The Pan-Cancer analysis of pseudogene expression reveals biologically and clinically relevant tumour subtypes

UBE2C: A pan-cancer diagnostic and prognostic biomarker revealed through bioinformatics analysis

BACKGROUND

The diverse and complex attributes of cancer have made it a daunting challenge to overcome globally and remains to endanger human life. Detection of critical cancer-related gene alterations in solid tumor samples better defines patient diagnosis and prognosis, and indicates what targeted therapies must be administered to improve cancer patients' outcome.

MATERIALS AND METHODS

To identify genes that have aberrant expression across different cancer types, differential expressed genes were detected within the TCGA datasets. Subsequently, the DEGs common to all pan cancers were determined. Furthermore, various methods were employed to gain genetic alterations, co-expression genes network and protein-protein interaction (PPI) network, pathway enrichment analysis of common genes. Finally, the gene regulatory network was constructed.

RESULTS

Intersectional analysis identified UBE2C as a common DEG between all 28 types of studied cancers. Upregulated UBE2C expression was significantly correlated with OS and DFS of 10 and 9 types of cancer patients. Also, UBE2C can be a diagnostic factor in CESC, CHOL, GBM, and UCS with AUC = 100% and diagnose 19 cancer types with AUC ≥90%. A ceRNA network constructed including UBE2C, 41 TFs, 10 shared miRNAs, and 21 circRNAs and 128 lncRNAs.

CONCLUSION

In summary, UBE2C can be a theranostic gene, which may serve as a reliable biomarker in diagnosing cancers, improving treatment responses and increasing the overall survival of cancer patients and can be a promising gene to be target by cancer drugs in the future.

Unlocking the role of EIF5A: A potential diagnostic marker regulating the cell cycle and showing negative correlation with immune infiltration in lung adenocarcinoma

BACKGROUND

Despite EIF5A upregulation related to tumor progression in LUAD (lung adenocarcinoma), the underlying mechanisms remain elusive. In addition, there are few comprehensive analyses of EIF5A in LUAD.

METHODS

We investigated the EIF5A expression level in LUAD patients using data from the TCGA and GEO databases. We employed qRT-PCR and western blot to verify EIF5A expression in cell lines, while immunohistochemistry was utilized for clinical sample analysis. We analyzed EIF5A expression in tumor-infiltrating immune cells using the TISCH database and assessed its association with immune infiltration in LUAD using the "ESTIMATE" R package. Bioinformatics approaches were developed to discover the EIF5A-related genes and explore EIF5A potential mechanisms in LUAD. Proliferation ability was verified through CCK-8, clone formation, and EdU assays, while flow cytometry assessed apoptosis and cell cycle. Western blot was used to detect the expression of pathway-related proteins.

RESULTS

EIF5A was significantly upregulated in LUAD. Moreover, we constructed a MAZ-hsa-miR-424-3p-EIF5A transcriptional network. We explored the potential mechanism of EIF5A in LUAD and further investigated the cAMP signaling pathway and the cell cycle. Finally, we proved that EIF5A silencing induced G1/S Cell Cycle arrest, promoted apoptosis, and inhibited proliferation via the cAMP/PKA/CREB signaling pathway.

CONCLUSION

EIF5A serves as a prognostic biomarker with a negative correlation to immune infiltrates in LUAD. It regulated the cell cycle in LUAD by inhibiting the cAMP/PKA/CREB signaling pathway.

Predictive value of machine learning algorithm of coronary artery calcium score and clinical factors for obstructive coronary artery disease in hypertensive patients

BACKGROUND

The addition of coronary artery calcium score (CACS) to prediction models has been verified to improve performance. Machine learning (ML) algorithms become important medical tools in an era of precision medicine, However, combined utility by CACS and ML algorithms in hypertensive patients to forecast obstructive coronary artery disease (CAD) on coronary computed tomography angiography (CCTA) is rare.

METHODS

This retrospective study was composed of 1,273 individuals with hypertension and without a history of CAD, who underwent dual-source computed tomography evaluation. We applied five ML algorithms, coupled with clinical factors, imaging parameters, and CACS to construct predictive models. Moreover, 80% individuals were randomly taken as a training set on which 5-fold cross-validation was done and the remaining 20% were regarded as a validation set.

RESULTS

16.7% (212 out of 1,273) of hypertensive patients had obstructive CAD. Extreme Gradient Boosting (XGBoost) posted the biggest area under the receiver operator characteristic curve (AUC) of 0.83 in five ML algorithms. Continuous net reclassification improvement (NRI) was 0.55 (95% CI (0.39-0.71), p < 0.001), and integrated discrimination improvement (IDI) was 0.04 (95% CI (0.01-0. 07), p = 0.0048) when the XGBoost model was compared with traditional Models. In the subgroup analysis stratified by hypertension levels, XGBoost still had excellent performance.

CONCLUSION

The ML model incorporating clinical features and CACS may accurately forecast the presence of obstructive CAD on CCTA among hypertensive patients. XGBoost is superior to other ML algorithms.

LncRNA PTTG3P promotes tumorigenesis and metastasis of NSCLC by binding with ILF3 to maintain mRNA stability and form a positive feedback loop with E2F1

Non-small cell lung cancer (NSCLC) is a highly lethal disease worldwide. We found the pseudogene-derived lncRNA PTTG3P is upregulated in NSCLC and associated with larger tumor size, advanced staging, and poor prognosis. This study investigated the oncogenic roles and mechanisms of PTTG3P in NSCLC. We demonstrate that PTTG3P promoted NSCLC cell proliferation, migration, tumorigenesis, and metastasis while inhibiting apoptosis in vitro and in vivo. Mechanistically, PTTG3P formed an RNA-protein complex with ILF3 to maintain MAP2K6 and E2F1 mRNA stability, two oncogenic factors involved in NSCLC progression. RNA-seq revealed MAP2K6 and E2F1 were downregulated upon PTTG3P knockdown. RIP and RNA stability assays showed PTTG3P/ILF3 interaction stabilized MAP2K6 and E2F1 transcripts. Interestingly, E2F1 transcriptionally upregulated PTTG3P by binding its promoter, forming a positive feedback loop. Knockdown of E2F1 or PTTG3P attenuated their mutual regulatory effects on cell growth and migration. Thus, a PTTG3P/ILF3/E2F1 axis enhances oncogene expression to promote NSCLC pathogenesis. Our study reveals PTTG3P exerts oncogenic functions in NSCLC via mRNA stabilization and a feedback loop, highlighting its potential as a prognostic biomarker and therapeutic target.

The non-coding competing endogenous RNAs in acute myeloid leukemia: biological and clinical implications

Acute myeloid leukemia (AML) is a hematologic carcinoma that has seen a considerable improvement in patient prognosis because of genetic diagnostics and molecularly-targeted therapies. Nevertheless, recurrence and drug resistance remain significant obstacles to leukemia treatment. It is critical to investigate the underlying molecular mechanisms and find solutions. Non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), circular RNAs, long non-coding RNAs, and pseudogenes, have been found to be crucial components in driving cancer. The competing endogenous RNA (ceRNA) mechanism has expanded the complexity of miRNA-mediated gene regulation. A great deal of literature has shown that ncRNAs are essential to the biological functions of the ceRNA network (ceRNET). NcRNAs can compete for the same miRNA response elements to influence miRNA-target RNA interactions. Recent evidence suggests that ceRNA might be a potential biomarker and therapeutic strategy. So far, however, there have been no comprehensive studies on ceRNET about AML. What is not yet clear is the clinical application of ceRNA in AML. This study attempts to summarize the development of research on the related ceRNAs in AML and the roles of ncRNAs in ceRNET. We also briefly describe the mechanisms of ceRNA and ceRNET. What's more significant is that we explore the clinical value of ceRNAs to provide accurate diagnostic and prognostic biomarkers as well as therapeutic targets. Finally, limitations and prospects are considered.

PA2G4P4 Promotes Glioma Cell Migration and Tumorigenesis through the PTEN/AKT/mTOR Signaling Pathway

Dysregulation of pseudogene expression is closely related to the progression of various cancers, including glioma. Proliferation-associated 2G4 pseudogene 4 (PA2G4P4) could affect cell viability and apoptosis of glioma cells. However, the specific regulatory mechanism of PA2G4P4 is not clear. In this paper, we found that PA2G4P4 overexpres-sion promoted glioma cell proliferation, migration and cell cycle progression, whereas PA2G4P4 knockdown inhibited cancer progression. Knockdown of PA2G4P4 also suppressed the tumorigenesis of glioma cells in vivo. Furthermore, knockdown of PA2G4 after overexpression of PA2G4P4 decreased the cell viability and migration ability to normal level. The protein level of a tumor suppressor gene phosphatase and tensing homolog (PTEN) was greatly decreased in U87 cells after PA2G4P4 overexpression, while increased after PA2G4 knockdown; on the contrary, the protein levels of P-AKT and P-S6 were obviously induced in U87 cells after PA2G4P4 overexpression, and decreased after PA2G4 knockdown. The cell ability, colony formation ability and cell migration ability were all recovered to normal level by adding an AKT inhibitor MK2206 to the glioma cells, which were induced by PA2G4P4 overexpression. Our results revealed that PA2G4P4 could regulate glioma cell proliferation and migration through PTEN/AKT/mTOR signaling pathway by targeting PA2G4 gene. PA2G4P4 may become a target for glioma treatment.

Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in dilated cardiomyopathy with heart failure

The etiologies and pathogenesis of dilated cardiomyopathy (DCM) with heart failure (HF) remain to be defined. Thus, exploring specific diagnosis biomarkers and mechanisms is urgently needed to improve this situation. In this study, three gene expression profiling datasets (GSE29819, GSE21610, GSE17800) and one single-cell RNA sequencing dataset (GSE95140) were obtained from the Gene Expression Omnibus (GEO) database. GSE29819 and GSE21610 were combined into the training group, while GSE17800 was the test group. We used the weighted gene co-expression network analysis (WGCNA) and identified fifteen driver genes highly associated with DCM with HF in the module. We performed the least absolute shrinkage and selection operator (LASSO) on the driver genes and then constructed five machine learning classifiers (random forest, gradient boosting machine, neural network, eXtreme gradient boosting, and support vector machine). Random forest was the best-performing classifier established on five Lasso-selected genes, which was utilized to select out NPPA, OMD, and PRELP for diagnosing DCM with HF. Moreover, we observed the up-regulation mRNA levels and robust diagnostic accuracies of NPPA, OMD, and PRELP in the training group and test group. Single-cell RNA-seq analysis further demonstrated their stable up-regulation expression patterns in various cardiomyocytes of DCM patients. Besides, through gene set enrichment analysis (GSEA), we found TGF-β signaling pathway, correlated with NPPA, OMD, and PRELP, was the underlying mechanism of DCM with HF. Overall, our study revealed NPPA, OMD, and PRELP serving as diagnostic biomarkers for DCM with HF, deepening the understanding of its pathogenesis.

Low expression of PEBP1P2 promotes metastasis of clear cell renal cell carcinoma by post-transcriptional regulation of PEBP1 and KLF13 mRNA

Background

Pseudogenes play an essential role in tumor occurrence and progression. However, the functions and mechanisms of pseudogenes in clear cell renal cell carcinoma (ccRCC) remain largely elusive.

Methods

We quantified PEBP1P2 expression in ccRCC tissues and cells using fluorescence in situ hybridization and real-time PCR. Besides, we evaluated the role of PEBP1P2 in ccRCC using a lung metastasis model and a transwell assay. Finally, we documented the interactions between PEBP1P2, PEBP1, and KLF13 by performing luciferase, RNA immunoprecipitation, RNA pulldown, and targeted RNA demethylation assays.

Results

Low PEBP1P2 expression correlates significantly with advanced stages and poor prognosis in ccRCC patients. Besides, PEBP1P2 overexpression inhibits ccRCC metastasis formation in vivo and in vitro. Interestingly, PEBP1P2 directly interacted with 5-methylcytosine (m5C)-containing PEBP1 mRNA and recruited the YBX1/ELAVL1 complex, stabilizing PEBP1 mRNA. In addition, PEBP1P2 increased KLF13 mRNA levels by acting as a sponge for miR-296, miR-616, and miR-3194.

Conclusions

PEBP1P2 inhibits ccRCC metastasis formation and regulates both PEBP1 and KLF13. Therefore, molecular therapies targeting PEBP1P2 might be an effective treatment strategy against ccRCC and other cancers with low PEBP1P2 levels.

Evolution and function of developmentally dynamic pseudogenes in mammals

BACKGROUND

Pseudogenes are excellent markers for genome evolution, which are emerging as crucial regulators of development and disease, especially cancer. However, systematic functional characterization and evolution of pseudogenes remain largely unexplored.

RESULTS

To systematically characterize pseudogenes, we date the origin of human and mouse pseudogenes across vertebrates and observe a burst of pseudogene gain in these two lineages. Based on a hybrid sequencing dataset combining full-length PacBio sequencing, sample-matched Illumina sequencing, and public time-course transcriptome data, we observe that abundant mammalian pseudogenes could be transcribed, which contribute to the establishment of organ identity. Our analyses reveal that developmentally dynamic pseudogenes are evolutionarily conserved and show an increasing weight during development. Besides, they are involved in complex transcriptional and post-transcriptional modulation, exhibiting the signatures of functional enrichment. Coding potential evaluation suggests that 19% of human pseudogenes could be translated, thus serving as a new way for protein innovation. Moreover, pseudogenes carry disease-associated SNPs and conduce to cancer transcriptome perturbation.

CONCLUSIONS

Our discovery reveals an unexpectedly high abundance of mammalian pseudogenes that can be transcribed and translated, and these pseudogenes represent a novel regulatory layer. Our study also prioritizes developmentally dynamic pseudogenes with signatures of functional enrichment and provides a hybrid sequencing dataset for further unraveling their biological mechanisms in organ development and carcinogenesis in the future.

Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.

Identification of potential biomarkers and their correlation with immune infiltration cells in schizophrenia using combinative bioinformatics strategy

Many studies have identified changes in gene expression in brains of schizophrenia patients and their altered molecular processes, but the findings in different datasets were inconsistent and diverse. Here we performed the most comprehensive analysis of gene expression patterns to explore the underlying mechanisms and the potential biomarkers for early diagnosis in schizophrenia. We focused on 10 gene expression datasets in post-mortem human brain samples of schizophrenia downloaded from gene expression omnibus (GEO) database using the integrated bioinformatics analyses including robust rank aggregation (RRA) algorithm, Weighted gene co-expression network analysis (WGCNA) and CIBERSORT. Machine learning algorithm was used to construct the risk prediction model for early diagnosis of schizophrenia. We identified 15 key genes (SLC1A3, AQP4, GJA1, ALDH1L1, SOX9, SLC4A4, EGR1, NOTCH2, PVALB, ID4, ABCG2, METTL7A, ARC, F3 and EMX2) in schizophrenia by performing multiple bioinformatics analysis algorithms. Moreover, the interesting part of the study is that there is a correlation between the expression of hub genes and the immune infiltrating cells estimated by CIBERSORT. Besides, the risk prediction model was constructed by using both these genes and the immune cells with a high accuracy of 0.83 in the training set, and achieved a high AUC of 0.77 for the test set. Our study identified several potential biomarkers for diagnosis of SCZ based on multiple bioinformatics algorithms, and the constructed risk prediction model using these biomarkers achieved high accuracy. The results provide evidence for an improved understanding of the molecular mechanism of schizophrenia.

Identification of critical genes associated with radiotherapy resistance in cervical cancer by bioinformatics

Background

Cervical cancer (CC) is one of the common malignant tumors in women, Currently, 30% of patients with intermediate to advanced squamous cervical cancer are still uncontrolled or recurrent after standard radical simultaneous radiotherapy; therefore, the search for critical genes affecting the sensitivity of radiotherapy may lead to new strategies for treatment.

Methods

Firstly, differentially expressed genes (DEGs) between radiotherapy-sensitivity and radiotherapy-resistance were identified by GEO2R from the gene expression omnibus (GEO) website, and prognosis-related genes for cervical cancer were obtained from the HPA database. Subsequently, the DAVID database analyzed gene ontology (GO). Meanwhile, the protein-protein interaction network was constructed by STRING; By online analysis of DEGs, prognostic genes, and CCDB data that are associated with cervical cancer formation through the OncoLnc database, we aim to search for the key DEGs associated with CC, Finally, the key gene(s) was further validated by immunohistochemistry.

Result

298 differentially expressed genes, 712 genes associated with prognosis, and 509 genes related to cervical cancer formation were found. The results of gene function analysis showed that DEGs were mainly significant in functional pathways such as variable shear and energy metabolism. By further verification, two genes, ASPH and NKAPP1 were identified through validation as genes that affect both sensitivities to radiotherapy and survival finally. Then, immunohistochemical results showed that the ASPH gene was highly expressed in the radiotherapy-resistant group and had lower Overall survival (OS) and Progression-free survival (PFS).

Conclusion

This study aims to better understand the characteristics of cervical cancer radiation therapy resistance-related genes through bioinformatics and provide further research ideas for finding new mechanisms and potential therapeutic targets related to cervical cancer radiation therapy.

Pan-cancer methylome analysis for cancer diagnosis and classification of cancer cell of origin

The accurate and early diagnosis and classification of cancer origin from either tissue or liquid biopsy is crucial for selecting the appropriate treatment and reducing cancer-related mortality. Here, we established the CAncer Cell-of-Origin (CACO) methylation panel using the methylation data of the 28 types of cancer in The Cancer Genome Atlas (7950 patients and 707 normal controls) as well as healthy whole blood samples (95 subjects). We showed that the CACO methylation panel had high diagnostic potential with high sensitivity and specificity in the discovery (maximum AUC = 0.998) and validation (maximum AUC = 1.000) cohorts. Moreover, we confirmed that the CACO methylation panel could identify the cancer cell type of origin using the methylation profile from liquid as well as tissue biopsy, including primary, metastatic, and multiregional cancer samples and cancer of unknown primary, independent of the methylation analysis platform and specimen preparation method. Together, the CACO methylation panel can be a powerful tool for the classification and diagnosis of cancer.

