Artificial intelligence (AI) and machine learning (ML) are rapidly advancing fields within computer science, driving significant progress in cancer diagnostics. Various ML models have been developed to assist diagnosis, guide therapy decisions, and facilitate early disease detection. In this review, we discuss diverse AI and ML approaches and critically evaluate their applications and limitations in pancreatic cancer histopathology, diagnostics, and biomarker discovery.

Pancreatic cancer is a highly malignant tumor with poor prognosis, emphasizing the need for accurate early diagnosis. EVs, as mediators of intercellular communication, carry DNA, RNA, and proteins that show differential but not tumor-specific expression patterns in pancreatic cancer. Studies have shown that combining RNA markers in EVs (such as miRNA, circRNA, and lncRNA) with serum CA 19-9 testing can significantly enhance diagnostic accuracy for pancreatic cancer. EV-associated proteins have exhibited favorable diagnostic performance in early-stage pancreatic cancer in preliminary studies, though their clinical applicability remains to be further validated. Furthermore, mutations in KRAS, TP53, and SMAD4 genes within EVs offer a promising avenue for non-invasive liquid biopsy. However, challenges such as standardization, low sensitivity, and specificity still hinder the clinical application of EVs. Future research should focus on strategies including multi-omics integration, AI-assisted analysis, multi-marker combined detection, and large-scale clinical validation to further improve the diagnostic capability for pancreatic cancer. Overcoming these obstacles may position EVs as a vital tool in the diagnosis of pancreatic cancer.

Pancreatic Ductal Adenocarcinoma (PDAC) accounts for a significant burden of global cancer deaths worldwide. The dismal outcomes associated with PDAC can be overcome by detecting the disease early and developing tools that predict response to treatment, allowing the selection of the most optimal treatment. Over the last couple of years, significant progress has been made in the development of novel biomarkers that aid in diagnosis, prognosis, treatment selection, and monitoring response. Blood-based biomarkers offer an alternative to tissue-based diagnosis and offer immense potential in managing PDAC. In this review, we have discussed the advances in blood-based biomarkers in PDAC, such as DNA (mutations and methylations), RNA, protein biomarkers and circulating tumor cells (CTC) over the last decade and also elucidated all aspects of practical implementation of these biomarkers in clinical practice. We have also discussed implementing multiomics utilizing more than one biomarker and targeted therapies that have been developed using these biomarkers.

Gastrointestinal (GI) cancers, which predominantly manifest in the stomach, colorectum, liver, esophagus, and pancreas, accounting for approximately 35% of global cancer-related mortality. The advent of liquid biopsy has introduced a pivotal diagnostic modality for the early identification of premalignant GI lesions and incipient cancers. This non-invasive technique not only facilitates prompt therapeutic intervention, but also serves as a critical adjunct in prognosticating the likelihood of tumor recurrence. The wealth of circulating exosomes present in body fluids is often enriched with proteins, lipids, microRNAs, and other RNAs derived from tumor cells. These specific cargo components are reflective of processes involved in GI tumorigenesis, tumor progression, and response to treatment. As such, they represent a group of promising biomarkers for aiding in the diagnosis of GI cancer. In this review, we delivered an exhaustive overview of the composition of exosomes and the pathways for cargo sorting within these vesicles. We laid out some of the clinical evidence that supported the utilization of exosomes as diagnostic biomarkers for GI cancers and discussed their potential for clinical application. Furthermore, we addressed the challenges encountered when harnessing exosomes as diagnostic and predictive instruments in the realm of GI cancers.

Pancreatic adenocarcinoma (PAAD) is a highly lethal malignancy that leads to patients missing optimal treatment opportunities due to its atypical clinical symptoms and the lack of effective diagnostic biomarkers. To develop a biomarker panel based on extracellular vesicle-derived transposable elements (EV-TEs) for non-invasive detection of PAAD, we analyzed 6.75 Tbp sequencing data of 852 EV-derived transcriptomes from two cohorts, and identified 31 EV-TEs features as the biomarker panel using recursive feature elimination. Predictive model constructed using the Support Vector Machine (SVM) algorithm demonstrated excellent performance for PAAD detection in the training set (AUC: 0.90, 95% CI: 0.86-0.93), the test set (AUC: 0.86, 95% CI: 0.79-0.92) and the independent external validation cohort of blood EV-derived samples (AUC: 0.88, 95% CI: 0.84-0.92). This study presents the first EV-TEs based predictive model for PAAD detection, showcasing the immense potential of these 'junk DNA' as innovative diagnostic biomarker for cancers.

Early-onset gastric cancer (EOGC) is a lethal malignancy. It differs from late-onset gastric cancer (LOGC) in clinical and molecular characteristics. The current strategies for EOGC detection have certain limitations in diagnostic performance due to the rising trend in EOGC.

We developed a liquid biopsy signature for EOGC detection.

We use a systematic discovery approach by analysing genome-wide transcriptomic profiling data from EOGC (n=43), LOGC (n=31) and age-matched non-disease controls (n=37) tissue samples. An extracellular vesicle-derived long non-coding RNA (EV-lncRNA) signature was identified in blood samples from a training cohort (n=299), and subsequently confirmed by qPCR in two external validation cohorts (n=462 and n=438), a preoperative/postoperative cohort (n=66) and a gastrointestinal tumour cohort (n=225).

