Current anatomic staging inadequately predicts outcomes in resectable pancreatic ductal adenocarcinoma (PDAC). This study integrates tumor-intrinsic KRT6A expression with host-derived prognostic nutritional index (PNI) to develop a novel prognostic model. This retrospective study enrolled 105 patients who underwent pancreaticoduodenectomy for histologically confirmed PDAC between January 2019 and December 2024. KRT6A expression was quantified by immunohistochemistry using computer-assisted image analysis. PNI was calculated as serum albumin (g/L) + 5 × lymphocyte count (10⁹/L). The primary endpoint was disease-free survival (DFS). Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors. A prognostic nomogram (KSI-Nomo) integrating KRT6A and PNI was constructed and validated using time-dependent ROC curves, calibration plots, and decision curve analysis. Risk stratification was performed based on nomogram total points. KRT6A expression was significantly elevated in poorly differentiated tumors and functionally promoted PDAC cell migration and invasion in vitro. Multivariate analysis identified KRT6A expression (HR = 6.337, 95% CI: 1.733-9.540, P = 0.034) and PNI (HR = 0.953, 95% CI: 0.245-1.702, P = 0.014) as the sole independent prognostic factors for DFS, outperforming conventional inflammatory indices (NLR, PLR, SII) and TNM staging. The KRT6A-PNI nomogram demonstrated excellent discriminative accuracy with AUC values of 0.838 (1-year), 0.836 (2-year), and 0.969 (3-year). Calibration curves showed good agreement between predicted and observed survival probabilities. Decision curve analysis confirmed superior net clinical benefit compared to treat-all or treat-none strategies. Risk stratification identified three distinct prognostic groups: low-risk (30% of patients, 3-year DFS rate 52.3%), intermediate-risk (40%, 38.6%), and high-risk (30%, 12.4%) (log-rank P < 0.001). The KRT6A-PNI model provides superior risk stratification for resectable PDAC, enabling personalized treatment decisions.

Objectives: The TNM staging system for distal cholangiocarcinoma (dCCA) has limited accuracy due to its anatomical basis. This study developed a prognostic model integrating inflammatory-nutritional markers and tumor biomarkers to improve risk stratification. Methods: We analyzed 208 dCCA patients undergoing pancreaticoduodenectomy (2017-2024). Independent prognostic factors for overall survival (OS) were identified via Cox regression, including tumor marker (corrected CA19-9) and host status markers (PLR, CAR, and PNI). A nomogram was constructed and evaluated using calibration, ROC, and DCA. Patients were risk-stratified using the model's score. Results: Four independent factors were identified: corrected CA19-9 (HR = 2.438), PLR (HR = 2.041), CAR (HR = 2.477), and PNI (HR = 0.415). The nomogram showed excellent discrimination for 1-, 3-, and 5-year OS (AUC: 0.847, 0.824, 0.858), good calibration, and clinical utility per DCA. Risk stratification significantly distinguished high-risk (n = 110) from low-risk (n = 98) groups (log-rank p < 0.0001). Discussion: This multidimensional model (tumor burden, inflammation, nutrition) outperforms TNM staging, highlighting host systemic status. Despite its single-center retrospective design, it shows promise for personalized risk assessment. Conclusion: The CINS (Cholangiocarcinoma Inflammation-Nutrition Score) accurately predicts prognosis and effectively risk-stratifies dCCA patients, aiding personalized treatment planning.

[This corrects the article DOI: 10.3389/fonc.2023.1112576.].

Patients undergoing pancreaticoduodenectomy for distal cholangiocarcinoma (dCCA) face a substantial risk of major postoperative cardiac complications (MPCC), which significantly impact mortality and recovery. Existing risk assessment tools lack objective cardiac functional parameters. This study aimed to develop and validate a novel prediction model integrating preoperative cardiac ultrasound parameters to individually predict the risk of MPCC within 30 days after dCCA surgery. A retrospective cohort study was conducted on 154 dCCA patients who underwent radical pancreaticoduodenectomy. Univariate and multivariate binary logistic regression analyses were performed to identify independent predictors of MPCC from clinical variables and preoperative transthoracic echocardiography parameters. A nomogram model was constructed based on the identified independent predictors. The model’s discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), with internal validation via bootstrapping. The incidence of MPCC was 34.4