CT-based radiomics analysis of different machine learning models for differentiating benign and malignant parotid tumors

OBJECTIVES

This study aimed to explore and validate the value of different radiomics models for differentiating benign and malignant parotid tumors preoperatively.

METHODS

This study enrolled 388 patients with pathologically confirmed parotid tumors (training cohort: n = 272; test cohort: n = 116). Radiomics features were extracted from CT images of the non-enhanced, arterial, and venous phases. After dimensionality reduction and selection, radiomics models were constructed by logistic regression (LR), support vector machine (SVM), and random forest (RF). The best radiomic model was selected by using ROC curve analysis. Univariate and multivariable logistic regression was applied to analyze clinical-radiological characteristics and identify variables for developing a clinical model. A combined model was constructed by incorporating radiomics and clinical features. Model performances were assessed by ROC curve analysis, and decision curve analysis (DCA) was used to estimate the models' clinical values.

RESULTS

In total, 2874 radiomic features were extracted from CT images. Ten radiomics features were deemed valuable by dimensionality reduction and selection. Among radiomics models, the SVM model showed greater predictive efficiency and robustness, with AUCs of 0.844 in the training cohort; and 0.840 in the test cohort. Ultimate clinical features constructed a clinical model. The discriminatory capability of the combined model was the best (AUC, training cohort: 0.904; test cohort: 0.854). Combined model DCA revealed optimal clinical efficacy.

CONCLUSIONS

The combined model incorporating radiomics and clinical features exhibited excellent ability to distinguish benign and malignant parotid tumors, which may provide a noninvasive and efficient method for clinical decision making.

KEY POINTS

The current study is the first to compare the value of different radiomics models (LR, SVM, and RF) for preoperative differentiation of benign and malignant parotid tumors. A CT-based combined model, integrating clinical-radiological and radiomics features, is conducive to distinguishing benign and malignant parotid tumors, thereby improving diagnostic performance and aiding treatment.

Non-Coding RNAs Are Brokers in Breast Cancer Interactome Networks and Add Discrimination Power between Subtypes

Despite the power of high-throughput genomics, most non-coding RNA (ncRNA) biotypes remain hard to identify, characterize, and validate. This is a clear indication that intensive next-generation sequencing research has led to great efficiency and accuracy in detecting ncRNAs, but not in their functionalization. Computational scientists continue to support the discovery process by spotting significant data features (expression or mutational profiles), elucidating phenotype uncertainty, and delineating complex regulation landscapes for biological pathways and pathophysiological processes. With reference to transcriptome regulation dynamics in cancer, this work introduces a novel network-driven inference approach designed to reveal the potential role of computationally identified ncRNAs in discriminating between breast cancer (BC) subtypes beyond the traditional gene expression signatures. As heterogeneity cast in the subtypes is a characteristic of most cancers, the proposed approach is generalizable beyond BC. Expression profiles of a wide transcriptome spectrum were obtained for a number of BC patients (and controls) listed in TCGA and processed with RNA-Seq. The well-known PAM50 subtype signature was available for the samples and used to move from differentially expressed transcript profiles to subtype-specific biclusters associating gene patterns with patients. Co-expressed gene networks were then generated and annotations were provided, focusing on the biclusters with basal and luminal signatures. These were used to build template maps, i.e., networks in which to embed the ncRNAs and contextually functionalize them based on their interactors. This inference approach is able to assess the influence of ncRNAs at the level of BC subtype. Network topology was considered through the brokerage measure to account for disruptiveness effects induced by the removal of nodes corresponding to ncRNAs. Equivalently, it is shown that ncRNAs can act as brokers of network interactome dynamics, and removing them allows the refinement of subtype-related characteristics previously obtained by gene signatures only. The results of the study elucidate the role of pseudogenes in two major BC subtypes, considering the contextual annotations. Put into a wider perspective, ncRNA brokers may help predictive functionalization studies targeted to new disease phenotypes, for instance those linked to the tumor microenvironment or metabolism, or those specifically involving metastasis. Overall, the approach may represent an in silico prioritization strategy toward the systems identification of new diagnostic and prognostic biomarkers.

ANXA2P2: A Potential Immunological and Prognostic Signature in Ovarian Serous Cystadenocarcinoma via Pan-Carcinoma Synthesis

Background

Although the effect of pseudogene ANXA2P2 on some tumors has been reported in a few literatures, the therapeutic potential and prognostic value of ANXA2P2 in ovarian serous cystadenocarcinoma (OV) have not been elucidated.

Methods

The correlation for ANXA2P2 expression patterns to prognostic characteristics, tumor immune microenvironment, immune cell infiltration level, tumor mutation burden (TMB), tumor microsatellite instability (MSI), drug sensitivity, and pathway function enrichment were investigated in pan-carcinoma via TCGA and GTEx databases. Subsequently, the role of ANXA2P2 expression levels in the pathway enrichments and prognosis prediction in OV were further explored using weighted correlation network analysis (WGCNA) analysis, gene mutation analysis, and risk-independent prognostic analysis.

Results

ANXA2P2 was frequently overexpressed in a variety of tumors compared with normal tissues. The correlation analysis for prognostic characteristics, tumor immune microenvironment, immune cell infiltration level, TMB, MSI, drug sensitivity, and pathway function enrichment revealed that ANXA2P2 expression patterns might deal a significant impact on the pathogenesis, development, and prognosis of various tumors. Then, GSVA, GSEA, WGCNA, gene mutation, and independent prognostic analysis for OV have indicated that high expression in ANXA2P2 could be mostly enriched in TNF-α signaling-via-NF-κB, epithelial-mesenchymal transition, apical junction, IL-6-JAK STAT3 signaling, etc., which were also proved to act as crucial factors on tumorigenesis, development, invasion, and metastasis. The mutation of TP53 (94%), TTN (24%), and CSMD3 (9%) in the biological process of tumor had been confirmed by relevant studies. Finally, the independent prognostic analysis demonstrated that ANXA2P2 expression in OV contributes greatly to the dependability of 3- and 5-year survival prediction.

Conclusion

In summary, our findings might provide a helpful foundation for prospective explorative researches, afford new strategies for the clinical treatment, deal prognosis prediction, and give new hope for OV patients.

PΨFinder: a practical tool for the identification and visualization of novel pseudogenes in DNA sequencing data

BACKGROUND

Processed pseudogenes (PΨgs) are disabled gene copies that are transcribed and may affect expression of paralogous genes. Moreover, their insertion in the genome can disrupt the structure or the regulatory region of a gene, affecting its expression level. These events have been identified as occurring mutations during cancer development, thus being able to identify PΨgs and their location will improve their impact on diagnostic testing, not only in cancer but also in inherited disorders.

RESULTS

We have implemented PΨFinder (P-psy-finder), a tool that identifies PΨgs, annotates known ones and predicts their insertion site(s) in the genome. The tool screens alignment files and provides user-friendly summary reports and visualizations. To demonstrate its applicability, we scanned 218 DNA samples from patients screened for hereditary colorectal cancer. We detected 423 PΨgs distributed in 96% of the samples, comprising 7 different parent genes. Among these, we confirmed the well-known insertion site of the SMAD4-PΨg within the last intron of the SCAI gene in one sample. While for the ubiquitous CBX3-PΨg, present in 82.6% of the samples, we found it reversed inserted in the second intron of the C15ORF57 gene.

CONCLUSIONS

PΨFinder is a tool that can automatically identify novel PΨgs from DNA sequencing data and determine their location in the genome with high sensitivity (95.92%). It generates high quality figures and tables that facilitate the interpretation of the results and can guide the experimental validation. PΨFinder is a complementary analysis to any mutational screening in the identification of disease-causing mutations within cancer and other diseases.

Identification of Methylation Immune Subtypes and Establishment of a Prognostic Signature for Gliomas Using Immune-Related Genes

DNA methylation patterns are essential in understanding carcinogenesis. However, the relationship between DNA methylation and the immune process has not been clearly established—this study aimed at elucidating the interaction between glioma and DNA methylation, consolidating glioma classification and prognosis. A total of 2,483 immune-related genes and 24,556 corresponding immune-related methylation probes were identified. From the Cancer Genome Atlas (TCGA) glioma cohort, a total of 683 methylation samples were stratified into two different clusters using unsupervised clustering, and eight types of other cancer samples from the TCGA database were shown to exhibit excellent distributions. A total of 3,562 differentially methylated probes (DMPs) were selected and used for machine learning. A five-probe signature was established to evaluate the prognosis of glioma as well as the potential benefits of radiotherapy and Procarbazine, CCNU, Vincristine (PCV) treatment. Other prognostic clinical models, such as nomogram and decision tree, were also evaluated. Our findings confirmed the interactions between immune-related methylation patterns and glioma. This novel approach for cancer molecular characterization and prognosis should be validated in further studies.

