Unbiasedly decoding the tumor microenvironment with single-cell multiomics analysis in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a poor prognosis and limited therapeutic options. Research on the tumor microenvironment (TME) of PDAC has propelled the development of immunotherapeutic and targeted therapeutic strategies with a promising future. The emergence of single-cell sequencing and mass spectrometry technologies, coupled with spatial omics, has collectively revealed the heterogeneity of the TME from a multiomics perspective, outlined the development trajectories of cell lineages, and revealed important functions of previously underrated myeloid cells and tumor stroma cells. Concurrently, these findings necessitated more refined annotations of biological functions at the cell cluster or single-cell level. Precise identification of all cell clusters is urgently needed to determine whether they have been investigated adequately and to identify target cell clusters with antitumor potential, design compatible treatment strategies, and determine treatment resistance. Here, we summarize recent research on the PDAC TME at the single-cell multiomics level, with an unbiased focus on the functions and potential classification bases of every cellular component within the TME, and look forward to the prospects of integrating single-cell multiomics data and retrospectively reusing bulk sequencing data, hoping to provide new insights into the PDAC TME.

Higher clearance rates of multiple HPV infections may explain their lower risk of HSIL: A retrospective study in Wenzhou, China.

Persistent human papilloma virus (HPV) infection is known to be associated with cervical lesions. The chief object of the study is to investigate if the pathogenicity of multiple HPV infections is different from a single infection. Furthermore, we would like to corroborate the discrepancy with clearance rates. Between August 1, 2020, and September 31, 2021, 5089 women underwent a colposcopy-directed biopsy in our hospital. We divided the 2999 patients who met the criteria into multiple and single HPV infection groups. The HPV genotypes were identified using the flow cytometry fluorescence hybridization technology. Binary logistic regression and survival analysis were used to perform statistics. Among HPV-positive individuals, 34.78% (1043/2999) were positive for 2 or more HPV types. After adjusting for the main factors, compared with single infection, multiple infections were associated with a significantly decreased risk of high squamous intraepithelial lesions (HSIL) (odds ratio [OR]: 0.570; 95% confidence interval [CI]: 0.468-0.694). In the mean time, the clearance rates of multiple infections were significantly higher (OR: 2.240; 95% CI: 1.919-2.614). When analyzing specific types covered by the 9-valent HPV vaccine, consistency between the lower risk of HSIL and the higher clearance rate was found in the most groups. Compared with a single infection, multiple HPV infections have a lower risk of HSIL, which may be related to its higher clearance rate. It suggests that aggressive treatment of multiple HPV infections early in their detection may be beneficial.

Epidemiology of pancreatic cancer: New version, new vision.

Pancreatic cancer (PC) is a devastating malignancy with an extremely high mortality rate and poses significant challenges to healthcare systems worldwide. The prevalence of PC risk factors spiked over the years, leading to a global increase in PC incidence rates. The contribution of different risk factors, however, varied from region to region due to genetic predisposition, environmental, social, and political factors underlying disease prevalence in addition to public health strategies. This comprehensive review aims to provide a thorough analysis of the epidemiology of PC, discussing its incidence, risk factors, screening strategies and socioeconomic burden. We compiled a wide range of seminal studies as well as epidemiological investigations to serve this review as a comprehensive guide for researchers, healthcare professionals, and policymakers keen for a more profound understanding of PC epidemiology. This review highlights the essentiality of persistent research efforts, interdisciplinary collaboration, and public health initiatives to address the expanding burden of this malignancy.

HIF-1α-regulated stanniocalcin-1 mediates gemcitabine resistance in pancreatic ductal adenocarcinoma via PI3K/AKT signaling pathway.

