Identification of a biomarker panel for colorectal cancer diagnosis

Background: Malignancies arising in the large bowel cause the second largest number of deaths from cancer in the Western World. Despite progresses made during the last decades, colorectal cancer remains one of the most frequent and deadly neoplasias in the western countries. Methods: A genomic study of human colorectal cancer has been carried out on a total of 31 tumoral samples, corresponding to different stages of the disease, and 33 non-tumoral samples. The study was carried out by hybridisation of the tumour samples against a reference pool of non-tumoral samples using Agilent Human 1A 60- mer oligo microarrays. The results obtained were validated by qRT-PCR. In the subsequent bioinformatics analysis, gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling were built. The consensus among all the induced models produced a hierarchy of dependences and, thus, of variables. Results: After an exhaustive process of pre-processing to ensure data quality–lost values imputation, probes quality, data smoothing and intraclass variability filtering–the final dataset comprised a total of 8, 104 probes. Next, a supervised classification approach and data analysis was carried out to obtain the most relevant genes. Two of them are directly involved in cancer progression and in particular in colorectal cancer. Finally, a supervised classifier was induced to classify new unseen samples. Conclusions: We have developed a tentative model for the diagnosis of colorectal cancer based on a biomarker panel. Our results indicate that the gene profile described herein can discriminate between non-cancerous and cancerous samples with 94.45% accuracy using different supervised classifiers (AUC values in the range of 0.997 and 0.955).

Pravastatin inhibits cell proliferation and increased MAT1A expression in hepatocarcinoma cells and in vivo models

Background: Statins may have therapeutic effects on hepatocarcinoma (HCC). This type of disorder is the most common malignant primary tumour in the liver. Our objective was to determine whether pravastatin had a therapeutic effect in vitro and in vivo models. Method: We design in vitro and in vivo model. In vitro we used PLC and determine cell proliferation. In vivo, we used and animal model to determined, PCNA and MAT1A expression and transaminases levels. Results: We found that pravastatin decreases cell proliferation in vitro (cell proliferation in pravastatin group was 82%, in sorafenib group 51% and in combined group 40%) and in vivo (in pravastatin group 80%, in sorafenib group 76.4% and in combined group 72.72%). The MAT1A levels, was significantly higher in Pravastatin group (D 62%, P 94%, S 71%, P + S 91%). The transaminases levels, decreased significantly in Pravastatin group (GOT and GPT levels D 619.5 U/L; 271 U/L) (P 117.5 U/L; 43.5 U/L) (S 147 U/L; 59 U/L) (P + S 142 U/L; 59 U/L). Conclusion: The combination of pravastatin + sorafenib were more effective than Sorafenib alone.

Is there a subgroup of long-term evolution among patients with advanced lung cancer?: Hints from the analysis of survival curves from cancer registry data

Background: Recently, with the access of low toxicity biological and targeted therapies, evidence of the existence of a long-term survival subpopulation of cancer patients is appearing. We have studied an unselected population with advanced lung cancer to look for evidence of multimodality in survival distribution, and estimate the proportion of long-term survivors. Methods: We used survival data of 4944 patients with non-small-cell lung cancer (NSCLC) stages IIIb-IV at diagnostic, registered in the National Cancer Registry of Cuba (NCRC) between January 1998 and December 2006. We fitted one-component survival model and two-component mixture models to identify short-and long-term survivors. Bayesian information criterion was used for model selection. Results: For all of the selected parametric distributions the two components model presented the best fit. The population with short-term survival (almost 4 months median survival) represented 64% of patients. The population of long-term survival included 35% of patients, and showed a median survival around 12 months. None of the patients of short-term survival was still alive at month 24, while 10% of the patients of long-term survival died afterwards. Conclusions: There is a subgroup showing long-term evolution among patients with advanced lung cancer. As survival rates continue to improve with the new generation of therapies, prognostic models considering short-and long-term survival subpopulations should be considered in clinical research.