A Pan-Cancer Analysis of Transcriptome and Survival Reveals Prognostic Differentially Expressed LncRNAs and Predicts Novel Drugs for Glioblastoma Multiforme Therapy

Numerous studies have identified various prognostic long non-coding RNAs (LncRNAs) in a specific cancer type, but a comprehensive pan-cancer analysis for prediction of LncRNAs that may serve as prognostic biomarkers is of great significance to be performed. Glioblastoma multiforme (GBM) is the most common and aggressive malignant adult primary brain tumor. There is an urgent need to identify novel therapies for GBM due to its poor prognosis and universal recurrence. Using available LncRNA expression data of 12 cancer types and survival data of 30 cancer types from online databases, we identified 48 differentially expressed LncRNAs in cancers as potential pan-cancer prognostic biomarkers. Two candidate LncRNAs were selected for validation in GBM. By the expression detection in GBM cell lines and survival analysis in GBM patients, we demonstrated the reliability of the list of pan-cancer prognostic LncRNAs obtained above. By constructing LncRNA-mRNA-drug network in GBM, we predicted novel drug-target interactions for GBM correlated LncRNA. This analysis has revealed common prognostic LncRNAs among cancers, which may provide insights into cancer pathogenesis and novel drug target in GBM.

Prediction model of laparoendoscopic single-site surgery in gynecology using machine learning algorithm

Introduction

Minimally invasive surgery has been widely used in gynecology. The laparoendoscopic single-site surgery (LESS) risk prediction model can provide evidence-based references for preoperative surgical procedure selection.

Aim

To determine whether the patients are suitable for LESS and to provide guidance for the clinical operation plan, we aimed to compare the clinical outcomes of LESS and conventional laparoscopic surgery (CLS) in gynecology. We constructed a LESS risk prediction model and predicted surgical conditions for the preoperative evaluation system.

Material and methods

A retrospective analysis was carried out among patients undergoing LESS (n = 1019) and CLS (n = 1055). Various clinical indicators were compared. Multiple machine model algorithms were evaluated. The optimal results were chosen as the model to form the risk prediction model.

Results

The LESS group showed advantages in the postoperative 12/24 h visual analog scale and Vancouver scar score compared with the CLS group (p < 0.05). The comparisons in other clinical indicators between the two groups showed that each group had advantages and the difference was statistically significant (p < 0.05), including operative time, estimated blood loss, and hospital stay. We evaluated the predictive value for various models using AUC values of 0.77, 0.77, 0.76, and 0.67 for XGBoost, random forest, GBDT, and logistic regression, respectively. The decision tree model was shown to be the optimal model.

Conclusions

LESS can reduce postoperative pain, shorten hospital stay and make scars acceptable. The risk prediction model based on a machine learning algorithm has manifested a high degree of accuracy and can satisfy the doctors’ demand for individualized preoperative evaluation and surgical safety in LESS.

Pseudogene-mediated DNA demethylation leads to oncogene activation

Pseudogenes, noncoding homologs of protein-coding genes, once considered nonfunctional evolutionary relics, have recently been linked to patient prognoses and cancer subtypes. Despite this potential clinical importance, only a handful of >12,000 pseudogenes in humans have been characterized in cancers to date. Here, we describe a previously unrecognized role for pseudogenes as potent epigenetic regulators that can demethylate and activate oncogenes. We focused on SALL4, a known oncogene in hepatocellular carcinoma (HCC) with eight pseudogenes. Using a locus-specific demethylating technology, we identified the critical CpG region for SALL4 expression. We demonstrated that SALL4 pseudogene 5 hypomethylates this region through interaction with DNMT1, resulting in SALL4 up-regulation. Intriguingly, pseudogene 5 is significantly up-regulated in a hepatitis B virus model before SALL4 induction, and both are increased in patients with HBV-HCC. Our results suggest that pseudogene-mediated demethylation represents a novel mechanism of oncogene activation in cancer.

Integrated analysis identifies a novel lncRNA prognostic signature associated with aerobic glycolysis and hub pathways in breast cancer

Long noncoding RNAs (lncRNAs) play a crucial role in cancer aerobic glycolysis. However, glycolysis‐related lncRNAs are still underexplored in breast cancer. In this study, we identified the five most glycolysis‐related lncRNAs in breast cancer to construct a prognostic signature, which could distinguish between patients with unfavorable and favorable prognoses. To investigate the role of signature lncRNAs in breast cancer, we profiled their expression levels in breast cancer progression cell line model. Real‐time PCR revealed that the five lncRNAs could contribute to breast cancer initiation or progression. Furthermore, we observed that the levels of four lncRNAs expression had a significant trend of gradient upregulation with the addition of glycolysis inhibitor in breast cancer cells. Afterward, random forest and logistic regression were conducted to assess the model's performance in stratifying glycolysis status. Finally, a nomogram including the lncRNA signature and clinical features was developed, and its efficacy in predicting the survival time and clinical utility was evaluated using a calibration curve, concordance index, and decision curve analysis. In this study, gene set enrichment analysis showed that the mTOR pathway, a central pathway in tumor initiation and progression, was significantly enriched in the high‐risk group. In addition, gene set variation analysis was performed to validate our findings in two independent datasets. Subsequent weighted gene co‐expression network analysis, followed by enrichment analysis, indicated that downstream cell growth‐related signaling was strikingly activated in the high‐risk group, and may directly promote tumor progression and escalate mortality risk in patients with high‐risk scores. Overall, our findings may provide novel insight into lncRNA‐related metabolic regulation, and help to develop promising prognostic indicators and therapeutic targets for breast cancer patients.

Systematic functional interrogation of human pseudogenes using CRISPRi

BACKGROUND

The human genome encodes over 14,000 pseudogenes that are evolutionary relics of protein-coding genes and commonly considered as nonfunctional. Emerging evidence suggests that some pseudogenes may exert important functions. However, to what extent human pseudogenes are functionally relevant remains unclear. There has been no large-scale characterization of pseudogene function because of technical challenges, including high sequence similarity between pseudogene and parent genes, and poor annotation of transcription start sites.

RESULTS

To overcome these technical obstacles, we develop an integrated computational pipeline to design the first genome-wide library of CRISPR interference (CRISPRi) single-guide RNAs (sgRNAs) that target human pseudogene promoter-proximal regions. We perform the first pseudogene-focused CRISPRi screen in luminal A breast cancer cells and reveal approximately 70 pseudogenes that affect breast cancer cell fitness. Among the top hits, we identify a cancer-testis unitary pseudogene, MGAT4EP, that is predominantly localized in the nucleus and interacts with FOXA1, a key regulator in luminal A breast cancer. By enhancing the promoter binding of FOXA1, MGAT4EP upregulates the expression of oncogenic transcription factor FOXM1. Integrative analyses of multi-omic data from the Cancer Genome Atlas (TCGA) reveal many unitary pseudogenes whose expressions are significantly dysregulated and/or associated with overall/relapse-free survival of patients in diverse cancer types.

CONCLUSIONS

Our study represents the first large-scale study characterizing pseudogene function. Our findings suggest the importance of nuclear function of unitary pseudogenes and underscore their underappreciated roles in human diseases. The functional genomic resources developed here will greatly facilitate the study of human pseudogene function.

Pseudogenes: Four Decades of Discovery

A pseudogene is defined as a genomic DNA sequence that looks like a mutated or truncated version of a known functional gene. Nearly four decades since their first discovery it has been estimated that between ~12,000 and ~20,000 pseudogenes exist in the human genome. Early efforts to characterize functions for pseudogenes were unsuccessful, thus they were considered functionless relics of evolutionary selection, junk DNA or genetic fossils. Remarkably, an increasing number of pseudogenes have been reported to be expressed as RNA transcripts above and beyond levels considered accidental or spurious transcription. There is emerging evidence that some expressed pseudogene transcripts have biological functions and should be defined as a subclass of functional long noncoding RNAs (lncRNA). In this introductory chapter, I briefly summarize the history and the current knowledge of pseudogenes, and highlight the emerging functions of some pseudogenes in human biology and disease. This second iteration of Pseudogenes in Methods in Molecular Biology highlights new methodological approaches to investigate this intriguing family of lncRNAs and the extent of their biological function.

Development and validation of a model to estimate the risk of acute ischemic stroke in geriatric patients with primary hypertension

Objectives

This study aimed to construct and validate a prediction model of acute ischemic stroke in geriatric patients with primary hypertension.

Methods

This retrospective file review collected information on 1367 geriatric patients diagnosed with primary hypertension and with and without acute ischemic stroke between October 2018 and May 2020. The study cohort was randomly divided into a training set and a testing set at a ratio of 70 to 30%. A total of 15 clinical indicators were assessed using the chi-square test and then multivariable logistic regression analysis to develop the prediction model. We employed the area under the curve (AUC) and calibration curves to assess the performance of the model and a nomogram for visualization. Internal verification by bootstrap resampling (1000 times) and external verification with the independent testing set determined the accuracy of the model. Finally, this model was compared with four machine learning algorithms to identify the most effective method for predicting the risk of stroke.

