Harnessing the tissue and plasma lncRNA-peptidome to discover peptide-based cancer biomarkers

CircRNA and lncRNA-encoded peptide in diseases, an update review

Non-coding RNAs (ncRNAs), including circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs), are unique RNA molecules widely identified in the eukaryotic genome. Their dysregulation has been discovered and played key roles in the pathogenesis of numerous diseases, including various cancers. Previously considered devoid of protein-coding ability, recent research has revealed that a small number of open reading frames (ORFs) within these ncRNAs endow them with the potential for protein coding. These ncRNAs-derived peptides or proteins have been proven to regulate various physiological and pathological processes through diverse mechanisms. Their emerging roles in disease diagnosis and targeted therapy underscore their potential utility in clinical settings. This comprehensive review aims to provide a systematic overview of proteins or peptides encoded by lncRNAs and circRNAs, elucidate their production and functional mechanisms, and explore their promising applications in cancer diagnosis, disease prediction, and targeted therapy.

Peptidomics Strategies to Evaluate Cancer Diagnosis, Prognosis, and Treatment

Peptides have potential bioactive functions, and the peptidomics landscape has been broadly investigated for various diseases, including cancer. In this chapter, we reviewed the past four years of literature available and selected 16 peer-reviewed publications exploring peptidomics in diagnosis, prognosis, and treatment in cancer research. We highlighted their main aims, mass spectrometry-based peptidomics, multi-omics, data-driven and in silico strategies, functional assays, and clinical applications. Moreover, we underscored several levels of difficulties in translating the peptidomics findings to clinical practice, aiming to learn with the accumulated knowledge and guide upcoming studies. Finally, this review reinforces the peptidomics robustness in discovering potential candidates for monitoring the several stages of cancer disease and therapeutic treatment, leveraging the management of cancer patients in the future.

Integrated plasma and exosome long noncoding RNA profiling is promising for diagnosing non-small cell lung cancer

OBJECTIVES

Non-small cell lung cancer (NSCLC) accounts for more than 80 % of all lung cancers, and its 5-year survival rate can be greatly improved by early diagnosis. However, early diagnosis remains elusive because of the lack of effective biomarkers. In this study, we aimed to develop an effective diagnostic model for NSCLC based on a combination of circulating biomarkers.

METHODS

Tissue-deregulated long noncoding RNAs (lncRNAs) in NSCLC were identified in datasets retrieved from the Gene Expression Omnibus (GEO, n=727) and The Cancer Genome Atlas (TCGA, n=1,135) databases, and their differential expression was verified in paired local plasma and exosome samples from NSCLC patients. Subsequently, LASSO regression was used to screen for biomarkers in a large clinical population, and a logistic regression model was used to establish a multi-marker diagnostic model. The area under the receiver operating characteristic (ROC) curve (AUC), calibration plots, decision curve analysis (DCA), clinical impact curves, and integrated discrimination improvement (IDI) were used to evaluate the efficiency of the diagnostic model.

RESULTS

Three lncRNAs-PGM5-AS1, SFTA1P, and CTA-384D8.35 were consistently expressed in online tissue datasets, plasma, and exosomes from local patients. LASSO regression identified nine variables (Plasma CTA-384D8.35, Plasma PGM5-AS1, Exosome CTA-384D8.35, Exosome PGM5-AS1, Exosome SFTA1P, Log10CEA, Log10CA125, SCC, and NSE) in clinical samples that were eventually included in the multi-marker diagnostic model. Logistic regression analysis revealed that Plasma CTA-384D8.35, exosome SFTA1P, Log10CEA, Exosome CTA-384D8.35, SCC, and NSE were independent risk factors for NSCLC (p<0.01), and their results were visualized using a nomogram to obtain personalized prediction outcomes. The constructed diagnostic model demonstrated good NSCLC prediction ability in both the training and validation sets (AUC=0.97).

CONCLUSIONS

In summary, the constructed circulating lncRNA-based diagnostic model has good NSCLC prediction ability in clinical samples and provides a potential diagnostic tool for NSCLC.