BACKGROUND:Portosystemic venous invasion (PSVI) depth critically influences prognosis in borderline resectable pancreatic cancer (BRPC), necessitating precise preoperative discrimination for personalized therapy. AIM:To develop and validate a preoperative nomogram integrating computed tomography parameters and carbohydrate antigen 19-9 (CA19-9) kinetics for predicting PSVI depth in treatment-naive BRPC. METHODS:This retrospective cohort study analyzed 167 BRPC patients undergoing radical resection between 2011 and 2023. Patients were stratified by pathological PSVI depth [no venous invasion (VI)/adventitial/muscularis propria/intimal]. Kaplan-Meier and ordinal logistic regression identified preoperative predictors from clinical/laboratory/computed tomography parameters (e.g., circumferential involvement and CA19-9). A nomogram was developed and validated via calibration curves/decision curve analysis. RESULTS:PSVI depth significantly stratified survival.: Intimal VI showed worst prognosis (median overall survival: 9 months, 5-year overall survival: 0% vs no VI: 17 months, 12.5%; P < 0.001). Independent predictors: CA19-9 [odds ratio (OR) = 3.819, Wald = 14.125, 95% confidence interval (CI): 1.980-7.410], circumferential involvement (OR = 8.271, Wald = 33.352, 95%CI: 3.950-17.320), and luminal compromise (OR = 3.544, Wald = 8.489, 95%CI: 1.818-6.447). The nomogram achieved C-index = 0.928 (95%CI: 0.889-0.967), with 100-250 points indicating high invasiveness risk. Decision curve analysis confirmed clinical utility (threshold: 0-0.7). CONCLUSION:This model integrates routine indicators to preoperatively quantify PSVI depth, guiding precision treatment.

Most life activities in organisms are regulated through protein complexes, which are mainly controlled via Protein-Protein Interactions (PPIs). Discovering new interactions between proteins and revealing their biological functions are of great significance for understanding the molecular mechanisms of biological processes and identifying the potential targets in drug discovery. Current experimental methods only capture stable protein interactions, which lead to limited coverage. In addition, expensive cost and time consuming are also the obvious shortcomings. In recent years, various computational methods have been successfully developed for predicting PPIs based only on protein homology, primary sequences of protein or gene ontology information. Computational efficiency and data complexity are still the main bottlenecks for the algorithm generalization. In this study, we proposed a novel computational framework, HNSPPI, to predict PPIs. As a hybrid supervised learning model, HNSPPI comprehensively characterizes the intrinsic relationship between two proteins by integrating amino acid sequence information and connection properties of PPI network. The experimental results show that HNSPPI works very well on six benchmark datasets. Moreover, the comparison analysis proved that our model significantly outperforms other five existing algorithms. Finally, we used the HNSPPI model to explore the SARS-CoV-2-Human interaction system and found several potential regulations. In summary, HNSPPI is a promising model for predicting new protein interactions from known PPI data.

Objective:To evaluate superior mesenteric artery preferential approach in the borderline resectable pancreatic head cancer.Methods:The clinical and follow-up data of 90 patients with borderline resectable pancreatic head cancer who underwent radical pancreatoduodenectomy at Beijing Chaoyang Hospital,Capital Medical University from Jan 2015 to Dec 2021 were analyzed.Results:After exploring the superior mesenteric artery in the lower colon area to confirm the vascular invasion meet the resection criteria, the blood supply is cut off first, then the tumors were resected en bloc, with the invaded vessels resected and reconstructed or replaced. All 90 patients successfully completed the operation without perioperative death. Pathology established pancreatic ductal adenocarcinoma. The 1-year, 2-year, and 3-year disease-free survival rates of patients in the arterial priority approach group were 68.2%, 60.4%, and 54.3%, while the 1-year, 2-year, and 3-year disease-free survival rates of patients by conventional approach were 58.4%, 26.4%, and 11.7% ( P=0.001). Conclusion:The superior mesenteric artery preferential approach in the inferior colon region can prolong the survival time of patients after surgery, and reduce the recurrence.

