The Landscape and Implications of Chimeric RNAs in Cervical Cancer

Readon: a novel algorithm to identify read-through transcripts with long-read sequencing data

MOTIVATION

There are many clustered transcriptionally active regions in the human genome, in which the transcription complex cannot immediately terminate transcription at the upstream gene termination site, but instead continues to transcribe intergenic regions and downstream genes, resulting in read-through transcripts. Several studies have demonstrated the regulatory roles of read-through transcripts in tumorigenesis and development. However, limited by the read length of next-generation sequencing, discovery of read-through transcripts has been slow. For long but also erroneous third-generation sequencing data, this study developed a novel minimizer sketch algorithm to accurately and quickly identify read-through transcripts.

RESULTS

Readon initially splits the reference sequence into distinct active regions. It employs a sliding window approach within each region, calculates minimizers, and constructs the specialized structured arrays for query indexing. Following initial alignment anchor screening of candidate read-through transcripts, further confirmation steps are executed. Comparative assessments against existing software reveal Readon's superior performance on both simulated and validated real data. Additionally, two downstream tools are provided: one for predicting whether a read-through transcript is likely to undergo nonsense-mediated decay or encodes a protein, and another for visualizing splicing patterns.

AVAILABILITY AND IMPLEMENTATION

Readon is freely available on GitHub (https://github.com/Bulabula45/Readon).

RTCpredictor: identification of read-through chimeric RNAs from RNA sequencing data

Read-through chimeric RNAs are being recognized as a means to expand the functional transcriptome and contribute to cancer tumorigenesis when mis-regulated. However, current software tools often fail to predict them. We have developed RTCpredictor, utilizing a fast ripgrep tool to search for all possible exon-exon combinations of parental gene pairs. We also added exonic variants allowing searches containing common SNPs. To our knowledge, it is the first read-through chimeric RNA specific prediction method that also provides breakpoint coordinates. Compared with 10 other popular tools, RTCpredictor achieved high sensitivity on a simulated and three real datasets. In addition, RTCpredictor has less memory requirements and faster execution time, making it ideal for applying on large datasets.

Discovery of a polymorphic gene fusion via bottom-up chimeric RNA prediction

Gene fusions and their chimeric products are commonly linked with cancer. However, recent studies have found chimeric transcripts in non-cancer tissues and cell lines. Large-scale efforts to annotate structural variations have identified gene fusions capable of generating chimeric transcripts even in normal tissues. In this study, we present a bottom-up approach targeting population-specific chimeric RNAs, identifying 58 such instances in the GTEx cohort, including notable cases such as SUZ12P1-CRLF3, TFG-ADGRG7 and TRPM4-PPFIA3, which possess distinct patterns across different ancestry groups. We provide direct evidence for an additional 29 polymorphic chimeric RNAs with associated structural variants, revealing 13 novel rare structural variants. Additionally, we utilize the All of Us dataset and a large cohort of clinical samples to characterize the association of the SUZ12P1-CRLF3-causing variant with patient phenotypes. Our study showcases SUZ12P1-CRLF3 as a representative example, illustrating the identification of elusive structural variants by focusing on those producing population-specific fusion transcripts.

NDUFA8 is transcriptionally regulated by EP300/H3K27ac and promotes mitochondrial respiration to support proliferation and inhibit apoptosis in cervical cancer