CoraL: interpretable contrastive meta-learning for the prediction of cancer-associated ncRNA-encoded small peptides

NcRNA-encoded small peptides (ncPEPs) have recently emerged as promising targets and biomarkers for cancer immunotherapy. Therefore, identifying cancer-associated ncPEPs is crucial for cancer research. In this work, we propose CoraL, a novel supervised contrastive meta-learning framework for predicting cancer-associated ncPEPs. Specifically, the proposed meta-learning strategy enables our model to learn meta-knowledge from different types of peptides and train a promising predictive model even with few labeled samples. The results show that our model is capable of making high-confidence predictions on unseen cancer biomarkers with only five samples, potentially accelerating the discovery of novel cancer biomarkers for immunotherapy. Moreover, our approach remarkably outperforms existing deep learning models on 15 cancer-associated ncPEPs datasets, demonstrating its effectiveness and robustness. Interestingly, our model exhibits outstanding performance when extended for the identification of short open reading frames derived from ncPEPs, demonstrating the strong prediction ability of CoraL at the transcriptome level. Importantly, our feature interpretation analysis discovers unique sequential patterns as the fingerprint for each cancer-associated ncPEPs, revealing the relationship among certain cancer biomarkers that are validated by relevant literature and motif comparison. Overall, we expect CoraL to be a useful tool to decipher the pathogenesis of cancer and provide valuable information for cancer research. The dataset and source code of our proposed method can be found at https://github.com/Johnsunnn/CoraL.

Modulating epigenetic modifications for cancer therapy (Review)

Cancer is a global public health concern. Alterations in epigenetic processes are among the earliest genomic aberrations occurring during cancer development and are closely related to progression. Unlike genetic mutations, aberrations in epigenetic processes are reversible, which opens the possibility for novel pharmacological treatments. Non‑coding RNAs (ncRNAs) represent an essential epigenetic mechanism, and emerging evidence links ncRNAs to carcinogenesis. Epigenetic drugs (epidrugs) are a group of promising target therapies for cancer treatment acting as coadjuvants to reverse drug resistance in cancer. The present review describes central epigenetic aberrations during malignant transformation and explains how epidrugs target DNA methylation, histone modifications and ncRNAs. Furthermore, clinical trials focused on evaluating the effect of these epidrugs alone or in combination with other anticancer therapies and other ncRNA‑based therapies are discussed. The use of epidrugs promises to be an effective tool for reversing drug resistance in some patients with cancer.

Selective Detection and Characterization of Small Cysteine-Containing Peptides with Cluster-Modified Nanopore Sensing

It was recently demonstrated that one can monitor ligand-induced structure fluctuations of individual thiolate-capped gold nanoclusters using resistive-pulse nanopore sensing. The magnitude of the fluctuations scales with the size of the capping ligand, and it was later shown one can observe ligand exchange in this nanopore setup. We expand on these results by exploring the different types of current fluctuations associated with peptide ligands attaching to tiopronin-capped gold nanoclusters. We show here that the fluctuations can be used to identify the attaching peptide through either the magnitude of the peptide-induced current jumps or the onset of high-frequency current fluctuations. Importantly, the peptide attachment process requires that the peptide contains a cysteine residue. This suggests that nanopore-based monitoring of peptide attachments with thiolate-capped clusters could provide a means for selective detection of cysteine-containing peptides. Finally, we demonstrate the cluster-based protocol with various peptide mixtures to show that one can identify more than one cysteine-containing peptide in a mixture.

Astrocytic IGF-1 and IGF-1R Orchestrate Mitophagy in Traumatic Brain Injury via Exosomal miR-let-7e

Defective brain hormonal signaling and autophagy have been associated with neurodegeneration after brain insults, characterized by neuronal loss and cognitive dysfunction. However, few studies have linked them in the context of brain injury. Insulin-like growth factor-1 (IGF-1) is an important hormone that contributes to growth, cell proliferation, and autophagy and is also expressed in the brain. Here, we assessed the clinical data from TBI patients and performed both in vitro and in vivo experiments with proteomic and gene-chip analysis to assess the functions of IGF-1 in mitophagy following TBI. We show that reduced plasma IGF-1 is correlated with cognition in TBI patients. Overexpression of astrocytic IGF-1 improves cognitive dysfunction and mitophagy in TBI mice. Mechanically, proteomics data show that the IGF-1-related NF-κB pathway transcriptionally regulates decapping mRNA2 (Dcp2) and miR-let-7, together with IGF-1R to orchestrate mitophagy in TBI. Finally, we demonstrate that brain injury induces impaired mitophagy at the chronic stage and that IGF-1 treatment could facilitate the mitophagy markers via exosomal miR-let-7e. By showing that IGF-1 is an important mediator of the beneficial effect of the neural-endocrine network in TBI models, our findings place IGF-1/IGF-1R as a potential target capable of noncoding RNAs and opposing mitophagy failure and cognitive impairment in TBI.

