The unique anatomical characteristics and blood supply of the rectosigmoid junction confer particular significance to its physiological functions and clinical surgeries. However, research on the prognosis of rectosigmoid junction cancer (RSC) is scarce, and reliable clinical prediction models are lacking.

This retrospective study included 524 patients diagnosed with RSC who were admitted to the Department of Gastrointestinal and Colorectal Surgery at the First Hospital of Jilin University between January 1, 2017, and June 1, 2019. Univariate and multivariate Cox regression analyses were conducted in this study to identify independent risk factors impacting the survival of RSC patients. Subsequently, models were constructed using six different machine learning algorithms. Finally, the discrimination, calibration, and clinical applicability of each model were evaluated to determine the optimal model.

Through univariate and multivariate Cox regression analyses, we identified seven independent risk factors associated with the survival of RSC patients: age (HR = 1.9, 95% CI: 1.3-2.8, P = 0.001), gender (HR = 0.6, 95% CI: 0.4-0.9, P = 0.013), diabetes (HR = 2.0, 95% CI: 1.3-3.1, P = 0.002), tumor differentiation (HR = 2.1, 95% CI: 1.4-3.1, P < 0.001), tumor N stage (HR = 2.02, 95% CI: 1.2-3.4, P = 0.009), distant metastasis (HR = 4.2, 95% CI: 2.7-6.7, P < 0.001), and anastomotic leakage (HR = 2.4, 95% CI: 1.1-5.3, P = 0.034). After evaluating each model, the prediction model based on XGBoost was determined to be the optimal model, with AUC of 0.7856, 0.8484, and 0.796 at 1, 3, and 5 years. It also had the lowest Brier scores at all time points, and decision curve analysis (DCA) demonstrated the best clinical decision benefits compared to other models.

We developed a prediction model based on the optimal machine learning, XGBoost, which can assist clinical decision-making and potentially extend the survival of patients with rectosigmoid junction cancer.

Despite significant efforts to improve clinical outcomes, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate. The poor prognosis associated with this disease is multifactorial and associated with a highly variable genetic profile associated with its pathogenesis. Epigenetic modifications including DNA methylation further affect the expression of genetic material. However, there is no comprehensive understanding of the clinical impact of DNA methylation in PDAC.

A systematic literature review was registered on the International Prospective Register of Systematic Reviews database (CRD42023451955) and followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. An electronic search was conducted using the following databases: CINAHL Plus, Cochrane Library, Embase, Web of Science, Ovid Medline, and Google Scholar. Inclusion criteria included studies of patients with a PDAC diagnosis and information regarding genes or CpG sites that potentially affect diagnosis, prognosis, or survival of PDAC.

The initial search retrieved 2402 articles, and 423 duplicates were excluded. After exclusion criteria was applied, 19 studies were included. The most common genes recorded as affecting tumor pathogenesis were SFRP1 (n = 3/19, 15.7 %) and NPTX2 (n = 2/19, 10,5 %). Studies indicated that hypermethylation of SFRP1 and NPTX2 were associated with poor prognosis.

PDAC is associated with a range of epigenetic modifications. Methylation of specific genes related to PDAC may influence survival and prognosis and be a therapeutic target. Individual patient epigenetic analysis may be a future direction in directing PDAC treatment and prognosis.

Resistance to immune checkpoint inhibitors (ICIs) represents a major challenge for the effective treatment of non-small cell lung cancer (NSCLC). Tumor heterogeneity has been identified as an important mechanism of treatment resistance in cancer and has been increasingly implicated in ICI resistance. The diversity and clonality of tumor neoantigens, which represent the target epitopes for tumor-specific immune cells, have been shown to impact the efficacy of immunotherapy. Advances in genomic techniques have further enhanced our understanding of clonal landscapes within NSCLC and their evolution in response to therapy. In this review, we examine the role of tumor heterogeneity during immune surveillance in NSCLC and highlight its spatial and temporal evolution as revealed by modern technologies. We explore additional sources of heterogeneity, including epigenetic and metabolic factors, that have come under greater scrutiny as potential mediators of the immune response. We finally discuss the implications of tumor heterogeneity on the efficacy of ICIs and highlight potential strategies for overcoming therapeutic resistance.

The increasing prevalence and poor prognosis of central nervous system (CNS) metastases pose a significant challenge in oncology, necessitating improved therapeutic strategies. Recent research has shed light on the complex genomic landscape of brain metastases, identifying unique and potentially actionable genetic alterations. These insights offer new avenues for targeted therapy, highlighting the potential of precision medicine approaches in treating CNS metastases. However, translating these discoveries into clinical practice requires overcoming challenges such as availability of tissue for characterization, access to molecular testing, drug delivery across the blood-brain barrier (BBB) and addressing intra- and intertumoral genetic heterogeneity. This review explores novel insights into the evolution of CNS metastases, the molecular mechanisms underlying their development, and implications for therapeutic interventions.

Metastasis is responsible for most cancer-related deaths. Different cancers have their own preferential sites of metastases, a phenomenon termed metastatic organotropism. The mechanisms underlying organotropism are multifactorial and include the generation of a pre-metastatic niche (PMN), metastatic homing, colonization, dormancy, and metastatic outgrowth. Historically, studies of metastatic organotropism have been limited by a lack of models allowing direct comparison of cells exhibiting different patterns of tropism. However, new innovative models and large-scale sequencing efforts have propelled organotropism research. Herein, we summarize the recent discoveries in metastatic organotropism regulation, focusing on lung, liver, brain, and bone tropism. We discuss how emerging technologies are continuing to improve our ability to model and, hopefully, predict and treat organotropism.

In the current study, we assessed whether repeated measurements of a panel of protein biomarkers with relevance to pancreatic ductal adenocarcinoma (PDAC) improves lead time performance for earlier detection over a single timepoint measurement. Specifically, CA125, CEA, LRG1, REG3A, THBS2, TIMP1, TNRFSF1A as well as CA19-9 were assayed in serially collected pre-diagnostic plasma from 242 PDAC cases and 242 age- and sex-matched non-case control participants in the PLCO cohort. We compared performance estimates of a parametric empirical Bayes (PEB) algorithm, which incorporates participant biomarker history, to that of a single-threshold (ST) method. We demonstrated improvements in AUC estimates (2-13 %) for all biomarkers when considering the PEB approach compared to ST. For CA19-9, the PEBCA19-9 yielded an AUC of 0.88 when at least one repeat measurement was within 3 years of clinical diagnosis. At a specificity of 98.5 %, the PEBCA19-9 identified 15 of the 41 PDAC cases and signaled positive at an average lead-time of 1.09 years whereas the ST approach captured 11 of the 41 PDAC cases with an average positive signal at 0.48 years. Among CA19-9 low individuals, a PEB algorithm based on repeat measurements of TIMP1 yielded an additional 14 % sensitivity at 98.5 % specificity. An adaptive algorithm that considers repeated CA19-9 measurements improves sensitivity and lead-time detection of PDAC compared to a single-threshold method. Additional protein biomarkers may improve sensitivity for earlier detection of PDAC among cases with low CA19-9.

Acute pancreatitis (AP) may obscure small pancreatic cancers (PCs) on computed tomography during the acute phase. Surveillance with magnetic resonance cholangiopancreatography (MRCP) and endoscopic ultrasonography (EUS) may enhance early detection and improve patient outcomes. This study evaluated the impact of early MRCP/EUS surveillance on PC outcomes in AP patients and identified high-risk subgroups for early screening.

We retrospectively analyzed 1562 AP patients treated between 2010 and 2021, categorizing them into early surveillance (MRCP/EUS within three months of AP onset; n = 760) and nonearly surveillance groups (n = 802). Key outcomes included time to PC diagnosis, surgical resection rate, tumor stage, and overall survival. Multivariate analysis was performed to identify risk factors for concealed PC in AP patients.

Among 27 PC cases analyzed, the early surveillance group achieved significantly earlier diagnosis, higher surgical resection rates, increased detection of early-stage PC, and improved overall survival compared with the nonearly surveillance group. Multivariate analysis revealed that subthreshold main pancreatic duct (MPD) dilation (≥2.5 mm) and moderately severe AP were significant predictors of PC.

Early MRCP/EUS surveillance in AP patients facilitates timely detection of occult PC and enhances patient prognosis. These findings support prioritizing early surveillance for AP patients with subthreshold MPD dilation and moderately severe disease. Further large-scale studies are warranted to validate these strategies in clinical practice.

The peritoneum is the second most common site of metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Up to half of all patients that undergo curative-intent resection eventually develop peritoneal metastasis (PM), which accounts for significant morbidity and drives mortality. Despite recent advances in management, PM is associated with very poor prognosis, which is often measured in weeks to months. Clinical manifestations including bowel obstruction, ascites, and urinary obstruction have profound impact on quality of life. Even with relatively advanced disease, PM often remains occult on imaging and thus tend to be underdiagnosed and understudied. Many patients with peritoneal-only PM are excluded from clinical trials because response cannot be measured by standard radiographic criteria. Furthermore, as patients with PM are not eligible for surgical resection and low-volume peritoneal disease is often not amenable to percutaneous biopsy, tissue samples for peritoneal-specific translational studies are limited. Intraperitoneal therapeutics have been proposed as an attractive option for PM, as better penetration of tumor tissue can be achieved with less systemic toxicity compared with intravenous chemotherapy. Heated intraperitoneal chemotherapy (HIPEC), typically combined with cytoreductive surgery (CRS), is an option for select patients with PM from gynecologic or gastrointestinal primary, and for patients with primary peritoneal mesothelioma. However, the incorporation of locoregional therapy for PM in patients with PDAC has been poorly studied given the aggressive nature of pancreatic cancer and overall poor prognosis. With recent advances in existing treatment options, there may be a subset of patients who may derive benefits from locoregional control with cytoreduction and/or intraperitoneal chemotherapy. Critically, additional work is needed to determine PM-favorable clinical and tumoral predictive biomarkers to identify patients who may benefit from a more aggressive approach. We describe the current state of management of patients with peritoneal metastasis from PDAC and review the available data exploring peritoneal-directed therapy with cytoreductive surgery and/or intraperitoneal chemotherapy.

Research on pancreatic cancer has transformed with the advent of organoid technology, providing a better platform that closely mimics cancer biology in vivo. This review highlights the critical advancements facilitated by pancreatic organoid models in understanding disease progression, evaluating therapeutic responses, and identifying biomarkers. These three-dimensional cultures enable the proper recapitulation of the cellular architecture and genetic makeup of the original tumors, providing insights into the complex molecular and cellular dynamics at various stages of pancreatic ductal adenocarcinoma (PDAC). We explore the applications of pancreatic organoids in dissecting the tumor microenvironment (TME); elucidating cancer progression, metastasis, and drug resistance mechanisms; and personalizing therapeutic strategies. By overcoming the limitations of traditional 2D cultures and animal models, the use of pancreatic organoids has significantly accelerated translational research, which is promising for improving diagnostic and therapeutic approaches in clinical settings, ultimately aiming to improve the outcomes of patients with pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) has one of the worst prognoses of all common solid cancers. For the large majority of PDAC patients, only systemic therapies with very limited efficacy are indicated. In addition, immunotherapies have not brought the advances seen in other cancer types. Several key characteristics of PDAC contribute to poor treatment outcomes, and in this review, we will discuss how these characteristics are best captured in currently available ex vivo or in vitro model systems. For instance, PDAC is hallmarked by a highly desmoplastic and immune-suppressed tumor microenvironment that impacts disease progression and therapy resistance. Also, large differences in tumor biology exist between and within tumors, complicating treatment decisions. Furthermore, PDAC has a very high propensity for locally invasive and metastatic growth. The use of animal models is often not desirable or feasible and several in vitro and ex vivo model systems have been developed, such as organotypic cocultures and tissue slices, among others. However, the absence of a full host organism impacts the ability of these models to accurately capture the characteristics that contribute to poor outcomes in PDAC. We will discuss the caveats and advantages of these model systems in the context of PDAC's key characteristics and provide recommendations on model choice and the possibilities for optimization. These considerations should be of use to researchers aiming to study PDAC in the in vitro setting.

Recently concluded, large-scale cancer genomics studies involving multiregion sequencing of primary tumors and paired metastases appear to indicate that many or most cancer patients have one or more "clonal" mutations in their tumors. Clonal mutations are those that are present in all of a patient's cancer cells. Clonally mutated proteins can potentially be targeted by inhibitors or E3 ligase small molecule glues, but developing new small molecule drugs for each patient is not feasible currently. Certain companies are using immunotherapies to target clonal mutations. I have devised another approach for exploiting clonal mutations, which I call "Oncolytic Vector Efficient Replication Contingent on Omnipresent Mutation Engagement" (OVERCOME). The ideal version of OVERCOME would likely employ a bioengineered facultative intracellular bacterium. The bacterium would initially be attenuated, but (transiently) reverse its attenuation upon clonal mutation detection.

Pancreatic ductal adenocarcinoma lacks suitable biomarkers for early diagnosis of disease. In gene panels developed for early diagnosis of pancreatic cancer, high AHNAK2 mRNA expression was one possible biomarker. In silico analysis of published human sample datasets (n = 177) and ex vivo analysis of human plasma samples (n = 30 PDAC with matched 30 healthy control) suggested AHNAK2 could be a diagnostic biomarker. At a plasma level of 421.47 ng/ml, AHNAK2 could potentially diagnose PDAC with a specificity and sensitivity of 83.33% and 86.67%. In vitro analysis suggests that in cell lines with diffuse cytoplasmic distribution of AHNAK2, there was colocalization of AHNAK2 with Cortactin in filipodia. This colocalization increased when cells were cultured on substrates such as Fibronectin and Collagen, as well as in hypoxia, and resulted in an augmented invasion of cancer cells. However, in cell lines with a vesicular AHNAK2 staining, such changes were not observed. Our study posits AHNAK2 as a valuable diagnostic biomarker in PDAC, now demanding prospective validation. Determination of mechanisms regulating AHNAK2 subcellular localisation may help explain its biological role.

Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed late, with an extremely poor prognosis. Treatment options like surgery, radiation, and chemotherapy are rarely curative. Tumor progression from primary to metastatic PDAC remains poorly understood at the molecular level.

In the current study, we analyzed the molecular profiles of metastatic PDAC obtained via the Oncomine Comprehensive Assay in comparison to primary PDAC.

The current study cohort consisted of 115 metastatic PDAC cases, of which 71 (62%) cases succeeded in molecular testing while the remaining 44 (38%) cases contained insufficient tumor cells. Molecular profiling of 71 cases revealed a total of 239 molecular alterations, 3.4 alterations per case on average, predominantly in the form of gene mutations. The most common gene mutations included KRAS (86%) and TP53 (83%) mutations. Gene copy number alterations were also detected in 19 (27%) cases involving genes such as CCNE1 and ERBB2. Compared to the molecular profiles of primary PDAC reported in our prior study and TCGA database, there seemed to be increased rates of TP53, ARID1A, BRAF, and PIK3CA mutations in the metastatic diseases.

These findings suggest that metastatic PDAC possesses unique genetic characteristics, offering potential therapeutic targets in advanced-stage pancreatic cancer.

Intraductal Papillary Mucinous Neoplasms (IPMNs) are cystic lesions and bona fide precursors for pancreatic ductal adenocarcinoma (PDAC). Recent studies have shown that pancreatic precancer is characterized by a transcriptomic program similar to gastric metaplasia. The aims of this study were to assay IPMN for pyloric markers, to identify molecular drivers, and to determine a functional role for this program in the pancreas.

Pyloric marker expression was evaluated by RNA-seq and multiplex immunostaining in patient samples. Cell lines and organoids expressing KrasG12D +/- GNASR201C underwent RNA sequencing. A PyScenic-based regulon analysis was performed to identify molecular drivers, and candidates were evaluated by RNA-seq, immunostaining, and small interfering RNA knockdown. Glycosylation profiling was performed to identify GNASR201C-driven changes. Glycan abundance was evaluated in patient samples.

Pyloric markers were identified in human IPMN. GNASR201C drove expression of this program as well as an indolent phenotype characterized by distinct glycosyltransferase changes. Glycan profiling identified an increase in LacdiNAcs and loss of pro-tumorigenic Lewis antigens. Knockdown of transcription factors Spdef or Creb3l1 or chitinase treatment reduced LacdiNAc deposition and reversed the indolent phenotype. LacdiNAc and 3-sulfoLeA/C abundance discriminated low from high grade patient IPMN.