Pancreatic ductal adenocarcinoma (PDAC) has a poor response to the first-line chemotherapy drug gemcitabine. We previously identified stanniocalcin-1 as a gemcitabine-resistant-related gene, but its specific role and function in pancreatic cancer remain unclear. RT-qPCR and Western blot were used to evaluate differential protein and mRNA expressions. The biological functions of genes were determined using proliferation and drug-resistance experiments. Subcutaneous tumorigenesis experiment was performed on nude mice. Prognostic analysis was performed using public databases and our clinical data. We found HIF-1α-regulated STC1 expression mediated chemoresistance in pancreatic cancer. Deeper, we explored the action mechanism of STC1 and identified PI3K/AKT as the downstream signaling pathway of STC1. Furthermore, we analyzed clinical data and found that STC1 expression was related to the prognosis of gemcitabine-treated patients after surgery. In general, we proved the HIF-1α/STC1/PI3K-AKT axis participated in PDAC progression and chemoresistance, and STC1 may serve as a potential prognostic factor and therapeutic target for PDAC treatment.

Optical coherence tomography measurements as potential imaging biomarkers for Parkinson's disease: A systematic review and meta-analysis.

BACKGROUND AND PURPOSE: Retinal pathological changes may precede or accompany the deterioration of brain tissue in Parkinson's disease (PD). The purpose of this meta-analysis was to assess the usefulness of optical coherence tomography (OCT) measurements as potential imaging biomarkers for PD. METHODS: PubMed, Embase, Web of Science and Cochrane Library databases were systematically searched for observational studies (published prior to 30 May 2020) comparing the OCT measurements between PD patients and healthy controls (HCs). Our main end-points were peripapillary retinal nerve fiber layer (pRNFL) thickness, macular ganglion cell complex thickness, macular thickness and macular volume. Pooled data were assessed by use of a random-effects model. RESULTS: A total of 36 observational studies were identified that included 1712 patients with PD (2548 eyes) and 1778 HCs (2646 eyes). Compared with the HC group, the PD group showed a significant reduction in mean pRNFL thickness (weighted mean difference [WMD] -3.51 µm, 95% confidence interval [CI] -4.84, -2.18; p = 0.000), all quadrants at the pRNFL (WMD range -7.65 to -2.44 µm, all p < 0.05), macular fovea thickness (WMD -5.62 µm, 95% CI -7.37, -3.87; p = 0.000), all outer sector thicknesses at the macula (WMD range -4.68 to -4.10 µm, all p < 0.05), macular volume (WMD -0.21 mm3 , 95% CI -0.36, -0.06; p < 0.05) and macular ganglion cell complex thickness (WMD -4.18 µm, 95% CI -6.07, -2.29; p < 0.05). CONCLUSIONS: Our pooled data confirmed robust associations between retinal OCT measurements and PD, highlighting the usefulness of OCT measurements as potential imaging biomarkers for PD.

Current epidemiology of pancreatic cancer: Challenges and opportunities.

Pancreatic cancer (PC) is an increasingly common disease worldwide. Having a better understanding of worldwide and regional epidemiologic features and risk factors of PC is essential to identify new approaches for prevention, early diagnosis, surveillance, and treatment. In this article, we review the epidemiologic features and risk factors for PC and discuss opportunities and challenges of PC future treatment.

Predictive modeling of postpartum blood pressure spikes.