Long Non-Coding RNAs ASB16-AS1 and AFAP1-AS1: Diagnostic, Prognostic Impact and Survival Analysis in Colorectal Cancer

Background

We aimed to evaluate the diagnostic roles of AFAP1-AS1 and ASB16-AS1 in colorectal cancer and highlight their roles in predicting colorectal cancer patients’ prognosis.

Methods

In this case–control study, 146 participants were involved. Group I included 47 patients with CRC. Group II composed of 49 patients with benign lesions in the colon, and Group III included 50 apparently normal subjects of coincided age and gender as controls. All participants were subjected to clinical and endoscopic evaluations, CA19-9, CEA, and quantification of relative expression of lncRNAs ASB16-AS1 and AFAP1-AS1.

Results

CRC patients had significantly elevated expression levels of both lncRNAs in tissue and plasma samples versus benign and control groups (p < 0.001). Despite the higher sensitivity of tissue samples results, the relative expression of both lncRNAs in plasma samples was very encouraging in the discrimination between patients with CRC versus control and benign groups. Furthermore, both lncRNAs could discriminate patients with early-stage CRC (stage I&II) from being colonic lesion and control groups with better sensitivity and specificity presented by ASB16-AS1 in tissue and plasma than results detailed by AFAP1-AS1. High expression levels of ASB16-AS1 in tissue and plasma and tissue lncRNA AFAP1-AS1 are significantly correlated with decreased overall survival (p < 0.001) and reduced progression-free (p < 0.001) compared to low expression in CRC patients.

Conclusion

We propose the utilization of lncRNA ASB16-AS1 and lncRNA AFAP1-AS1 as biomarkers in diagnosis and prognosis estimation for CRC patients. Moreover, their value in early CRC patients may affect the assortment of target therapy and treatment protocols.

Multi-Omics Integration-Based Prioritisation of Competing Endogenous RNA Regulation Networks in Small Cell Lung Cancer: Molecular Characteristics and Drug Candidates

Background

The competing endogenous RNA (ceRNA) network-mediated regulatory mechanisms in small cell lung cancer (SCLC) remain largely unknown. This study aimed to integrate multi-omics profiles, including the transcriptome, regulome, genome and pharmacogenome profiles, to elucidate prioritised ceRNA characteristics, pathways and drug candidates in SCLC.

Method

We determined the plasma messenger RNA (mRNA), microRNA (miRNA), long noncoding RNA (lncRNA) and circular RNA (circRNA) expression levels using whole-transcriptome sequencing technology in our SCLC plasma cohort. Significantly expressed plasma mRNAs were then overlapped with the Gene Expression Omnibus (GEO) tissue mRNA data (GSE 40275, SCLC tissue cohort). Next, we applied a multistep multi-omics (transcriptome, regulome, genome and pharmacogenome) integration analysis to first construct the network and then to identify the lncRNA/circRNA-miRNA-mRNA ceRNA characteristics, genomic alterations, pathways and drug candidates in SCLC.

Results

The multi-omics integration-based prioritisation of SCLC ceRNA regulatory networks consisted of downregulated mRNAs (CSF3R/GAA), lncRNAs (AC005005.4-201/DLX6-AS1-201/NEAT1-203) and circRNAs (hsa_HLA-B_1/hsa_VEGFC_8) as well as upregulated miRNAs (hsa-miR-4525/hsa-miR-6747-3p). lncRNAs (lncRNA-AC005005.4-201 and NEAT1-203) and circRNAs (circRNA-hsa_HLA-B_1 and hsa_VEGFC_8) may regulate the inhibited effects of hsa-miR-6747-3p for CSF3R expression in SCLC, while lncRNA-DLX6-AS1-201 or circRNA-hsa_HLA-B_1 may neutralise the negative regulation of hsa-miR-4525 for GAA in SCLC. CSF3R and GAA were present in the genomic alteration, and further identified as targets of FavId and Trastuzumab deruxtecan, respectively. In the SCLC-associated pathway analysis, CSF3R was involved in the autophagy pathways, while GAA was involved in the glucose metabolism pathways.

Conclusions

We identified potential lncRNA/cirRNA-miRNA-mRNA ceRNA regulatory mechanisms, pathways and promising drug candidates in SCLC, providing novel potential diagnostics and therapeutic targets in SCLC.

