Identification of the CD8 + T-cell Related Signature for Predicting the Prognosis of Gastric Cancer Based on Integrated Analysis of Bulk and Single-cell RNA Sequencing Data.

The infiltration of CD8 + T cells in the tumor microenvironment is associated with better survival and immunotherapy response. However, their roles in gastric cancer have not been explored so far. In here, the profiles of GC gene expression were collected from The Cancer Genome Atlas database. Single-cell transcriptomic data originated from GSE134520. Cell clustering, annotation, and CD8 + T-cell differential genes were from the TISCH database. We determined 896 CD8 + T-cell differential genes by scRNA-seq analysis. After integrating immune-related genes, 174 overlapping genes were obtained and a novel risk model was subsequently built. The performance of CD8 + T-cell-associated gene signature was assessed in the training and external validation sets. The gene signature showed independent risk factors of overall survival for GC. A quantitative nomogram was built to enhance the clinical efficacy of this signature. Furthermore, low-risk individuals showed higher mutation status, higher immune checkpoint expression, low Tumour Immune Dysfunction and Exclusion (TIDE) scores, and higher IPS-PD-1 combined IPS-CTLA4 scores, indicating a greater response to immunotherapy. In addition, analysis of IMvigor210 immunotherapy cohort demonstrated that low-risk individuals had a favorable response to prognosis and immunotherapy. In conclusion, we generated a CD8 + T-cell-related signature that can serve as a promising tool for personalized prognosis prediction and guiding decisions regarding immunotherapy in GC patients.

Dermoscopy-based Radiomics Help Distinguish Basal Cell Carcinoma and Actinic Keratosis: A Large-scale Real-world Study Based on a 207-combination Machine Learning Computational Framework.

This study has used machine learning algorithms to develop a predictive model for differentiating between dermoscopic images of basal cell carcinoma (BCC) and actinic keratosis (AK). We compiled a total of 904 dermoscopic images from two sources - the public dataset (HAM10000) and our proprietary dataset from the First Affiliated Hospital of Dalian Medical University (DAYISET 1) - and subsequently categorised these images into four distinct cohorts. The study developed a deep learning model for quantitative analysis of image features and integrated 15 machine learning algorithms, generating 207 algorithmic combinations through random combinations and cross-validation. The final predictive model, formed by integrating XGBoost with Lasso regression, exhibited effective performance in the differential diagnosis of BCC and AK. The model demonstrated high sensitivity in the training set and maintained stable performance in three validation sets. The area under the curve (AUC) value reached 1.000 in the training set and an average of 0.695 in the validation sets. The study concludes that the constructed discriminative diagnostic model based on machine learning algorithms has excellent predictive capabilities that could enhance clinical decision-making efficiency, reduce unnecessary biopsies, and provide valuable guidance for further treatment.

Energy-guided diffusion model for CBCT-to-CT synthesis.

Cone Beam Computed Tomography (CBCT) plays a crucial role in Image-Guided Radiation Therapy (IGRT), providing essential assurance of accuracy in radiation treatment by monitoring changes in anatomical structures during the treatment process. However, CBCT images often face interference from scatter noise and artifacts, posing a significant challenge when relying solely on CBCT for precise dose calculation and accurate tissue localization. There is an urgent need to enhance the quality of CBCT images, enabling a more practical application in IGRT. This study introduces EGDiff, a novel framework based on the diffusion model, designed to address the challenges posed by scatter noise and artifacts in CBCT images. In our approach, we employ a forward diffusion process by adding Gaussian noise to CT images, followed by a reverse denoising process using ResUNet with an attention mechanism to predict noise intensity, ultimately synthesizing CBCT-to-CT images. Additionally, we design an energy-guided function to retain domain-independent features and discard domain-specific features during the denoising process, enhancing the effectiveness of CBCT-CT generation. We conduct numerous experiments on the thorax dataset and pancreas dataset. The results demonstrate that EGDiff performs better on the thoracic tumor dataset with SSIM of 0.850, MAE of 26.87 HU, PSNR of 19.83 dB, and NCC of 0.874. EGDiff outperforms SoTA CBCT-to-CT synthesis methods on the pancreas dataset with SSIM of 0.754, MAE of 32.19 HU, PSNR of 19.35 dB, and NCC of 0.846. By improving the accuracy and reliability of CBCT images, EGDiff can enhance the precision of radiation therapy, minimize radiation exposure to healthy tissues, and ultimately contribute to more effective and personalized cancer treatment strategies.