Cervical cancer, a common malignancy in women, poses a significant health burden worldwide. In this study, we aimed to investigate the expression, function, and potential mechanisms of NADH: ubiquinone oxidoreductase subunit A8 (NDUFA8) in cervical cancer. The Gene Expression Profiling Interactive Analysis (GEPIA) database and immunohistochemical scoring were used to analyze NDUFA8 expression in cervical cancer tissues and normal tissues. Quantitative real-time PCR and Western blot analyses were performed to assess the expression level of NDUFA8 in cervical cancer cell lines. NDUFA8 knockdown or overexpression experiments were conducted to evaluate its impact on cell proliferation and apoptosis. The mitochondrial respiratory status was analyzed by measuring cellular oxygen consumption, adenosine triphosphate (ATP) levels, and the expression levels of Mitochondrial Complex I activity, and Mitochondrial Complex IV-associated proteins Cytochrome C Oxidase Subunit 5B (COX5B) and COX6C. NDUFA8 exhibited high expression levels in cervical cancer tissues, and these levels were correlated with reduced survival rates. A significant upregulation of NDUFA8 expression was observed in cervical cancer cell lines compared to normal cells. Silencing NDUFA8 hindered cell proliferation, promoted apoptosis, and concurrently suppressed cellular mitochondrial respiration, resulting in decreased levels of available ATP. Conversely, NDUFA8 overexpression induced the opposite effects. Herein, we also found that E1A Binding Protein P300 (EP300) overexpression facilitated Histone H3 Lysine 27 (H3K27) acetylation enrichment, enhancing the activity of the NDUFA8 promoter region. NDUFA8, which is highly expressed in cervical cancer, is regulated by transcriptional control via EP300/H3K27 acetylation. By promoting mitochondrial respiration, NDUFA8 contributes to cervical cancer cell proliferation and apoptosis. These findings provide novel insights into NDUFA8 as a therapeutic target in cervical cancer.

CTNNBIP1-CLSTN1 functions as a housekeeping chimeric RNA and regulates cell proliferation through SERPINE2

The conventional understanding that chimeric RNAs are unique to carcinoma and are the products of chromosomal rearrangement is being challenged. However, experimental evidence supporting the function of chimeric RNAs in normal physiology is scarce. We decided to focus on one particular chimeric RNA, CTNNBIP1-CLSTN1. We examined its expression in various tissues and cell types and compared it quantitatively among cancer and noncancer cells. We further investigated its role in a panel of noncancer cells and investigated the functional mechanism. We found that this fusion transcript is expressed in almost all tissues and a wide range of cell types, including fibroblasts, epithelial cells, stem cells, vascular endothelial cells, and hepatocytes. In addition, the CTNNBIP1-CLSTN1 expression level in noncancerous cell lines was not evidently different from that in cancer cell lines. Furthermore, in at least three cell types, silencing CTNNBIP1-CLSTN1 significantly reduced the cell proliferation rate by inducing G2/M arrest and apoptosis. Importantly, rescue experiments confirmed that cell cycle arrest was restored by exogenous expression of the chimera but not the wild-type parental gene. Further evidence is provided that CTNNBIP1-CLSTN1 regulates cell proliferation through SERPINE2. Thus, CTNNBIP1-CLSTN1 is an example of a new class of fusion RNAs, dubbed "housekeeping chimeric RNAs".

CTNNBIP1-CLSTN1 Functions as a Housekeeping Chimeric RNA, and Regulates Cell Proliferation through SERPINE2

The conventional wisdom that chimeric RNAs being peculiarity of carcinoma, and the products of chromosomal rearrangement is being challenged, However, experimental evidence supporting chimeric RNAs in normal physiology being functional is scarce. We decided to focus on one particular chimeric RNA, CTNNBIP1-CLSTN1 . We examined its expression among various tissues and cell types, and compared quantitatively among cancer and non-cancer cells. We further investigated its role in a panel of non-cancer cells and probed the functional mechanism. We found that this fusion transcript is expressed in almost all tissues, and a wide range of cell types including fibroblasts, epithelial, stem, vascular endothelial cells, and hepatocytes. The expression level in non-cancerous cell lines is also not evidently different from that in the cancer cell lines. Furthermore, silencing CTNNBIP1-CLSTN1 significantly reduces cell proliferation rate, by inducing G2/M arrest in cell cycle progress and apoptosis in at least three cell types. Importantly, rescue experiments confirmed that the cell cycle arrest can be regained by exogenous expression of the chimera, but not the wild type parental gene. Further evidence is provided that CTNNBIP1-CLSTN1 regulates cell proliferation through SERPINE2 . Thus, CTNNBIP1-CLSTN1 represents an example of a new class of fusion RNA, dubbed "housekeeping chimeric RNAs".

