lncRNA BSG-AS1 is hypoxia-responsive and promotes hepatocellular carcinoma by enhancing BSG mRNA stability

Long non-coding RNAs in hepatocellular carcinoma

Long noncoding RNAs (lncRNAs) refer to a class of RNAs greater than 200 nucleotides in length, most of which are considered unable to encode proteins, thus deemed to be junk genes formerly. But with emerging studies about lncRNAs coming out in recent years, it is much more clearly depicted that they can regulate gene expression at different levels, with various mechanisms, thus participating in diverse biological or pathological processes, including complicated tumor-associated pathways. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, the third leading cause of cancer-related mortality worldwide, which has been found to tightly associate with aberrant expression of a variety of lncRNAs regulating tumor proliferation, invasion, drug resistance, and so on, making it a potential novel tumor marker and therapeutic target. In this review, we highlight a few lncRNAs that are closely related to the occurrence and progression of HCC and try to cover their multifarious roles from different layers.

Making Sense of Antisense lncRNAs in Hepatocellular Carcinoma

Transcriptome complexity is emerging as an unprecedented and fascinating domain, especially by high-throughput sequencing technologies that have unveiled a plethora of new non-coding RNA biotypes. This review covers antisense long non-coding RNAs, i.e., lncRNAs transcribed from the opposite strand of other known genes, and their role in hepatocellular carcinoma (HCC). Several sense-antisense transcript pairs have been recently annotated, especially from mammalian genomes, and an understanding of their evolutionary sense and functional role for human health and diseases is only beginning. Antisense lncRNAs dysregulation is significantly involved in hepatocarcinogenesis, where they can act as oncogenes or oncosuppressors, thus playing a key role in tumor onset, progression, and chemoradiotherapy response, as deduced from many studies discussed here. Mechanistically, antisense lncRNAs regulate gene expression by exploiting various molecular mechanisms shared with other ncRNA molecules, and exploit special mechanisms on their corresponding sense gene due to sequence complementarity, thus exerting epigenetic, transcriptional, post-transcriptional, and translational controls. The next challenges will be piecing together the complex RNA regulatory networks driven by antisense lncRNAs and, ultimately, assigning them a function in physiological and pathological contexts, in addition to defining prospective novel therapeutic targets and innovative diagnostic tools.

Molecular regulation of hypoxia through the lenses of noncoding RNAs and epitranscriptome

Cells maintain homeostasis in response to environmental stress through specific cell stress responses. Hypoxic stress, well known to be associated with diverse solid tumors, is one of the main reasons for cancer-related mortality. Although cells can balance themselves well during hypoxic stress, the underlying molecular mechanisms are not well understood. The enhanced appreciation of diverse roles played by noncoding transcriptome and epigenome in recent years has brought to light the involvement of noncoding RNAs and epigenetic modifiers in hypoxic regulation. The emergence of techniques like deep sequencing has facilitated the identification of large numbers of long noncoding RNAs (lncRNAs) that are differentially regulated in various cancers. Similarly, proteomic studies have identified diverse epigenetic modifiers such as HATs, HDACs, DNMTs, polycomb groups of proteins, and their possible roles in the regulation of hypoxia. The crosstalk between lncRNAs and epigenetic modifiers play a pivotal role in hypoxia-induced cancer initiation and progression. Besides the lncRNAs, several other noncoding RNAs like circular RNAs, miRNAs, and so forth are also expressed during hypoxic conditions. Hypoxia has a profound effect on the expression of noncoding RNAs and epigenetic modifiers. Conversely, noncoding RNAs/epigenetic modifies can regulate the hypoxia signaling axis by modulating the stability of the hypoxia-inducible factors (HIFs). The focus of this review is to illustrate the molecular orchestration underlying hypoxia biology, especially in cancers, which can help in identifying promising therapeutic targets in hypoxia-induced cancers. This article is categorized under: RNA Turnover and Surveillance > Regulation of RNA Stability RNA in Disease and Development > RNA in Disease RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry.

