Long noncoding RNA (lncRNA) metallothionein 1 J, pseudogene (MT1JP) is downregulated in triple-negative breast cancer and upregulates microRNA-138 (miR-138) to downregulate hypoxia-inducible factor-1α (HIF-1α)

Regulation of the HIF switch in human endothelial and cancer cells

Hypoxia-inducible factors (HIFs) are transcription factors that reprogram the transcriptome for cells to survive hypoxic insults and oxidative stress. They are important during embryonic development and reprogram the cells to utilize glycolysis when the oxygen levels are extremely low. This metabolic change facilitates normal cell survival as well as cancer cell survival. The key feature in survival is the transition between acute hypoxia and chronic hypoxia, and this is regulated by the transition between HIF-1 expression and HIF-2/HIF-3 expression. This transition is observed in many human cancers and endothelial cells and referred to as the HIF Switch. Here we discuss the mechanisms involved in the HIF Switch in human endothelial and cancer cells which include mRNA and protein levels of the alpha chains of the HIFs. A major continuing effort in this field is directed towards determining the differences between normal and tumor cell utilization of this important pathway, and how this could lead to potential therapeutic approaches.

The strict regulation of HIF-1α by non-coding RNAs: new insight towards proliferation, metastasis, and therapeutic resistance strategies

The hypoxic environment is prominently witnessed in most solid tumors and is associated with the promotion of cell proliferation, epithelial-mesenchymal transition (EMT), angiogenesis, metabolic reprogramming, therapeutic resistance, and metastasis of tumor cells. All the effects are mediated by the expression of a transcription factor hypoxia-inducible factor-1α (HIF-1α). HIF-1α transcriptionally modulates the expression of genes responsible for all the aforementioned functions. The stability of HIF-1α is regulated by many proteins and non-coding RNAs (ncRNAs). In this article, we have critically discussed the crucial role of ncRNAs [such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), Piwi-interacting RNAs (piRNAs), and transfer RNA (tRNA)-derived small RNAs (tsRNAs)] in the regulation of stability and expression of HIF-1α. We have comprehensively discussed the molecular mechanisms and relationship of HIF-1α with each type of ncRNA in either promotion or repression of human cancers and therapeutic resistance. We have also elaborated on ncRNAs that are in clinical examination for the treatment of cancers. Overall, the majority of aspects concerning the relationship between HIF-1α and ncRNAs have been discussed in this article.

Pseudogenes in Cancer: State of the Art

Pseudogenes are duplicates of protein-coding genes that have accumulated multiple detrimental alterations, rendering them unable to produce the protein they encode. Initially disregarded as "junk DNA" due to their perceived lack of functionality, research on their biological roles has been hindered by this assumption. Nevertheless, recent focus has shifted towards these molecules due to their abnormal expression in cancer phenotypes. In this review, our objective is to provide a thorough overview of the current understanding of pseudogene formation, the mechanisms governing their expression, and the roles they may play in promoting tumorigenesis.

Hypoxia-driven ncRNAs in breast cancer

Low oxygen tension, or hypoxia is the driving force behind tumor aggressiveness, leading to therapy resistance, metastasis, and stemness in solid cancers including breast cancer, which now stands as the leading cause of cancer-related mortality in women. With the great advancements in exploring the regulatory roles of the non-coding genome in recent years, the wide spectrum of hypoxia-responsive genome is not limited to just protein-coding genes but also includes multiple types of non-coding RNAs, such as micro RNAs, long non-coding RNAs, and circular RNAs. Over the years, these hypoxia-responsive non-coding molecules have been greatly implicated in breast cancer. Hypoxia drives the expression of these non-coding RNAs as upstream modulators and downstream effectors of hypoxia inducible factor signaling in the favor of breast cancer through a myriad of molecular mechanisms. These non-coding RNAs then contribute in orchestrating aggressive hypoxic tumor environment and regulate cancer associated cellular processes such as proliferation, evasion of apoptotic death, extracellular matrix remodeling, angiogenesis, migration, invasion, epithelial-to-mesenchymal transition, metastasis, therapy resistance, stemness, and evasion of the immune system in breast cancer. In addition, the interplay between hypoxia-driven non-coding RNAs as well as feedback and feedforward loops between these ncRNAs and HIFs further contribute to breast cancer progression. Although the current clinical implications of hypoxia-driven non-coding RNAs are limited to prognostics and diagnostics in breast cancer, extensive explorations have established some of these hypoxia-driven non-coding RNAs as promising targets to treat aggressive breast cancers, and future scientific endeavors hold great promise in targeting hypoxia-driven ncRNAs at clinics to treat breast cancer and limit global cancer burden.

Predicting latent lncRNA and cancer metastatic event associations via variational graph auto-encoder

Long non-coding RNA (lncRNA) are shown to be closely associated with cancer metastatic events (CME, e.g., cancer cell invasion, intravasation, extravasation, proliferation) that collaboratively accelerate malignant cancer spread and cause high mortality rate in patients. Clinical trials may accurately uncover the relationships between lncRNAs and CMEs; however, it is time-consuming and expensive. With the accumulation of data, there is an urgent need to find efficient ways to identify these relationships. Herein, a graph embedding representation-based predictor (VGEA-LCME) for exploring latent lncRNA-CME associations is introduced. In VGEA-LCME, a heterogeneous combined network is constructed by integrating similarity and linkage matrix that can maintain internal and external characteristics of networks, and a variational graph auto-encoder serves as a feature generator to represent arbitrary lncRNA and CME pair. The final robustness predicted result is obtained by ensemble classifier strategy via cross-validation. Experimental comparisons and literature verification show better remarkable performance of VGEA-LCME, although the similarities between CMEs are challenging to calculate. In addition, VGEA-LCME can further identify organ-specific CMEs. To the best of our knowledge, this is the first computational attempt to discover the potential relationships between lncRNAs and CMEs. It may provide support and new insight for guiding experimental research of metastatic cancers. The source code and data are available at https://github.com/zhuyuan-cug/VGAE-LCME.