Results

The prediction model identified six variables (smoking, alcohol abuse, blood pressure management, stroke history, diabetes, and carotid artery stenosis). The AUC was 0.736 in the training set and 0.730 and 0.725 after resampling and in the external verification, respectively. The calibration curve illustrated a close overlap between the predicted and actual diagnosis of stroke in both the training set and testing validation. The multivariable logistic regression analysis and support vector machine with radial basis function kernel were the best models with an AUC of 0.710.

Conclusion

The prediction model using multiple logistic regression analysis has considerable accuracy and can be visualized in a nomogram, which is convenient for its clinical application.

An immune-related pseudogene signature to improve prognosis prediction of endometrial carcinoma patients

Background

Pseudogenes show multiple functions in various cancer types, and immunotherapy is a promising cancer treatment. Therefore, this study aims to identify immune-related pseudogene signature in endometrial cancer (EC).

Methods

Gene transcriptome data of EC tissues and corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) through UCSC Xena browser. Spearman correlation analysis was performed to identify immune-related pseudogenes (IRPs) between the immune genes and pseudogenes. Univariate Cox regression, LASSO, and multivariate were performed to develop a risk score signature to investigate the different overall survival (OS) between high- and low-risk groups. The prognostic significance of the signature was assessed by the Kaplan–Meier curve, time-dependent receiver operating characteristic (ROC) curve. The abundance of 22 immune cell subtypes of EC patients was evaluated using CIBERSORT.

Results

Nine IRPs were used to build a prognostic signature. Survival analysis revealed that patients in the low-risk group presented longer OS than those in the high-risk group as well as in multiple subgroups. The signature risk score was independent of other clinical covariates and was associated with several clinicopathological variables. The prognostic signature reflected infiltration by multiple types of immune cells and revealed the immunotherapy response of patients with anti-programmed death-1 (PD-1) and anti-programmed cell death 1 ligand 1 (PD-L1) therapy. Function enrichment analysis revealed that the nine IRPs were mainly involved in multiple cancer-related pathways.

Conclusion

We identified an immune-related pseudogene signature that was strongly correlated with the prognosis and immune response to EC. The signature might have important implications for improving the clinical survival of EC patients and provide new strategies for cancer treatment.

Positive natural selection of N6-methyladenosine on the RNAs of processed pseudogenes

BACKGROUND

Canonical nonsense-mediated decay (NMD) is an important splicing-dependent process for mRNA surveillance in mammals. However, processed pseudogenes are not able to trigger NMD due to their lack of introns. It is largely unknown whether they have evolved other surveillance mechanisms.

RESULTS

Here, we find that the RNAs of pseudogenes, especially processed pseudogenes, have dramatically higher m6A levels than their cognate protein-coding genes, associated with de novo m6A peaks and motifs in human cells. Furthermore, pseudogenes have rapidly accumulated m6A motifs during evolution. The m6A sites of pseudogenes are evolutionarily younger than neutral sites and their m6A levels are increasing, supporting the idea that m6A on the RNAs of pseudogenes is under positive selection. We then find that the m6A RNA modification of processed, rather than unprocessed, pseudogenes promotes cytosolic RNA degradation and attenuates interference with the RNAs of their cognate protein-coding genes. We experimentally validate the m6A RNA modification of two processed pseudogenes, DSTNP2 and NAP1L4P1, which promotes the RNA degradation of both pseudogenes and their cognate protein-coding genes DSTN and NAP1L4. In addition, the m6A of DSTNP2 regulation of DSTN is partially dependent on the miRNA miR-362-5p.

CONCLUSIONS

Our discovery reveals a novel evolutionary role of m6A RNA modification in cleaning up the unnecessary processed pseudogene transcripts to attenuate their interference with the regulatory network of protein-coding genes.

Dysregulation of Transcription Factor Networks Unveils Different Pathways in Human Papillomavirus 16-Positive Squamous Cell Carcinoma and Adenocarcinoma of the Uterine Cervix

Squamous cell carcinoma (SCC) and adenocarcinoma (ADC) are the most common histological types of cervical cancer (CC). The worse prognosis of ADC cases highlights the need for better molecular characterization regarding differences between these CC types. RNA-Seq analysis of seven SCC and three ADC human papillomavirus 16-positive samples and the comparison with public data from non-tumoral human papillomavirus-negative cervical tissue samples revealed pathways exclusive to each histological type, such as the epithelial maintenance in SCC and the maturity-onset diabetes of the young (MODY) pathway in ADC. The transcriptional regulatory network analysis of cervical SCC samples unveiled a set of six transcription factor (TF) genes with the potential to positively regulate long non-coding RNA genes DSG1-AS1, CALML3-AS1, IGFL2-AS1, and TINCR. Additional analysis revealed a set of MODY TFs regulated in the sequence predicted to be repressed by miR-96-5p or miR-28-3p in ADC. These microRNAs were previously described to target LINC02381, which was predicted to be positively regulated by two MODY TFs upregulated in cervical ADC. Therefore, we hypothesize LINC02381 might act by decreasing the levels of miR-96-5p and miR-28-3p, promoting the MODY activation in cervical ADC. The novel TF networks here described should be explored for the development of more efficient diagnostic tools.

Increased expression of peptides from non-coding genes in cancer proteomics datasets suggests potential tumor neoantigens

Neoantigen-based immunotherapy has yielded promising results in clinical trials. However, it is limited to tumor-specific mutations, and is often tailored to individual patients. Identifying suitable tumor-specific antigens is still a major challenge. Previous proteogenomics studies have identified peptides encoded by predicted non-coding sequences in human genome. To investigate whether tumors express specific peptides encoded by non-coding genes, we analyzed published proteomics data from five cancer types including 933 tumor samples and 275 matched normal samples and compared these to data from 31 different healthy human tissues. Our results reveal that many predicted non-coding genes such as DGCR9 and RHOXF1P3 encode peptides that are overexpressed in tumors compared to normal controls. Furthermore, from the non-coding genes-encoded peptides specifically detected in cancers, we predict a large number of “dark antigens” (neoantigens from non-coding genomic regions), which may provide an alternative source of neoantigens beyond standard tumor specific mutations.

Rong Xiang et al. analyze the expression of non-coding genes encoded peptides in publicly-available proteomics data from five cancer types and matched controls. They identify peptides from non-coding genes including DGCR9 and RHOXF1P3 that are upregulated in tumors compared to controls, suggesting that non-coding gene-encoded peptides may be a source of neoantigens in some cancers.

Circulating Biomarkers for Early Stage Non-Small Cell Lung Carcinoma Detection: Supplementation to Low-Dose Computed Tomography

Lung cancer is currently the leading cause of cancer death in both developing and developed countries. Given that lung cancer has poor prognosis in later stages, it is essential to achieve an early diagnosis to maximize patients’ overall survival. Non-small cell lung cancer (NSCLC) is the most common form of primary lung cancer in both smokers and non-smokers. The current standard screening method, low‐dose computed tomography (LDCT), is the only radiological method that demonstrates to have mortality benefits across multiple large randomized clinical trials (RCT). However, these RCTs also found LDCT to have a significant false positive rate that results in unnecessary invasive biopsies being performed. Due to the lack of both sensitive and specific screening methods for the early detection of lung cancer, there is an urgent need for alternative minimally or non-invasive biomarkers that may provide diagnostic, and/or prognostic information. This has led to the identification of circulating biomarkers that can be readily detectable in blood and have been extensively studied as prognosis markers. Circulating microRNA (miRNA) in particular has been investigated for these purposes as an augmentation to LDCT, or as direct diagnosis of lung cancer. There is, however, a lack of consensus across the studies on which miRNAs are the most clinically useful. Besides miRNA, other potential circulating biomarkers include circulating tumor cells (CTCs), circulating tumor DNA (ctDNAs) and non-coding RNAs (ncRNAs). In this review, we provide the current outlook of several of these biomarkers for the early diagnosis of NSCLC.

Next Generation Sequencing Technology in the Clinic and Its Challenges

Simple Summary

Precise identification and annotation of mutations are of utmost importance in clinical oncology. Insights of the DNA sequence can provide meaningful knowledge to unravel the underlying genetics of disease. Hence, tailoring of personalized medicine often relies on specific genomic alteration for treatment efficacy. The aim of this review is to highlight that sequencing harbors much more than just four nucleotides. Moreover, the gradual transition from first to second generation sequencing technologies has led to awareness for choosing the most appropriate bioinformatic analytic tools based on the aim, quality and demand for a specific purpose. Thus, the same raw data can lead to various results reflecting the intrinsic features of different datamining pipelines.

Abstract

Data analysis has become a crucial aspect in clinical oncology to interpret output from next-generation sequencing-based testing. NGS being able to resolve billions of sequencing reactions in a few days has consequently increased the demand for tools to handle and analyze such large data sets. Many tools have been developed since the advent of NGS, featuring their own peculiarities. Increased awareness when interpreting alterations in the genome is therefore of utmost importance, as the same data using different tools can provide diverse outcomes. Hence, it is crucial to evaluate and validate bioinformatic pipelines in clinical settings. Moreover, personalized medicine implies treatment targeting efficacy of biological drugs for specific genomic alterations. Here, we focused on different sequencing technologies, features underlying the genome complexity, and bioinformatic tools that can impact the final annotation. Additionally, we discuss the clinical demand and design for implementing NGS.

