The use of temozolomide (TMZ) has improved the prognosis for glioblastoma multiforme patients. However, TMZ resistance may be one of the main reasons why treatment fails. Although this resistance has frequently been linked to the expression of O6-methylguanine-DNA methyltransferase (MGMT) it seems that this enzyme is not the only molecular mechanism that may account for the appearance of drug resistance in glioblastoma multiforme patients as the mismatch repair (MMR) complex, P-glycoprotein, and/or the presence of cancer stem cells may also be implicated. Four nervous system tumor cell lines were used to analyze the modulation of MGMT expression and MGMT promoter methylation by TMZ treatment. Furthermore, 5-aza-2’-deoxycytidine was used to demethylate the MGMT promoter and O(6)-benzylguanine to block GMT activity. In addition, MMR complex and P-glycoprotein expression were studied before and after TMZ exposure and correlated with MGMT expression. Finally, the effect of TMZ exposure on CD133 expression was analyzed. Our results showed two clearly differentiated groups of tumor cells characterized by low (A172 and LN229) and high (SF268 and SK-N-SH) basal MGMT expression. Interestingly, cell lines with no MGMT expression and low TMZ IC₅₀ showed a high MMR complex expression, whereas cell lines with high MGMT expression and high TMZ IC₅₀ did not express the MMR complex. In addition, modulation of MGMT expression in A172 and LN229 cell lines was accompanied by a significant increase in the TMZ IC₅₀, whereas no differences were observed in SF268 and SK-N-SH cell lines. In contrast, P-glycoprotein and CD133 was found to be unrelated to TMZ resistance in these cell lines. These results may be relevant in understanding the phenomenon of TMZ resistance, especially in glioblastoma multiforme patients laking MGMT expression, and may also aid in the design of new therapeutic strategies to improve the efficacy of TMZ in glioblastoma multiforme patients.

Pancreatic ductal adenocarcinoma is a deadly disease because of late diagnosis and chemoresistance. We aimed to find a panel of serum cytokines representing diagnostic and predictive biomarkers for pancreatic cancer. A cytokine antibody array was performed to simultaneously identify 507 cytokines in sera of patients with pancreatic cancer and healthy controls. The nonparametric Mann-Whitney U test was used to pairwise compare the controls, the pretreated patients, and the posttreated patients. Fold changes greater than or equal to 1.5 or less than or equal to 1/1.5 were considered significant. Receiver operating characteristic curves were used to assess the performance of the model. A leave-one-out cross-validation was used for estimating prediction error. Comparing the sera of pretreated patients against the control samples, the cytokines fibroblast growth factor 10 (FGF-10/keratinocyte growth factor-2 (KGF-2), chemokine (C-X-C motif) ligand 11 interferon inducible T cell alpha chemokine (I-TAC)/chemokine [C-X-C motif] ligand 11 (CXCL11), oncostatin M (OSM), osteoactivin/glycoprotein nonmetastatic melanoma protein B, and stem cell factor (SCF) were found significantly overexpressed. Besides, the cytokines CD30 ligand/tumor necrosis factor superfamily, member 8 (TNFSF8), chordin-like 2, FGF-10/KGF-2, growth/differentiation factor 15, I-TAC/CXCL11, OSM, and SCF were differentially expressed in response to treatment. We propose a role for FGF-10/KGF-2, I-TAC/CXCL11, OSM, osteoactivin/glycoprotein nonmetastatic melanoma protein B, and SCF as novel diagnostic biomarkers. CD30 ligand/TNFSF8, chordin-like 2, FGF-10/KGF-2, growth/differentiation factor 15, I-TAC/CXCL11, OSM, and SCF might represent as predictive biomarkers for gemcitabine and erlotinib response of patients with pancreatic cancer.

In more recent years, a significant increase in the number of available biological experiments has taken place due to the widespread use of massive sequencing data. Furthermore, the continuous developments in the machine learning and in the high performance computing areas, are allowing a faster and more efficient analysis and processing of this type of data. However, biological information about a certain disease is normally widespread due to the use of different sequencing technologies and different manufacturers, in different experiments along the years around the world. Thus, nowadays it is of paramount importance to attain a correct integration of biologically-related data in order to achieve genuine benefits from them. For this purpose, this work presents an integration of multiple Microarray and RNA-seq platforms, which has led to the design of a multiclass study by collecting samples from the main four types of leukemia, quantified at gene expression. Subsequently, in order to find a set of differentially expressed genes with the highest discernment capability among different types of leukemia, an innovative parameter referred to as coverage is presented here. This parameter allows assessing the number of different pathologies that a certain gen is able to discern. It has been evaluated together with other widely known parameters under assessment of an ANOVA statistical test which corroborated its filtering power when the identified genes are subjected to a machine learning process at multiclass level. The optimal tuning of gene extraction evaluated parameters by means of this statistical test led to the selection of 42 highly relevant expressed genes. By the use of minimum-Redundancy Maximum-Relevance (mRMR) feature selection algorithm, these genes were reordered and assessed under the operation of four different classification techniques. Outstanding results were achieved by taking exclusively the first ten genes of the ranking into consideration. Finally, specific literature was consulted on this last subset of genes, revealing the occurrence of practically all of them with biological processes related to leukemia. At sight of these results, this study underlines the relevance of considering a new parameter which facilitates the identification of highly valid expressed genes for simultaneously discerning multiple types of leukemia.

Cancer is the second leading cause of death in the Western world. The limited successes of available treatments for cancer mean that new strategies need to be developed. The possibility of modifying the cancer cell with the introduction of genetic material opens the way to a new approach based on gene therapy. There are still many technical difficulties to be overcome, but recent advances in the molecular and cellular biology of gene transfer have made it likely that gene therapy will soon start to play an increasing role in clinical practice, particularly in the treatment of cancer. Gene therapy will probably be the therapeutic option in cases in which conventional treatments such as surgery, radiotherapy and chemotherapy have failed. The development of modified vectors, and an improved understanding of interactions between the vector and the human host, are generating inventions that are being protected by patents due to the considerable interest of industry for their possible commercialization. We review the latest strategies, patented and/or under clinical trial, in cancer gene therapy. These include patents that cover the use of modified vectors to increase the security and specificity, recombining adenovirus that leads to loss or gain of gene function, activation of the patient's own immune cells to eliminate cancer cells by expression of molecules that enhance immune responses, silencing genes related to the development of drug resistance in patients, inhibition of angiogenesis of solid tumors by targeting the tumor vasculature, and the development of enzymes that destroy viral or cancerous genetic material.

Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses (‘gained’ genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease.