Background. As an iron-dependent type of programmed cell death, ferroptosis plays an important role in the pathogenesis and progression of hepatocellular carcinoma (HCC). Long noncoding RNAs (lncRNAs) have been linked to the prognosis of patients with HCC in a number of studies. Nevertheless, the predictive value of lncRNAs (FRLs) associated with ferroptosis in HCC has not been fully elucidated. Methods. Download RNA sequencing data and clinical profiles of HCC patients from The Cancer Genome Atlas (TCGA) database. The FRLs associated with prognosis were determined by Pearson’s correlation analysis. After that, prognostic signature for FRLs was established using Cox and LASSO regression analyses. Meanwhile, survival analysis, correlation analysis of clinicopathological features, Cox regression, receiver operating characteristic (ROC) curve, and nomogram were used to analyze the FRL signature’s predictive capacity. The relationship between signature risk score, immune cell infiltration, and chemotherapy drug sensitivity is further studied.Results. In total, 93 FRLs were found to be of prognostic value in patients with HCC. A five-FRL signature comprising AC015908.3, LINC01138, AC009283.1, Z83851.1, and LUCAT1 was created in order to enhance the prognosis prediction with HCC patients. The signature demonstrated a good predictive potency, according to the Kaplan-Meier and ROC curves. The five-FRL signature was found to be a risk factor independent of various clinical factors using Cox regression and stratified survival analysis. The high-risk group was shown to be enriched in tumorigenesis and immune-related pathways according to GSEA analysis. Additionally, immune cell infiltration, immune checkpoint molecules, and half-inhibitory concentrations differed considerably between risk groups, implying that this signature could be used to evaluate the clinical efficacy of chemotherapy and immunotherapy. Conclusion. The five-FRL risk signature is helpful for assessing the prognosis of HCC patients and improving therapy options, so it can be further applied clinically.

Background Pancreaticoduodenectomy (PD) and distal pancreatectomy with splenectomy (DPS) are considered the standard procedures for pancreatic lesions. However, long-term metabolic consequences of PD and DPS applied for benign or low-grade malignant tumors need to be addressed. This study aimed to investigate the short- and long-term outcomes of organ-sparing pancreatectomy for benign or low-grade malignant pancreatic tumors in our institution. Material/Methods The clinical data of 101 patients with benign or low-grade malignant pancreatic tumors who underwent organ-sparing pancreatectomy from January 2009 to September 2021 were retrospectively analyzed, including 40 tumor enucleations (EN), 22 central pancreatectomies (CP), 25 spleen-preserving distal pancreatectomies (SPDP), 7 pylorus-preserving pancreaticoduodenectomies (PPPD) and 7 duodenum-preserving pancreatic head resections (DPPHR). Results The mean operative time, intraoperative blood loss, and length of hospital stay were 182.9±74.6 min, 191.9±127.8 mL, and 11.6±8.1 days, respectively. EN had the shortest operative time, while DPPHR had the longest operative time. The mean intraoperative blood loss of DPPHR and PPPD was significantly greater than the others (all P <0.05). The length of hospital stay of PPPD was longest. The overall morbidity was 33.6%. The reoperation rate was 1.0% and there was no mortality. The incidence of pancreatic endocrine insufficiency and exocrine insufficiency were 5.9% and 6.9%, respectively. None patients had tumor recurrence during the follow-up period. Conclusions Organ-sparing pancreatectomy is associated with acceptable perioperative risk and postoperative complications and better long-term outcomes in the aspects of preservation of function and curability in benign or low-grade malignant pancreatic tumors.

Background Necroptosis plays an important role in tumor genesis and progression. Long non-coding RNAs (IncRNAs) have been proven a regulatory factor of necroptosis in various tumors. However, the real role of necroptosis-related lncRNAs (NRLs) and their potential to predict the prognosis of pancreatic cancer (PC) remain largely unclear. Methods 178 PC patients' RNA sequencing data and clinical profiles were downloaded from The Cancer Genome Atlas (TCGA) database. NRLs were identified using Pearson correlation analysis. Then, patients were divided into the training set and the validation set at a 1 : 1 ratio. Subsequently, Cox and LASSO regression analyses were conducted to establish a prognostic NRLs signature in the training set and validation set. The predictive efficacy of the 5-NRLs signature was assessed by survival analysis, nomogram, COX regression, clinicopathological features correlation analysis, and the operating characteristic (ROC) curve. Furthermore, correlations between the risk score (RS) and immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and anticancer drug sensitivity were analyzed. Results A 5-NRLs signature was established to predict the prognostic of PC, including LINC00857, AL672291.1, PTPRN2-AS1, AC141930.2, and MEG9. The 5-NRLs signature demonstrated a high degree of predictive power according to ROC and Kaplan-Meier curves, and was revealed to be an independent risk factor for prognosis via stratified survival analysis. Nomogram and calibration curves indicated the clinical adaptability of the signature. Additionally, immune cell infiltration, immune checkpoint molecules, somatic gene mutations and half-inhibitory concentration were significantly different between two risk subtypes. Conclusions The novel 5-NRLs signature is helpful for assessing the prognosis of PC patients and improving therapy options, so it can be further applied clinically.

BackgroundNecroptosis plays an important role in tumor genesis and progression. Long non-coding RNAs (IncRNAs) have been proven a regulatory factor of necroptosis in various tumors. However, the real role of necroptosis-related lncRNAs (NRLs) and their potential to predict the prognosis of pancreatic cancer (PC) remain largely unclear.Methods 178 PC patients' RNA sequencing data and clinical pro les were downloaded from The Cancer Genome Atlas (TCGA) database. NRLs were identi ed using Pearson correlation analysis. Then, patients were divided into the training set and the validation set at a 1 : 1 ratio. Subsequently, Cox and LASSO regression analyses were conducted to establish a prognostic NRLs signature in the training set and validation set. The predictive e cacy of the 5-NRLs signature was assessed by survival analysis, nomogram, COX regression, clinicopathological features correlation analysis, and the operating characteristic (ROC) curve. Furthermore, correlations between the risk score (RS) and immune cell in ltration, immune checkpoint molecules, somatic gene mutations, and anticancer drug sensitivity were analyzed.