BACKGROUND: Hypertensive disorders of pregnancy are one of the leading causes of maternal morbidity and mortality worldwide. Management of these conditions can pose many clinical dilemmas and can be particularly challenging during the immediate postpartum period. Models for predicting and managing postpartum hypertension are necessary to help address this clinical challenge. OBJECTIVE: This study aimed to evaluate predictive models of blood pressure spikes in the postpartum period and to investigate clinical management strategies to optimize care. STUDY DESIGN: This was a retrospective cohort study of postpartum women who participated in remote blood pressure monitoring. A postpartum blood pressure spike was defined as a blood pressure measurement of ≥140/90 mm Hg while on an antihypertensive medication and a blood pressure measurement of ≥150/100 mm Hg if not on an antihypertensive medication. We identified 3 risk level patient clusters (low, medium, and high) when predicting patient risk for a blood pressure spike on postpartum days 3 to 7. The variables used in defining these clusters were peak systolic blood pressure before discharge, body mass index, patient systolic blood pressure per trimester, heart rate, gestational age, maternal age, chronic hypertension, and gestational hypertension. For each risk cluster, we focused on 2 treatments, namely (1) postpartum length of stay (<3 days or ≥3 days) and (2) discharge with or without blood pressure medications. We evaluated the effectiveness of the treatments in different subgroups of patients by estimating the conditional average treatment effect values in each cluster using a causal forest. Moreover, for all patients, we considered discharge with medication policies depending on different discharge blood pressure thresholds. We used a doubly robust policy evaluation method to compare the effectiveness of the policies. RESULTS: A total of 413 patients were included, and among those, 267 (64.6%) had a postpartum blood pressure spike. The treatments for patients at medium and high risk were considered beneficial. The 95% confidence intervals for constant marginal average treatment effect for antihypertensive use at discharge were -3.482 to 4.840 and - 5.539 to 4.315, respectively; and for a longer stay they were -5.544 to 3.866 and -7.200 to 4.302, respectively. For patients at low risk, the treatments were not critical in preventing a blood pressure spike with 95% confidence intervals for constant marginal average treatment effect of 1.074 to 15.784 and -2.913 to 9.021 for the different treatments. We considered the option to discharge patients with antihypertensive use at different blood pressure thresholds, namely (1) ≥130 mm Hg and/or ≥80 mm Hg, (2) ≥140 mm Hg and/or ≥90 mm Hg, (3) ≥150 mm Hg and/or ≥ 100 mm Hg, or (4) ≥160 mm Hg and/or ≥ 110 mm Hg. We found that policy (2) was the best option with P<.05. CONCLUSION: We identified 3 possible strategies to prevent outpatient blood pressure spikes during the postpartum period, namely (1) medium- and high-risk patients should be considered for a longer postpartum hospital stay or should participate in daily home monitoring, (2) medium- and high-risk patients should be prescribed antihypertensives at discharge, and (3) antihypertensive treatment should be prescribed if patients are discharged with a blood pressure of ≥140/90 mm Hg.

Predictive Modeling of Hypertension-Related Postpartum Readmission: Retrospective Cohort Analysis.

BACKGROUND: Hypertension is the most common reason for postpartum hospital readmission. Better prediction of postpartum readmission will improve the health care of patients. These models will allow better use of resources and decrease health care costs. OBJECTIVE: This study aimed to evaluate clinical predictors of postpartum readmission for hypertension using a novel machine learning (ML) model that can effectively predict readmissions and balance treatment costs. We examined whether blood pressure and other measures during labor, not just postpartum measures, would be important predictors of readmission. METHODS: We conducted a retrospective cohort study from the PeriData website data set from a single midwestern academic center of all women who delivered from 2009 to 2018. This study consists of 2 data sets; 1 spanning the years 2009-2015 and the other spanning the years 2016-2018. A total of 47 clinical and demographic variables were collected including blood pressure measurements during labor and post partum, laboratory values, and medication administration. Hospital readmissions were verified by patient chart review. In total, 32,645 were considered in the study. For our analysis, we trained several cost-sensitive ML models to predict the primary outcome of hypertension-related postpartum readmission within 42 days post partum. Models were evaluated using cross-validation and on independent data sets (models trained on data from 2009 to 2015 were validated on the data from 2016 to 2018). To assess clinical viability, a cost analysis of the models was performed to see how their recommendations could affect treatment costs. RESULTS: Of the 32,645 patients included in the study, 170 were readmitted due to a hypertension-related diagnosis. A cost-sensitive random forest method was found to be the most effective with a balanced accuracy of 76.61% for predicting readmission. Using a feature importance and area under the curve analysis, the most important variables for predicting readmission were blood pressures in labor and 24-48 hours post partum increasing the area under the curve of the model from 0.69 (SD 0.06) to 0.81 (SD 0.06), (P=.05). Cost analysis showed that the resulting model could have reduced associated readmission costs by US $6000 against comparable models with similar F1-score and balanced accuracy. The most effective model was then implemented as a risk calculator that is publicly available. The code for this calculator and the model is also publicly available at a GitHub repository. CONCLUSIONS: Blood pressure measurements during labor through 48 hours post partum can be combined with other variables to predict women at risk for postpartum readmission. Using ML techniques in conjunction with these data have the potential to improve health outcomes and reduce associated costs. The use of the calculator can greatly assist clinicians in providing care to patients and improve medical decision-making.