A three EV-lncRNA (NALT1, PTENP1 and HOTTIP) liquid biopsy signature was developed for EOGC detection with an area under the receiver operating characteristic curve (AUROC) of 0.924 (95% CI 0.889 to 0.953). This EV-lncRNA signature provided robust diagnostic performance in two external validation cohorts (Xi'an cohort: AUROC, 0.911; Beijing cohort: AUROC, 0.9323). Furthermore, the EV-lncRNA signature reliably identified resectable stage EOGC patients (stage I/II) and demonstrated better diagnostic performance than traditional GC-related biomarkers in distinguishing early-stage EOGC (stage I) from precancerous lesions. The low levels of this biomarker in postsurgery and other gastrointestinal tumour plasma samples indicated its GC specificity.

The newly developed EV-lncRNA signature effectively identified EOGC patients at a resectable stage with enhanced precision, thereby improving the prognosis of patients who would have otherwise missed the curative treatment window.

Cancer remains the second leading cause of death worldwide, surpassed only by cardiovascular disease. From the different types of cancer, pancreatic cancer (PaC) has one of the lowest survival rates, with a survival rate of about 20% after the first year of diagnosis and about 8% after 5 years. The lack of highly sensitive and specific biomarkers, together with the absence of symptoms in the early stages, determines a late diagnosis, which is associated with a decrease in the effectiveness of medical intervention, regardless of its nature - surgery and/or chemotherapy. This review provides an updated overview of recent studies combining multi-OMICs approaches (e.g., proteomics, metabolomics) with analytical tools, highlighting the synergy between high-throughput molecular data generation and precise analytical tools such as LC-MS, GC-MS and MALDI-TOF MS. This combination significantly improves the detection, quantification and identification of biomolecules in complex biological systems and represents the latest advances in understanding PaC management and the search for effective diagnostic tools. Large-scale data analysis coupled with bioinformatics tools enables the identification of specific genetic mutations, gene expression patterns, pathways, networks, protein modifications and metabolic signatures associated with PaC pathogenesis, progression and treatment response through the integration of multi-OMICs data.

Cancer remains a leading global cause of mortality, making early detection crucial for improving survival outcomes. The study aims to develop a machine learning-enabled blood-derived exosomal RNA profiling platform for multi-cancer detection and localization.

In this multi-phase, multi-center study, we analyzed RNA from exosomes derived from peripheral blood plasma in 818 participants across eight cancer types during the discovery phase. Machine learning techniques were applied to identify potential pan-cancer biomarkers. During the screening and model validation phases, the sample size was progressively expanded to 1,385 participants in two steps, while the candidate biomarkers were refined into a set of 12 exosomal tumor RNA signatures (ETR.sig). In the subsequent model construction phase, diagnostic models were developed using the expanded cohort and ETR.sig. Statistical analyses included the calculation of receiver operating characteristic (ROC) curves and AUC values to assess the models' ability to distinguish cancer cases from controls and determine tumor origins. To further validate and explore the biological relevance of the identified biomarkers, we integrated tissue RNA-seq, single-cell data, and clinical information.

Machine learning analysis initially identified 33 candidate biomarkers, which were narrowed down to 20 ETR.sig in the screening phase and 12 ETR.sig in the validation phase. In the model construction phase, a diagnostic model based on ETR.sig, built using the Random Forest (RF) algorithm, showed excellent performance with an AUC of 0.915 for distinguishing pan-cancer from controls. The multi-class classification model also demonstrated strong classification power, with macro-average and micro-average AUCs of 0.983 and 0.985, respectively, for differentiating between eight cancer types. Additionally, tumor origin classification using the RF-based diagnostic models achieved high AUC values: BRCA 0.976, COAD 0.98, KIRC 0.947, LIHC 0.967, LUAD 0.853, OV 0.972, PAAD 0.977, and PRAD 0.898. Integration of tissue RNA-seq, single-cell data, and clinical information revealed key associations between ETR.sig-related genes and tumor development.

The study demonstrates the robust potential of exosomal RNA as a minimally invasive biomarker resource for cancer detection. The developed ETR.sig platform offers a promising tool for precision oncology and broad-spectrum cancer screening, integrating advanced computational models with nanoscale vesicle biology for accurate and rapid diagnosis.

The invasion and metastasis of pancreatic cancer (PC) are key factors contributing to disease progression and poor prognosis. This process is primarily driven by EMT, which has been the focus of recent studies highlighting the role of long non-coding RNAs (lncRNAs) as crucial regulators of EMT. However, the mechanisms by which lncRNAs influence invasive metastasis are multifaceted, extending beyond EMT regulation alone.

This review primarily aims to characterize lncRNAs affecting invasion and metastasis in pancreatic cancer. We summarize the regulatory roles of lncRNAs across multiple molecular pathways and highlight their translational potential, considering the implications for clinical applications in diagnostics and therapeutics.

The review focuses on three principal scientific themes. First, we primarily summarize lncRNAs orchestrate various signaling pathways, such as TGF-β/Smad, Wnt/β-catenin, and Notch, to regulate molecular changes associated with EMT, thereby enhancing cellular motility and invasivenes. Second, we summarize the effects of lncRNAs on autophagy and ferroptosis and discuss the role of exosomal lncRNAs in the tumor microenvironment to regulate the behavior of neighboring cells and promote cancer cell invasion. Third, we emphasize the effects of RNA modifications (such as m6A and m5C methylation) on stabilizing lncRNAs and enhancing their capacity to mediate invasive metastasis in PC. Lastly, we discuss the translational potential of these findings, emphasizing the inherent challenges in using lncRNAs as clinical biomarkers and therapeutic targets, while proposing prospective research strategies.

