Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironment.

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.

Ion release and local effects of titanium metal particles from dental implants: An experimental study in rats.

BACKGROUND: The objective of this study was to evaluate the accumulation of ions in blood and organs caused by titanium (Ti) metal particles in a mandibular defect in rats, together with a description of the local reaction of oral tissues to this Ti alloy debris. METHODS: Twenty Sprague-Dawley rats were randomly distributed into three groups: an experimental group with a mandibular bone defect filled with metallic debris obtained by implantoplasty; a positive control group; and a negative control group. Thirty days after surgery, the rats were euthanized and perilesional tissue surrounding the mandibular defect was removed, together with the lungs, spleen, liver, and brain. Two blood samples were collected: immediately before surgery and before euthanasia. The perilesional tissue was histologically analyzed using hematoxylin-eosin staining, and Ti, aluminum, and vanadium ion concentrations in blood and organs were measured by TQ-ICP-MS. Descriptive and bivariate analyses of the data were performed. RESULTS: All rats with implanted metal debris showed metal particles and a bone fracture callus on the osseous defect. The metal particles were surrounded by a foreign body reaction characterized by the presence of histiocytes and multinucleated giant cells (MNGCs). The experimental group had a significant higher concentration of Ti ions in all studied organs except lung tissue (p < 0.05). In addition, there were more V ions in the brain in the experimental group (p = 0.008). CONCLUSIONS: Although further studies are required to confirm the clinical relevance of these results, Ti metal particles in the jaw might increase the concentration of metal ions in vital organs and induce a foreign body reaction.

Decreased miR-497-5p Suppresses IL-6 Induced Atrophy in Muscle Cells.

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.

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma.

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.

The expression landscape of cachexia-inducing factors in human cancers.

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.

Tumor Transcriptome Reveals High Expression of IL-8 in Non-Small Cell Lung Cancer Patients with Low Pectoralis Muscle Area and Reduced Survival.

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.

The Pathway to Cancer Cachexia: MicroRNA-Regulated Networks in Muscle Wasting Based on Integrative Meta-Analysis.

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.