Ribosome profiling: a powerful tool in oncological research.

Neoplastic cells need to adapt their gene expression pattern to survive in an ever-changing or unfavorable tumor microenvironment. Protein synthesis (or mRNA translation), an essential part of gene expression, is dysregulated in cancer. The emergence of distinct translatomic technologies has revolutionized oncological studies to elucidate translational regulatory mechanisms. Ribosome profiling can provide adequate information on diverse aspects of translation by aiding in quantitatively analyzing the intensity of translating ribosome-protected fragments. Here, we review the primary currently used translatomics techniques and highlight their advantages and disadvantages as tools for translatomics studies. Subsequently, we clarified the areas in which ribosome profiling could be applied to better understand translational control. Finally, we summarized the latest advances in cancer studies using ribosome profiling to highlight the extensive application of this powerful and promising translatomic tool.

The NF-κB/NUAK2 signaling axis regulates pancreatic cancer progression by targeting SMAD2/3.

Nuclear factor kappa B (NF-κB) plays a pivotal role in the development of pancreatic cancer, and its phosphorylation has previously been linked to the regulation of NUAK2. However, the regulatory connection between NF-κB and NUAK2, as well as NUAK2's role in pancreatic cancer, remains unclear. In this study, we observed that inhibiting NUAK2 impeded the proliferation, migration, and invasion of pancreatic cancer cells while triggering apoptosis. NUAK2 overexpression partially resisted apoptosis and reversed the inhibitory effects of the NF-κB inhibitor. NF-κB transcriptionally regulated NUAK2 transcription by binding to the promoter region of NUAK2. Mechanistically, NUAK2 knockdown remarkably reduced the expression levels of p-SMAD2/3 and SMAD2/3, resulting in decreased nuclear translocation of SMAD4. In SMAD4-negative cells, NUAK2 knockdown impacted FAK signaling by downregulating SMAD2/3. Moreover, NUAK2 knockdown heightened the sensitivity of pancreatic cancer cells to gemcitabine, suggesting that NUAK2 inhibitors could be a promising strategy for pancreatic cancer treatment.

FBXO32 Stimulates Protein Synthesis to Drive Pancreatic Cancer Progression and Metastasis.

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death worldwide, primarily due to its rapid progression. The current treatment options for PDAC are limited, and a better understanding of the underlying mechanisms responsible for PDAC progression is required to identify improved therapeutic strategies. In this study, we identified FBXO32 as an oncogenic driver in PDAC. FBXO32 was aberrantly upregulated in PDAC, and high FBXO32 expression was significantly associated with an unfavorable prognosis in patients with PDAC. FRG1 deficiency promoted FBXO32 upregulation in PDAC. FBXO32 promoted cell migration and invasion in vitro and tumor growth and metastasis in vivo. Mechanistically, FBXO32 directly interacted with eEF1A1 and promoted its polyubiquitination at the K273 site, leading to enhanced activity of eEF1A1 and increased protein synthesis in PDAC cells. Moreover, FBXO32-catalyzed eEF1A1 ubiquitination boosted the translation of ITGB5 mRNA and activated focal adhesion kinase (FAK) signaling, thereby facilitating focal adhesion assembly and driving PDAC progression. Importantly, interfering with the FBXO32-eEF1A1 axis or pharmaceutical inhibition of FAK by defactinib, an FDA-approved FAK inhibitor, substantially inhibited PDAC growth and metastasis driven by aberrantly activated FBXO32-eEF1A1 signaling. Overall, this study uncovers a mechanism by which PDAC cells rely on FBXO32-mediated eEF1A1 activation to drive progression and metastasis. FBXO32 may serve as a promising biomarker for selecting eligible patients with PDAC for treatment with defactinib. Significance: FBXO32 upregulation in pancreatic cancer induced by FRG1 deficiency increases eEF1A1 activity to promote ITGB5 translation and stimulate FAK signaling, driving cancer progression and sensitizing tumors to the FAK inhibitor defactinib.