Pancreatic ductal adenocarcinoma (PDAC) is a malignant neoplasm that typically manifests with subtle clinical manifestations in its early stages and frequently eludes diagnosis until the advanced phases of the disease. The limited therapeutic options available for PDAC significantly contribute to its high mortality rate, highlighting the urgent need for novel biomarkers capable of effectively identifying early clinical manifestations and facilitating precise diagnosis. The pivotal role of cellular exosomes in both the pathogenesis and therapeutic interventions for PDAC has been underscored. Furthermore, researchers have acknowledged the potential of exosomes as targeted drug carriers against regulatory cells in treating PDAC. The present article aims to provide a comprehensive review encompassing recent advancements in utilizing exosomes for elucidating mechanisms underlying disease development, patterns of metastasis, diagnostic techniques and treatment strategies associated with PDAC.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer characterized by dismal prognosis. Late diagnosis, resistance to chemotherapy, and lack of efficacious targeted therapies render PDAC almost untreatable. Dysregulation of splicing, the process that excises the introns from nascent transcripts, is emerging as a hallmark of PDAC and a possible vulnerability of this devastating cancer. Splicing factors are deregulated in PDAC and contribute to all steps of tumorigenesis, from inflammation-related early events to metastasis and acquisition of chemoresistance. At the same time, splicing dysregulation offers a therapeutic opportunity to target cancer-specific vulnerabilities. We discuss mounting evidence that splicing plays a key role in PDAC and the opportunities that this essential process offers for developing new targeted therapies.

Identifying differentially expressed genes (DEGs) is a core task of transcriptome analysis, as DEGs can reveal the molecular mechanisms underlying biological processes. However, interpreting the biological significance of large DEG lists is challenging. Currently, gene ontology, pathway enrichment and protein-protein interaction analysis are common strategies employed by biologists. Additionally, emerging analytical strategies/approaches (such as network module analysis, knowledge graph, drug repurposing, cell marker discovery, trajectory analysis, and cell communication analysis) have been proposed. Despite these advances, comprehensive guidelines for systematically and thoroughly mining the biological information within DEGs remain lacking.

This review aims to provide an overview of essential concepts and methodologies for the biological interpretation of DEGs, enhancing the contextual understanding. It also addresses the current limitations and future perspectives of these approaches, highlighting their broad applications in deciphering the molecular mechanism of complex diseases and phenotypes. To assist users in extracting insights from extensive datasets, especially various DEG lists, we developed DEGMiner (https://www.ciblab.net/DEGMiner/), which integrates over 300 easily accessible databases and tools.

This review offers strong support and guidance for exploring DEGs, and also will accelerate the discovery of hidden biological insights within genomes.

Cancer has a high mortality rate across the globe, and tissue biopsy remains the gold standard for tumor diagnosis due to its high level of laboratory standardization, good consistency of results, relatively stable samples, and high accuracy of results. However, there are still many limitations and drawbacks in the application of tissue biopsy in tumor. The emergence of liquid biopsy provides new ideas for early diagnosis and prognosis of tumor. Compared with tissue biopsy, liquid biopsy has many advantages in the diagnosis and treatment of various types of cancer, including non-invasive, quickly and so on. Currently, the application of liquid biopsy in tumor detection has received widely attention. It is now undergoing rapid progress, and it holds significant potential for future applications. Around now, liquid biopsies encompass several components such as circulating tumor cells, circulating tumor DNA, exosomes, microRNA, circulating RNA, tumor platelets, and tumor endothelial cells. In addition, advances in the identification of liquid biopsy indicators have significantly enhanced the possibility of utilizing liquid biopsies in clinical settings. In this review, we will discuss the application, advantages and challenges of liquid biopsy in some common tumors from the perspective of diverse systems of tumors, and look forward to its future development prospects in the field of cancer diagnosis and treatment.

Colorectal cancer (CRC) exhibits significant genetic and epigenetic diversity, evolving into sub-clonal populations with varied metastatic potentials and treatment responses. Predicting metastatic disease in CRC patients remains challenging, underscoring the need for reliable biomarkers. While most research on therapeutic targets and biomarkers has focused on proteins, non-coding RNAs such as long non-coding RNAs (lncRNAs) comprise most of the transcriptome and demonstrate superior tissue- and cancer-specific expression. We utilised spatial transcriptomics to investigate lncRNAs in CRC tumours, offering more precise cell-type-specific expression data compared to bulk RNA sequencing. Our analysis identified 301 lncRNAs linked to malignant CRC regions, which we validated with public data. Further validation using RNA-FISH revealed three lncRNAs (LINC01978, PLAC4, and LINC01303) that are detectable in stage II tumours but not in normal epithelium and are upregulated in metastatic tissues. These lncRNAs hold potential as biomarkers for early risk assessment of metastatic disease.

Cholangiocarcinoma is a malignancy with high aggressiveness, and extrahepatic cholangiocarcinoma (ECCA) represents the predominant subtype. However, the molecular architecture and underlying pathogenic mechanisms of ECCA remain poorly understood. The objective of this study is to elucidate the molecular markers and biological pathways associated with ECCA.

In order to identify the factors influencing ECCA, we conducted transcriptome sequencing on a cohort of 8 surgically resected ECCA specimens. To validate our findings, we integrated data from The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases using batch integration analysis. Finally, we confirmed our results using clinical samples.

The findings of this study reveal that through the analysis of sequencing data, we have successfully identified the genes that are differentially expressed and have a significant role in the development of ECCA. Utilizing the Weighted Gene Co-expression Network Analysis approach, we have integrated these identified gene modules with the GEO dataset, leading to the identification of four key genes (PTGDS, ITIH2, LSAMP, HBB) that are strongly associated with the progression-free survival of ECCA. We screened a key gene LSAMP from four genes using immunohistochemistry. The gene primarily participate in crucial biological processes such as the ECCA cell cycle and DNA replication. The qRT-PCR reaction and Western Blot conducted on the tissues provided confirmation of the expression levels of the gene, which exhibited consistency with the outcomes of our analysis.