Discovery of A Polymorphic Gene Fusion via Bottom-Up Chimeric RNA Prediction

Gene fusions and their chimeric products are typically considered hallmarks of cancer. However, recent studies have found chimeric transcripts in non-cancer tissues and cell lines. In addition, efforts to annotate structural variation at large scale have found examples of gene fusions with potential to produce chimeric transcripts in normal tissues. In this report, we provide a means for targeting population-specific chimeric RNAs to enrich for those generated by gene fusion events. We identify 57 such chimeric RNAs from the GTEx cohort, including SUZ12P1-CRLF3 and TFG-ADGRG7 , whose distribution we assessed across the populations of the 1000 Genomes Project. We reveal that SUZ12P1-CRLF3 results from a common complex structural variant in populations with African heritage, and identify its likely mechanism for formation. Additionally, we utilize a large cohort of clinical samples to characterize the SUZ12P1-CRLF3 chimeric RNA, and find an association between the variant and indications of Neurofibramatosis Type I. We present this gene fusion as a case study for identifying hard-to-find and potentially functional structural variants by selecting for those which produce population-specific fusion transcripts.

KEY POINTS

- Discovery of 57 polymorphic chimeric RNAs- Characterization of SUZ12P1-CRLF3 polymorphic chimeric RNA and corresponding rearrangement- Novel bottom-up approach to identify structural variants which produce transcribed gene fusions.

RTCpredictor: Identification of Read-Through Chimeric RNAs from RNA Sequencing Data

Read-through chimeric RNAs are gaining attention in cancer and other research fields, yet current tools often fail in predicting them. We have thus developed the first read-through chimeric RNA specific prediction method, RTCpredictor, utilizing a fast ripgrep algorithm to search for all possible exon-exon combinations of parental gene pairs. Compared with other ten popular tools, RTCpredictor achieved top performance on both simulated and real datasets. We randomly selected up to 30 candidate read-through chimeras predicted from each software method and experimentally validated a total of 109 read-throughs and on this set, RTCpredictor outperformed all the other methods. In addition, RTCpredictor ( https://github.com/sandybioteck/RTCpredictor ) has less memory requirements and faster execution time.

Recent advances in cancer fusion transcript detection

Extensive investigation of gene fusions in cancer has led to the discovery of novel biomarkers and therapeutic targets. To date, most studies have neglected chromosomal rearrangement-independent fusion transcripts and complex fusion structures such as double or triple-hop fusions, and fusion-circRNAs. In this review, we untangle fusion-related terminology and propose a classification system involving both gene and transcript fusions. We highlight the importance of RNA-level fusions and how long-read sequencing approaches can improve detection and characterization. Moreover, we discuss novel bioinformatic tools to identify fusions in long-read sequencing data and strategies to experimentally validate and functionally characterize fusion transcripts.

Chimeric RNA TNNI2-ACTA1-V1 Regulates Cell Proliferation by Regulating the Expression of NCOA3

Chimeric RNA is a crucial target for tumor diagnosis and drug therapy, also having its unique biological role in normal tissues. TNNI2-ACTA1-V1 (TA-V1), a chimeric RNA discovered by our laboratory in porcine muscle tissue, can inhibit the proliferation of Porcine Skeletal Muscle Satellite Cells (PSCs). The regulatory mechanism of TA-V1 in PSCs remains unclear, but we speculate that NCOA3, DDR2 and RDX may be the target genes of TA-V1. In this study, we explored the effects of NCOA3, DDR2 and RDX on cell viability and cell proliferation by CCK-8 assay, EdU staining and flow cytometry. Furthermore, the regulatory pathway of proliferation in PSCs mediated by TA-V1 through NCOA3 or CyclinD1 was elucidated by co-transfection and co-immunoprecipitation (Co-IP). The results revealed that overexpression of NCOA3 significantly increased cell viability and the expression level of CyclinD1, and also promotes cell proliferation by changing cells from the G1 phase to the S phase. In addition, inhibiting the expression of NCOA3 substantially reduced cell viability and inhibited cell proliferation. Overexpression of DDR2 and RDX had no significant effect on cell viability and proliferation. Co-transfection experiments showed that NCOA3 could rescue the proliferation inhibition of PSCs caused by TA-V1. Co-IP assay indicated that TA-V1 directly interacts with NCOA3. Our study explores the hypothesis that TA-V1 directly regulates NCOA3, indirectly regulating CyclinD1, thereby regulating PSCs proliferation. We provide new putative mechanisms of porcine skeletal muscle growth and lay the foundation for the study of chimeric RNA in normal tissues.