E3 ubiquitin ligase RBCK1 confers ferroptosis resistance in pancreatic cancer by facilitating MFN2 degradation.

Ferroptosis, a novel form of iron-dependent non-apoptotic cell death, plays an active role in the pathogenesis of diverse diseases, including cancer. However, the mechanism through which ferroptosis is regulated in pancreatic ductal adenocarcinoma (PDAC) remains unclear. Here, our study, via combining bioinformatic analysis with experimental validation, showed that ferroptosis is inhibited in PDAC. Genome-wide sequencing further revealed that the ferroptosis activator imidazole ketone erastin (IKE) induced upregulation of the E3 ubiquitin ligase RBCK1 in PDAC cells at the transcriptional or translational level. RBCK1 depletion or knockdown rendered PDAC cells more vulnerable to IKE-induced ferroptotic death in vitro. In a mouse xenograft model, genetic depletion of RBCK1 increased the killing effects of ferroptosis inducer on PDAC cells. Mechanistically, RBCK1 interacts with and polyubiquitylates mitofusin 2 (MFN2), a key regulator of mitochondrial dynamics, to facilitate its proteasomal degradation under ferroptotic stress, leading to decreased mitochondrial reactive oxygen species (ROS) production and lipid peroxidation. These findings not only provide new insights into the defense mechanisms of PDAC cells against ferroptotic death but also indicate that targeting the RBCK1-MFN2 axis may be a promising option for treating patients with PDAC.

Multiomics integration reveals NETosis heterogeneity and TLR2 as a prognostic biomarker in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant neoplasm characterized by a poor prognosis and limited therapeutic strategy. The PDAC tumor microenvironment presents a complex heterogeneity, where neutrophils emerge as the predominant constituents of the innate immune cell population. Leveraging the power of single-cell RNA-seq, spatial RNA-seq, and multi-omics approaches, we included both published datasets and our in-house patient cohorts, elucidating the inherent heterogeneity in the formation of neutrophil extracellular traps (NETs) and revealed the correlation between NETs and immune suppression. Meanwhile, we constructed a multi-omics prognostic model that suggested the patients exhibiting downregulated expression of NETs may have an unfavorable outcome. We also confirmed TLR2 as a potent prognosis factor and patients with low TLR2 expression had more effective T cells and an overall survival extension for 6 months. Targeting TLR2 might be a promising strategy to reverse immunosuppression and control tumor progression for an improved prognosis.

Single-cell RNA-seq reveals heterogeneity in metastatic renal cell carcinoma and effect of anti-angiogenesis therapy in the pancreas metastatic lesion.

Metastatic clear cell renal cell carcinoma has heterogenous tumor microenvironment (TME). Among the metastatic lesions, pancreas metastasis is rare and controversy in treatment approaches. Here, extensive primary and metastatic lesion samples were included by single-cell RNA-seq to decipher the distinct metastasis TME. The hypoxic and inflammatory TME of pancreas metastasis was decoded in this study, and the activation of PAX8-myc signaling, and metabolic reprogramming were observed. The active components including endothelial cells, fibroblasts and T cells were profiled. Meanwhile, we also evaluated the effect of anti-angiogenesis treatment in the pancreas metastasis patient. The potential mechanisms of pancreatic tropism, instability of genome, and the response of immunotherapy were also discussed in this work. Taken together, our findings suggest a clue to the heterogeneity in metastasis TME and provide evidence for the treatment of pancreas metastasis in renal cell carcinoma patients.