An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence.

Background: This study aimed to establish and validate a prognostic model based on immune-related genes (IRGPM) for predicting disease-free survival (DFS) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy, and to elucidate the immune profiles associated with different prognostic outcomes. Methods: Transcriptomic and clinical data were sourced from the Gene Expression Omnibus (GEO) database and the West China Hospital database. We focused on genes from the RNA immune-oncology panel. The elastic net approach was employed to pinpoint immune-related genes significantly impacting DFS. We developed the IRGPM for rectal cancer using the random forest technique. Based on the IRGPM, we calculated prognostic risk scores to categorize patients into high-risk and low-risk groups. Comparative analysis of immune characteristics between these groups was conducted. Results: In this study, 407 LARC samples were analyzed. The elastic net identified a signature of 20 immune-related genes, forming the basis of the IRGPM. Kaplan-Meier survival analysis revealed a lower 5-year DFS in the high-risk group compared to the low-risk group. The receiver operating characteristic (ROC) curve affirmed the model's robust predictive capability. Validation of the model was performed in the GSE190826 cohort and our institution's cohort. Gene expression differences between high-risk and low-risk groups predominantly related to cytokine-cytokine receptor interactions. Notably, the low-risk group exhibited higher immune scores. Further analysis indicated a greater presence of activated B cells, activated CD8 T cells, central memory CD8 T cells, macrophages, T follicular helper cells, and type 2 helper cells in the low-risk group. Additionally, immune checkpoint analysis revealed elevated PDCD1 expression in the low-risk group. Conclusions: The IRGPM, developed through random forest and elastic net methodologies, demonstrates potential in distinguishing DFS among LARC patients receiving standard treatment. Notably, the low-risk group, as defined by the IRGPM, showed enhanced activation of adaptive immune responses within the tumor microenvironment.

A SwinTransformer-Based Segmentation Framework With Self-Supervised Strategy for Post-Operative Prostate Cancer Radiotherapy.

Radical prostatectomy (prostate removal) is a standard treatment for clinically localized prostate cancer and is often followed by postoperative radiotherapy. Postoperative radiotherapy requires accurate delineation of the clinical target volume (CTV) and lymph node drainage area (LNA) on computed tomography (CT) images. However, the CTV contour cannot be determined by the simple prostate expansion after resection of the prostate in the CT image. Constrained by this factor, the manual delineation process in postoperative radiotherapy is more time-consuming and challenging than in radical radiotherapy. In addition, CTV and LNA have no boundaries that can be distinguished by pixel values in CT images, and existing automatic segmentation models cannot get satisfactory results. Radiation oncologists generally determine CTV and LNA profiles according to clinical consensus and guidelines regarding surrounding organs at risk (OARs). In this work, we design a cascade segmentation block to explicitly establish correlations between CTV, LNA, and OARs, leveraging OARs features to guide CTV and LNA segmentation. Furthermore, inspired by the success of the self-attention mechanism and self-supervised learning, we adopt SwinTransformer as our backbone and propose a pure SwinTransformer-based segmentation network with self-supervised learning strategies. We performed extensive quantitative and qualitative evaluations of the proposed method. Compared to other competitive segmentation models, our model shows higher dice scores with minor standard deviations, and the detailed visualization results are more consistent with the ground truth. We believe this work can provide a feasible solution to this problem, making the postoperative radiotherapy process more efficient.

