Muscle mass, assessed at diagnosis by L3-CT scan as a prognostic marker of clinical outcomes in patients with gastric cancer: A systematic review and meta-analysis

Rinninella, Emanuele; Cintoni, Marco; Raoul, Pauline; Pozzo, Carmelo; Strippoli, Antonia; Bria, Emilio; Tortora, Giampaolo; Gasbarrini, Antonio; Mele, Maria Cristina

doi:10.1016/j.clnu.2019.10.021

Cited by 96 publications

(80 citation statements)

References 62 publications

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“…In addition, The autophagy marker protein LC3 is also associated with low prognosis of gastric cancer. [15]. These ndings con rm the relationship between autophagy and gastric cancer, and indicate the great potential of autophagy related genes as a prognostic marker of gastric cancer.…”

Section: Introductionsupporting

confidence: 57%

Identification and Validation of an Individualized Autophagy-Clinical Prognostic Index in Gastric Cancer Patients

Qiu

Sun

Wang

et al. 2020

Preprint

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Background : The purpose of this study is to perform bioinformatics analysis of autophagy-related genes in gastric cancer, and to construct a multi-gene joint signature for predicting the prognosis of gastric cancer. Methods : GO and KEGG analysis were applied for differentially expressed autophagy-related genes in gastric cancer, and PPI network was constructed in Cytoscape software. In order to optimize the prognosis evaluation system of gastric cancer, we established a prognosis model integrating autophagy-related genes. We used single factor Cox proportional risk regression analysis to screen genes related to prognosis from 204 autophagy-related genes in The Atlas Cancer Genome (TCGA) gastric cancer cohort. Then, the generated genes were applied to the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, the selected genes were further included in the multivariate Cox proportional hazard regression analysis to establish the prognosis model. According to the median risk score, patients were divided into high-risk group and low-risk group, and survival analysis was conducted to evaluate the prognostic value of risk score. Finally, by combining clinic-pathological features and prognostic gene signatures, a nomogram was established to predict individual survival probability. Results : GO analysis showed that the 28 differently expressed autophagy-related genes was enriched in cell growth, neuron death, and regulation of cell growth. KEGG analysis showed that the 28 differently expressed autophagy-related genes were related to platinum drug resistance, apoptosis and p53 signaling pathway. The risk score was constructed based on 4 genes (GRID2, ATG4D,GABARAPL2, CXCR4), and gastric cancer patients were significantly divided into high-risk and low-risk groups according to overall survival. In multivariate Cox regression analysis, risk score was still an independent prognostic factor (HR = 1.922, 95% CI = 1.573-2.349, P < 0.001). Cumulative curve showed that the survival time of patients with low-risk score was significantly longer than that of patients with high-risk score (P < 0.001). The external data GSE62254 proved that nomograph had a great ability to evaluate the prognosis of individual gastric cancer patients. Conclusions: This study provides a potential prognostic marker for predicting the prognosis of GC patients and the molecular biology of GC autophagy.

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Section: Introductionsupporting

confidence: 57%

Identification and Validation of an Individualized Autophagy-Clinical Prognostic Index in Gastric Cancer Patients

Qiu

Sun

Wang

et al. 2020

Preprint

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“…The definition of cut-offs for skeletal muscle index or sketetal muscle radiodensity varied according to the thresholds used in each study, and specifically for the skeletal muscle index, according to the demographic characteristics of the patients. Lack of standardization of assessment of muscle mass influences not only the findings of the present meta-analysis but also other studies that have examined the role of muscle mass in patients with cancer 34. Due to the limited number of studies and the non-significance of results, subgroup analyses were not performed to avoid misinterpretation of the data.…”

Section: Discussionmentioning

confidence: 95%

Skeletal muscle mass as a prognostic indicator of outcomes in ovarian cancer: a systematic review and meta-analysis