Application of peptide biomarkers in life analysis based on liquid chromatography-mass spectrometry technology

Biomedicine is developing rapidly in the 21st century. Among them, the qualitative and quantitative analysis of peptide biomarkers is of considerable importance for the diagnosis and therapy of diseases and the quality evaluation of drugs and food. The identification and quantitative analysis of peptides have been going on for decades. Traditionally, immunoassays or biological assays are generally used to quantify peptides in biological matrices. However, the selectivity and sensitivity of these methods cannot meet the requirements of the application. The separation and analysis technique of liquid chromatography-mass spectrometry (LC-MS) supplies a reliable alternative. In contrast to immunoassays, LC-MS methods are capable of providing the analytical prowess necessary to satisfy the demands of peptide biomarker research in the life sciences arena. This review article provides a historical account of the in-roads made by LC-MS technology for the detection of peptide biomarkers in the past 10 years, with the focus on the qualification/quantification developments and their applications.

Peptidomics and proteogenomics: background, challenges and future needs

INTRODUCTION

With available genomic data and related information, it is becoming possible to better highlight mutations or genomic alterations associated with a particular disease or disorder. The advent of high-throughput sequencing technologies has greatly advanced diagnostics, prognostics, and drug development.

AREAS COVERED

Peptidomics and proteogenomics are the two post-genomic technologies that enable the simultaneous study of peptides and proteins/transcripts/genes. Both technologies add a remarkably large amount of data to the pool of information on various peptides associated with gene mutations or genome remodeling. Literature search was performed in the PubMed database and is up to date.

EXPERT OPINION

This article lists various techniques used for peptidomic and proteogenomic analyses. It also explains various bioinformatics workflows developed to understand differentially expressed peptides/proteins and their role in disease pathogenesis. Their role in deciphering disease pathways, cancer research, and biomarker discovery using biofluids is highlighted. Finally, the challenges and future requirements to overcome the current limitations for their effective clinical use are also discussed.

LncRNA-Encoded Peptide: Functions and Predicting Methods

Long non-coding RNA (lncRNA) was originally defined as the representative of the non-coding RNAs and unable to encode. However, recent reports suggest that some lncRNAs actually contain open reading frames that encode peptides. These coding products play important roles in the pathogenesis of many diseases. Here, we summarize the regulatory pathways of mammalian lncRNA-encoded peptides in influencing muscle function, mRNA stability, gene expression, and so on. We also address the promoting and inhibiting functions of the peptides in different cancers and other diseases. Then we introduce the computational predicting methods and data resources to predict the coding ability of lncRNA. The intention of this review is to provide references for further coding research and contribute to reveal the potential prospects for targeted tumor therapy.

Alteration of Proteotranscriptomic Landscape Reveals the Transcriptional Regulatory Circuits Controlling Key-Signaling Pathways and Metabolic Reprogramming During Tumor Evolution

The proteotranscriptomic landscape depends on the transcription, mRNA-turnover, translation, and regulated-destruction of proteins. Gene-specific mRNA-to-protein correlation is the consequence of the dynamic interplays of the different regulatory processes of proteotranscriptomic landscape. So far, the critical impact of mRNA and protein stability on their subsequent correlation on a global scale remained unresolved. Whether the mRNA-to-protein correlations are constrained by their stability and conserved across mammalian species including human is unknown. Moreover, whether the stability-dependent correlation pattern is altered in the tumor has not been explored. To establish the quantitative relationship between stability and correlation between mRNA and protein levels, we performed a multi-omics data integration study across mammalian systems including diverse types of human tissues and cell lines in a genome-wide manner. The current study illuminated an important aspect of the mammalian proteotranscriptomic landscape by providing evidence that stability-constrained mRNA-to-protein correlation follows a hierarchical pattern that remains conserved across different tissues and mammalian species. By analyzing the tumor and non-tumor tissues, we further illustrated that mRNA-to-protein correlations deviate in tumor tissues. By gene-centric analysis, we harnessed the hierarchical correlation patterns to identify altered mRNA-to-protein correlation in tumors and characterized the tumor correlation-enhancing and -repressing genes. We elucidated the transcriptional regulatory circuits controlling the correlation-enhancing and -repressing genes that are associated with metabolic reprogramming and cancer-associated pathways in tumor tissue. By tightly controlling the mRNA-to-protein correlation of specific genes, the transcriptional regulatory circuits may enable the tumor cells to evolve in varying tumor microenvironment. The mRNA-to-protein correlation analysis thus can serve as a unique approach to identify the pathways prioritized by the tumor cells at different clinical stages. The component of transcriptional regulatory circuits identified by the current study can serve as potential candidates for stage-dependent anticancer therapy.