GNASR201C drives an indolent phenotype in IPMN by amplifying a differentiated, pyloric phenotype through SPDEF/CREB3L1 which is characterized by distinct glycans. Acting as a glycan rheostat, mutant GNAS elevates LacdiNAcs at the expense of pro-tumorigenic acidic Lewis epitopes, inhibiting cancer cell invasion and disease progression. LacdiNAc and 3-Sulfo-LeA/C are mutually exclusive and may serve as markers of disease progression.

Despite advances in understanding genetic susceptibility to cancer, much of cancer heritability remains unidentified. At the same time, the makeup of industrial chemicals in our environment only grows more complex. This gap in knowledge on cancer risk has prompted calls to expand cancer research to the comprehensive, discovery-based study of nongenetic environmental influences, conceptualized as the "exposome."

Our scoping review aimed to describe the exposome and its application to cancer epidemiology and to study design limitations, challenges in analytical methods, and major unmet opportunities in advanced exposome profiling methods that allow the quantification of complex chemical exposure profiles in biological matrices. To evaluate progress on incorporating measurements of the exposome into cancer research, we performed a review of such "cancer exposome" studies published through August 2023.

We found that only 1 study leveraged untargeted chemical profiling of the exposome as a method to measure tens of thousands of environmental chemicals and identify prospective associations with future cancer risk. The other 13 studies used hypothesis-driven exposome approaches that targeted a set of preselected lifestyle, occupational, air quality, social determinant, or other external risk factors. Many of the included studies could only leverage sample sizes with less than 400 cancer cases (67% of nonecologic studies) and exposures experienced after diagnosis (29% of studies). Six cancer types were covered, most commonly blood (43%), lung (21%), or breast (14%) cancer.

The exposome is underutilized in cancer research, despite its potential to unravel complex relationships between environmental exposures and cancer and to inform primary prevention.

Human tumors are diverse in their natural history and response to treatment, which in part results from genetic and transcriptomic heterogeneity. In clinical practice, single-site needle biopsies are used to sample this diversity, but cancer biomarkers may be confounded by spatiogenomic heterogeneity within individual tumors. Here we investigate clonally expressed genes as a solution to the sampling bias problem by analyzing multiregion whole-exome and RNA sequencing data for 450 tumor regions from 184 patients with lung adenocarcinoma in the TRACERx study. We prospectively validate the survival association of a clonal expression biomarker, Outcome Risk Associated Clonal Lung Expression (ORACLE), in combination with clinicopathological risk factors, and in stage I disease. We expand our mechanistic understanding, discovering that clonal transcriptional signals are detectable before tissue invasion, act as a molecular fingerprint for lethal metastatic clones and predict chemotherapy sensitivity. Lastly, we find that ORACLE summarizes the prognostic information encoded by genetic evolutionary measures, including chromosomal instability, as a concise 23-transcript assay.

Pancreas is a vital gland of gastrointestinal system with exocrine and endocrine secretory functions, interweaved into essential metabolic circuitries of the human body. Pancreatic ductal adenocarcinoma (PDAC) represents one of the most lethal malignancies, with a 5-year survival rate of 11%. This poor prognosis is primarily attributed to the absence of early symptoms, rapid metastatic dissemination, and the limited efficacy of current therapeutic interventions. Despite recent advancements in understanding the etiopathogenesis and treatment of PDAC, there remains a pressing need for improved individualized models, identification of novel molecular targets, and development of unbiased predictors of disease progression. Here we aim to explore the concept of precision medicine utilizing 3-dimensional, patient-specific cellular models of pancreatic tumors and discuss their potential applications in uncovering novel druggable molecular targets and predicting clinical parameters for individual patients.

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the worst solid malignancies in regard to outcomes and metabolic dysfunction leading to cachexia. It is alarming that PDAC incidence rates continue to increase and warrant the need for innovative approaches to combat this disease. Due to its relatively slow progression (10-20 years), prevention strategies represent an effective means to improve outcomes. One of the risk factors for many cancers and for pancreatic cancer in particular is diet. Hence, our objective is to understand how a diet rich in ω3 and ω6 polyunsaturated fatty acids affects the progression of this disease. Methods: We investigated polyunsaturated fatty acid (PUFA) effects on disease progression employing both in vitro (PDAC cell lines) and in vivo (EL-Kras and KC mice) approaches. Also, we gathered data from the National Health and Nutrition Examination Survey (NHANES) and the National Cancer Institute (NCI) from 1999 to 2017 for a retrospective observational study. Results: The consumption of PUFAs in a patient population correlates with increased PDAC incidence, particularly when the ω3 intake increases to a lesser extent than ω6. Our data demonstrate dietary PUFAs can be incorporated into plasma membrane lipids affecting PI3K/AKT signaling and support the emergence of membrane-targeted therapies. Moreover, we show that the phospholipid composition of a lipid nanoparticle (LNP) can impact the cell membrane integrity and, ultimately, cell viability after administration of these LNPs. Conclusions: Cancer prevention is impactful particularly for those with very poor prognosis, including pancreatic cancer. Our results point to the importance of dietary intervention in this disease when detected early and the potential to improve the antiproliferative effect of drug efficacy when combined with these regimens in later stages of pancreatic cancer.