Identification of Anoikis-Related Subgroups and Prognosis Model in Liver Hepatocellular Carcinoma

Resistance to anoikis is a key characteristic of many cancer cells, promoting cell survival. However, the mechanism of anoikis in hepatocellular carcinoma (HCC) remains unknown. In this study, we applied differentially expressed overlapping anoikis-related genes to classify The Cancer Genome Atlas (TCGA) samples using an unsupervised cluster algorithm. Then, we employed weighted gene coexpression network analysis (WGCNA) to identify highly correlated genes and constructed a prognostic risk model based on univariate Cox proportional hazards regression. This model was validated using external datasets from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). Finally, we used a CIBERSORT algorithm to investigate the correlation between risk score and immune infiltration. Our results showed that the TCGA cohorts could be divided into two subgroups, with subgroup A having a lower survival probability. Five genes (BAK1, SPP1, BSG, PBK and DAP3) were identified as anoikis-related prognostic genes. Moreover, the prognostic risk model effectively predicted overall survival, which was validated using ICGC and GEO datasets. In addition, there was a strong correlation between infiltrating immune cells and prognostic genes and risk score. In conclusion, we identified anoikis-related subgroups and prognostic genes in HCC, which could be significant for understanding the molecular mechanisms and treatment of HCC.

A cell cycle-related lncRNA signature predicts the progression-free interval in papillary thyroid carcinoma

The cell cycle plays a vital role in tumorigenesis and progression. Long non-coding RNAs (lncRNAs) are key regulators of cell cycle processes. Therefore, understanding cell cycle-related lncRNAs (CCR-lncRNAs) is crucial for determining the prognosis of papillary thyroid carcinoma (PTC). RNA-seq and clinical data of PTC were acquired from The Cancer Genome Atlas, and CCR-lncRNAs were selected based on Pearson's correlation coefficients. According to univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses, a five-CCR-lncRNA signature (FOXD2-AS1, LOC100507156, BSG-AS1, EGOT, and TMEM105) was established to predict the progression-free interval (PFI) in PTC. Kaplan-Meier survival, time-dependent receiver operating characteristic curve, and multivariate Cox regression analyses proved that the signature had a reliable prognostic capability. A nomogram consisting of the risk signature and clinical characteristics was constructed that effectively predicted the PFI in PTC. Functional enrichment analyses indicted that the signature was involved in cell cycle- and immune-related pathways. Furthermore, we also analyzed the correlation between the signature and immune cell infiltration. Finally, we verified the differential expression of CCR-lncRNAs in vitro using quantitative real-time polymerase chain reaction. Overall, the newly developed prognostic risk signature based on five CCR-lncRNAs may become a marker for predicting the PFI in PTC.

Dynamic landscape of long noncoding RNAs during leaf aging in Arabidopsis

Leaf senescence, the last stage of leaf development, is essential for whole-plant fitness as it marks the relocation of nutrients from senescing leaves to reproductive or other developing organs. Temporally coordinated physiological and functional changes along leaf aging are fine-tuned by a highly regulated genetic program involving multi-layered regulatory mechanisms. Long noncoding RNAs (lncRNAs) are newly emerging as hidden players in many biological processes; however, their contribution to leaf senescence has been largely unknown. Here, we performed comprehensive analyses of RNA-seq data representing all developmental stages of leaves to determine the genome-wide lncRNA landscape along leaf aging. A total of 771 lncRNAs, including 232 unannotated lncRNAs, were identified. Time-course analysis revealed 446 among 771 developmental age-related lncRNAs (AR-lncRNAs). Intriguingly, the expression of AR-lncRNAs was regulated more dynamically in senescing leaves than in growing leaves, revealing the relevant contribution of these lncRNAs to leaf senescence. Further analyses enabled us to infer the function of lncRNAs, based on their interacting miRNA or mRNA partners. We considered functionally diverse lncRNAs including antisense lncRNAs (which regulate overlapping protein-coding genes), competitive endogenous RNAs (ceRNAs; which regulate paired mRNAs using miRNAs as anchors), and mRNA-interacting lncRNAs (which affect the stability of mRNAs). Furthermore, we experimentally validated the senescence regulatory function of three novel AR-lncRNAs including one antisense lncRNA and two mRNA-interacting lncRNAs through molecular and phenotypic analyses. Our study provides a valuable resource of AR-lncRNAs and potential regulatory networks that link the function of coding mRNA and AR-lncRNAs. Together, our results reveal AR-lncRNAs as important elements in the leaf senescence process.

