Transcriptomic signature of cancer cachexia by integration of machine learning, literature mining and meta-analysis.

BACKGROUND: Cancer cachexia is a severe metabolic syndrome marked by skeletal muscle atrophy. A successful clinical intervention for cancer cachexia is currently lacking. The study of cachexia mechanisms is largely based on preclinical animal models and the availability of high-throughput transcriptomic datasets of cachectic mouse muscles is increasing through the extensive use of next generation sequencing technologies. METHODS: Cachectic mouse muscle transcriptomic datasets of ten different studies were combined and mined by seven attribute weighting models, which analysed both categorical variables and numerical variables. The transcriptomic signature of cancer cachexia was identified by attribute weighting algorithms and was used to evaluate the performance of eleven pattern discovery models. The signature was employed to find the best combination of drugs (drug repurposing) for developing cancer cachexia treatment strategies, as well as to evaluate currently used cachexia drugs by literature mining. RESULTS: Attribute weighting algorithms ranked 26 genes as the transcriptomic signature of muscle from mice with cancer cachexia. Deep Learning and Random Forest models performed better in differentiating cancer cachexia cases based on muscle transcriptomic data. Literature mining revealed that a combination of melatonin and infliximab has negative interactions with 2 key genes (Rorc and Fbxo32) upregulated in the transcriptomic signature of cancer cachexia in muscle. CONCLUSIONS: The integration of machine learning, meta-analysis and literature mining was found to be an efficient approach to identifying a robust transcriptomic signature for cancer cachexia, with implications for improving clinical diagnosis and management of this condition.

Identification of Potential Biomarkers for Cancer Cachexia and Anti-Fn14 Therapy.

BACKGROUND: Developing therapies for cancer cachexia has not been successful to date, in part due to the challenges of achieving robust quantitative measures as a readout of patient treatment. Hence, identifying biomarkers to assess the outcomes of treatments for cancer cachexia is of great interest and important for accelerating future clinical trials. METHODS: We established a novel xenograft model for cancer cachexia with a cachectic human PC3* cell line, which was responsive to anti-Fn14 mAb treatment. Using RNA-seq and secretomic analysis, genes differentially expressed in cachectic and non-cachectic tumors were identified and validated by digital droplet PCR (ddPCR). Correlation analysis was performed to investigate their impact on survival in cancer patients. RESULTS: A total of 46 genes were highly expressed in cachectic PC3* tumors, which were downregulated by anti-Fn14 mAb treatment. High expression of the top 10 candidates was correlated with low survival and high cachexia risk in different cancer types. Elevated levels of LCN2 were observed in serum samples from cachectic patients compared with non-cachectic cancer patients. CONCLUSION: The top 10 candidates identified in this study are candidates as potential biomarkers for cancer cachexia. The diagnostic value of LCN2 in detecting cancer cachexia is confirmed in patient samples.

Biomarkers for Cancer Cachexia: A Mini Review.

Cancer cachexia is a common condition in many cancer patients, particularly those with advanced disease. Cancer cachexia patients are generally less tolerant to chemotherapies and radiotherapies, largely limiting their treatment options. While the search for treatments of this condition are ongoing, standards for the efficacy of treatments have yet to be developed. Current diagnostic criteria for cancer cachexia are primarily based on loss of body mass and muscle function. However, these criteria are rather limiting, and in time, when weight loss is noticeable, it may be too late for treatment. Consequently, biomarkers for cancer cachexia would be valuable adjuncts to current diagnostic criteria, and for assessing potential treatments. Using high throughput methods such as "omics approaches", a plethora of potential biomarkers have been identified. This article reviews and summarizes current studies of biomarkers for cancer cachexia.

Generation of reporter cell lines for factors inducing muscle wasting in cancer cachexia.

Rapidly identifying cachexia-inducing factors that directly induce muscle wasting is an existing challenge. We developed two reporter cell lines that allow swift detection of such factors in blood from patients. C2C12 myoblasts were used for the establishment of reporter cells. A luciferase reporter gene, driven by promoters of wasting genes, Muscle RING-finger protein-1 (MuRF1) and Muscle Atrophy F-Box Protein (MAFbx/Atrogin-1) were used for the construction of reporter constructs. Increased expression of these genes in muscle tissue under wasting conditions was shown in vivo and in vitro. We found these reporter cell lines could detect factors associated with cancer cachexia, such as myostatin (Mstn), activin A, and TNF-α. We further investigated the capacity to directly detect a cachectic state using plasma samples from cachectic mice and cancer patients. Activation of the reporter cell lines was observed by the addition of plasma from mice with cancer cachexia and serum samples from patients with pancreatic or colorectal cancer. These results indicate that the reporter cell lines are competent as a tool for screening cachexia-inducing factors and potentially distinguishing a cachectic state induced by cancer.

Activation of Src induces mitochondrial localisation of de2-7EGFR (EGFRvIII) in glioma cells: implications for glucose metabolism.

A common mutation of the epidermal growth factor receptor in glioma is the de2-7EGFR (or EGFRvIII). Glioma cells expressing de2-7EGFR contain an intracellular pool of receptor with high levels of mannose glycosylation, which is consistent with delayed processing. We now show that this delay occurs in the Golgi complex. Low levels of de2-7EGFR were also seen within the mitochondria. Src activation dramatically increased the amount of mitochondrial de2-7EGFR, whereas its pharmacological inhibition caused a significant reduction. Because de2-7EGFR is phosphorylated by Src at Y845, we generated glioma cells expressing a Y845F-modified de2-7EGFR. The de2-7EGFR(845F) mutant failed to show mitochondrial localisation, even when co-expressed with constitutive active Src. Low levels of glucose enhanced mitochondrial localisation of de2-7EGFR, and glioma cells expressing the receptor showed increased survival and proliferation under these conditions. Consistent with this, de2-7EGFR reduced glucose dependency by stimulating mitochondrial oxidative metabolism. Thus, the mitochondrial localisation of de2-7EGFR contributes to its tumorigenicity and might help to explain its resistance to some EGFR-targeted therapeutics.