In this study, we evaluated the effects of vitamin E δ-tocotrienol (DT3) and aspirin on Wnt signaling in human colon cancer stem cells (CCSCs) and in the prevention of adenoma formation in APCmin/+ mice. We found that knockdown of the adenomatous polyposis coli (APC) gene led to subsequent activation of Wnt signaling in colon epithelial cells (NCM460-APCsiRNA) and induction of β-catenin and its downstream target proteins c-MYC, cyclin D1, and survivin. When aspirin and DT3 were combined, cell growth and survival were inhibited and apoptosis was induced in colon epithelial cells and CCSCs. However, DT3 and/or aspirin had little or no effect on the control of normal colon epithelial cells (NCM460-NCsiRNA). The induction of apoptosis was directly related to the activation of caspase 8 and cleavage of BH3-interacting-domain (BID) to truncated BID. In addition, DT3- and/or aspirin-induced apoptosis was associated with cleaved Poly (ADP-ribose) polymerase (PARP), elevated levels of cytosolic cytochrome c and BAX, and depletion of antiapoptotic protein BCl-2 in CCSCs. The combination of aspirin and DT3 inhibited the self-renewal capacity, Wnt/β-catenin receptor activity, and expression of β-catenin and its downstream targets c-MYC, cyclin D1, and survivin in CCSCs. We also found that treatment with DT3 alone or combined with aspirin significantly inhibited intestinal adenoma formation and Wnt/β-catenin signaling and induced apoptosis, compared with vehicle, in APCmin/+ mice. Our study demonstrated a rationale for further investigation of the combination of DT3 and aspirin for colorectal cancer prevention and therapy.

Genetic mutations arising from various internal and external factors drive cells to become cancerous. Cancerous cells undergo numerous changes, including metabolic reprogramming and epigenetic modifications, to support their abnormal proliferation. This metabolic reprogramming leads to the altered expression of many metabolic enzymes and the accumulation of metabolites. Recent studies have shown that these enzymes and metabolites can serve as substrates or cofactors for chromatin-modifying enzymes, thereby participating in epigenetic modifications and promoting carcinogenesis. Additionally, epigenetic modifications play a role in the metabolic reprogramming and immune evasion of cancer cells, influencing cancer progression. This review focuses on the origins of cancer, particularly the metabolic reprogramming of cancer cells and changes in epigenetic modifications. We discuss how metabolites in cancer cells contribute to epigenetic remodeling, including lactylation, acetylation, succinylation, and crotonylation. Finally, we review the impact of epigenetic modifications on tumor immunity and the latest advancements in cancer therapies targeting these modifications.

Metastatic cancer affects millions of people worldwide annually and is the leading cause of cancer-related deaths. Most patients with metastatic disease are not eligible for surgical resection, and current therapeutic regimens have varying success rates, some with 5-year survival rates below 5%. Here, we test the hypothesis that metastatic cancer can be genetically targeted by exploiting single base substitution mutations unique to individual cells that occur as part of normal aging prior to transformation. These mutations are targetable because ~10% of them form novel tumor-specific "NGG" protospacer adjacent motif (PAM) sites targetable by CRISPR-Cas9.

Whole genome sequencing was performed on five rapid autopsy cases of patient-matched primary tumor, normal and metastatic tissue from pancreatic ductal adenocarcinoma decedents. CRISPR-Cas9 PAM targets were determined by bioinformatic tumor-normal subtraction for each patient and verified in metastatic samples by high-depth capture-based sequencing.

We found that 90% of PAM targets were maintained between primary carcinomas and metastases overall. We identified rules that predict PAM loss or retention, where PAMs located in heterozygous regions in the primary tumor can be lost in metastases (private LOH), but PAMs occurring in regions of loss of heterozygosity (LOH) in the primary tumor were universally conserved in metastases.

Regions of truncal LOH are strongly retained in the presence of genetic instability and, therefore, represent genetic vulnerabilities in pancreatic adenocarcinomas. A CRISPR-based gene therapy approach targeting these regions may be a novel way to genetically target metastatic cancer.

Cancer has long been believed to be a genetic disease caused by the accumulation of mutations in key genes involved in cellular processes. However, recent advances in sequencing technology have demonstrated that cells with cancer driver mutations are also present in normal tissues in response to aging, environmental damage, and chronic inflammation, suggesting that not only intrinsic factors within cancer cells, but also environmental alterations are important key factors in cancer development and progression. Pancreatic cancer tissue is mostly comprised of stromal cells and immune cells. The desmoplasmic microenvironment characteristic of pancreatic cancer is hypoxic and hypotrophic. Pancreatic cancer cells may adapt to this environment by rewiring their metabolism through epigenomic changes, enhancing intrinsic plasticity, creating an acidic and immunosuppressive tumor microenvironment, and inducing noncancerous cells to become tumor-promoting. In addition, pancreatic cancer has often metastasized to local and distant sites by the time of diagnosis, suggesting that a similar mechanism is operating from the precancerous stage. Here, we review key recent findings on how pancreatic cancers acquire plasticity, undergo metabolic reprogramming, and promote immunosuppressive microenvironment formation during their evolution. Furthermore, we present the following two signaling pathways that we have identified: one based on the small G-protein ARF6 driven by KRAS/TP53 mutations, and the other based on the RNA-binding protein Arid5a mediated by inflammatory cytokines, which promote both metabolic reprogramming and immune evasion in pancreatic cancer. Finally, the striking diversity among pancreatic cancers in the relative importance of mutational burden and the tumor microenvironment, their clinical relevance, and the potential for novel therapeutic strategies will be discussed.

The circulation of tumor cells through the bloodstream is a significant step in tumor metastasis. To better understand the metastatic process, circulating tumor cell (CTC) survival in the circulation must be explored. While immune interactions with CTCs in recent decades have been examined, research has yet to sufficiently explain some CTC behaviors in blood flow. Studies related to CTC mechanical responses in the bloodstream have recently been conducted to further study conditions under which CTCs might die. While experimental methods can assess the mechanical properties and death of CTCs, increasingly sophisticated computational models are being built to simulate the blood flow and CTC mechanical deformation under fluid shear stresses (FSS) in the bloodstream.Several factors contribute to the mechanical deformation and death of CTCs as they circulate. While FSS can damage CTC structure, diverse interactions between CTCs and blood components may either promote or hinder the next metastatic step-extravasation at a remote site. Overall understanding of how these factors influence the deformation and death of CTCs could serve as a basis for future experiments and simulations, enabling researchers to predict CTC death more accurately. Ultimately, these efforts can lead to improved metastasis-specific therapeutics and diagnostics specific in the future.