A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules

Background

Lung cancer remains the leading cause of cancer deaths across the world. Early detection of lung cancer by low-dose computed tomography (LDCT) can reduce the mortality rate. However, making a definitive preoperative diagnosis of malignant pulmonary nodules (PNs) found by LDCT is a clinical challenge. This study aimed to develop a prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant pulmonary nodules from benign PNs.

Methods

We assessed three DNA methylation biomarkers (PTGER4, RASSF1A, and SHOX2) and clinically-relevant variables in a training cohort of 110 individuals with PNs. Four machine-learning-based prediction models were established and compared, including the K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and logistic regression (LR) algorithms. Variables of the best-performing algorithm (LR) were selected through stepwise use of Akaike’s information criterion (AIC). The constructed prediction model was compared with the methylation biomarkers and the Mayo Clinic model using the non-parametric approach of DeLong et al. with the area under a receiver operator characteristic curve (AUC) analysis.

Results

A prediction model was finally constructed based on three DNA methylation biomarkers and one radiological characteristic for identifying malignant from benign PNs. The developed prediction model achieved an AUC value of 0.951 in malignant PNs diagnosis, significantly higher than the three DNA methylation biomarkers (0.912, 95% CI:0.843–0.958, p = 0.013) or Mayo Clinic model (0.823, 95% CI:0.739–0.890, p = 0.001). Validation of the prediction model in the testing cohort of 100 subjects with PNs confirmed the diagnostic value.

Conclusion

We have shown that integrating DNA methylation biomarkers and radiological characteristics could more accurately identify lung cancer in subjects with CT-found PNs. The prediction model developed in our study may provide clinical utility in combination with LDCT to improve the over-all diagnosis of lung cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08002-4.

Visualization and Analysis in the Field of Pan-Cancer Studies and Its Application in Breast Cancer Treatment

Although all cancers are molecularly distinct, many share common driver mutations. Pan-cancer analysis, utilizes next-generation sequencing (NGS), pan-cancer model systems, and pan-cancer projects such as The Cancer Genome Atlas (TCGA), to assess frequently mutated genes and other genomic abnormalities that are common among many cancer types, regardless of the tumor origin, providing new directions for tumor biology research. However, there is currently no study that has objectively analyzed the results of pan-cancer studies on cancer biology. For this study, 999 articles on pan-cancer published from 2006 to 2020 were obtained from the Scopus database, and bibliometric methods were used to analyze citations, international cooperation, co-authorship and keyword co-occurrence clusters. Furthermore, we also focused on and summarized the application of pan-cancer in breast cancer. Our result shows that the pan-cancer studies were first published in 2006 and entered a period of rapid development after 2013. So far, 86 countries have carried out international cooperation in sharing research. Researchers form the United States and Canada have published the most articles and have made the most extensive contribution to this field, respectively. Through author keyword analysis of the 999 articles, TCGA, biomarkers, NGS, immunotherapy, DNA methylation, prognosis, and several other keywords appear frequently, and these terms are hot spots in pan-cancer studies. There are four subtypes of breast cancer (luminalA, luminalB, HER2, and basal-like) according to pan-cancer analysis of breast cancer. Meanwhile, it was found that breast cancer has genetic similarity to pan-gynecological cancers, such as ovarian cancer, which indicates related etiology and possibly similar treatments. Collectively, with the emergence of new detection methods, new cancer databases, and the involvement of more researchers, pan-cancer analyses will play a greater role in cancer biology research.

Cancer, Retrogenes, and Evolution

This review summarizes the knowledge about retrogenes in the context of cancer and evolution. The retroposition, in which the processed mRNA from parental genes undergoes reverse transcription and the resulting cDNA is integrated back into the genome, results in additional copies of existing genes. Despite the initial misconception, retroposition-derived copies can become functional, and due to their role in the molecular evolution of genomes, they have been named the “seeds of evolution”. It is convincing that retrogenes, as important elements involved in the evolution of species, also take part in the evolution of neoplastic tumors at the cell and species levels. The occurrence of specific “resistance mechanisms” to neoplastic transformation in some species has been noted. This phenomenon has been related to additional gene copies, including retrogenes. In addition, the role of retrogenes in the evolution of tumors has been described. Retrogene expression correlates with the occurrence of specific cancer subtypes, their stages, and their response to therapy. Phylogenetic insights into retrogenes show that most cancer-related retrocopies arose in the lineage of primates, and the number of identified cancer-related retrogenes demonstrates that these duplicates are quite important players in human carcinogenesis.

Pseudogenes as Biomarkers and Therapeutic Targets in Human Cancers

Pseudogenes are commonly labeled as "junk DNA" given their perceived nonfunctional status. However, the advent of large-scale genomics projects prompted a revisit of pseudogene biology, highlighting their key functional and regulatory roles in numerous diseases, including cancers. Integrative analyses of cancer data have shown that pseudogenes can be transcribed and even translated, and that pseudogenic DNA, RNA, and proteins can interfere with the activity and function of key protein coding genes, acting as regulators of oncogenes and tumor suppressors. Capitalizing on the available clinical research, we are able to get an insight into the spread and variety of pseudogene biomarker and therapeutic potential. In this chapter, we describe pseudogenes that fulfill their role as diagnostic or prognostic biomarkers, both as unique elements and in collaboration with other genes or pseudogenes. We also report that the majority of prognostic pseudogenes are overexpressed and exert an oncogenic role in colorectal, liver, lung, and gastric cancers. Finally, we highlight a number of pseudogenes that can establish future therapeutic avenues.

Translation of noncoding RNAs and cancer

The human genome contains thousands of noncoding RNAs (ncRNAs), which are thought to lack open reading frames (ORFs) and cannot be translated. Some ncRNAs reportedly have important functions, including epigenetic regulation, chromatin remolding, protein modification, and RNA degradation, but the functions of most ncRNAs remain elusive. Through the application and development of ribosome profiling and sequencing technologies, an increasing number of studies have discovered the translation of ncRNAs. Although ncRNAs were initially defined as noncoding RNAs, a number of ncRNAs actually contain ORFs that are translated into peptides. Here, we summarize the available methods, tools, and databases for identifying and validating ncRNA-encoded peptides/proteins, and the recent findings regarding ncRNA-encoded small peptides/proteins in cancer are compiled and synthesized. Importantly, the role of ncRNA-encoding peptides/proteins has application prospects in cancer research, but some potential challenges remain unresolved. The aim of this review is to provide a theoretical basis that might promote the discovery of more peptides/proteins encoded by ncRNAs and aid the further development of novel diagnostic and prognostic cancer markers and therapeutic targets.

Positive feedback loop of AKR1B10P1/miR-138/SOX4 promotes cell growth in hepatocellular carcinoma cells

Potential functions of pseudogenes on tumorigenesis and development of human malignancies have been gradually revealed recently. However, the specific regulation and intracellular events associated with pseudogenes have not been illustrated clearly in hepatocellular carcinoma (HCC). AKR1B10P1 is an isoform pseudogene of oncogenic AKR1B10, and is barely transcribed in normal hepatocytes. In this study, anomalous transcript of AKR1B10P1 was detected in both HCC tissues and cell lines, and is positively correlated with its parental genes. High level of AKR1B10P1 transcript is correlated with dismal clinicopathologic features, including large tumor dimension, high level of serum Alpha-fetoprotein (AFP), advanced TNM stages, tumor microsatellite formation and venous invasion. Loss-of and gain-of function assays demonstrated the exact impact of AKR1B10P1 on promoting HCC cell proliferation. Furthermore, transcription factor SOX4 was discovered facilitating the activation of AKR1B10P1 transcription, and was validated as a down-stream target degraded by tumor-suppressing miR-138. Meanwhile, we discovered the existence of a positive feedback from AKR1B10P1, by which miR-138 interacts with AKR1B10P1 via a competing endogenous RNA (ceRNA) way. Thus, we suggest a positive feedback loop of AKR1B10P1/miR-138/SOX4, promoting HCC cell proliferation. In summary, the AKR1B10P1/miR-138/SOX4 loop in HCC cells provides us potential and probable targets contributing to HCC prevention and therapeutic treatment.

Protein-Coding Genes in Euarchontoglires with Pseudogene Homologs in Humans

An original bioinformatics technique is developed to identify the protein-coding genes in rodents, lagomorphs and nonhuman primates that are pseudogenized in humans. The method is based on per-gene verification of local synteny, similarity of exon-intronic structures and orthology in a set of genomes. It is applicable to any genome set, even with the number of genomes exceeding 100, and efficiently implemented using fast computer software. Only 50 evolutionary recent human pseudogenes were predicted. Their functional homologs in model species are often associated with the immune system or digestion and mainly express in the testes. According to current evidence, knockout of most of these genes leads to an abnormal phenotype. Some genes were pseudogenized or lost independently in human and nonhuman hominoids.