A deep understanding of Protein-protein interactions (PPIs) can provide comprehensive insights into many biological functions, thereby facilitating drug target identification and novel therapeutic design. Recent developments in artificial intelligence (AI)-driven computational methods have enabled the discovery of previously uncharacterized PPIs from large-scale interactome datasets. Almost all existing machine learning methods rely on Subcellular Localization (SL) to construct balanced datasets based on positive interactions to achieve predictions. Despite high fitting accuracy, the generalization ability of these models is questionable. To solve this problem, we analyzed existing methods and found that the high false positives in these methods are due to the bias in data distribution caused by SL. Therefore, we proposed a new strategy for negative instance sampling in PPI prediction and developed a Hybrid Graph Neural Network framework for Protein-protein Interaction Prediction (HGNNPIP). The experimental results showed that HGNNPIP works well on six benchmark datasets. Comparison analysis demonstrated that our model outperformed the other four existing methods. We also used HGNNPIP to explore the molecular contacts involved in the rice-pathogen interaction system. In vivo experiments confirmed multiple regulations related to disease resistance in rice. In summary, this study provides new insights into establishing a computational framework for PPI prediction with high reliability. ### Competing Interest Statement The authors have declared no competing interest.

BACKGROUND:Pancreatic ductal adenocarcinoma (PDAC) is a malignancy characterized by challenging early diagnosis and poor prognosis. It is believed that coagulation has an impact on the tumor microenvironment of PDAC. The aim of this study is to further distinguish coagulation-related genes and investigate immune infiltration in PDAC.METHODS:We gathered two subtypes of coagulation-related genes from the KEGG database, and acquired transcriptome sequencing data and clinical information on PDAC from The Cancer Genome Atlas (TCGA) database. Using an unsupervised clustering method, we categorized patients into distinct clusters. We investigated the mutation frequency to explore genomic features and performed enrichment analysis, utilizing Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) to explore pathways. CIBERSORT was used to analyze the relationship between tumor immune infiltration and the two clusters. A prognostic model was created for risk stratification, and a nomogram was established to assist in determining the risk score. The response to immunotherapy was assessed using the IMvigor210 cohort. Finally, PDAC patients were recruited, and experimental samples were collected to validate the infiltration of neutrophils using immunohistochemistry. In addition, and identify the ITGA2 expression and function were identified by analyzing single cell sequencing data.RESULTS:Two coagulation-related clusters were established based on the coagulation pathways present in PDAC patients. Functional enrichment analysis revealed different pathways in the two clusters. Approximately 49.4% of PDAC patients experienced DNA mutation in coagulation-related genes. Patients in the two clusters displayed significant differences in terms of immune cell infiltration, immune checkpoint, tumor microenvironment and TMB. We developed a 4-gene prognostic stratified model through LASSO analysis. Based on the risk score, the nomogram can accurately predict the prognosis in PDAC patients. We identified ITGA2 as a hub gene, which linked to poor overall survival (OS) and short disease-free survival (DFS). Single-cell sequencing analysis demonstrated that ITGA2 was expressed by ductal cells in PDAC.CONCLUSIONS:Our study demonstrated the correlation between coagulation-related genes and the tumor immune microenvironment. The stratified model can predict the prognosis and calculate the benefits of drug therapy, thus providing the recommendations for clinical personalized treatment.

BackgroundUnderstanding the spatial heterogeneity of the tumor microenvironment (TME) in pancreatic cancer (PC) remains challenging.MethodsIn this study, we performed spatial transcriptomics (ST) to investigate the gene expression features across one normal pancreatic tissue, PC tissue, adjacent tumor tissue, and tumor stroma. We divided 18,075 spatial spots into 22 clusters with t-distributed stochastic neighbor embedding based on gene expression profiles. The biological functions and signaling pathways involved in each cluster were analyzed with gene set enrichment analysis.ResultsThe results revealed that KRT13+FABP5+ malignant cell subpopulation had keratinization characteristics in the tumor tissue. Fibroblasts from adjacent tumor tissue exhibited a tumor-inhibiting role such as “B-cell activation” and “positive regulation of leukocyte activation.” The FGG+CRP+ inflammatory cancer-associated fibroblasts replaced the islets in tumor stroma. During PC progression, the damage to pancreatic structure and function was heavier in the pancreatic exocrine (AMYA2+PRSS1+) than in the endocrine (INS+GCG+).ConclusionOur results revealed the spatial heterogeneity of dynamic changes and highlighted the significance of impaired exocrine function in PC.