A rise in blood glucose is an early warning sign of underlying pancreatic cancer (PC) and may be an indicator of genetic events in PC progression. However, there is still a lack of mechanistic research on pancreatic cancer-associated new-onset diabetes (PCAND). In the present study, we identified a gene SRI, which possesses a SNP with the potential to distinguish PCAND and Type 2 diabetes mellitus (T2DM), by machine learning on the basis of the UK Biobank database. In vitro and in vivo, sorcin overexpression induced pancreatic β-cell dysfunction. Sorcin can form a positive feedback loop with STAT3 to increase the transcription of serpin E1 and CCL5, which may directly induce β-cell dysfunction. In 88 biopsies, the expression of sorcin was elevated in PC tissues, especially in PCAND samples. Furthermore, plasma serpin E1 levels are higher in peripheral blood samples from PCAND patients than in those from T2DM patients. In conclusion, sorcin may be the key driver in PCAND, and further study on the sorcin-STAT3-serpin E1/CCL5 signaling axis may help us better understand the pathogenesis of PCAND and identify potential biomarkers.

The pancreas is involved in digestion and glucose regulation in the human body. Given the recognized link between chronic pancreatitis and pancreatic cancer, addressing pancreatic disorders and pancreatic cancer is particularly challenging. This review aims to highlight the limitations of traditional methods in diagnosing pancreatic disorders and cancer and explore several next-generation sequencing (NGS) approaches as a promising alternative. There are distinct clinical symptoms that are shared by a number of clinical phenotypes of pancreatic illness induced by particular genetic mutations. Traditional diagnostic methods encompass computed tomography, magnetic resonance imaging, contrast-enhanced Doppler ultrasound, endoscopic ultrasound, endoscopic retrograde cholangiopancreatography, transabdominal ultrasound, laparoscopy, and positron emission tomography have a prognostic ability of only 5% or less and a 5-year survival rate. Genetic sequencing can be employed as an alternative to conventional diagnostic techniques. Sanger sequencing and NGS are currently largely operated genome analysis, with no exception for pancreatic disease diagnosis. The NGS methods can sequence millions to billions of short DNA fragments, enabling enormous sample screening in a short amount of time with low-abundance detection, like in 0.1%-1% mutation prevalence declining approximate cost. Whole-genome sequencing, whole-exome sequencing, RNA sequencing, and single-cell NGS are a few NGS methods utilized to diagnose pancreatic disease. For both research and clinical applications, the NGS techniques can provide a precise diagnosis of pancreatic disorders in a short amount of time at a reasonable expenditure.

For the past 3-4 decades, the discovery of Sanger's method of pyrosequencing was the only method unparalleled till 2005 being employed as a method of whole genome sequencing (WGS). Following this, a revolutionary extensive parallel sequencing method, Next Generation Sequencing (NGS), was engineered. NGS supported a substantial number of bases under a high throughput metagenomic interrogation. Bioinformatics contributed notably to this advancement. It provided alignment tools, assembly algorithms, and protocols such as Illumina and hybridization capture which have metamorphosed clinical and translational diagnostics. With the extension in precision medicine and targeted therapy under NGS sectors such as epigenetics, transcriptomics, mutation detection, prognosis, therapeutics, and patient management have been gaining progress. Using NGS in real-time clinical settings has been proven to produce positive outcomes. The most recent instrumental benefaction of NGS has been decoding the SARS-CoV-2 virus epidemiology with the assistance of multiplex PCR. So far, it had been employed to inspect different levels of viral loads from low to mid. This has been executed by amplification and phylogenetic examination of the load to raise a connective link with the evolutionary history leading up to the period of origin. The depletion in the consumed time and extensive genome size under analysis was further coupled by a cutback in the cost of sequencing while executing NGS. With the aid of this review paper, we aspire to manifest how the above-mentioned elements have boosted, tissue, microbial, and molecular data interrogation. Along with this, promoting, and stimulating an extensive evaluation and expansion in the paradigm of morphological and phenotypic study, via bioinformatics can facilitate further advancement in personalized and concise clinical research.

Altered metabolism is a hallmark of cancer, and reprogramming of energy metabolism, known as the "Warburg effect", has long been associated with cancer. Cancer cells use the process of glycolysis to quickly manufacture energy from glucose, pyruvic acid, and lactate, which in turn accelerates the growth of cancer and glycolysis becomes a key target for anti-cancer therapies. Recent groundbreaking discoveries regarding long noncoding RNAs (lncRNAs) have opened a new chapter in the mechanism of cancer occurrence. It is widely recognized that lncRNAs regulate energy metabolism through glycolysis in cancer cells. LncRNAs have been demonstrated to engage in several cancer processes such as proliferation, apoptosis, migration, invasion, and chemoresistance, whereas glycolysis is enhanced or inhibited by the dysregulation of lncRNAs. As a result, cancer survival and development are influenced by different signaling pathways. In this review, we summarize the roles of lncRNAs in a variety of cancers and describe the mechanisms underlying their role in glycolysis. Additionally, the predictive potential of glycolysis and lncRNAs in cancer therapy is discussed.