Chimeric RNAs Discovered by RNA Sequencing and Their Roles in Cancer and Rare Genetic Diseases

Chimeric RNAs are transcripts that are generated by gene fusion and intergenic splicing events, thus comprising nucleotide sequences from different parental genes. In the past, Northern blot analysis and RT-PCR were used to detect chimeric RNAs. However, they are low-throughput and can be time-consuming, labor-intensive, and cost-prohibitive. With the development of RNA-seq and transcriptome analyses over the past decade, the number of chimeric RNAs in cancer as well as in rare inherited diseases has dramatically increased. Chimeric RNAs may be potential diagnostic biomarkers when they are specifically expressed in cancerous cells and/or tissues. Some chimeric RNAs can also play a role in cell proliferation and cancer development, acting as tools for cancer prognosis, and revealing new insights into the cell origin of tumors. Due to their abilities to characterize a whole transcriptome with a high sequencing depth and intergenically identify spliced chimeric RNAs produced with the absence of chromosomal rearrangement, RNA sequencing has not only enhanced our ability to diagnose genetic diseases, but also provided us with a deeper understanding of these diseases. Here, we reviewed the mechanisms of chimeric RNA formation and the utility of RNA sequencing for discovering chimeric RNAs in several types of cancer and rare inherited diseases. We also discussed the diagnostic, prognostic, and therapeutic values of chimeric RNAs.

The exon 12-containing LHX6 isoforms promote cervical cancer cell proliferation by regulating the MAPK signaling pathway

LIM homeobox 6 (LHX6) has been reported to be downregulated and inhibits cell proliferation in various cancers. Alternative splicing of LHX6 leads to six annotated isoforms, which can be found in the NCBI database. However, the expression patterns and potential roles of these isoforms remain poorly characterized in cervical cancer. Here, we demonstrated that the LHX6 isoforms containing exon 12 (LHX6^EX(+12) group) and isoforms lacking exon 12 (LHX6^EX(-12) group) were differentially expressed in cervical tissue by qRT-PCR. The mRNA expression level of LHX6^EX(+12) group was higher than that of LHX6^EX(-12) group in cervical cancer tissue. Knockdown of LHX6^EX(+12) group and all LHX6 isoforms (LHX6^All group) inhibited cell growth, increased cell apoptosis, and induced cell cycle arrest from G0/G1 phase to S phase in vitro. Consistently, overexpression of the LHX6^EX(+12) group promoted cervical cancer cell proliferation in vitro. In contrast, no significant differences in cell proliferation were found between LHX6^EX(-12) isoform knockdown group and its control. RNA-sequencing suggested that the LHX6^EX(+12) isoform group might exert its cancer-promoting effects in cervical cancer via regulating MAPK signaling pathway. Downregulation of the LHX6^EX(+12) group significantly suppressed the phosphorylation of MRK, ERK, JNK, and P38 at the protein level. We also identified some unique biological processes and signaling pathways in which each isoform group might be involved. In summary, our results indicated that LHX6^EX(+12) isoform group was the dominant oncogenic type of LHX6 in cervical cancer, which may be a new biomarker and a potential precise therapeutic target for cervical cancer in the future.

Generation and Application of Inducible Chimeric RNA ASTN2-PAPPAas Knockin Mouse Model

Chimeric RNAs (chiRNAs) play many previously unrecognized roles in different diseases including cancer. They can not only be used as biomarkers for diagnosis and prognosis of various diseases but also serve as potential therapeutic targets. In order to better understand the roles of chiRNAs in pathogenesis, we inserted human sequences into mouse genome and established a knockin mouse model of the tamoxifen-inducible expression of ASTN2-PAPPA antisense chimeric RNA (A-P_aschiRNA). Mice carrying the A-P_aschiRNA knockin gene do not display any apparent abnormalities in growth, fertility, histological, hematopoietic, and biochemical indices. Using this model, we dissected the role of A-P_aschiRNA in chemical carcinogen 4-nitroquinoline 1-oxide (4NQO)-induced carcinogenesis of esophageal squamous cell carcinoma (ESCC). To our knowledge, we are the first to generate a chiRNA knockin mouse model using the Cre-loxP system. The model could be used to explore the roles of chiRNA in pathogenesis and potential targeted therapies.