Our study has successfully identified potential biomarkers LSAMP for ECCA, which can serve as valuable tools for early detection and targeted therapeutic interventions in clinical settings.

Extracellular vesicles (EVs) contain cell-derived lipids, proteins and RNAs; however, determining the tissue- and cell-type-specific EV abundances in body fluids remains a significant hurdle for our understanding of EV biology. While tissue- and cell-type-specific EV abundances can be estimated by matching the EV's transcriptome to a tissue's/cell type's expression signature using deconvolutional methods, a comparative assessment of deconvolution methods' performance on EV transcriptome data is currently lacking. We benchmarked 11 deconvolution methods using data from four cell lines and their EVs, in silico mixtures, 118 human plasma and 88 urine EVs. We identified deconvolution methods that estimated cell type-specific abundances of pure and in silico mixed cell line-derived EV samples with high accuracy. Using data from two urine EV cohorts with different EV isolation procedures, four deconvolution methods produced highly similar results. The three methods were also concordant in their tissue- and cell-type-specific plasma EV abundance estimates. We identified driving factors for deconvolution accuracy and highlighted the importance of implementing biological knowledge in creating the tissue/cell type signature. Overall, our analyses demonstrate that the deconvolution algorithms DWLS and CIBERSORTx produce highly similar and accurate estimates of tissue- and cell-type-specific EV abundances in biological fluids.

The DNA damage repair (DDR) pathway is a complex signaling cascade that can sense DNA damage and trigger cellular responses to DNA damage to maintain genome stability and integrity. A typical hallmark of cancer is genomic instability or nonintegrity, which is closely related to the accumulation of DNA damage within cancer cells. The treatment principles of radiotherapy and chemotherapy for cancer are based on their cytotoxic effects on DNA damage, which are accompanied by severe and unnecessary side effects on normal tissues, including dysregulation of the DDR and induced therapeutic tolerance. As a driving factor for oncogenes or tumor suppressor genes, noncoding RNA (ncRNA) have been shown to play an important role in cancer cell resistance to radiotherapy and chemotherapy. Recently, it has been found that ncRNA can regulate tumor treatment tolerance by altering the DDR induced by radiotherapy or chemotherapy in cancer cells, indicating that ncRNA are potential regulatory factors targeting the DDR to reverse tumor treatment tolerance. This review provides an overview of the basic information and functions of the DDR and ncRNAs in the tolerance or sensitivity of tumors to chemotherapy and radiation therapy. We focused on the impact of ncRNA (mainly microRNA [miRNA], long noncoding RNA [lncRNA], and circular RNA [circRNA]) on cancer treatment by regulating the DDR and the underlying molecular mechanisms of their effects. These findings provide a theoretical basis and new insights for tumor-targeted therapy and the development of novel drugs targeting the DDR or ncRNAs.

It takes more than 20 years for normal colorectal mucosa to develop into metastatic carcinoma. The long time window provides a golden opportunity for early detection to terminate the malignant progression. Here, we aim to enable liquid biopsy of T1a stage colorectal cancer (CRC) and precancerous advanced adenoma (AA) by profiling circulating small extracellular vesicle (sEV)-derived RNAs. We exhibited a full RNA landscape for the circulating sEVs isolated from 60 participants. A total of 58,333 annotated RNAs were detected from plasma sEVs, among which 1,615 and 888 sEV-RNAs were found differentially expressed in plasma from T1a stage CRC and AA compared to normal controls (NC). Then we further categorized these sEV-RNAs into six modules by a weighted gene coexpression network analysis and constructed a 60-gene t-SNE model consisting of the top 10 RNAs of each module that could well distinguish T1a stage CRC/AA from NC samples. Some sEV-RNAs were also identified as indicators of specific endoscopic and morphological features of different colorectal lesions. The top-ranked biomarkers were further verified by RT-qPCR, proving that these candidate sEV-RNAs successfully identified T1a stage CRC/AA from NC in another cohort of 124 participants. Finally, we adopted different algorithms to improve the performance of RT-qPCR-based models and successfully constructed an optimized classifier with 79.3% specificity and 99.0% sensitivity. In conclusion, circulating sEVs of T1a stage CRC and AA patients have distinct RNA profiles, which successfully enable the detection of both T1a stage CRC and AA via liquid biopsy.

The introduction of Immune Checkpoint Inhibitors (ICIs) has marked a paradigm shift in treating Lung Squamous Cell Carcinoma (LUSC), emphasizing the urgent need for precise molecular biomarkers to reliably forecast therapeutic efficacy. This study aims to identify potential biomarkers for immunochemotherapy efficacy by focusing on plasma extracellular vesicle (EV)-derived long RNAs (exLRs).

We enrolled 78 advanced LUSC patients undergoing first-line immunochemotherapy. Plasma samples were collected, and exLR sequencing was conducted to establish baseline profiles. A retrospective analysis was performed on 42 patients to identify differentially expressed exLRs. Further validation of the top differentially expressed exLRs was conducted using quantitative reverse transcription PCR (qRT-PCR). Univariate Cox analysis was applied to determine the prognostic significance of these exLRs. Based on these findings, we developed a predictive signature (p-Signature).

In the retrospective analysis of 42 patients, we identified 460 differentially expressed exLRs, with pathways related to leukocyte migration notably enriched among non-responders. Univariate Cox analysis revealed 45 exLRs with prognostic significance. The top 6 protein-coding exLRs were validated using qRT-PCR, identifying CXCL8, SSH3, and SDHAF1 as differentially expressed between responders and non-responders. The p-Signature, comprising these three exLRs, demonstrated high accuracy in distinguishing responders from non-responders, with an Area Under the Curve (AUC) of 0.904 in the retrospective cohort and 0.812 in the prospective cohort.