CALB2 drives pancreatic cancer metastasis through inflammatory reprogramming of the tumor microenvironment.

BACKGROUND: Early dissemination to distant organs accounts for the dismal prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). Chronic, dysregulated, persistent and unresolved inflammation provides a preferred tumor microenvironment (TME) for tumorigenesis, development, and metastasis. A better understanding of the key regulators that maintain inflammatory TME and the development of predictive biomarkers to identify patients who are most likely to benefit from specific inflammatory-targeted therapies is crucial for advancing personalized cancer treatment. METHODS: This study identified cell-specific expression of CALB2 in human PDAC through single-cell RNA sequencing analysis and assessed its clinicopathological correlations in tissue microarray using multi-color immunofluorescence. Co-culture systems containing cancer-associated fibroblasts (CAFs) and patient-derived organoids (PDOs) in vitro and in vivo were employed to elucidate the effects of CALB2-activated CAFs on PDAC malignancy. Furthermore, CUT&RUN assays, luciferase reporter assays, RNA sequencing, and gain- or loss-of-function assays were used to unravel the molecular mechanisms of CALB2-mediated inflammatory reprogramming and metastasis. Additionally, immunocompetent KPC organoid allograft models were constructed to evaluate CALB2-induced immunosuppression and PDAC metastasis, as well as the efficacy of inflammation-targeted therapy. RESULTS: CALB2 was highly expressed both in CAFs and cancer cells and correlated with an unfavorable prognosis and immunosuppressive TME in PDAC patients. CALB2 collaborated with hypoxia to activate an inflammatory fibroblast phenotype, which promoted PDAC cell migration and PDO growth in vitro and in vivo. In turn, CALB2-activated CAFs upregulated CALB2 expression in cancer cells through IL6-STAT3 signaling-mediated direct transcription. In cancer cells, CALB2 further activated Ca2+-CXCL14 inflammatory axis to facilitate PDAC metastatic outgrowth and immunosuppression. Genetic or pharmaceutical inhibition of CXCL14 significantly suppressed CALB2-mediated metastatic colonization of PDAC cells in vivo and extended mouse survival. CONCLUSIONS: These findings identify CALB2 as a key regulator of inflammatory reprogramming to promote PDAC metastatic progression. Combination therapy with αCXCL14 monoclonal antibody and gemcitabine emerges as a promising strategy to suppress distant metastasis and improve survival outcomes in PDAC with CALB2 overexpression.

Organoid: Bridging the gap between basic research and clinical practice.

Nowadays, the diagnosis and treatment system of malignant tumors has increasingly tended to be more precise and personalized while the existing tumor models are still unable to fully meet the needs of clinical practice. Notably, the emerging organoid platform has been proven to have huge potential in the field of basic-translational medicine, which is expected to promote a paradigm shift in personalized medicine. Here, given the unique advantages of organoid platform, we mainly explore the prominent role of organoid models in basic research and clinical practice from perspectives of tumor biology, tumorigenic microbes-host interaction, clinical decision-making, and regenerative strategy. In addition, we also put forward some practical suggestions on how to construct a new generation of organoid platform, which is destined to vigorously promote the reform of basic-translational medicine.

From basic research to clinical application: targeting fibroblast activation protein for cancer diagnosis and treatment.