Identification of multi-omics biomarkers and construction of the novel prognostic model for hepatocellular carcinoma

Genome changes play a crucial role in carcinogenesis, and many biomarkers can be used as effective prognostic indicators in various tumors. Although previous studies have constructed many predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance is unsatisfactory. Because multi-omics data can more comprehensively reflect the biological phenomenon of disease, we hope to build a more accurate predictive model by multi-omics analysis. We use the TCGA to identify crucial biomarkers and construct prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. The performances of predictive models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Multiple mRNAs, lncRNAs, miRNAs, CNV genes, and SNPs were significantly associated with the prognosis of HCC. We constructed five single-omic models, and the mRNA and lncRNA models showed good performance with c-indexes over 0.70. The multi-omics model presented a robust predictive ability with a c-index over 0.77. This study identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC. In addition, we constructed multiple single-omic models and an integrated multi-omics model that may provide practical and reliable guides for prognosis assessment.

Comprehensive analysis of metabolic pathway activity subtypes derived prognostic signature in hepatocellular carcinoma

OBJECTIVE

Metabolic reprogramming is one of the hallmarks of cancer, but metabolic pathway activity-related subtypes of hepatocellular carcinoma (HCC) have not been identified.

METHODS

Based on the quantification results of 41 metabolic pathway activities by gene set variation analysis, the training cohort (n = 609, merged by TCGA and GSE14520) was clustered into three subtypes (C1, C2, and C3) with the nonnegative matrix factorization method. Totally 1371 differentially expressed genes among C1, C2, and C3 were identified, and an 8-gene risk score was established by univariable Cox regression analysis, least absolute shrinkage and selection operator method, and multivariable Cox regression analysis.

RESULTS

C1 had the strongest metabolic activity, good prognosis, the highest CTNNB1 mutation rate, with massive infiltration of eosinophils and natural killer cells. C2 had the weakest metabolic activity, poor prognosis, was younger, was inclined to vascular invasion and advanced stage, had the highest TP53 mutation rate, exhibited a higher expression level of immune checkpoints, accompanied by massive infiltration of regulatory T cells. C3 had moderate metabolic activity and prognosis, the highest LRP1B mutation rate, and a higher infiltration level of neutrophils and macrophages. Internal cohorts (TCGA, n = 370; GSE14520, n = 239), external cohorts (ICGC, n = 231; GSE116174, n = 64), and clinical subgroup validation showed that the risk score was applicable for patients with diverse clinical features and was effective in predicting the prognosis and malignant progression of patients with HCC. Compared with the low-risk group, the high-risk group had a poor prognosis, enhanced cancer stem cell characteristics, activated DNA damage repair, weakened metabolic activity, cytolytic activity, and interferon response.

CONCLUSION

We identified HCC subtypes from the perspective of metabolism-related pathway activity and proposed a robust prognostic signature for HCC.

Extracellular Matrix-Based Gene Expression Signature Defines Two Prognostic Subtypes of Hepatocellular Carcinoma With Different Immune Microenvironment Characteristics

Background: Accumulating evidence has suggested that the extracellular matrix (ECM) plays a vital role in the development and progression of cancer, and could be recognized as a biomarker of the response to immunotherapy. However, the effect of the ECM signature in hepatocellular carcinoma (HCC) is not well understood.

Methods: HCC patients derived from the TCGA-LIHC dataset were clustered according to the ECM signature. The differences in prognosis, functional enrichment, immune infiltration, and mutation characteristics between distinct molecular clusters were examined, and its predictive value on the sensitivities to chemotherapy and immunotherapy was further analyzed. Then, a prognostic model was built based on the ECM-related gene expression pattern.

Results: HCC patients were assigned into two molecular subtypes. Approximately 80% of HCC patients were classified into cluster A with poor prognosis, more frequent TP53 mutation, and lower response rate to immunotherapy. In contrast, patients in cluster B had better survival outcomes and higher infiltration levels of dendritic cells, macrophages, and regulatory T cells. The prognostic risk score model based on the expression profiles of six ECM-related genes (SPP1, ADAMTS5, MMP1, BSG, LAMA2, and CDH1) demonstrated a significant association with higher histologic grade and advanced TNM stage. Moreover, the prognostic risk score showed good performance in both the training dataset and validation dataset, as well as improved prognostic capacity compared with TNM stage.

Conclusions: We characterized two HCC subtypes with distinct clinical outcomes, immune infiltration, and mutation characteristics. A novel prognostic model based on the ECM signature was further developed, which may contribute to individualized prognostic prediction and aid in clinical decision-making.