This study highlighted the potential of plasma exLR profiles in predicting LUSC treatment efficacy. Intriguingly, lower p-Signature scores were associated with increased abundance of activated CD4+ and CD8+ T cells, indicating a more robust immune environment. These findings suggest that the p-Signature could serve as a valuable tool in guiding personalized and effective therapeutic strategies for LUSC.

Neoadjuvant chemotherapy (NACT) is routinely used to treat patients with advanced gastric cancer (AGC). However, the identification of reliable markers to determine which AGC patients would benefit from NACT remains challenging.

A systematic screening of plasma proteins between NACT-sensitive and NACT-resistant AGC patients was performed by a mass spectrometer (n = 6). The effect of the most differential plasma protein was validated in two independent cohorts with AGC patients undergoing NACT (ELISA cohort: n = 155; Validated cohort: n = 203). The expression of this candidate was examined in a cohort of AGC tissues using immunohistochemistry (n = 34). The mechanism of this candidate on 5-Fluorouracil (5-FU) resistance was explored by cell-biology experiments in vitro and vivo.

A series of differential plasma proteins between NACT-sensitive and NACT-resistant AGC patients was identified. Among them, plasma HIST1H2BK was validated as a significant biomarker for predicting NACT response and prognosis. Moreover, HIST1H2BK was over-expression in NACT-resistant tissues compared to NACT-sensitive tissues in AGC. Mechanistically, HIST1H2BK inhibited 5-FU-induced apoptosis by upregulating A2M transcription and then activating LRP/PI3K/Akt pathway, thereby promoting 5-FU resistance in GC cells. Intriguingly, HIST1H2BK-overexpressing 5-FU-resistant GC cells propagated resistance to 5-FU-sensitive GC cells through the secretion of HIST1H2BK.

This study highlights significant differences in plasma protein profiles between NACT-resistant and NACT-sensitive AGC patients. Plasma HIST1H2BK emerged as an effective biomarker for achieving more accurate NACT in AGC. The mechanism of intracellular and secreted HIST1H2BK on 5-FU resistance provided a novel insight into chemoresistance in AGC.

Pancreatic cancer remains one of the most lethal diseases worldwide. Cancer-derived exosomes, benefiting from the protective role of the lipid membrane, exhibit remarkable stability in the circulatory system. These exosomes, released by tumor microenvironment, contain various biomolecules such as proteins, RNAs, and lipids that plays a pivotal role in mediating distant communication between the local pancreatic tumor and other organs or tissues. They facilitate the transfer of oncogenic factors to distant sites, contributing to the compromised body immune system, distant metastasis, diabetes, cachexia, and promoting a microenvironment conducive to tumor growth and metastasis in pancreatic cancer patients. Beyond their intrinsic roles, circulating exosomes in peripheral blood can be detected to facilitate accurate liquid biopsy. This approach offers a novel and promising method for the diagnosis and management of pancreatic cancer. Consequently, circulating exosomes are not only crucial mediators of systemic cell-cell communication during pancreatic cancer progression but also hold great potential as precise tools for pancreatic cancer management and treatment. Exosome-based liquid biopsy and therapy represent promising advancements in the diagnosis and treatment of pancreatic cancer. Exosomes can serve as drug delivery vehicles, enhancing the targeting and efficacy of anticancer treatments, modulating the immune system, and facilitating gene editing to suppress tumor growth. Ongoing research focuses on biomarker identification, drug delivery systems, and clinical trials to validate the safety and efficacy of exosome-based therapies, offering new possibilities for early diagnosis and precision treatment in pancreatic cancer. Leveraging the therapeutic potential of exosomes, including their ability to deliver targeted drugs and modulate immune responses, opens new avenues for innovative treatment strategies.

Cell-free long RNAs in human plasma and extracellular vesicles (EVs) have shown promise as biomarkers in liquid biopsy, despite their fragmented nature.

To investigate these fragmented cell-free RNAs (cfRNAs), we developed a cost-effective cfRNA sequencing method called DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing). DETECTOR-seq utilised a meticulously tailored set of customised guide RNAs to remove large amounts of unwanted RNAs (i.e., fragmented ribosomal and mitochondrial RNAs) in human plasma. Early barcoding strategy was implemented to reduce costs and minimise plasma requirements.

Using DETECTOR-seq, we conducted a comprehensive analysis of cell-free transcriptomes in both whole human plasma and EVs. Our analysis revealed discernible distributions of RNA types in plasma and EVs. Plasma exhibited pronounced enrichment in structured circular RNAs, tRNAs, Y RNAs and viral RNAs, while EVs showed enrichment in messenger RNAs (mRNAs) and signal recognition particle RNAs (srpRNAs). Functional pathway analysis highlighted RNA splicing-related ribonucleoproteins (RNPs) and antimicrobial humoral response genes in plasma, while EVs demonstrated enrichment in transcriptional activity, cell migration and antigen receptor-mediated immune signals. Our study indicates the comparable potential of cfRNAs from whole plasma and EVs in distinguishing cancer patients (i.e., colorectal and lung cancer) from healthy donors. And microbial cfRNAs in plasma showed potential in classifying specific cancer types.

Our comprehensive analysis of total and EV cfRNAs in paired plasma samples provides valuable insights for determining the need for EV purification in cfRNA-based studies. We envision the cost effectiveness and efficiency of DETECTOR-seq will empower transcriptome-wide investigations in the fields of cfRNAs and liquid biopsy.

DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing) enabled efficient and specific depletion of sequences derived from fragmented ribosomal and mitochondrial RNAs in plasma. Distinct human and microbial cell-free RNA (cfRNA) signatures in whole Plasma versus extracellular vesicles (EVs) were revealed. Both Plasma and EV cfRNAs were capable of distinguishing cancer patients from normal individuals, while microbial RNAs in Plasma cfRNAs enabled better classification of cancer types than EV cfRNAs.