Rinninella

Fagotti

Cintoni

et al. 2020

Int J Gynecol Cancer

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BackgroundMuscle mass plays a key role in predicting clinical outcomes in cancer. This systematic review and meta-analysis aimed to evaluate whether computed tomography (CT) scan indexes of muscle mass quantity and quality could be used as prognostic factors in ovarian cancer.MethodsThree electronic bibliographic databases (MEDLINE, Web of Science, and Cochrane Central Register of Controlled Trials) were used to conduct a systematic literature search from inception to January 2020. The primary outcome was overall survival. Pooled analyses of hazard ratios (HRs) and 95% confidence intervals (CIs) were performed with Review Manager 5.3. Heterogeneity was assessed by measuring inconsistency (I2 based on the χ2 test). Secondary outcomes included progression free survival, disease free survival, postoperative complications, and chemotoxicity. Study quality and quality of evidence were assessed.ResultsA total of 15 studies were included in the systematic review, of which six studies (1226 patients) were included in the meta-analysis. Summary unadjusted HRs (HR 1.11, 95% CI 0.84 to 1.46, p=0.47) and adjusted HRs (HR 1.10, 95% CI 0.84 to 1.43, p=0.49) did not show a significant association between low skeletal muscle index and overall survival (p>0.05) in ovarian cancer. Instead, although the quality of evidence was low, pooled data of three studies, comprising 679 patients, showed a significant association between low skeletal muscle radiodensity and poor overall survival (HR 1.63, 95% CI 1.28 to 2.07, p<0.0001). Moreover, the heterogeneity between studies precluded the possibility of performing a meta-analysis and reaching conclusions for progression free survival, disease free survival, surgical complications, and chemotoxicity.ConclusionsThis work suggested that the measurement of skeletal muscle radiodensity by routine CT scan at diagnosis, with standardization of diagnostic criteria, could be a reliable tool to select at risk patients and to individualize effective nutritional strategies. However, prospective homogeneous studies with a larger number of patients are required to confirm these results.

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“…Patients with poor nutritional status are often not able to tolerate chemotherapy and discontinue treatment. Low skeletal muscle mass was already shown to be a significant predictor of chemotherapy toxicity and survival in cancer patients [ 5 , 6 , 7 ]. Thus, research increasingly focuses on the importance of preserving skeletal muscle mass of patients receiving chemotherapy.…”

Section: Introductionmentioning

confidence: 99%

Skeletal Muscle Loss during Multikinase Inhibitors Therapy: Molecular Pathways, Clinical Implications, and Nutritional Challenges

Rinninella

Cintoni²,

Raoul

et al. 2020

Nutrients

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In cancer patients, loss of muscle mass is significantly associated with low tolerability of chemotherapy and poor survival. Despite the great strides in the treatment of cancer, targeted therapies such as tyrosine kinase inhibitors (TKIs) could exacerbate muscle wasting. Over recent years, the impact of skeletal muscle loss during TKI therapy on clinical outcomes has been in the spotlight. In this review, we focus on the different molecular pathways of TKIs potentially involved in muscle wasting. Then, we report the results of the studies assessing the effects of different TKI therapies—such as sorafenib, regorafenib, sunitinib, and lenvatinib—on muscle mass, and highlight their potential clinical implications. Finally, we discuss an integrative nutritional approach to be adopted during TKI treatment. The assessment of muscle mass from computerized tomography imaging could be helpful in predicting toxicity and prognosis in patients treated with TKI such as sorafenib. Early recognition of low muscle mass and effective personalized nutritional support could prevent or attenuate muscle mass wasting. However, the role of nutrition is still overlooked, and future clinical trials are needed to find the optimal nutritional support to countermeasure muscle mass depletion during TKI therapy.

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Muscle mass, assessed at diagnosis by L3-CT scan as a prognostic marker of clinical outcomes in patients with gastric cancer: A systematic review and meta-analysis

Cited by 96 publications

References 62 publications

Identification and Validation of an Individualized Autophagy-Clinical Prognostic Index in Gastric Cancer Patients

Identification and Validation of an Individualized Autophagy-Clinical Prognostic Index in Gastric Cancer Patients

Skeletal muscle mass as a prognostic indicator of outcomes in ovarian cancer: a systematic review and meta-analysis

Skeletal Muscle Loss during Multikinase Inhibitors Therapy: Molecular Pathways, Clinical Implications, and Nutritional Challenges

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