Redox Neutral Radical-Relay Nickel-Catalyzed Remote Carbonylation.

A novel and efficient method for the catalytic installation of a carbonyl group via remote radical coupling is disclosed. The nickel-catalyzed reaction proceeds to undergo a sequential single-electron transfer, 1,5-hydrogen atom transfer, and carbonyl insertion, thus providing the α-substituted ketone. Further, this reaction could be carried out smoothly under normal pressure and redox-neutral conditions, and demonstrated functional-group compatibility and excellent site-selectivity.

The anti-TMV potency of the tobacco-derived fungus Aspergillus versicolor and its active alkaloids, as anti-TMV activity inhibitors.

Nicotiana tabacum (tobacco) has attracted interest as one of the most economically important industrial crops widely cultivated in China, whose dried leaves are popularly consumed medicinally and recreationally by human societies. In this study, five undescribed alkaloids derivatives, isoaspergillines A-E, together with eight known alkaloids, notoamide D, (1R,4S)-4-benzyl-1-isopropyl-2,4-dihydro-1H-pyrazino-[2,1-b]quinazoline-3,6-dione, protuboxepin K, notoamide C, notoamide M, deoxybrevianamide E, cyclo (D-Pro-L-Trp), and versicolamide B, were obtained from the culture of the Nicotiana tabacum-derived fungus Aspergillus versicolor. Their structures were mainly elucidated through comprehensive analyses of spectroscopic data. Bioactivity evaluation of all isolated compounds revealed that isoaspergilline A and notoamide M exhibited anti-TMV activities with IC50 values of 20.0 and 22.8 µM, respectively. Molecular docking suggested that isoaspergilline A and notoamide M were well located into the active site of anti-TMV by interacting with SER138, SER143, and ASN73 residues. This study enlightens the therapeutic potential of the endophytic fungus A. versicolor and it is helpful to find undescribed anti-TMV activity inhibitors, as well as searching for new anti-TMV candidates from natural sources.

Discovery of novel hydroxyamidine based indoleamine 2,3-dioxygenase 1 (IDO1) and thioredoxin reductase 1 (TrxR1) dual inhibitors.

In order to take advantage of both immunotherapeutic and metabolic antitumor agents, novel dual indoleamine 2,3- dioxygenase 1 (IDO1) and thioredoxin reductase 1 (TrxR1) inhibitors were designed. Thioredoxin reductase 1 (TrxR1) is a main ROS modulator within CRC cells. Indoleamine 2,3-dioxygenase (IDO1) is crucial controller for tryptophan (Trp) metabolism that is also important for CRC immunotherapy. Herein, ten compounds 12a-j containing hydroxyamidine scaffold were designed, synthesized and evaluated for inhibitory activities against IDO1/TrxR1 enzyme and CRC cells. Among these compounds, the most active compound 12d (ZC0109) showed excellent and balanced activity against both IDO1 (IC50 = 0.05 µM) and TrxR1 (IC50 = 3.00 ± 0.25 µM) were selected for further evaluation. Compound ZC0109 exhibited good dual inhibition against IDO1 and TrxR1 both in vitro and in vivo. Further mechanistic studies reveal that, through IDO1 and TrxR1 inhibition by ZC0109 treatment, accumulated ROS effectively induced apoptosis and G1/S cell cycle arrest in cancer cells. In vivo evaluation demonstrated excellent anti-tumor effect of ZC0109 with the notable ability of promoting ROS-induced apoptosis, reducing kynurenine level in plasma and restoring anti-tumor immune response. Thus, ZC0109 represents a potential CRC therapy agent for further development.

Integrated analysis of senescence-associated genes in pancreatic ductal adenocarcinoma.