Liquid biopsy-derived RNA sequencing (lbRNA-seq) exhibits significant promise for clinic-oriented cancer diagnostics due to its non-invasiveness and ease of repeatability. Despite substantial advancements, obstacles like technical artefacts and process standardisation impede seamless clinical integration. Alongside addressing technical aspects such as normalising fluctuating low-input material and establishing a standardised clinical workflow, the lack of result validation using independent datasets remains a critical factor contributing to the often low reproducibility of liquid biopsy-detected biomarkers. Considering the outlined drawbacks, our objective was to establish a workflow/methodology characterised by: 1. Harness the rich diversity of biological features accessible through lbRNA-seq data, encompassing a holistic range of molecular and functional attributes. These components are seamlessly integrated via a Machine Learning-based Ensemble Classification framework, enabling a unified and comprehensive analysis of the intricate information encoded within the data. 2. Implementing and rigorously benchmarking intra-sample normalisation methods to heighten their relevance within clinical settings. 3. Thoroughly assessing its efficacy across independent test sets to ascertain its robustness and potential utility. Using ten datasets from several studies comprising three different sources of biological material, we first show that while the best-performing normalisation methods depend strongly on the dataset and coupled Machine Learning method, the rather simple Counts Per Million method is generally very robust, showing comparable performance to cross-sample methods. Subsequently, we demonstrate that the innovative biofeature types introduced in this study, such as the Fraction of Canonical Transcript, harbour complementary information. Consequently, their inclusion consistently enhances prediction power compared to models relying solely on gene expression-based biofeatures. Finally, we demonstrate that the workflow is robust on completely independent datasets, generally from different labs and/or different protocols. Taken together, the workflow presented here outperforms generally employed methods in prediction accuracy and may hold potential for clinical diagnostics application due to its specific design.

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignancies worldwide, while it persists as the fourth most prevalent cause of cancer-related death in the United States of America. Although there are several novel therapeutic strategies for the approach of this intensely aggressive tumor, it remains a clinical challenge, as it is hard to identify in early stages, due to its asymptomatic course. A diagnosis is usually established when the disease is already in its late stages, while its chemoresistance constitutes an obstacle to the optimal management of this malignancy. The discovery of novel diagnostic and therapeutic tools is considered a necessity for this tumor, due to its low survival rates and treatment failures. One of the most extensively investigated potential diagnostic and therapeutic modalities is extracellular vesicles (EVs). These vesicles constitute nanosized double-lipid membraned particles that are characterized by a high heterogeneity that emerges from their distinct biogenesis route, their multi-variable sizes, and the particular cargoes that are embedded into these particles. Their pivotal role in cell-to-cell communication via their cargo and their implication in the pathophysiology of several diseases, including pancreatic cancer, opens new horizons in the management of this malignancy. Meanwhile, the interplay between pancreatic carcinogenesis and short non-coding RNA molecules (micro-RNAs or miRs) is in the spotlight of current studies, as they can have either a role as tumor suppressors or promoters. The deregulation of both of the aforementioned molecules leads to several aberrations in the function of pancreatic cells, leading to carcinogenesis. In this review, we will explore the role of extracellular vesicles and miRNAs in pancreatic cancer, as well as their potent utilization as diagnostic and therapeutic tools.

The "omics" revolution has transformed the biomedical research landscape by equipping scientists with the ability to interrogate complex biological phenomenon and disease processes at an unprecedented level. The volume of "big" data generated by the different omics studies such as genomics, transcriptomics, proteomics, and metabolomics has led to the concurrent development of computational tools to enable in silico analysis and aid data deconvolution. Considering the intensive resources and high costs required to generate and analyze big data, there has been centralized, collaborative efforts to make the data and analysis tools freely available as "Open Source," to benefit the wider research community. Pancreatology research studies have contributed to this "big data rush" and have additionally benefitted from utilizing the open source data as evidenced by the increasing number of new research findings and publications that stem from such data. In this review, we briefly introduce the evolution of open source omics data, data types, the "FAIR" guiding principles for data management and reuse, and centralized platforms that enable free and fair data accessibility, availability, and provide tools for omics data analysis. We illustrate, through the case study of our own experience in mining pancreatitis omics data, the power of repurposing open source data to answer translationally relevant questions in pancreas research.

Liquid biopsy has emerged as a promising non-invasive approach for detecting, monitoring diseases, and predicting their recurrence. However, the effective utilization of liquid biopsy data to identify reliable biomarkers for various cancers and other diseases requires further exploration. Here, we present cfOmics, a web-accessible database (https://cfomics.ncRNAlab.org/) that integrates comprehensive multi-omics liquid biopsy data, including cfDNA, cfRNA based on next-generation sequencing, and proteome, metabolome based on mass-spectrometry data. As the first multi-omics database in the field, cfOmics encompasses a total of 17 distinct data types and 13 specimen variations across 69 disease conditions, with a collection of 11345 samples. Moreover, cfOmics includes reported potential biomarkers for reference. To facilitate effective analysis and visualization of multi-omics data, cfOmics offers powerful functionalities to its users. These functionalities include browsing, profile visualization, the Integrative Genomic Viewer, and correlation analysis, all centered around genes, microbes, or end-motifs. The primary objective of cfOmics is to assist researchers in the field of liquid biopsy by providing comprehensive multi-omics data. This enables them to explore cell-free data and extract profound insights that can significantly impact disease diagnosis, treatment monitoring, and management.

