The Detection and Bioinformatic Analysis of Alternative 3' UTR Isoforms as Potential Cancer Biomarkers

Differential expression of mRNA 3'-end isoforms in cervical and ovarian cancers

Early diagnosis and therapeutic targeting are continuing challenges for gynecological cancers. Here, we focus on cancer transcriptomes and describe the differential expression of 3'UTR isoforms in patients using an algorithm to detect differential poly(A) site usage. We find primarily 3'UTR shortening cases in cervical cancers compared with the normal cervix. We show differential expression of alternate 3'-end isoforms of FOXP1, VPS4B, and OGT in HPV16-positive patients who develop high-grade cervical lesions compared with the infected but non-progressing group. In contrast, in ovarian cancers, 3'UTR lengthening is more evident compared with normal ovary tissue. Nevertheless, highly malignant ovarian tumors have unique 3'UTR shortening events (e.g., CHRAC1, SLC16A1, and TOP2A), some of which correlate with upregulated protein levels in tumors. Overall, our study shows isoform level deregulation in gynecological cancers and highlights the complexity of the transcriptome. This transcript diversity could help identify novel cancer genes and provide new possibilities for diagnosis and therapy.

A Survey on Methods for Predicting Polyadenylation Sites from DNA Sequences, Bulk RNA-seq, and Single-cell RNA-seq

Alternative polyadenylation (APA) plays important roles in modulating mRNA stability, translation, and subcellular localization, and contributes extensively to shaping eukaryotic transcriptome complexity and proteome diversity. Identification of poly(A) sites (pAs) on a genome-wide scale is a critical step toward understanding the underlying mechanism of APA-mediated gene regulation. A number of established computational tools have been proposed to predict pAs from diverse genomic data. Here we provided an exhaustive overview of computational approaches for predicting pAs from DNA sequences, bulk RNA sequencing (RNA-seq) data, and single-cell RNA sequencing (scRNA-seq) data. Particularly, we examined several representative tools using bulk RNA-seq and scRNA-seq data from peripheral blood mononuclear cells and put forward operable suggestions on how to assess the reliability of pAs predicted by different tools. We also proposed practical guidelines on choosing appropriate methods applicable to diverse scenarios. Moreover, we discussed in depth the challenges in improving the performance of pA prediction and benchmarking different methods. Additionally, we highlighted outstanding challenges and opportunities using new machine learning and integrative multi-omics techniques, and provided our perspective on how computational methodologies might evolve in the future for non-3' untranslated region, tissue-specific, cross-species, and single-cell pA prediction.

Transcriptome-wide measurement of poly(A) tail length and composition at subnanogram total RNA sensitivity by PAIso-seq

Poly(A) tails are added to the 3' ends of most mRNAs in a non-templated manner and play essential roles in post-transcriptional regulation, including mRNA export, stability and translation. Measuring poly(A) tails is critical for understanding their regulatory roles in almost every aspect of biological and medical studies. Previous methods for analyzing poly(A) tails require large amounts of input RNA (microgram-level total RNA), which limits their application. We recently developed a poly(A) inclusive full-length RNA isoform-sequencing method (PAIso-seq) at single-oocyte-level sensitivity (a single mammalian oocyte contains ~0.5 ng of total RNA) based on PacBio sequencing that enabled accurate measurement of the poly(A) tail length and non-A residues within the body of poly(A) tails along with the full-length cDNA, providing the opportunity to study precious in vivo samples with very limited input material. Here, we describe a detailed protocol for PAIso-seq library preparation from single mouse oocytes or bulk oocyte samples. In addition, we provide a complete bioinformatic pipeline to perform the analysis from the raw data to downstream analysis. The minimum time required is ~14.5 h for PAIso-seq double-stranded cDNA preparation, 2 d for PacBio sequencing in HiFi mode and 8 h for the initial data analysis.