RNA editing affects cis-regulatory elements and predicts adverse cancer survival

BACKGROUND

RNA editing exerts critical impacts on numerous biological processes and thus are implicated in crucial human phenotypes, including tumorigenesis and prognosis. While previous studies have analyzed aggregate RNA editing activity at the sample level and associated it with overall cancer survival, there is not yet a large-scale disease-specific survival study to examine genome-wide RNA editing sites' prognostic value taking into account the host gene expression and clinical variables.

METHODS

In this study, we solved comprehensive Cox proportional models of disease-specific survival on individual RNA-editing sites plus host gene expression and critical demographic covariates. This allowed us to interrogate the prognostic value of a large number of RNA-editing sites at single-nucleotide resolution.

RESULTS

As a result, we identified 402 gene-proximal RNA-editing sites that generally predict adverse cancer survival. For example, an RNA-editing site residing in ZNF264 indicates poor survival of uterine corpus endometrial carcinoma, with a hazard ratio of 2.13 and an adjusted p-value of 4.07 × 10-7 . Some of these prognostic RNA-editing sites mediate the binding of RNA binding proteins and microRNAs, thus propagating their impacts to extensive regulatory targets.

CONCLUSIONS

In conclusion, RNA editing affects cis-regulatory elements and predicts adverse cancer survival.

Comparative study of bioinformatic tools for the identification of chimeric RNAs from RNA Sequencing

Chimeric RNAs are gaining more and more attention as they have broad implications in both cancer and normal physiology. To date, over 40 chimeric RNA prediction methods have been developed to facilitate their identification from RNA sequencing data. However, a limited number of studies have been conducted to compare the performance of these tools; additionally, previous studies have become outdated as more software tools have been developed within the last three years. In this study, we benchmarked 16 chimeric RNA prediction software, including seven top performers in previous benchmarking studies, and nine that were recently developed. We used two simulated and two real RNA-Seq datasets, compared the 16 tools for their sensitivity, positive prediction value (PPV), F-measure, and also documented the computational requirements (time and memory). We noticed that none of the tools are inclusive, and their performance varies depending on the dataset and objects. To increase the detection of true positive events, we also evaluated the pair-wise combination of these methods to suggest the best combination for sensitivity and F-measure. In addition, we compared the performance of the tools for the identification of three classes (read-through, inter-chromosomal and intra-others) of chimeric RNAs. Finally, we performed TOPSIS analyses and ranked the weighted performance of the 16 tools.

Landscape of Chimeric RNAs in Non-Cancerous Cells

Gene fusions and their products (RNA and protein) have been traditionally recognized as unique features of cancer cells and are used as ideal biomarkers and drug targets for multiple cancer types. However, recent studies have demonstrated that chimeric RNAs generated by intergenic alternative splicing can also be found in normal cells and tissues. In this study, we aim to identify chimeric RNAs in different non-neoplastic cell lines and investigate the landscape and expression of these novel candidate chimeric RNAs. To do so, we used HEK-293T, HUVEC, and LO2 cell lines as models, performed paired-end RNA sequencing, and conducted analyses for chimeric RNA profiles. Several filtering criteria were applied, and the landscape of chimeric RNAs was characterized at multiple levels and from various angles. Further, we experimentally validated 17 chimeric RNAs from different classifications. Finally, we examined a number of validated chimeric RNAs in different cancer and non-cancer cells, including blood from healthy donors, and demonstrated their ubiquitous expression pattern.

TIF-Seq2 disentangles overlapping isoforms in complex human transcriptomes

Eukaryotic transcriptomes are complex, involving thousands of overlapping transcripts. The interleaved nature of the transcriptomes limits our ability to identify regulatory regions, and in some cases can lead to misinterpretation of gene expression. To improve the understanding of the overlapping transcriptomes, we have developed an optimized method, TIF-Seq2, able to sequence simultaneously the 5' and 3' ends of individual RNA molecules at single-nucleotide resolution. We investigated the transcriptome of a well characterized human cell line (K562) and identified thousands of unannotated transcript isoforms. By focusing on transcripts which are challenging to be investigated with RNA-Seq, we accurately defined boundaries of lowly expressed unannotated and read-through transcripts putatively encoding fusion genes. We validated our results by targeted long-read sequencing and standard RNA-Seq for chronic myeloid leukaemia patient samples. Taking the advantage of TIF-Seq2, we explored transcription regulation among overlapping units and investigated their crosstalk. We show that most overlapping upstream transcripts use poly(A) sites within the first 2 kb of the downstream transcription units. Our work shows that, by paring the 5' and 3' end of each RNA, TIF-Seq2 can improve the annotation of complex genomes, facilitate accurate assignment of promoters to genes and easily identify transcriptionally fused genes.

