Postmyocardial Infarct Remodeling and Heart Failure: Potential Contributions from Pro- and Antiaging Factors

Myocardial infarction and adverse postinfarct remodeling in older persons lead to poor outcome and need greater understanding of the contributions of age-related factors on abnormal cardiac function and management. In this perspective, how normal aging processes could contribute to the events of post-myocardial infarction and remodeling is reviewed. Post-myocardial infarction and remodeling involve cardiomechanical factors and neurohormonal response. Many factors prevent or accelerate aging including immunosenescence, recruitment and regeneration of stem cells, telomere shortening, oxidative damage, antiaging hormones klotho and melatonin, nutrition, and Sirtiun protein family, and these factors could affect post-MI remodeling and heart failure. Interest in stem cell repair of myocardial infarcts to mitigate post-MI remodeling needs more information on aging of stem cells, and potential effects on stem cell use in infarct repair. Integrating genomics and proteomics methods may help find clinically novel therapy in the management of post-MI remodeling and heart failure in aged individuals

Human Cancer Classification: A Systems Biology- Based Model Integrating Morphology, Cancer Stem Cells, Proteomics, and Genomics

Human cancer classification is currently based on the idea of cell of origin, light and electron microscopic attributes of the cancer. What is not yet integrated into cancer classification are the functional attributes of these cancer cells. Recent innovative techniques in biology have provided a wealth of information on the genomic, transcriptomic and proteomic changes in cancer cells. The emergence of the concept of cancer stem cells needs to be included in a classification model to capture the known attributes of cancer stem cells and their potential contribution to treatment response, and metastases. The integrated model of cancer classification presented here incorporates all morphology, cancer stem cell contributions, genetic, and functional attributes of cancer. Integrated cancer classification models could eliminate the unclassifiable cancers as used in current classifications. Future cancer treatment may be advanced by using an integrated model of cancer classification

Galectin-3 and Beclin1/Atg6 Genes In Human Cancers: Using cDNA Tissue Panel, qRT-PCR, and Logistic Regression Model to Identify Cancer Cell Biomarkers

Cancer biomarkers are sought to support cancer diagnosis, predict cancer patient response to treatment and survival. Identifying reliable biomarkers for predicting cancer treatment response needs understanding of all aspects of cancer cell death and survival. Galectin-3 and Beclin1 are involved in two coordinated pathways of programmed cell death, apoptosis and autophagy and are linked to necroptosis/necrosis. The aim of the study was to quantify galectin-3 and Beclin1 mRNA in human cancer tissue cDNA panels and determine their utility as biomarkers of cancer cell survival.A panel of 96 cDNAs from eight (8) different normal and cancer tissue types were used for quantitative real-time polymerase chain reaction (qRT-PCR) using ABI7900HT. Miner2.0, a web-based 4- and 3-parameter logistic regression software was used to derive individual well polymerase chain reaction efficiencies (E) and cycle threshold (Ct) values. Miner software derived formula was used to calculate mRNA levels and then fold changes. The ratios of cancer to normal tissue levels of galectin-3 and Beclin1 were calculated (using the mean for each tissue type). Relative mRNA expressions for galectin-3 were higher than for Beclin1 in all tissue (normal and cancer) types. In cancer tissues, breast, kidney, thyroid and prostate had the highest galectin-3 mRNA levels compared to normal tissues. High levels of Beclin1 mRNA levels were in liver and prostate cancers when compared to normal tissues. Breast, kidney and thyroid cancers had high galectin-3 levels and low Beclin1 levels.Galectin-3 expression patterns in normal and cancer tissues support its reported roles in human cancer. Beclin1 expression pattern supports its roles in cancer cell survival and in treatment response. qRT-PCR analysis method used may enable high throughput studies to generate molecular biomarker sets for diagnosis and predicting cancer treatment response

Primers for Galectin-3, Beclin1 and β-Actin used for qRT-PCR.

<p>LGALS3 amplicon size = 161, location 5′-3′ = 13-33, 3′-5′ = 173-155. BECN1 amplicon size = 188, location 5′-3′ = 104-123, 3′-5′ = 291-271. ACTB amplicon size = 250, location 5′-3′ = 393-413, 3′-5′ = 642-622.</p

Beclin1/Atg6 relative mRNA levels in Human Normal and Cancer Tissues.

<p>(<b>A</b>). Box plot of the overall expression in all tissue types. (<b>B</b>) Box plots of Beclin1 distribution of mRNA levels in normal tissues (Na = normal breast, Nb = Normal colon, Nc = Normal kidney, Nd = Normal liver, Ne = Normal lung, Nf = Normal ovary, Ng = Normal Prostate, Nh = Normal thyroid). (<b>C</b>) Box-whisker plots of the relative Beclin1 mRNA levels in cancer tissues. BrCa = Breast cancer, Col_Ca = colon cancer, Ca_Kid = Kidney cancer, Ca_Liv = liver cancer, Ca_Lun = lung cancer, Ca_Ova = ovarian cancer, Ca_Pros = Prostate cancer, Ca_Thyr = thyroid cancer (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0026150#pone-0026150-t003" target="_blank">Table 3a and b</a> for values).</p