Second mitochondria-derived activator of caspase/direct inhibitor of apoptosis-binding protein with low pI (Smac/DIABLO) is a proapoptogenic mitochondrial protein that is released to the cytosol in response to diverse apoptotic stimuli, including commonly used chemotherapeutic drugs. In the cytosol, Smac/DIABLO interacts and antagonizes inhibitors of apoptosis proteins (IAPs), thus allowing the activation of caspases and apoptosis. This activity has prompted the synthesis of peptidomimetics that could potentially be used in cancer therapy. For these reasons, several authors have analyzed the expression levels of Smac/DIABLO in samples of patients from different tumors. Although dissimilar results have been found, a tissue-specific role of this protein emerges from the data. The objective of this review is to present the current knowledge of the Smac/DIABLO role in cancer and its possible use as a marker or therapeutic target for drug design.

Background: MicroRNAs (miRNAs) are noncoding RNA molecules heavily involved in human tumors, in which few of them circulating the human body. Finding a tumor-associated signature of miRNA, that is, the minimum miRNA entities to be measured for discriminating both different types of cancer and normal tissues, is of utmost importance. Feature selection techniques applied in machine learning can help however they often provide naive or biased results.Results: An ensemble feature selection strategy for miRNA signatures is proposed. miRNAs are chosen based on consensus on feature relevance from high-accuracy classifiers of different typologies. This methodology aims to identify signatures that are considerably more robust and reliable when used in clinically relevant prediction tasks. Using the proposed method, a 100-miRNA signature is identified in a dataset of 8023 samples, extracted from TCGA. When running eight-state-of-the-art classifiers along with the 100-miRNA signature against the original 1046 features, it could be detected that global accuracy differs only by 1.4%. Importantly, this 100-miRNA signature is sufficient to distinguish between tumor and normal tissues. The approach is then compared against other feature selection methods, such as UFS, RFE, EN, LASSO, Genetic Algorithms, and EFS-CLA. The proposed approach provides better accuracy when tested on a 10-fold cross-validation with different classifiers and it is applied to several GEO datasets across different platforms with some classifiers showing more than 90% classification accuracy, which proves its cross-platform applicability.Conclusions: The 100-miRNA signature is sufficiently stable to provide almost the same classification accuracy as the complete TCGA dataset, and it is further validated on several GEO datasets, across different types of cancer and platforms. Furthermore, a bibliographic analysis confirms that 77 out of the 100 miRNAs in the signature appear in lists of circulating miRNAs used in cancer studies, in stem-loop or mature-sequence form. The remaining 23 miRNAs offer potentially promising avenues for future research.

Yin-Yang transcription factor 1 (YY1) is involved in tumor progression, metastasis and has been shown to be elevated in different cancers, including leukemia. The regulatory mechanism underlying YY1 expression in leukemia is still not understood. Bioinformatics analysis reveal three Hypoxia-inducible factor 1-alpha (HIF-1α) putative binding sites in the YY1 promoter region. The regulation of YY1 by HIF-1α in leukemia was analyzed. Mutation of the putative YY1 binding sites in a reporter system containing the HIF-1α promoter region and CHIP analysis confirmed that these sites are important for YY1 regulation. Leukemia cell lines showed that both proteins HIF-1α and YY1 are co-expressed under hypoxia. In addition, the expression of mRNA of YY1 was increased after 3 h of hypoxia conditions and affect several target genes expression. In contrast, chemical inhibition of HIF-1α induces downregulation of YY1 and sensitizes cells to chemotherapeutic drugs. The clinical implications of HIF-1α in the regulation of YY1 were investigated by evaluation of expression of HIF-1α and YY1 in 108 peripheral blood samples and by RT-PCR in 46 bone marrow samples of patients with pediatric acute lymphoblastic leukemia (ALL). We found that the expression of HIF-1α positively correlates with YY1 expression in those patients. This is consistent with bioinformatic analyses of several databases. Our findings demonstrate for the first time that YY1 can be transcriptionally regulated by HIF-1α, and a correlation between HIF-1α expression and YY1 was found in ALL clinical samples. Hence, HIF-1α and YY1 may be possible therapeutic target and/or biomarkers of ALL.