Fusion transcript detection using spatial transcriptomics

BACKGROUND

Fusion transcripts are involved in tumourigenesis and play a crucial role in tumour heterogeneity, tumour evolution and cancer treatment resistance. However, fusion transcripts have not been studied at high spatial resolution in tissue sections due to the lack of full-length transcripts with spatial information. New high-throughput technologies like spatial transcriptomics measure the transcriptome of tissue sections on almost single-cell level. While this technique does not allow for direct detection of fusion transcripts, we show that they can be inferred using the relative poly(A) tail abundance of the involved parental genes.

METHOD

We present a new method STfusion, which uses spatial transcriptomics to infer the presence and absence of poly(A) tails. A fusion transcript lacks a poly(A) tail for the 5' gene and has an elevated number of poly(A) tails for the 3' gene. Its expression level is defined by the upstream promoter of the 5' gene. STfusion measures the difference between the observed and expected number of poly(A) tails with a novel C-score.

RESULTS

We verified the STfusion ability to predict fusion transcripts on HeLa cells with known fusions. STfusion and C-score applied to clinical prostate cancer data revealed the spatial distribution of the cis-SAGe SLC45A3-ELK4 in 12 tissue sections with almost single-cell resolution. The cis-SAGe occurred in disease areas, e.g. inflamed, prostatic intraepithelial neoplastic, or cancerous areas, and occasionally in normal glands.

CONCLUSIONS

STfusion detects fusion transcripts in cancer cell line and clinical tissue data, and distinguishes chimeric transcripts from chimeras caused by trans-splicing events. With STfusion and the use of C-scores, fusion transcripts can be spatially localised in clinical tissue sections on almost single cell level.

HPV-CCDC106 integration alters local chromosome architecture and hijacks an enhancer by three-dimensional genome structure remodeling in cervical cancer

Integration of human papillomavirus (HPV) DNA into the human genome is a reputed key driver of cervical cancer. However, the effects of HPV integration on chromatin structural organization and gene expression are largely unknown. We studied a cohort of 61 samples and identified an integration hot spot in the CCDC106 gene on chromosome 19. We then selected fresh cancer tissue that contained the unique integration loci at CCDC106 with no HPV episomal DNA and performed whole-genome, RNA, chromatin immunoprecipitation and high-throughput chromosome conformation capture (Hi-C) sequencing to identify the mechanisms of HPV integration in cervical carcinogenesis. Molecular analyses indicated that chromosome 19 exhibited significant genomic variation and differential expression densities, with correlation found between three-dimensional (3D) structural change and gene expression. Importantly, HPV integration divided one topologically associated domain (TAD) into two smaller TADs and hijacked an enhancer from PEG3 to CCDC106, with a decrease in PEG3 expression and an increase in CCDC106 expression. This expression dysregulation was further confirmed using 10 samples from our cohort, which exhibited the same HPV-CCDC106 integration. In summary, we found that HPV-CCDC106 integration altered local chromosome architecture and hijacked an enhancer via 3D genome structure remodeling. Thus, this study provides insight into the 3D structural mechanism underlying HPV integration in cervical carcinogenesis.

Landscape characterization of chimeric RNAs in colorectal cancer

Gene fusions and their fusion products have been recognized as ideal biomarkers and drug targets for cancer. However, few recurrent gene fusions were found in colorectal cancer (CRC), despite comprehensive studies. We believe that chimeric RNAs, in the absence of chromosomal rearrangement, may represent a new repertoire of biomarkers and/or therapeutic targets in CRC. In this study, we aim to identify such recurrent chimeric RNAs, and investigate their clinical implications. To do so, we performed extensive data mining for chimeric RNAs using The Cancer Genome Atlas CRC RNA-Seq datasets. Multiple filtering criteria were applied, and the landscape of chimeric RNAs at multiple levels, from various angles, was analyzed. Eleven frequent, cancer biased chimeric RNAs were validated. The expression of RRM2-C2orf48 correlates with poor clinical outcomes, while the expression of parental RRM2 and C2orf48 correlates with positive clinical outcomes. Mechanistically, it is a product of cis-splicing between adjacent genes. Silencing of RRM2-C2orf48 resulted in reduced cellular proliferation in colon cancer cells, whereas overexpressed chimera promoted cell proliferation. These findings suggest that frequent chimeric RNAs are present in CRCs, and that chimeric RNAs may have different expression profiles and functions from parental genes, thus representing a new repertoire of biomarkers and therapeutic targets.