Pancreatic ductal adenocarcinoma is one of the most lethal malignant diseases, with a mortality rate being close to incidence. Due to its heterogeneity and plasticity, as well as the lack of distinct symptoms in the early phases, it is very often diagnosed at an advanced stage, resulting in poor prognosis. Traditional tissue biopsies remain the gold standard for making a diagnosis, but have an obvious disadvantage in their inapplicability for frequent sampling. Blood-based biopsies represent a non-invasive method which potentially offers easy and repeated sampling, leading to the early detection and real-time monitoring of the disease and hopefully an accurate prognosis. Given the urgent need for a reliable biomarker that can estimate a patient's condition and response to an assigned treatment, blood-based biopsies are emerging as a potential new tool for improving patients' survival and surveillance. In this article, we discuss the current advances and challenges in using liquid biopsies for pancreatic cancer, focusing on circulating tumour DNA (ctDNA), extracellular vesicles (EVs), and circulating tumour cells (CTCs), and compare the performance and reliability of different biomarkers and combinations of biomarkers.

Colorectal cancer (CRC) is one of the most threatening tumors in the world, and chemotherapy remains dominant in the treatment of metastatic CRC (mCRC) patients. The purpose of this study was to develop a biomarker panel to predict the response of the first line chemotherapy in mCRC patients.

Totally 190 mCRC patients treated with FOLFOX or XEOLX chemotherapy in 3 different institutions were included. We extracted the plasma extracellular vesicle (EV) RNA, performed RNA sequencing, constructed a model and generated a signature through shrinking the number of variables by the random forest algorithm and the least absolute shrinkage and selection operator (LASSO) algorithm in the training cohort (n = 80). We validated it in an internal validation cohort (n = 62) and a prospective external validation cohort (n = 48).

We established a signature consisted of 22 EV RNAs which could identify responders, and the area under the receiver operating characteristic curve (AUC) values was 0.986, 0.821, and 0.816 in the training, internal validation, and external validation cohort respectively. The signature could also identify the progression-free survival (PFS) and overall survival (OS). Besides, we constructed a 7-gene signature which could predict tumor response to first-line oxaliplatin-containing chemotherapy and simultaneously resistance to second-line irinotecan-containing chemotherapy.

The study was first to develop a signature of EV-derived RNAs to predict the response of the first line chemotherapy in mCRC with high accuracy using a non-invasive approach, indicating that the signature could help to select the optimal regimen for mCRC patients.

Extracellular vesicles (EVs) are bilayered membrane vesicles produced by living cells and secreted into the extracellular matrix. Bile is a special body fluid that is secreted by the liver cells, and extracellular vesicles long RNAs (exLRs) have not been explored in bile. In this study, exLR sequencing (exLR-seq) was performed on 19 bile samples from patients with malignant cancer or patients with biliary stones. A total of 8649 mRNAs, 13 823 circRNAs and 1105 lncRNAs were detected. The KEGG pathway analysis revealed that differentially expressed exLRs were enriched in mTOR and AMPK signaling pathway. We identified five mRNAs (EID2, LLPH, ATP6V0A2, RRP9 and MTRNR2L10), three lncRNAs (AC015922.2, AL135905.1 and LINC00921) and six circRNAs (circASH1L, circATP9A, circCLIP1, circRNF138, circTIMMDC1 and circANKRD12) were enriched in bile EV samples with cancer, and these exLRs may be potential markers used to distinguish malignant cancers from benign biliary diseases. Moreover, the tissue/cellular source components of EVs were analyzed using the EV-origin algorithm. The absolute abundance of CD4_naive and Th1 cell source in bile EVs from cancer patients were significantly increased. In summary, our study presented abundant exLRs in human bile EVs and provides some basis for the selection of tumor diagnostic markers.

Pancreatic ductal adenocarcinoma (PDAC) is an extremely deadly form of cancer, with limited progress in 5-year survival rates despite significant research efforts. The main challenges in treating PDAC include difficulties in early detection, and resistance to current therapeutic approaches due to aggressive molecular and microenvironment features. These challenges emphasize the importance of identifying clinically validated biomarkers for early detection and clinical management. Extracellular vesicles (EVs), particularly exosomes, have emerged as crucial mediators of intercellular communication by transporting molecular cargo. Recent research has unveiled their role in initiation, metastasis, and chemoresistance of PDAC. Consequently, utilizing EVs in liquid biopsies holds promise for the identification of biomarkers for early detection, prognosis, and monitoring of drug efficacy. However, numerous limitations, including challenges in isolation and characterization of homogeneous EVs populations, as well as the absence of standardized protocols, can affect the reliability of studies involving EVs as biomarkers, underscoring the necessity for a prudent approach. EVs have also garnered considerable attention as a promising drug delivery system and novel therapy for tumors. The loading of biomolecules or chemical drugs into exosomes and their subsequent delivery to target cells can effectively impede tumor progression. Nevertheless, there are obstacles that must be overcome to ensure the accuracy and efficacy of therapies relying on EVs for the treatment of tumors. In this review, we examine both recent advancements and remaining obstacles, exploring the potential of utilizing EVs in biomarker discovery as well as for the development of drug delivery vehicles.

TAMs (tumor-associated macrophages) infiltration promotes the progression of esophageal cancer (EC). However, the underlying mechanisms remain unclear.

Abnormal expression of LINC01592 from EC microarrays of the TCGA database was analyzed. LINC01592 expression level was validated in both EC cell lines and tissues. Stable LINC01592 knockdown and overexpression of EC cell lines were established. In vitro and in vivo trials were conducted to test the impact of LINC01592 knockdown and overexpression on EC cells. RNA binding protein immunoprecipitation (RIP), RNA pulldown assays, and Immunofluorescence (IF) were used to verify the combination of E2F6 and LINC01592. The combination of E2F6 and NBR1 was verified through the utilization of ChIP and dual luciferase reporter assays.