Pseudo2GO: A Graph-Based Deep Learning Method for Pseudogene Function Prediction by Borrowing Information From Coding Genes

Pseudogenes are indicating more and more functional potentials recently, though historically were regarded as relics of evolution. Computational methods for predicting pseudogene functions on Gene Ontology is important for directing experimental discovery. However, no pseudogene-specific computational methods have been proposed to directly predict their Gene Ontology (GO) terms. The biggest challenge for pseudogene function prediction is the lack of enough features and functional annotations, making training a predictive model difficult. Considering the close functional similarity between pseudogenes and their parent coding genes that share great amount of DNA sequence, as well as that coding genes have rich annotations, we aim to predict pseudogene functions by borrowing information from coding genes in a graph-based way. Here we propose Pseudo2GO, a graph-based deep learning semi-supervised model for pseudogene function prediction. A sequence similarity graph is first constructed to connect pseudogenes and coding genes. Multiple features are incorporated into the model as the node attributes to enable the graph an attributed graph, including expression profiles, interactions with microRNAs, protein-protein interactions (PPIs), and genetic interactions. Graph convolutional networks are used to propagate node attributes across the graph to make classifications on pseudogenes. Comparing Pseudo2GO with other frameworks adapted from popular protein function prediction methods, we demonstrated that our method has achieved state-of-the-art performance, significantly outperforming other methods in terms of the M-AUPR metric.

Sharing of Genetic Association Signals by Age-Related Macular Degeneration and Alzheimer's Disease at Multiple Levels

Age-related macular degeneration and Alzheimer's disease are closely related complex diseases that may share overlapping pathogenesis in gene networks. This study was conducted to investigate the genetic factors shared by both diseases. We analyzed genome-wide association studies' summary statistics from both diseases using a new platform known as functional mapping and annotation (FUMA) and a co-localization analysis. We obtained disease-related gene expression profile data from the Gene Expression Omnibus and analyzed these data using weighted gene co-expression network analysis. FUMA analysis and Bayesian co-localization analysis showed that ten genes on chromosome 7, one pathway (complement and coagulation cascade), and forty-two biological processes were common for both diseases. Among these ten genes, two protein-coding genes, two pseudogenes, and two RNA genes were, for the first time, identified to be associated with both diseases. Weighted gene co-expression network analysis identified 19 age-related macular degeneration hub genes and 19 Alzheimer's disease hub genes and revealed that these two diseases shared nine pathways and 63 biological processes. Using FUMA, co-localization analysis, and weighted gene co-expression network analysis, 10 genes on chromosome 7, 10 pathways, and 105 biological processes were found to be associated with the two degenerative diseases. This suggests that these10 genes and the hub genes of these modules associated with the shared pathways are potential diagnostic markers for both diseases.

A three-lncRNA signature of pretreatment biopsies predicts pathological response and outcome in esophageal squamous cell carcinoma with neoadjuvant chemoradiotherapy

BACKGROUND

Current strategies are insufficient to predict pathologically complete response (pCR) for esophageal squamous cell carcinomas (ESCCs) before treatment. Here, we aim to develop a novel long noncoding RNA (lncRNA) signature for pCR and outcome prediction of ESCCs through a multicenter analysis for a Chinese population.

METHODS

Differentially expressed lncRNAs (DELs) between pCRs and less than pCR (

RESULTS

Twelve DELs were identified from Guangzhou cohort and six lncRNAs were verified. Then, a classifier of three lncRNAs (SCAT1, PRKAG2-AS1, and FLG-AS1) was established and achieved a high accuracy with an area under the receiver operating characteristic curve (AUC) of 0.952 in the training cohort, which was well validated in the internal validation cohort and external cohort with the AUCs of 0.856 and 0.817, respectively. Furthermore, the predictive score was identified as the only independent predictor for pCR. Patients with high discriminant score showed a significantly longer overall and relapse-free survival (P < .05).

CONCLUSIONS

We developed the first and applicable three-lncRNA signature of pCR and outcome prediction, which is robust and reproducible in multicenter cohorts for ESCCs with nCRT.

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Key Words

• esophageal squamous cell carcinoma
• individualized medicine
• lncRNAs
• neoadjuvant chemoradiotherapy
• pathologically complete response

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MESH Headings Collapse

Grants

• 2017-I2M-1-005 CAMS Innovation Fund for Medical Sciences
• 2016-I2M-1-001 CAMS Innovation Fund for Medical Sciences
• 2016YFC1303201 National Key R&D Program of China
• 81802299 National Natural Science Foundation of China
• 81502514 National Natural Science Foundation of China
• 3332018070 Fundamental Research Funds for the Central Universities
• 2018YFC1312105 National Key Basic Research Development Plan
• 7204291 Beijing Natural Science Foundation
• LC2019B18 Beijing Hope Run Special Fund of Cancer Foundation of China
• National Natural Science Foundation of China
• Fundamental Research Funds for the Central Universities
• Beijing Natural Science Foundation

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Affiliation(s)

Chaoqi Zhang Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Zhihui Zhang Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Guochao Zhang Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Liyan Xue Department of PathologyNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Haijun Yang Department of PathologyAnyang Cancer HospitalThe Fourth Affiliated Hospital of Henan University of Science and TechnologyAnyangHenanChina
Yuejun Luo Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Xiaoli Zheng Department of radiotherapyThe Affiliated Cancer hospital of Zhengzhou UniversityZhengzhouHenanChina
Yonglei Zhang Department of General SurgeryThe Affiliated Cancer Hospital of Zhengzhou UniversityZhengzhouHenanChina
Yufen Yuan Department of PathologyAnyang Cancer HospitalThe Fourth Affiliated Hospital of Henan University of Science and TechnologyAnyangHenanChina
Ruixue Lei Department of PathologyAnyang Cancer HospitalThe Fourth Affiliated Hospital of Henan University of Science and TechnologyAnyangHenanChina
Zhaoyang Yang Department of PathologyNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Bo Zheng Department of PathologyNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Zhen Zhang Biotherapy CenterThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
Le Wang Department of OtologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
Yun Che Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Sihui Wang Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Feng Wang Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Lingling Fang Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Qingpeng Zeng Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Jiagen Li Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Shugeng Gao Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Qi Xue Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Nan Sun Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Jie He Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina

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Identification of a Six-Immune-Related Long Non-coding RNA Signature for Predicting Survival and Immune Infiltrating Status in Breast Cancer

Long non-coding RNAs (lncRNAs) play critical roles in tumor immunity; however, the functional roles of immune-related lncRNAs in breast cancer (BC) remain elusive. To further explore the immune-related lncRNAs in BC, whole genomic expression data and corresponding clinical information were obtained from multiple BC datasets. Based on correlation with the immune-related genes within the training set, we screened out the most promising immune-related lncRNAs. Subsequently, Lasso penalized Cox regression analysis followed by stepwise multivariate Cox regression analysis identified six survival-related lncRNAs (AC116366.1, AC244502.1, AC100810.1, MIAT, AC093297.2, and AL356417.2) and constructed a prognostic signature. The cohorts in the high-risk group had significantly poor survival time compared to those in the low-risk group. In addition, a nomogram integrated with clinical features and the prognostic signature was developed on the basis of the training set. Importantly, all the findings had a similar performance in three validated datasets. In the following studies, our integrative analyses indicated that the infiltration of CD8-positive (CD8) T cells associated with a good prognosis was strikingly activated in the low-risk group. To further provide an interpretation of biological mechanisms for the prognostic signature, we performed weighted gene co-expression network analysis (WGCNA) followed by KEGG pathway-enrichment analysis. Our results showed that the antigen presentation pathway involved in protein processing in endoplasmic reticulum and antigen processing and presentation was markedly altered in the high-risk group, which might promote tumor immune evasion and associate with poor clinical outcomes in BC patients with high risk scores. In conclusion, we aimed to take advantage of bioinformatics analyses to explore immune-related lncRNAs, which could function as prognostic indicators and promising therapeutic targets for BC patients.

Pseudogene-gene functional networks are prognostic of patient survival in breast cancer

BACKGROUND

Given the vast range of molecular mechanisms giving rise to breast cancer, it is unlikely universal cures exist. However, by providing a more precise prognosis for breast cancer patients through integrative models, treatments can become more individualized, resulting in more successful outcomes. Specifically, we combine gene expression, pseudogene expression, miRNA expression, clinical factors, and pseudogene-gene functional networks to generate these models for breast cancer prognostics. Establishing a LASSO-generated molecular gene signature revealed that the increased expression of genes STXBP5, GALP and LOC387646 indicate a poor prognosis for a breast cancer patient. We also found that increased CTSLP8 and RPS10P20 and decreased HLA-K pseudogene expression indicate poor prognosis for a patient. Perhaps most importantly we identified a pseudogene-gene interaction, GPS2-GPS2P1 (improved prognosis) that is prognostic where neither the gene nor pseudogene alone is prognostic of survival. Besides, miR-3923 was predicted to target GPS2 using miRanda, PicTar, and TargetScan, which imply modules of gene-pseudogene-miRNAs that are potentially functionally related to patient survival.