Chimeric RNAs in cancer

Chimeric RNAs are hybrid transcripts containing exons from two separate genes. Chimeric RNAs are traditionally considered to be transcribed from fusion genes caused by chromosomal rearrangement. These canonical chimeric RNAs are well characterized to be expressed in a cancer-unique pattern and/or act as oncogene products. However, benefited by the development of advanced deep sequencing technologies, novel types of non-canonical chimeric RNAs have been discovered to be generated from intergenic splicing without genomic aberrations. They can be formed through trans-splicing or cis-splicing between adjacent genes (cis-SAGe) mechanisms. Non-canonical chimeric RNAs are widely detected in normal physiology, although several have been shown to have a cancer-specific expression pattern. Further studies have indicated that some of them play fundamental roles in controlling cell growth and motility, and may have functions independent of the parental genes. These discoveries are unveiling a new layer of the functional transcriptome and are also raising the possibility of utilizing non-canonical chimeric RNAs as cancer diagnostic markers and therapeutic targets. In this chapter, we will overview different categories of chimeric RNAs and their expression in various types of cancerous and normal samples. Acknowledging that chimeric RNAs are not unique to cancer, we will discuss both bioinformatic and biological methods to identify credible cancer-specific chimeric RNAs. Furthermore, we will describe downstream methods to explore their molecular processing mechanisms and potential functions. A better understanding of the biogenesis mechanisms and functional products of cancer-specific chimeric RNAs will pave ways for the development of novel cancer biomarkers and therapeutic targets.

GOLT1A-KISS1 fusion is associated with metastasis in adenoid cystic carcinomas

Genetic alterations can drive carcinogenesis. Numerous studies have shown that gene fusion is associated with cancer progression and could provide valuable biomarkers for clinical diagnosis or targets for cancer therapy. Adenoid cystic carcinoma (ACC) is a rare form of adenocarcinoma, characterized by frequent local recurrence and high rates of distant metastasis, ultimately resulting in low survival rates. Owing to the lack of effective therapeutic targets and limited biomarkers for diagnosis, a deeper understanding of the molecular basis of ACC is urgently needed. Here, we show that gene fusion is associated with ACC metastasis. We identified a metastasis suppressor KISS1 fused with a close-by gene, GOLT1A, in highly metastatic ACC cell lines and human specimens. Such fusion blocks KISS1 translation, but not transcription, by introducing 5' upstream open reading frames (uORFs) in the GOLT1A-KISS1 fusion transcript. Deletion of these uORFs rescued KISS1 expression and reduced invasion and migration of metastatic ACC cells. We also detected GOLT1A-KISS1 fusion transcripts in other types of highly metastatic cancer cell lines. Taken together, our results highlight the significance of this novel GOLT1A-KISS1 gene fusion in tumor metastasis and provide a valuable biomarker for clinical diagnosis and future therapeutic targeting of ACC.

Validation of Chimeric Fusion Peptides Using Proteomics Data

Chimeric RNAs as well as their fused protein products have therapeutic applications ranging from diagnostics to being used as therapeutic target. Many algorithms have been developed to identify chimeric RNAs, however, identification and validation of fused protein product of the chimeric RNA is still an emerging field. These chimeric proteins can be validated by searching and identifying them in publicly available proteomics datasets. Here we describe the detailed steps for (1) downloading and processing publicly available proteomics datasets, (2) developing fusion peptide database by performing in silico tryptic digestion of chimeric proteins, and (3) software used to identify chimeric peptides in the proteomics data.