Background: Cellular senescence plays a critical role in the occurrence and development, and immune modulation of cancer. This research primarily investigated the role of senescence-associated genes (SAGs) in the survival and tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC). Methods: From the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database, the gene expression profiles and clinical data of PDAC samples were downloaded. SAGs in the TCGA cohort were used to build a novel prognostic model and validated in the ICGC cohort. The relationship of signature with the immune landscape, tumor mutational burden (TMB), as well as the sensitivity of different therapies, was explored. Moreover, a nomogram was developed to predict the overall survival of PDAC patients. Results: A prognostic signature was constructed on basis of three SAGs, and patients in the low-risk score group had a longer survival time. The accuracy of the signature to distinguish different score groups was confirmed through principal component analysis (PCA) and the Receiver operator curves curve. The mRNA expression of the three signature genes was also verified in normal pancreatic and PDAC cell lines by RT-qPCR. The signature could independently predict the prognosis of PDAC patients and had broad applicability. Meanwhile, the nomogram predicted that 1- and 3-years survival rates were in good agreement with the observed overall survival rates. Low-risk patients had lower tumor mutational burden, and low-TMB patients had a better prognosis. Low- and high-risk patients exhibit distinct immune cell infiltration and immune checkpoint changes. By further analyzing the risk score, patients in the low-risk group were more responsive to immunotherapy and a variety of commonly used chemotherapeutic drugs. Conclusion: The prognostic signature can well predict the prognosis and assess the possibility of immunotherapy in personalized PDAC treatment.

Design, synthesis, and biological evaluation of a novel indoleamine 2,3-dioxigenase 1 (IDO1) and thioredoxin reductase (TrxR) dual inhibitor.

Targeting the Trp-Kyn pathway is an attractive approach for cancer immunotherapy. Thioredoxin reductase (TrxR) enzymes are reactive oxygen species (ROS) modulators that are involved in the tumor cell growth and survival processes. The 4-phenylimidazole scaffold is well-established as useful for indoleamine 2,3-dioxygenase 1 (IDO1) inhibition, while piperlongumine (PL) and its derivatives have been reported to be inhibitors of TrxR. To take advantage of both immunotherapy and TrxR inhibition, we designed a first-generation dual IDO1 and TrxR inhibitor (ZC0101) using the structural combination of 4-phenylimidazole and PL scaffolds. ZC0101 exhibited better dual inhibition against IDO1 and TrxR in vitro and in cell enzyme assays than the uncombined forms of 4-phenylimidazole and PL. It also showed antiproliferative activity in various cancer cell lines, and a selective killing effect between normal and cancer cells. Furthermore, ZC0101 effectively induced apoptosis and ROS accumulation in cancer cells. Knockdown of TrxR1 and IDO1 expression induced cellular enzyme inhibition and ROS accumulation effects during ZC0101 treatment, but only reduced TrxR1 expression was able to improve ZC0101's antiproliferation effect. This proof-of-concept study provides a novel strategy for cancer treatment. ZC0101 represents a promising lead compound for the development of novel antitumor agents that can also be used as a valuable probe to clarify the relationships and mechanisms of cancer immunotherapy and ROS modulators.

Multiomic Analysis of Methylation and Transcriptome Reveals a Novel Signature in Esophageal Cancer.

BACKGROUND: Epigenetic alterations have been shown to lead to human carcinogenesis. The aim of this study was to perform an integrative analysis to develop an epigenetic signature to predict overall survival (OS) of esophageal cancer. METHODS: DNA methylation and messenger RNA expression data of esophageal cancer samples were downloaded from The Cancer Genome Atlas database and were incorporated and analyzed using an R package MethylMix. Functional enrichment analysis of the methylation-related differentially expressed genes (DEGs) was performed. Epigenetic signature and nomogram associated with the OS of esophageal cancer were established by the multivariate Cox model. RESULTS: A total of 71 methylation-related DEGs were identified. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that these genes were involved in the biological process related to the initiation and progression of esophageal cancer. Two-gene (FAM24B and FAM200A) risk signature for OS was developed by multivariate Cox analysis, of which had high accuracy. The signature is independent of clinicopathological variables and indicated better predictive power than other clinicopathological variables. Moreover, we developed a novel prognostic nomogram based on risk score and 3 clinicopathological factors. CONCLUSIONS: Our study indicated possible methylation-related DEGs and established an epigenetic signature, which may provide novel insights for understanding the pathogenesis of esophageal cancer.

