genes of these secretome-related proteins was upregulated in PDAC samples from The Cancer Genome Atlas (TCGA) when compared to control samples from TCGA and The Genotype-Tissue Expression (GTEx). The prognostic value of these 39 secreted proteins in predicting survival outcome was confirmed using gene expression data from four PDAC datasets (validation set). The gene expression of these secreted proteins was able to distinguish high- and low-survival patients in nine additional tumor types from TCGA, demonstrating that deregulation of these secreted proteins may also contribute to the prognosis in multiple cancers types. Finally, we compared the prognostic value of the identified secreted proteins in PDAC biomarkers studies from the literature. This analysis revealed that our gene signature performed equally well or better than the signatures from these previous studies. In conclusion, our integrated meta-analysis of PDAC proteome and secretome identified 39 secreted proteins as potential biomarkers, and the tumor gene expression profile of these proteins in patients with PDAC is associated with worse overall survival.