Cancer cachexia is a multifactorial syndrome characterized by progressive weight loss and a disease process that nutritional support cannot reverse. Although progress has been made in preclinical research, there is still a long way to go in translating research findings into clinical practice. One of the main reasons for this is that existing preclinical models do not fully replicate the conditions seen in clinical patients. Therefore, it is important to understand the characteristics of existing preclinical models of cancer cachexia and pay close attention to the latest developments in preclinical models. The main models of cancer cachexia used in current research are allogeneic and xenograft models, genetically engineered mouse models, chemotherapy drug-induced models, Chinese medicine spleen deficiency models, zebrafish and Drosophila models, and cellular models. This review aims to revisit and summarize the commonly used animal models of cancer cachexia by evaluating existing preclinical models, to provide tools and support for translational medicine research.

Background/Objectives: Early and aggressive metastasis is a major feature of pancreatic ductal adenocarcinoma. Understanding the processes underlying metastasis is crucial for making a difference to disease outcome. Towards these ends, we looked in a comprehensive manner for genes that are metastasis-specific. Methods: A genome-wide CRISPR-Cas9 gene knockout screen with 259,900 single guide RNA constructs was performed on pancreatic cancer cell lines with very high or very low metastatic capacity, respectively. Functional aspects of some of the identified genes were analysed in vitro. The injection of tumour cells with or without a gene knockout into mice was used to confirm the effect on metastasis. Results: The knockout of 590 genes-and, with higher analysis stringency, 67 genes-affected the viability of metastatic cells substantially, while these genes were not vital to non-metastasizing cells. Further evaluations identified different molecular processes related to this observation. One of the genes was MYBL2, encoding for a well-known transcription factor involved in the regulation of cell survival, proliferation, and differentiation in cancer tissues. In our metastasis-focussed study, no novel functional activity was detected for MYBL2, however. Instead, a metastasis-specific transformation of its genetic interaction with FOXM1 was observed. The interaction was synergistic in cells of low metastatic capacity, while there was a strong switch to a buffering mode in metastatic cells. In vivo analyses confirmed the strong effect of MYBL2 on metastasis. Conclusions: The genes found to be critical for the viability of metastatic cells form a basis for further investigations of the processes responsible for triggering and driving metastasis. As shown for MYBL2, unexpected processes of regulating metastasis might also be involved.

Patients with pancreatic ductal adenocarcinoma present early postoperative systemic metastases, despite complete oncological resection. The aim of this study was to assess two pancreatoduodenectomy approaches with regard to intraoperative circulating tumour cells and cluster mobilization and their potential association with the development of distant metastasis.

Patients with periampullary tumours who underwent open pancreatoduodenectomy were randomly allocated to either the no-touch approach or the superior mesenteric artery approach. A total of four intraoperative portal vein samples (at the beginning of the intervention, after portal vein disconnection from the tumour, after tumour resection, and before abdominal closure) were collected to measure circulating tumour cells and cluster numbers. Primary outcomes were the intraoperative number of circulating tumour cells and cluster mobilization. Further, their potential impact on 3-year distant metastasis disease-free survival and overall survival was assessed.

A total of 101 patients with periampullary tumours were randomized (51 in the superior mesenteric artery group and 50 in the no-touch group) and 63 patients with pancreatic ductal adenocarcinoma (34 in the superior mesenteric artery group and 29 in the no-touch group) were analysed. Circulating tumour cells and cluster mobilization were similar in both the no-touch group and the superior mesenteric artery group at all time points. There were no significant differences between surgical groups with regard to the median metastasis disease-free survival (12.4 (interquartile range 6.1-not reached) months in the superior mesenteric artery group and 18.1 (interquartile range 12.1-not reached) months in the no-touch group; P = 0.730). Patients with intraoperative cluster mobilization from the beginning to the end of surgery developed significantly more distant metastases within the first year after surgery (P = 0.023). Two intraoperative factors (the superior mesenteric artery approach (P = 0.025) and vein resection (P < 0.001)) were predictive factors for cluster mobilization.

Patients undergoing pancreatoduodenectomy using either the no-touch approach or the superior mesenteric artery approach had similar circulating tumour cells and cluster mobilization and similar overall survival and metastasis disease-free survival. A high intraoperative cluster dissemination during pancreatoduodenectomy was a predictive factor for early metastases in patients with pancreatic ductal adenocarcinoma.

NCT03340844 (http://www.clinicaltrials.gov)-CETUPANC trial.

While pancreatic adenocarcinoma requires surgical resection definitive cure, treatment paradigms are shifting toward a neoadjuvant approach to systemic therapy. Rationale is twofold: micro-metastatic disease is likely present in a majority of patients, reinforcing the importance of systemic therapy regardless of resectability; moreover, systemic therapy is well-tolerated and improves surgical outcomes when delivered preoperatively. Second, a neoadjuvant approach allows for selection of biology and patients most likely to benefit from potentially morbid surgery. This review examines the increasing body of evidence in support of empiric neoadjuvant therapy in pancreatic adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor survival. Novel biomarkers are urgently needed to improve the outcome through early detection. Here, we aimed to discover novel biomarkers for early PDAC detection using multi-omics profiling in pre-diagnostic plasma samples biobanked after routine health examinations. A nested case-control study within the Northern Sweden Health and Disease Study was designed. Pre-diagnostic plasma samples from 37 future PDAC patients collected within 2.3 years before diagnosis and 37 matched healthy controls were included. We analyzed metabolites using liquid chromatography mass spectrometry and gas chromatography mass spectrometry, microRNAs by HTG edgeseq, proteins by multiplex proximity extension assays, as well as three clinical biomarkers using milliplex technology. Supervised and unsupervised multi-omics integration were performed as well as univariate analyses for the different omics types and clinical biomarkers. Multiple hypothesis testing was corrected using Benjamini-Hochberg's method and a false discovery rate (FDR) below 0.1 was considered statistically significant. Carbohydrate antigen (CA) 19-9 was associated with PDAC risk (OR [95 % CI] = 3.09 [1.31-7.29], FDR = 0.03) and increased closer to PDAC diagnosis. Supervised multi-omics models resulted in poor discrimination between future PDAC cases and healthy controls with obtained accuracies between 0.429-0.500. No single metabolite, microRNA, or protein was differentially altered (FDR < 0.1) between future PDAC cases and healthy controls. CA 19-9 levels increase up to two years prior to PDAC diagnosis but extensive multi-omics analysis including metabolomics, microRNAomics and proteomics in this cohort did not identify novel early biomarkers for PDAC.

