Next-generation sequencing has uncovered thousands of long noncoding RNAs (lncRNA). Many are reported to be aberrantly expressed in various cancers, including hepatocellular carcinoma (HCC), and play key roles in tumorigenesis. This review provides an in-depth discussion of the oncogenic mechanisms reported to be associated with deregulated HCC-associated lncRNAs. Transcriptional expression of lncRNAs in HCC is modulated through transcription factors, or epigenetically by aberrant histone acetylation or DNA methylation, and posttranscriptionally by lncRNA transcript stability modulated by miRNAs and RNAbinding proteins. Seventy-four deregulated lncRNAs have been identified in HCC, of which, 52 are upregulated. This review maps the oncogenic roles of these deregulated lncRNAs by integrating diverse datasets including clinicopathologic features, affected cancer phenotypes, associated miRNA and/or protein-interacting partners as well as modulated gene/protein expression. Notably, 63 deregulated lncRNAs are significantly associated with clinicopathologic features of HCC. Twenty-three deregulated lncRNAs associated with both tumor and metastatic clinical features were also tumorigenic and prometastatic in experimental models of HCC, and eight of these mapped to known cancer pathways. Fifty-two upregulated lncRNAs exhibit oncogenic properties and are associated with prominent hallmarks of cancer, whereas 22 downregulated lncRNAs have tumorsuppressive properties. Aberrantly expressed lncRNAs in HCC exert pleiotropic effects on miRNAs, mRNAs, and proteins. They affect multiple cancer phenotypes by altering miRNA and mRNA expression and stability, as well as through effects on protein expression, degradation, structure, or interactions with transcriptional regulators. Hence, these insights reveal novel lncRNAs as potential biomarkers and may enable the design of precision therapy for HCC.

Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type-specific spatial patterns and gene expression variation. To address this challenge, we develop STdeconvolve as a reference-free approach to deconvolve underlying cell types comprising such multi-cellular pixel resolution spatial transcriptomics (ST) datasets. Using simulated as well as real ST datasets from diverse spatial transcriptomics technologies comprising a variety of spatial resolutions such as Spatial Transcriptomics, 10X Visium, DBiT-seq, and Slide-seq, we show that STdeconvolve can effectively recover cell-type transcriptional profiles and their proportional representation within pixels without reliance on external single-cell transcriptomics references. STdeconvolve provides comparable performance to existing reference-based methods when suitable single-cell references are available, as well as potentially superior performance when suitable single-cell references are not available. STdeconvolve is available as an open-source R software package with the source code available at https://github.com/JEFworks-Lab/STdeconvolve.

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Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type spatial co-localization patterns. We developed STdeconvolve as an unsupervised approach to deconvolve underlying cell-types comprising such multi-cellular pixel resolution spatially resolved transcriptomics datasets. We show that STdeconvolve effectively recovers the putative transcriptomic profiles of cell-types and their proportional representation within spatially resolved pixels without reliance on external single-cell transcriptomics references.

Statins are used extensively for the clinical treatment of cardiovascular diseases. Recent studies suggest that statins increase the risk of new‐onset diabetes mellitus (NODM). However, the mechanisms of statin‐induced NODM remain unclear. The present study investigated the effects of autophagy on insulin secretion impairment induced by rosuvastatin (RS) in rat insulinoma cells (INS‐1E) cells. INS‐1E cells were cultured and treated with RS at different concentrations (0.2–20 μM) for 24 h. Insulin secretion in INS‐1E cells was detected by enzyme‐linked immunosorbent assay, and the co‐localization of microtubule‐associated protein light chain 3 (LC3) and lysosome‐associated membrane protein 2 (LAMP‐2) was observed by immunofluorescence staining. Western blotting was used to assess the conversion of LC3 and p62. The results showed that the insulin secretion and cell viability decrease induced by RS treatment for 24 h occurred in a dose‐dependent manner in INS‐1E cells. RS significantly inhibited the expression of LC3‐II but increased the protein expression of p62. Simultaneously, RS diminished the co‐localization of LC3‐II and LAMP‐2 fluorescence signals. These results suggested that RS‐inhibited autophagy in INS‐1E cells. Rapamycin, an autophagy agonist, reversed the insulin secretion and cell viability suppression induced by RS in INS‐1E cells. RS also decreased the phosphorylation of the mammalian target of rapamycin (mTOR). The results indicated that RS impairs insulin secretion in INS‐1E cells, which may be partly due to the inhibition of autophagy via an mTOR‐dependent pathway.