PURPOSE: This study aims to review the multifaceted roles of a membrane protein named Fibroblast Activation Protein (FAP) expressed in tumor tissue, including its molecular functionalities, regulatory mechanisms governing its expression, prognostic significance, and its crucial role in cancer diagnosis and treatment. METHODS: Articles that have uncovered the regulatory role of FAP in tumor, as well as its potential utility within clinical realms, spanning diagnosis to therapeutic intervention has been screened for a comprehensive review. RESULTS: Our review reveals that FAP plays a pivotal role in solid tumor progression by undertaking a multitude of enzymatic and nonenzymatic roles within the tumor stroma. The exclusive presence of FAP within tumor tissues highlights its potential as a diagnostic marker and therapeutic target. The review also emphasizes the prognostic significance of FAP in predicting tumor progression and patient outcomes. Furthermore, the emerging strategies involving FAPI inhibitor (FAPI) in cancer research and clinical trials for PET/CT diagnosis are discussed. And targeted therapy utilizing FAP including FAPI, chimeric antigen receptor (CAR) T cell therapy, tumor vaccine, antibody-drug conjugates, bispecific T-cell engagers, FAP cleavable prodrugs, and drug delivery system are also introduced. CONCLUSION: FAP's intricate interactions with tumor cells and the tumor microenvironment make it a promising target for diagnosis and treatment. Promising strategies such as FAPI offer potential avenues for accurate tumor diagnosis, while multiple therapeutic strategies highlight the prospects of FAP targeting treatments which needs further clinical evaluation.

Novel strategies optimize immunotherapy by improving the cytotoxic function of T cells for pancreatic cancer treatment.

Pancreatic cancer (PC) is considered highly malignant due to its unsatisfying prognosis and limited response to therapies. Immunotherapy has therefore been developed to harness the antigen-specific properties and cytotoxicity of the immune system, aiming to induce a robust anti-tumor immune response that specifically demolishes PC cells while minimizing lethality in healthy tissue. The activation and augmentation of cytotoxic T cells play a critical role in the initiation and final success of immunotherapy. PC, however, is often immunotherapy resistant due to its intrinsic immunosuppressive tumor microenvironment that consequently hampers effective T cell priming. Emerging therapeutic approaches are orientated to modulate the tumor microenvironment in PC to enhance immune system involvement and heighten T cell efficacy. These novel strategies have shown promising therapeutic effects in the treatment of PC either as standalone approaches or combinatorial with other therapeutic schemes. The objective of this article is to explore innovative approaches to optimize immunotherapy for PC patients through T cell cytotoxic function augmentation.

Genomic analysis and filtration of novel prognostic biomarkers based on metabolic and immune subtypes in pancreatic cancer.

PURPOSE: Patients with pancreatic cancer (PC) can be classified into various molecular subtypes and benefit from some precise therapy. Nevertheless, the interaction between metabolic and immune subtypes in the tumor microenvironment (TME) remains unknown. We hope to identify molecular subtypes related to metabolism and immunity in pancreatic cancer METHODS: Unsupervised consensus clustering and ssGSEA analysis were utilized to construct molecular subtypes related to metabolism and immunity. Diverse metabolic and immune subtypes were characterized by distinct prognoses and TME. Afterward, we filtrated the overlapped genes based on the differentially expressed genes (DEGs) between the metabolic and immune subtypes by lasso regression and Cox regression, and used them to build risk score signature which led to PC patients was categorized into high- and low-risk groups. Nomogram were built to predict the survival rates of each PC patient. RT-PCR, in vitro cell proliferation assay, PC organoid, immunohistochemistry staining were used to identify key oncogenes related to PC RESULTS: High-risk patients have a better response for various chemotherapeutic drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. We built a nomogram with the risk group, age, and the number of positive lymph nodes to predict the survival rates of each PC patient with average 1-year, 2-year, and 3-year areas under the curve (AUCs) equal to 0.792, 0.752, and 0.751. FAM83A, KLF5, LIPH, MYEOV were up-regulated in the PC cell line and PC tissues. Knockdown of FAM83A, KLF5, LIPH, MYEOV could reduce the proliferation in the PC cell line and PC organoids CONCLUSION: The risk score signature based on the metabolism and immune molecular subtypes can accurately predict the prognosis and guide treatments of PC, meanwhile, the metabolism-immune biomarkers may provide novel target therapy for PC.