Confirmation of Transcriptional Read-Through Events by RT-PCR

Chimeric RNAs can be formed by trans-splicing from different transcripts or cis-splicing of adjacent genes (cis-SAGe). Cis-SAGe results from read-through transcription of two neighbor genes. To investigate the mechanisms underlying intergenic splicing of adjacent genes, it is important to develop an assay to detect transcriptional read-through. Here, we describe a general RT-PCR based method to confirm the process for cis-SAGe candidates. In this method, we use PCR to amplify cDNA that is reverse transcribed from the read-through precursor mRNA. The result provides a foundation for further downstream mechanistic studies.

Potentially Critical Roles of NDUFB5, TIMMDC1, and VDAC3 in the Progression of Septic Cardiomyopathy Through Integrated Bioinformatics Analysis

Septic cardiomyopathy (SC) is a rare and harmful cardiovascular disease with decreased left ventricular (LV) output and multiple organ failure, which poses a serious threat to human life. Despite the advances in SC, its diagnostic basis and treatment methods are limited, and the specific diagnostic biomarkers and its candidate regulatory targets have not yet been fully established. In this study, the GSE79962 gene expression profile was retrieved, with 20 patients with SC and 11 healthy donors as control. Weighted gene coexpression network analysis (WGCNA) was employed to investigate gene modules that were strongly correlated with clinical phenotypes. Blue module was found to be most significantly related to SC. Moreover, Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the coexpression genes in blue module and showed that it was associated with metabolic pathways, oxidative phosphorylation, and cardiac muscle contraction. Furthermore, a total of 10 hub genes NDUFB5, TIMMDC1, VDAC3, COQ10A, MRPL16 (mitochondrial ribosomal protein L16), C3orf43, TMEM182, DLAT, NDUFA8, and PDHB (pyruvate dehydrogenase E1 beta subunit) in the blue module were identified at transcriptional level and further validated at translational level in myocardium of an lipopolysaccharide-induced septic cardiac dysfunction mouse model. Overall, the results of quantitative real-time polymerase chain reaction were consistent with most of the microarray analysis results. Intriguingly, we observed that the highest change was NDUFB5, TIMMDC1, and VDAC3. These identified and validated genes provided references that would advance the understanding of molecular mechanisms of SC. Taken together, using WGCNA, the hub genes NDUFB5, TIMMDC1, and VDAC3 might serve as potential biomarkers for diagnosis and/or therapeutic targets for precise treatment of SC in the future.

Fusion Transcripts of Adjacent Genes: New Insights into the World of Human Complex Transcripts in Cancer

The awareness of genome complexity brought a radical approach to the study of transcriptome, opening eyes to single RNAs generated from two or more adjacent genes according to the present consensus. This kind of transcript was thought to originate only from chromosomal rearrangements, but the discovery of readthrough transcription opens the doors to a new world of fusion RNAs. In the last years many possible intergenic cis-splicing mechanisms have been proposed, unveiling the origins of transcripts that contain some exons of both the upstream and downstream genes. In some cases, alternative mechanisms, such as trans-splicing and transcriptional slippage, have been proposed. Five databases, containing validated and predicted Fusion Transcripts of Adjacent Genes (FuTAGs), are available for the scientific community. A comparative analysis revealed that two of them contain the majority of the results. A complete analysis of the more widely characterized FuTAGs is provided in this review, including their expression pattern in normal tissues and in cancer. Gene structure, intergenic splicing patterns and exon junction sequences have been determined and here reported for well-characterized FuTAGs. The available functional data and the possible roles in cancer progression are discussed.

FAL1: A critical oncogenic long non-coding RNA in human cancers

Long noncoding RNAs (lncRNAs) are characterized as a group of endogenous RNAs that are more than 200 nucleotides in length and have no protein-encoding function. More and more evidence indicates that lncRNAs play vital roles in various human diseases, especially in tumorigenesis. Focally amplified lncRNA on chromosome 1 (FAL1), a novel lncRNA with enhancer-like activity, has been identified as an oncogene in multiple cancers and high expression level of FAL1 is usually associated with poor prognosis. Dysregulation of FAL1 has been shown to promote the proliferation and metastasis of cancer cells. In the present review, we summarized and illustrated the functions and underlying molecular mechanisms of FAL1 in the occurrence and development of different cancers and other diseases. FAL1 has the potential to appear as a feasible diagnostic and prognostic tool and new therapeutic target for cancer patients though further investigation is needed so as to accelerate clinical application.