RESULTS

In our LASSO-based model, we take into account features including pseudogenes, genes and candidate pseudogene-gene interactions. Key biomarkers were identified from the features. The identification of key biomarkers in combination with significant clinical factors (such as stage and radiation therapy status) should be considered as well, enabling a specific prognostic prediction and future treatment plan for an individual patient. Here we used our PseudoFuN web application to identify the candidate pseudogene-gene interactions as candidate features in our integrative models. We further identified potential miRNAs targeting those features in our models using PseudoFuN as well. From this study, we present an interpretable survival model based on LASSO and decision trees, we also provide a novel feature set which includes pseudogene-gene interaction terms that have been ignored by previous prognostic models. We find that some interaction terms for pseudogenes and genes are significantly prognostic of survival. These interactions are cross-over interactions, where the impact of the gene expression on survival changes with pseudogene expression and vice versa. These may imply more complicated regulation mechanisms than previously understood.

CONCLUSIONS

We recommend these novel feature sets be considered when training other types of prognostic models as well, which may provide more comprehensive insights into personalized treatment decisions.

Role of PA2G4P4 pseudogene in bladder cancer tumorigenesis

BACKGROUND

Many pseudogenes possess biological activities and play important roles in the pathogenesis of various types of cancer including bladder cancer (BlCa), which still lacks suitable molecular biomarkers. Recently, pseudogenes were found to be significantly enriched in a pan-cancer classification based on the Cancer Genome Atlas gene expression data. Among them, the top-ranking pseudogene was the proliferation-associated 2G4 pseudogene 4 (PA2G4P4).

METHODS

Genomic and transcript features of PA2G4P4 were determined by GeneBank database analysis followed by 5' RACE experiments. Therefore, we conducted a retrospective molecular study on a cohort of 45 patients of BlCa. PA2G4P4 expression was measured by RT-qPCR, whereas PA2G4P4 transcript distribution was analyzed by in situ hybridization on both normal and cancerous histological sections and compared to the immunolocalization of its parental PA2G4/EBP1 protein. Finally, we tested the effects of PA2G4P4 depletion on proliferation, migration, and death of BlCa cells.

RESULTS

We showed for the first time PA2G4P4 overexpression in BlCa tissues and in cell lines. PA2G4P4 distribution strictly overlaps PA2G4/EBP1 protein localization. Moreover, we showed that PA2G4P4 knockdown affects both proliferation and migration of BlCa cells, highlighting its potential oncogenic role.

CONCLUSIONS

PA2G4P4 may play a functional role as an oncogene in BlCa development, suggesting it as a good candidate for future investigation and new clinical applications.

Prognostic Value and Molecular Regulatory Mechanism of MSTO2P in Hepatocellular Carcinoma: A Comprehensive Study Based on Bioinformatics, Clinical Analysis and in vitro Validation

Background

Accumulating studies have shown that pseudogenes could become key regulators in human cancers. Misato family member 2, pseudogene (MSTO2P) is overexpressed in lung and gastric cancer and affects the biological functions of tumor cells. However, the role of MSTO2P in hepatocellular carcinoma (HCC) is unreported.

Purpose

This study aimed to examine the diagnostic and prognostic value of MSTO2P in HCC, to investigate the effects of MSTO2P on the biological functions of HCC cells, and to explore the potential mechanisms of MSTO2P in HCC.

Methods

Relevant data on HCC were downloaded from the Gene Expression Omnibus database and the Cancer Genome Atlas database and used to analyze MSTO2P expression and the role of MSTO2P in HCC prognosis. MSTO2P in HCC cell lines was knocked down by shRNA to study the effects of MSTO2P on cell proliferation, apoptosis, metastasis and invasion in HCC. Expressions of the main proteins involved in epithelial–mesenchymal transition and the PI3K/AKT/mTOR signaling pathway in HCC were examined via Western blot analysis.

Results

MSTO2P had significant diagnostic and prognostic value in HCC. MSTO2P was highly expressed in HCC tissues and cells, and MSTO2P increased HCC cell proliferation, invasion and metastasis. MSTO2P knockdown also increased E-cadherin expression and decreased N-cadherin and Vimentin expression. Additionally, MSTO2P increased the expressions of proteins in the PI3K/AKT/mTOR pathway, including PI3K, p-AKT and p-mTOR.

Conclusion

MSTO2P might be used as a potential target for diagnosing and curing HCC. MSTO2P may affect HCC cell proliferation, apoptosis, metastasis and invasion through the PI3K/AKT/mTOR pathway.

MYC-regulated pseudogene HMGA1P6 promotes ovarian cancer malignancy via augmenting the oncogenic HMGA1/2

Pseudogenes have long been considered as nonfunctional genomic sequences. Recent studies have shown that they can potentially regulate the expression of protein-coding genes and are dysregulated in diseases including cancer. However, the potential roles of pseudogenes in ovarian cancer have not been well studied. Here we characterized the pseudogene expression profile in HGSOC (high-grade serous ovarian carcinoma) by microarray. We identified 577 dysregulated pseudogenes and most of them were up-regulated (538 of 577). HMGA1P6 (High mobility group AT-hook 1 pseudogene 6) was one of the overexpressed pseudogenes and its expression was inversely correlated with patient survival. Mechanistically, HMGA1P6 promoted ovarian cancer cell malignancy by acting as a ceRNA (competitive endogenous RNA) that led to enhanced HMGA1 and HMGA2 expression. Importantly, HMGA1P6 was transcriptionally activated by oncogene MYC in ovarian cancer. Our findings reveal that MYC may contribute to oncogenesis through transcriptional regulation of pseudogene HMGA1P6 in ovarian cancer.

Pseudogene-Derived lncRNAs and Their miRNA Sponging Mechanism in Human Cancer

Pseudogenes, abundant in the human genome, are traditionally considered as non-functional “junk genes.” However, recent studies have revealed that pseudogenes act as key regulators at DNA, RNA or protein level in diverse human disorders (including cancer), among which pseudogene-derived long non-coding RNA (lncRNA) transcripts are extensively investigated and has been reported to be frequently dysregulated in various types of human cancer. Growing evidence demonstrates that pseudogene-derived lncRNAs play important roles in cancer initiation and progression by serving as competing endogenous RNAs (ceRNAs) through competitively binding to shared microRNAs (miRNAs), thus affecting both their cognate genes and unrelated genes. Herein, we retrospect those current findings about expression, functions and potential ceRNA mechanisms of pseudogene-derived lncRNAs in human cancer, which may provide us with some crucial clues in developing potential targets for cancer therapy in the future.

Application of Machine Learning in Developing a Novelty Five-Pseudogene Signature to Predict Prognosis of Head and Neck Squamous Cell Carcinoma: A New Aspect of "Junk Genes" in Biomedical Practice

Head and neck squamous cell carcinoma (HNSCC) is the sixth malignancy, which is characterized by poor prognosis or high mortality because of the lack of predicting markers. Aberrant cancer pseudogenes have been found predictive for prognosis. We aim to identify a pseudogene-based prognosis signature for HNSCC by machine learning. RNA-seq data were downloaded from The Cancer Genome Atlas, and 700 differentially-expressed pseudogenes were identified. The survival-related pseudogenes were screened through COX-regression analysis, which includes univariate regression, least absolute shrinkage and selection operator regression, and multivariate regression, and a five-pseudogene signature was constructed. The value of prediction for the signature was validated in multiple subgroups in terms of survival. Gene set enrichment analysis (GSEA) and coexpression analysis were used to determine the underlying biological functions. Seven hundred dysregulated pseudogenes were identified, and the five-pseudogene signature can distinguish the low-risk and high-risk patients for both training and testing sets and predicted prognosis with high sensitivity and specificity. Furthermore, the signature was applicable to patients of different genders, ages, stages, and grades. Coexpression analysis revealed that the five-pseudogene is associated with immune system. GSEA showed cancer-related biological process and pathways the five-pseudogene involved in. The five-pseudogene signature is not only a novel marker for prognosis but also a promising signature for monitoring therapeutic schedule. Therefore, our findings may have potential clinical significance.

Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples

PURPOSE

Many antineoplastics are designed to target upregulated genes, but quantifying upregulation in a single patient sample requires an appropriate set of samples for comparison. In cancer, the most natural comparison set is unaffected samples from the matching tissue, but there are often too few available unaffected samples to overcome high intersample variance. Moreover, some cancer samples have misidentified tissues of origin or even composite-tissue phenotypes. Even if an appropriate comparison set can be identified, most differential expression tools are not designed to accommodate comparisons to a single patient sample.

METHODS

We propose a Bayesian statistical framework for gene expression outlier detection in single samples. Our method uses all available data to produce a consensus background distribution for each gene of interest without requiring the researcher to manually select a comparison set. The consensus distribution can then be used to quantify over- and underexpression.

RESULTS

We demonstrate this method on both simulated and real gene expression data. We show that it can robustly quantify overexpression, even when the set of comparison samples lacks ideally matched tissue samples. Furthermore, our results show that the method can identify appropriate comparison sets from samples of mixed lineage and rediscover numerous known gene-cancer expression patterns.

CONCLUSION

This exploratory method is suitable for identifying expression outliers from comparative RNA sequencing (RNA-seq) analysis for individual samples, and Treehouse, a pediatric precision medicine group that leverages RNA-seq to identify potential therapeutic leads for patients, plans to explore this method for processing its pediatric cohort.