Background:Colorectal cancer (CRC) is the third most common cancer in the world and has a high mortality rate. Colorectal adenoma (CRA) is precancerous lesions of CRC. The purpose of the present study was to construct a nomogram predictive model for CRA with low-grade intraepithelial neoplasia (LGIN) in order to identify high-risk individuals, facilitating early diagnosis and treatment, and ultimately reducing the incidence of CRC. Methods:We conducted a single-center case-control study. Based on the results of colonoscopy and pathology, 320 participants were divided into the CRA group and the control group, the demographic and laboratory test data were collected. A development cohort (n = 223) was used for identifying the risk factors for CRA with LGIN and to develop a predictive model, followed by an internal validation. An independent validation cohort (n = 97) was used for external validation. Receiver operating characteristic curve, calibration plot and decision curve analysis were used to evaluate discrimination ability, accuracy and clinical practicability of the model. Results:Four predictors, namely sex, age, albumin and monocyte count, were included in the predictive model. In the development cohort, internal validation and external validation cohort, the area under the curve (AUC) of this risk predictive model were 0.946 (95%CI: 0.919-0.973), 0.909 (95 % CI: 0.869-0.940) and 0.928 (95%CI: 0.876-0.980), respectively, which demonstrated the model had a good discrimination ability. The calibration plots showed a good agreement and the decision curve analysis (DCA) suggested the predictive model had a high clinical net benefit. Conclusion:The nomogram model exhibited good performance in predicting CRA with LGIN, which can aid in the early detection of high-risk patients, improve early treatment, and ultimately reduce the incidence of CRC.

BACKGROUND:Tumor hypoxia is a feature of tumor micro-environment (TME), which provides a suitable environment for tumor cells migration and invasion. However, up to now, the function of exosomes derived from hypoxic tumor cells is still not fully understood. The present study is aimed to explore the underlying mechanisms of lung cancer-secreted exosomes-mediated tumor metastasis under hypoxia.METHODS & RESULTS:Exosomes were isolated from normoxic or hypoxic NCI-H446 cells. Some characteristic proteins were detected by western blots. Levels of CD63, CD 9 and CD 81 proteins were up-regulated on the membrane of exosomes secreted by hypoxic NCI-H446 cells. Basing on the results from miRNA sequencing, qRT-PCR and wound healing assay, hsa-miR-625-3p was discovered to be accumulated inside hypoxic exosomes and responsible for the metastasis of lung cancer cell. Further experiments from luciferase reporter gene assay demonstrated hsa-miR-625-3p could directly inhibit SCAI expression through binding with its 3'UTR, which suggested the mechanisms by which exosomal hsa-miR-625-3p suppressed tumor cells migration.CONCLUSIONS:Exosomal miR-625-3p derived from hypoxic small lung cancer cells accelerated tumor cells migration through inhibiting SCAI directly.

Lung cancer is a life-threatening malignant tumour that is prevalent worldwide. Here, the GCNT3 gene in lung adenocarcinoma was studied via public databases, and cytology and molecular biology experiments were performed to further explore the role of this gene in lung adenocarcinoma. In this study, abnormally high GCNT3 expression levels were observed in tumour tissues compared with normal tissues at both the mRNA and protein levels. In the pancancer analysis, abnormal GCNT3 expression was observed in many tumour types. Moreover, the survival analysis revealed that among patients receiving radiotherapy, those with high GCNT3 expression levels had a worse prognosis. Cell and molecular biology experiments showed that the proliferation, migration and invasion capabilities of the A549 cell line were decreased after knockdown of GCNT3, and epithelial-mesenchymal transformation was significantly inhibited. In subsequent studies, we found that the sensitivity of cells to radiotherapy was enhanced after GCNT3 knockdown. Overall, our findings reveal that GCNT3 is an important factor affecting the radiotherapy sensitivity of lung adenocarcinoma, and GCNT3 inhibition deserves further study as a radiotherapy sensitising strategy.

Micro(mi)RNAs play an essential role in the epithelial-mesenchymal transition (EMT) process in human cancers. This study aimed to uncover the regulatory mechanism of miR-1301-3p on EMT in pancreatic cancer (PC). The miRNA profilings from Gene Expression Omnibus data sets (GSE31568, GSE41372, and GSE32688) demonstrated the downregulation of miR-1301-3p in PC tissues, which was validated with 72 paired PC tissue samples through qRT-PCR detection. The low level of miR-1301-3p was associated with a poor prognosis for PC patients from the PC cohort of The Cancer Genome Atlas and the validation cohort. Gene Ontology analyses indicated that the target genes of miR-1301-3p were involved in cell cycle and adherent junction regulation. In vitro assays revealed that miR-1301-3p suppressed the proliferation and migration abilities of PC cells. Western blotting and luciferase reporter assays suggested that miR-1301-3p inhibited RhoA expression by targeting its 3′-untranslated region; RhoA upregulated N-cadherin and vimentin levels; however, it downregulated the E-cadherin level. In conclusion, our study showed that miR-1301-3p could serve as a prognostic biomarker for PC and suppress PC cell malignancy by targeting the RhoA-induced EMT process.