LINC01592 is carried and transferred by exosomes secreted by M2-TAMs to tumor cells. The molecular mechanism underlying the promotion of NBR1 transcription involves the direct binding of LINC01592 to E2F6, which facilitates the nuclear entry of E2F6. The collaborative action of LINC01592 and E2F6 results in improved NBR1 transcription. The elevation of NBR1 binding to the ubiquitinated protein MHC-I via the ubiquitin domain caused a higher degradation of MHC-I in autophagolysosomes and a reduction in MHC-I expression on the exterior of cancerous cell. Consequently, this caused cancerous cells to escape from CD8+ CTL immune attack. The tumor-promoting impacts of LINC01592, as well as the growth of M2-type macrophage-driven tumors, were significantly suppressed by the interruption of E2F6/NBR1/MHC-I signaling through the effect of siRNA or the corresponding antibody blockade. Significantly, the suppression of LINC01592 resulted in an upregulation of MHC-I expression on the tumor cell membrane, thereby enhancing the efficacy of CD8+ T cell reinfusion therapy.

The investigation conducted has revealed a significant molecular interaction between TAMs and EC via the LINC01592/E2F6/NBR1/MHC-I axis, which facilitates the progression of malignant tumors. This suggests that a therapeutic intervention targeting this axis may hold promise for the treatment of the disease.

Pancreatic cancer (PC) is a highly malignant disease with a 5-year survival rate of only 10%. Families with PC are at greater risk, as are type 2 diabetes, pancreatitis, and other factors. Insufficient early detection methods make this cancer have a poor prognosis. Additionally, the molecular mechanisms underlying PC development remain unclear. Increasing evidence suggests that long non-coding RNAs (lncRNAs) contribute to PC pathology,which may control gene expression by recruiting histone modification complexes to chromatin and interacting with proteins and RNAs. In recent studies, abnormal regulation of lncRNAs has been implicated in PC proliferation, metastasis, invasion, angiogenesis, apoptosis, and chemotherapy resistance suggesting potential clinical implications. The paper reviews the progress of lncRNA research in PC about diabetes mellitus, pancreatitis, cancer metastasis, tumor microenvironment regulation, and chemoresistance. Furthermore, lncRNAs may serve as potential therapeutic targets and biomarkers for PC diagnosis and prognosis. This will help improve PC patients' survival rate from a lncRNA perspective.

Renal cell carcinoma (RCC) is one of the most malignant urological tumors. Currently, there is a lack of molecular markers for early diagnosis of RCC. The 5-year survival rate for early-stage RCC is generally favorable; however, the prognosis takes a significant downturn when the tumor progresses to distant metastasis. Therefore, the identification of molecular markers for RCC is crucial in enhancing early diagnosis rates. Exosomes are a type of extracellular vesicle (EV) typically ranging in size from 30 nm to 150 nm, which contain RNA, DNA, proteins, lipids, etc. They can impact neighboring receptor cells through the autocrine or paracrine pathway, influence cellular communication, and regulate the local immune cells, consequently shaping the tumor immune microenvironment and closely associating with tumor development. The clinical application of exosomes as tumor markers and therapeutic targets has ignited significant interest within the research community. This review aims to provide a comprehensive summary of the advancements in exosome research within the context of RCC.

In recent years, conventional coagulation (CC) and thromboelastography (TEG) parameters have been reported to be closely related to the progression of pancreatic cancer (PC). However, the potential utility of these parameters in differentiating benign and malignant pancreatic diseases is still unclear.

A retrospective study was conducted to evaluate the efficacy of coagulation parameters in differentiating pancreatic cancer/early stage pancreatic cancer (EPC, TNM stages I and II) from benign control conditions, and to further explore whether coagulation parameters could improve the differential value of CA199.

Receiver operating characteristic (ROC) curves and logistic regression analysis were used to identify the diagnostic value of each coagulation parameter or combination of parameters.

Compared with benign pancreatic disease (BPD), patients with pancreatic malignant tumors had significant coagulation disorders, specifically manifested as abnormal increases or decreases in several CC and TEG parameters (such as activated partial thromboplastin time (APTT), fibrinogen (FIB), D-dimer (DD2), K time, R time, Angle, maximum amplitude (MA), coagulation index (CI), and Ly30). In the training group, ROC curve showed that FIB, DD2, Angle, MA, and CI had favorable efficacy at differentiating PC or EPC from BPD (for PC, AUC = 0.737, 0.654, 0.627, 0.602, 0.648; for EPC, AUC = 0.723, 0.635, 0.630, 0.614, 0.648). However, several combined diagnostic indicators based on FIB, DD2 and CI failed to outperform the individual coagulation indexes in diagnostic efficiency. Combinations of certain coagulation indexes with CA199 outperformed CA199 alone at identifying PC or EPC, especially FIB + CA199 (for PC, AUC = 0.904; for EPC, AUC = 0.905), FIB + DD2 + CA199 (for PC, AUC = 0.902; for EPC, AUC = 0.900), FIB + CI + CA199 (for PC, AUC = 0.906; for EPC, AUC = 0.906), and FIB + DD2 + CI + CA199 (for PC, AUC = 0.905; for EPC, AUC = 0.900). The results from a validation set also confirmed that these combinations have advantageous diagnostic value for PC and EPC.

A significant hypercoagulable state was common in PC. Some CC and TEG parameters are valuable in the differential diagnosis of benign and malignant pancreatic diseases. In addition, coagulation indexes combined with CA199 can further enhance the differential diagnosis efficacy of CA199 in PC and EPC.