To evaluate the suitability of the MIA PaCa-2 cell line for studying pancreatic cancer intratumor heterogeneity, we aim to further characterize the nature of MIA PaCa-2 cells' phenotypic, genomic, and transcriptomic heterogeneity.

MIA PaCa-2 single-cell clones were established through flow cytometry. For the phenotypic study, we quantified the cellular morphology, proliferation rate, migration potential, and drug sensitivity of the clones. The chromosome copy number and transcriptomic profiles were quantified using SNPa and RNA-seq, respectively.

Four MIA PaCa-2 clones showed distinctive phenotypes, with differences in cellular morphology, proliferation rate, migration potential, and drug sensitivity. We also observed a degree of genomic variations between these clones in form of chromosome copy number alterations and single nucleotide variations, suggesting the genomic heterogeneity of the population, and the intrinsic genomic instability of MIA PaCa-2 cells. Lastly, transcriptomic analysis of the clones also revealed gene expression profile differences between the clones, including the uniquely regulated ITGAV , which dictates the morphology of MIA PaCa-2 clones.

MIA PaCa-2 is comprised of cells with distinctive phenotypes, heterogeneous genomes, and differential transcriptomic profiles, suggesting its suitability as a model to study the underlying mechanisms behind pancreatic cancer heterogeneity.

Pancreatic ductal adenocarcinoma (PDAC) is notorious for its resistance to various treatment modalities. The genetic heterogeneity of PDAC, coupled with the presence of a desmoplastic stroma within the tumor microenvironment (TME), contributes to an unfavorable prognosis. The mechanisms and consequences of interactions among different cell types, along with spatial variations influencing cellular function, potentially play a role in the pathogenesis of PDAC. Understanding the diverse compositions of the TME and elucidating the functions of microscopic neighborhoods may contribute to understanding the immune microenvironment status in pancreatic cancer. As we delve into the spatial biology of the microscopic neighborhoods within the TME, aiding in deciphering the factors that orchestrate this intricate ecosystem. This overview delineates the fundamental constituents and the structural arrangement of the PDAC microenvironment, highlighting their impact on cancer cell biology.

The liver is a frequent site of metastasis in advanced gastric cancer (GC). Despite significant advancements in diagnostic and therapeutic techniques, the overall survival rate for patients afflicted with gastric cancer liver metastasis (GCLM) remains dismally low. Precision oncology has made significant progress in identifying therapeutic targets and enhancing our understanding of metastasis mechanisms through genome sequencing and molecular characterization. Therefore, it is crucial to have a comprehensive understanding of the various molecular processes involved in GCLM and the fundamental principles of systemic therapy to develop new treatment approaches. This paper aims to review recent findings on the diagnosis, potential biomarkers, and therapies targeting the multiple molecular processes of GCLM, with the goal of improving treatment strategies for patients with GCLM.

Globally, liver cancer as one of the most frequent fatal malignancies, hits hard and fast. And the lack of effective treatments for liver hepatocellular carcinoma (LIHC), activates the researchers to promote promising precision medicine. Interestingly, emerging evidence proves that cellular senescence is involved in the progression of cancers and is recognized for its hallmark-promoting capabilities. Hence, efforts have been made to construct and validate the senescence risk score signature (SRSS) model as a novel prognostic biomarker for LIHC.

The existing databases were mined for the following bioinformatics analyses. GSE22405, GSE57957, and senescence-related genes (SRGs) from public databases were utilized as a training set and the validation set was constituted by LIHC and pancreatic adenocarcinoma (PAAD) from The Cancer Genome Atlas (TCGA). After overlapping differentially expressed genes (DEGs) with SRGs, differentially expressed SRGs were identified with the progression of liver cancer through univariate and multivariate Cox regression and enrichment analyses. The model that utilized three SRGs was constructed using the least absolute shrinkage and selection operator (LASSO) regression algorithm. Next, to evaluate the predictive performance of the SRSS model, the overall survival (OS) and survival rates were assessed through Kaplan-Meier (KM) and the receiver operating characteristic (ROC) curves. The predictive value for LIHC prognosis was further evaluated by capitalizing on risk score, nomograms, decision curve analysis (DCA) curves, and clinical information including tumor stages, gender, age, and race.

DEGs were revealed as enriching in multiple tumor-related biological processes (BPs) and pathways. IGFBP3, SOCS2, and RACGAP1 were identified as the three considerable SRGs for the model. The high-risk group had a worse prognosis [both hazard ratio (HR) >1, P<0.001] and ROC curves showed a reliable predictive model with area under the curve (AUC) predictive values ranging from 0.673-0.816 for different-year survival rates respectively. The univariate and multivariate Cox regression analyses exhibited that risk score was the only credible prognostic predictor (HR >1, P<0.001) among clinical features such as tumor stage, age, etc., in LIHC. The nomograms, and DCA curves, combined with multiple clinical information, proved that the predictive ability of SRSS was strongest, followed by nomogram and traditional tumor node metastasis (TNM) stage was the weakest.

In summary, comprehensive analyses supported that the SRSS model can better predict survival and risk in LIHC patients. Promisingly, it may point out a brand-new direction for LIHC therapy.