Background: Pancreatic ductal adenocarcinoma (PDAC) is known to have a poor prognosis, early local invasion, and distant metastasis. Surgical resection is the most effective treatment, and tumor recurrence can be the key factor affecting the surgical outcome. Serum carbohydrate antigen 19-9 (CA19-9) is a tumor marker with high sensitivity to pancreatic cancer; elevated CA19-9 levels often indicate poor biological behavior. Tumor size is also a crucial factor that affects the prognosis. Therefore, we developed a program to evaluate the effect of the ratio of CA19-9 to total tumor volume (CA19-9/TTV) as a prognostic marker on tumor recurrence and long-term survival in patients with PDAC following pancreaticoduodenectomy (PD). Methods: Data from 200 patients who underwent PD for PDAC were retrospectively analyzed. CA19-9/TTV was calculated according to preoperative CA19-9 and TTV, and patients were divided into two groups according to the optimal cut-off value. Univariate and multivariate analyses were performed on the clinicopathological data to screen the risk factors affecting postoperative recurrence and long-term prognosis of patients with PDAC undergoing PD. Results: The receiver operating characteristic curve showed that the best cut-off value was 5.62 (area under curve [AUC], 0.633; 95% CI: 0.548-0.718). Multivariate analysis showed that tumor differentiation and CA19-9/TTV were independent risk factors for the long-term prognosis of PDAC (P = 0.004, P = 0.007), as well as for tumor recurrence (P = 0.008, P = 0.008). Conclusion: CA19-9/TTV is an independent risk factor for the prognosis of PDAC and may be a new marker for lower survival benefits.

本文回顾性分析2013年1月至2019年12月在首都医科大学附属北京朝阳医院肝胆外科因胰头癌行胰十二指肠切除术的154例患者的临床及随访资料。根据患者术前CA19-9/GGT与1年生存情况绘制ROC曲线，确定CA19-9/GGT的最佳cut-off值，并以此将患者分为低比值组和高比值组。单因素及多因素分析筛选出CA19-9/GGT（ RR=1.842，95% CI：1.081~3.193）和淋巴结转移（ RR=1.780，95% CI：1.118~2.835）是影响胰头癌术后远期生存的独立预后因素。本研究显示CA19-9/GGT相对于单纯CA19-9而言，在判断胰头癌远期生存方面更具有价值。

Superior mesenteric artery‑first approach has been proposed for the surgical treatment of pancreatic head cancer. However, little is known about its effects on resectable pancreatic head cancer. In the present study, data from patients with resectable pancreatic head cancer, who underwent radical pancreatoduodenectomy with or without the superior mesenteric artery‑first approach at from January, 2014 to December, 2019, were retrospectively collected and analyzed. A total of 204 patients were included in the study. The blood loss and blood transfusion of the arterial approach group (n=94) were less than those of the conventional approach group (n=110). Diarrhea occurred in 31 cases (15.2%) of the arterial approach group and in 18 cases (8.8%) of the conventional approach group (P<0.05). A higher rate of R0 resection and a higher number of lymph nodes harvested were achieved in the arterial approach group (P<0.05). The 1‑, 2‑ and 3‑year tumor‑free survival rates of the patients in the arterial approach group were 60.9, 43.2 and 37.9%, respectively, and those of the patients in the conventional approach group were 64.2, 24.9 and 15.6%, respectively (P<0.05). Moreover, the 1‑, 2‑, and 3‑year overall survival rates of the patients in the arterial approach group were 79.5, 49.7 and 36.7%, and those of the patients in the conventional approach group were and 75.9, 38.6 and 18.7%, respectively (P<0.05). On the whole, the present study demonstrates that the superior mesenteric artery‑first approach can reduce intraoperative blood loss and consequent blood transfusion, facilitate the achievement of an R0 resection, and thus prolong the survival of patients, despite resulting in a higher rate of diarrhea.