Antisense transcription, considered until recently as transcriptional noise, is a very common phenomenon in human and eukaryotic transcriptomes, operating in two ways based on whether the antisense RNA acts in cis or in trans. This process can generate long non-coding RNAs (lncRNAs), one of the most diverse classes of cellular transcripts, which have demonstrated multifunctional roles in fundamental biological processes, including embryonic pluripotency, differentiation and development. Antisense lncRNAs have been shown to control nearly every level of gene regulation—pretranscriptional, transcriptional and posttranscriptional—through DNA–RNA, RNA–RNA or protein–RNA interactions. This review is centered on functional studies of antisense lncRNA-mediated regulation of neighboring gene expression. Specifically, it addresses how these transcripts interact with other biological molecules, nucleic acids and proteins, to regulate gene expression through chromatin remodeling at the pretranscriptional level and modulation of transcriptional and post-transcriptional processes by altering the sense mRNA structure or the cellular compartmental distribution, either in the nucleus or the cytoplasm.

Cancer is one of the leading causes of death, and despite increased research in recent years, control of advanced-stage disease and optimal therapeutic responses remain elusive. Recent technological improvements have increased our understanding of human cancer as a heterogeneous disease. For instance, four hallmarks of cancer have recently been included, which in addition to being involved in cancer development, could be involved in therapeutic responses and resistance. One of these hallmarks is chromosome instability (CIN), a source of genetic variation in either altered chromosome number or structure. CIN has become a hot topic in recent years, not only for its implications in cancer diagnostics and prognostics, but also for its role in therapeutic responses. Chromosomal alterations are mainly used to determine genetic heterogeneity in tumors, but CIN could also reveal treatment efficacy, as many therapies are based on increasing CIN, which causes aberrant cells to undergo apoptosis. However, it should be noted that contradictory findings on the implications of CIN for the therapeutic response have been reported, with some studies associating high CIN with a better therapeutic response and others associating it with therapeutic resistance. Considering these observations, it is necessary to increase our understanding of the role CIN plays not only in tumor development, but also in therapeutic responses. This review focuses on recent studies that suggest possible mechanisms and consequences of CIN in different disease types, with a primary focus on cancer outcomes and therapeutic responses.

17β-Estradiol (E2) plays a pivotal role in the development and progression of breast cancer. As a result, blockade of the E2 signal through either tamoxifen (TAM) or aromatase inhibitors is an important therapeutic strategy to treat or prevent estrogen receptor (ER) positive breast cancer. However, resistance to TAM is the major obstacle in endocrine therapy. This resistance occurs either de novo or is acquired after an initial beneficial response. The underlying mechanisms for TAM resistance are probably multifactorial and remain largely unknown. Considering that breast cancer is a very heterogeneous disease and patients respond differently to treatment, the molecular analysis of TAM’s biological activity could provide the necessary framework to understand the complex effects of this drug in target cells. Moreover, this could explain, at least in part, the development of resistance and indicate an optimal therapeutic option. This review highlights the implications of TAM in breast cancer as well as the role of receptors/signal pathways recently suggested to be involved in the development of TAM resistance. G protein—coupled estrogen receptor, Androgen Receptor and Hedgehog signaling pathways are emerging as novel therapeutic targets and prognostic indicators for breast cancer, based on their ability to mediate estrogenic signaling in ERα-positive or -negative breast cancer.

The incidence and mortality of breast cancer (BC) have increased in recent years, and BC is the main cause of cancer-related death in women worldwide. One of the most significant clinical problems in the treatment of patients with BC is the development of therapeutic resistance. Therefore, elucidating the molecular mechanisms involved in drug resistance is critical. The therapeutic decision for the management of patients with BC is based not only on the assessment of prognostic factors but also on the evaluation of clinical and pathological parameters. Although this has been a successful approach, some patients relapse and/or eventually develop resistance to treatment. This review is focused on recent studies on the possible biological and molecular mechanisms involved in both response and resistance to treatment in BC. Additionally, emerging treatments that seek to overcome resistance and reduce side effects are also described. A greater understanding of the mechanisms of action of treatments used in BC might contribute not only to the enhancement of our understanding of the mechanisms involved in the development of resistance but also to the optimization of the existing treatment regimens.

Noncoding RNAs (ncRNAs) play prominent roles in the regulation of gene expression via their interactions with other biological molecules such as proteins and nucleic acids. Although much of our knowledge about how these ncRNAs operate in different biological processes has been obtained from experimental findings, computational biology can also clearly substantially boost this knowledge by suggesting possible novel interactions of these ncRNAs with other molecules. Computational predictions are thus used as an alternative source of new insights through a process of mutual enrichment because the information obtained through experiments continuously feeds through into computational methods. The results of these predictions in turn shed light on possible interactions that are subsequently validated experimentally. This review describes the latest advances in databases, bioinformatic tools, and new in silico strategies that allow the establishment or prediction of biological interactions of ncRNAs, particularly miRNAs and lncRNAs. The ncRNA species described in this work have a special emphasis on those found in humans, but information on ncRNA of other species is also included.