Integration of Online Omics-Data Resources for Cancer Research

The manifestations of cancerous phenotypes necessitate alterations at different levels of information-flow from genome to proteome. The molecular alterations at different information processing levels serve as the basis for the cancer phenotype to emerge. To understand the underlying mechanisms that drive the acquisition of cancer hallmarks it is required to interrogate cancer cells using multiple levels of information flow represented by different omics - such as genomics, epigenomics, transcriptomics, and proteomics. The advantage of multi-omics data integration comes with a trade-off in the form of an added layer of complexity originating from inherently diverse types of omics-datasets that may pose a challenge to integrate the omics-data in a biologically meaningful manner. The plethora of cancer-specific online omics-data resources, if able to be integrated efficiently and systematically, may facilitate the generation of new biological insights for cancer research. In this review, we provide a comprehensive overview of the online single- and multi-omics resources that are dedicated to cancer. We catalog various online omics-data resources such as The Cancer Genome Atlas (TCGA) along with various TCGA-associated data portals and tools for multi-omics analysis and visualization, the International Cancer Genome Consortium (ICGC), Catalogue of Somatic Mutations in Cancer (COSMIC), The Pathology Atlas, Gene Expression Omnibus (GEO), and PRoteomics IDEntifications (PRIDE). By comparing the strengths and limitations of the respective online resources, we aim to highlight the current biological and technological challenges and possible strategies to overcome these challenges. We outline the available schemes for the integration of the multi-omics dimensions for stratifying cancer patients and biomarker prediction based on the integrated molecular-signatures of cancer. Finally, we propose the multi-omics driven systems-biology approaches to realize the potential of precision onco-medicine as the future of cancer research. We believe this systematic review will encourage scientists and clinicians worldwide to utilize the online resources to explore and integrate the available omics datasets that may provide a window of opportunity to generate new biological insights and contribute to the advancement of the field of cancer research.

De novo sequencing of proteins by mass spectrometry

INTRODUCTION

Proteins are crucial for every cellular activity and unraveling their sequence and structure is a crucial step to fully understand their biology. Early methods of protein sequencing were mainly based on the use of enzymatic or chemical degradation of peptide chains. With the completion of the human genome project and with the expansion of the information available for each protein, various databases containing this sequence information were formed.

AREAS COVERED

De novo protein sequencing, shotgun proteomics and other mass-spectrometric techniques, along with the various software are currently available for proteogenomic analysis. Emphasis is placed on the methods for de novo sequencing, together with potential and shortcomings using databases for interpretation of protein sequence data.

EXPERT OPINION

As mass-spectrometry sequencing performance is improving with better software and hardware optimizations, combined with user-friendly interfaces, de-novo protein sequencing becomes imperative in shotgun proteomic studies. Issues regarding unknown or mutated peptide sequences, as well as, unexpected post-translational modifications (PTMs) and their identification through false discovery rate searches using the target/decoy strategy need to be addressed. Ideally, it should become integrated in standard proteomic workflows as an add-on to conventional database search engines, which then would be able to provide improved identification.

Bioinformatics analysis of prognosis-related long non-coding RNAs in invasive breast carcinoma

Breast cancer is one of the most common types of cancer among women worldwide and needs more sensitive prognostic biomarkers to improve its treatment. In the present study, differentially expressed long non-coding RNAs (lncRNAs) in invasive breast carcinoma from The Cancer Genome Atlas and cBioPortal database were investigated, identifying 292 differentially expressed lncRNAs in 1,100 cases. By analyzing the overall survival rate, 10 lncRNAs were significantly correlated with poor prognosis. To explore the underlying molecular mechanisms of the 10 prognosis-related lncRNAs, bioinformatic methods were used to predict the potential target miRNAs, mRNAs and proteins, and to construct a lncRNA-miRNA-mRNA regulatory network and lncRNA-protein interaction network. Finally, the functions of the target genes and proteins were insvestigated using Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses. The results showed that these 10 lncRNAs could be novel prognostic markers for invasive breast carcinoma and the present study aimed to provide novel insight into the diagnosis and treatment of breast cancer.