Obesity and colorectal cancer:Role of adipokines in tumor initiation and progression

Obesity-associated diseases account for a large portion of public health challenges. Among obesity-related disorders, a direct and independent relationship has been ascertained for colorectal cancer (CRC). The evidence that adipocyte hypertrophy and excessive adipose tissue accumulation (mainly visceral) can promote pathogenic adipocyte and adipose tissue-related diseases, has led to formulate the concept of “adiposopathy”, defined as adipocyte and adipose tissue dysfunction that contributes to metabolic syndrome. Adipose tissue can, indeed, be regarded as an important and highly active player of the innate immune response, in which cytokine/adipokine secretion is responsible for a paracrine loop between adipocytes and macrophages, thus contributing to the systemic chronic low-grade inflammation associated with visceral obesity, which represents a favorable niche for tumor development. The adipocyte itself participates as a central mediator of this inflammatory response in obese individuals by secreting hormones, growth factors and proinflammatory cytokines, which are of particular relevance for the pathogenesis of CRC. Among adipocyte-secreted hormones, the most relevant to colorectal tumorigenesis are adiponectin, leptin, resistin and ghrelin. All these molecules have been involved in cell growth and proliferation, as well as tumor angiogenesis and it has been demonstrated that their expression changes from normal colonic mucosa to adenoma and adenocarcinoma, suggesting their involvement in multistep colorectal carcinogenesis. These findings have led to the hypothesis that an unfavorable adipokine profile, with a reduction of those with an anti-inflammatory and anti-cancerous activity, might serve as a prognostic factor in CRC patients and that adipokines or their analogues/antagonists might become useful agents in the management or chemoprevention of CRC.

Clinical significance of glycemic parameters on venous thromboembolism risk prediction in gastrointestinal cancer

To investigate the possible predictive role of routinely used glycemic parameters for a first venous thromboembolism (VTE) episode in gastrointestinal (GI) cancer ambulatory patients - with or without clinically diagnosed type 2 diabetes (T2D) or obesity - treated with chemotherapy.METHODSPre-treatment fasting blood glucose, insulin, glycated hemoglobin (HbA1c) and homeostasis model of risk assessment (HOMA) were retrospectively evaluated in a cohort study of 342 GI cancer patients. Surgery was performed in 142 (42%) patients with primary cancer, 30 (21%) and 112 (79%) of whom received neoadjuvant and adjuvant therapies, respectively. First-line chemotherapy was administered in 200 (58%) patients with metastatic disease. The study outcome was defined as the occurrence of a first symptomatic or asymptomatic VTE episode during active treatment.RESULTSImpaired glucose tolerance (IGT) or T2D were diagnosed in 30% of GI cancer patients, while overweight/obesity had an incidence of 41%. VTE occurred in 9.4% of patients (7% of non-diabetic non-obese), especially in those with a high ECOG score (P = 0.025). No significant association was found between VTE incidence and T2D, obesity, different tumor types, metastatic disease, Khorana class of risk, or different anti-cancer drugs, although VTE rates were substantially higher in patients receiving bevacizumab (17% vs 8%, P = 0.044). Conversely, all glucose metabolic indexes were associated with increased VTE risk at ROC analysis. Multivariate Cox proportional analyses confirmed that HOMA index (HR = 4.13, 95%CI: 1.63-10.5) or fasting blood glucose (HR = 3.56, 95%CI: 1.51-8.39) were independent predictors of VTE occurrence during chemotherapy.CONCLUSIONThe results here reported demonstrate that evaluating glucose metabolic asset may allow for VTE risk stratification in GI cancer, helping to identify chemotherapy-treated patients who might benefit from thromboprophylaxis. Further multicenter prospective studies involving a larger number of patients are presently needed.

Prognostic value of glycated hemoglobin in colorectal cancer

AIM To investigate the clinical significance of routinely used glycemic parameters in a cohort of colorectal cancer(CRC) patients.METHODS Pre-treatment fasting blood glucose, insulin, Hb A1 c and homeostasis model of risk assessment(HOMA-IR) were retrospectively evaluated in a case-control study of 224 CRC and 112 control subjects matched for sex, obesity and diabetes frequency and blood lipid profile.Furthermore, the prognostic value of routinely used glycemic parameters towards progression-free(PFS) and overall survival(OS) was prospectively evaluated.RESULTS Fasting blood glucose, insulin, HOMA-IR and HbA 1c(all P < 0.0001) levels were higher in non-diabetic CRC patients compared with obesity-matched controls. All parameters were associated with increased CRC risk at ROC analysis, but no relationship with clinical-pathological variables or survival outcomes was observed for glycemia, insulinemia or HOMA-IR. Conversely, advanced CRC stage(P = 0.018) was an independent predictor of increased Hb A1 c levels, which were also higher in patients who had disease progression compared with those who did not(P = 0.05). Elevated Hb A1 c levels showed a negative prognostic value both in terms of PFS(HR = 1.24) and OS(HR = 1.36) after adjustment for major confounders, which was further confirmed in a subgroup analysis performed after exclusion of diabetic patients.CONCLUSION HbA 1c might have a negative prognostic value in CRC, thus suggesting that glycemic metabolic markers should be carefully monitored in these patients, independently of overt diabetes.

Artificial intelligence for the study of colorectal cancer tissue slides

Artificial intelligence(AI)is gaining incredible momentum as a companion diagnostic in a number of fields in oncology.In the present mini-review,we summarize the main uses and findings of AI applied to the analysis of digital histopathological images of slides from colorectal cancer(CRC)patients.Machine learning tools have been developed to automatically and objectively recognize specific CRC subtypes,such as those with microsatellite instability and high lymphocyte infiltration that would optimally respond to specific therapies.Also,AI-based classification in distinct prognostic groups with no studies of the basic biological features of the tumor have been attempted in a methodological approach that we called“biology-agnostic”.