XAF1 is a tumour suppressor gene that compromises cell viability by modulating different cellular events such as mitosis, cell cycle progression and apoptosis. In cancer, the XAF1 gene is commonly silenced by CpG-dinucleotide hypermethylation of its promoter. DNA demethylating agents induce transcriptional reactivation of XAF1, sensitizing cancer cells to therapy. The molecular mechanisms that mediate promoter CpG methylation have not been previously studied. Here, we demonstrate that CTCF interacts with the XAF1 promoter in vivo in a methylation-sensitive manner. By transgene assays, we demonstrate that CTCF mediates the open-chromatin configuration of the XAF1 promoter, inhibiting both CpG-dinucleotide methylation and repressive histone posttranslational modifications. In addition, the absence of CTCF in the XAF1 promoter inhibits transcriptional activation induced by well-known apoptosis activators. We report for the first time that epigenetic silencing of the XAF1 gene is a consequence of the loss of CTCF binding.

In mammals, apoptosis is the main mechanism to eliminate unwanted cells, securing tissue homeostasis and consequently maintaining the health in the organism. Classically, apoptosis culminates with the activation of caspases, which are enzymes that display cysteine protease activity to degrade specific substrates implied in essential cellular processes. This process is highly regulated. A key regulation mechanism is mediated by the Inhibitor of Apoptosis Proteins (IAPs) family members, which inhibit the activated forms of caspases through physical interaction with them. Smac/DIABLO, a mitochondrial protein that is translocated to the cytoplasm in apoptotic conditions, derepresses the IAP-mediated caspase inhibition through physical interaction with IAPs. The first four amino acids (AVPI) of Smac/DIABLO mediate the interaction with IAPs and subsequent apoptosis induction. This interaction has lead to the creation of small molecules mimicking the AVPI segment for potential anticancer therapy. Nevertheless, several studies have pointed out the existence of AVPI-independent functions of Smac/DIABLO. The aim of this review was to provide a landscape of these underestimated AVPI-independent biological functions that have been observed using different approaches, such as the study of endogenous splice variant isoforms and truncated and mutated artificial proteins.

The role of microRNAs (miRNAs) in cellular processes captured the attention of many researchers, since their dysregulation is shown to affect the cancer disease landscape by sustaining proliferative signaling, evading program cell death, and inhibiting growth suppressors. Thus, miRNAs have been considered important diagnostic and prognostic biomarkers for several types of tumors. Machine learning algorithms have proven to be able to exploit the information contained in thousands of miRNAs to accurately predict and classify cancer types. Nevertheless, extracting the most relevant miRNA expressions is fundamental to allow human experts to validate and make sense of the results obtained by automatic algorithms. We propose a novel feature selection approach, able to identify the most important miRNAs for tumor classification, based on consensus on feature relevance from high-accuracy classifiers of different typologies. The proposed methodology is tested on a real-world dataset featuring 8,129 patients, 29 different types of tumors, and 1,046 miRNAs per patient, taken from The Cancer Genome Atlas (TCGA) database. A new miRNA signature is suggested, containing the 100 most important oncogenic miRNAs identified by the presented approach. Such a signature is proved to be sufficient to identify all 29 types of cancer considered in the study, with results nearly identical to those obtained using all 1,046 features in the original dataset. Subsequently, a meta-analysis of the medical literature is performed to find references to the most important biomarkers extracted by the methodology. Besides known oncomarkers, 15 new miRNAs previously not ranked as important biomarkers for diagnosis and prognosis in cancer pathologies are uncovered. Such miRNAs, considered relevant by the machine learning algorithms, but still relatively unexplored by specialized literature, could provide further insights in the biology of cancer.