Integrated analysis of circular RNA-associated ceRNA network in pancreatic ductal adenocarcinoma.

Circular RNAs (circRNAs) have displayed dysregulated expression in several types of cancer. However, the functions of the majority of circRNAs in pancreatic ductal adenocarcinoma (PDAC) remain unknown. The present study aimed to investigate the expression, functions and molecular mechanisms of circRNAs in PDAC. The circRNAs, mRNAs and the microRNA (miRNAs) expression profiles were obtained from three Gene Expression Omnibus microarray datasets, and a circRNA-miRNA-mRNA and circRNA-miRNA-hubgene network was established. The interactions between proteins were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins database, and hubgenes were identified using the MCODE plugin. A total of eight differentially expressed circRNAs (DEcircRNAs), 44 differentially expressed miRNAs (DEmiRNAs), and 2,052 differentially expressed mRNAs (DEmRNAs) were identified. The present study successfully constructed a circRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network based on four circRNAs, six miRNAs and 111 mRNAs in PDAC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathways analyses indicated that DEmRNAs may participate in the pathogenesis and progression of PDAC. The protein-protein interaction network and module analysis identified six hubgenes (THBS1, FN1, TIMP3, TGFB2, ITGA1 and ITGA3). Furthermore, the circRNA-miRNA-hubgene regulatory modules were constructed based on the three DEcircRNAs, one DEmiRNAs and five DEmRNAs. In conclusion, the results of the present study improve the current understanding of the pathogenesis of PDAC.

Piperlongumine Inhibits Akt Phosphorylation to Reverse Resistance to Cisplatin in Human Non-Small Cell Lung Cancer Cells via ROS Regulation.

Resistance is a major concern when administering chemotherapy to patients with non-small cell lung cancer (NSCLC). Chemosensitizer are agents that can reverse resistance to chemotherapeutic drugs, thereby enhancing the chemosensitivity of tumor cells. Thus, their development will improve therapeutic efficacy in cancer. However, few effective chemosensitizer have been identified to date. Piperlongumine (PL) has been shown to effectively reverse resistance to chemotherapeutic drugs in several types of cancers. However, the mechanisms associated with the chemotherapy resistance reversal effect of PL and its regulation of target factors in chemotherapy resistance cells are still unclear. This study investigated the reversal effect of PL both in vitro and in vivo, and provided evidence that PL inhibited the phosphorylation of Akt via the accumulation of reactive oxygen species in chemotherapy resistance cells. Consequently, various Akt activation-dependent genes caused a reduction of drug efflux and induction of apoptosis in cisplatin-resistant A549 NSCLC cells. Our results indicate that Akt phosphorylation may play a functional role in the reversal effect of PL and contribute, at least in part, to the treatment outcomes of patients with chemotherapy resistance.

Long-noncoding RNA HOTAIR inhibits immunologic rejection of mouse leukemia cells through activating the Wnt/ß-catenin signaling pathway in a mouse model of leukemia.

Long-noncoding RNAs (lncRNAs) is involved in the development of diverse diseases, including leukemia, while the role lncRNA HOX transcript antisense RNA (HOTAIR) played in leukemia remains unclear and in need of further investigation. Therefore, this study was conducted to explore the effects of lncRNA HOTAIR on the immunologic rejection of leukemia cells through the Wnt/ß-catenin in mice. Mice were administrated with HOTAIR mimics as well as small interfering RNA HOTAIR to explore the regulatory role of HOTAIR. The numbers of white blood cell (WBC) and platelet (PLT) and the content of hemoglobin in peripheral blood (PB) were determined. The cytokine level in PB was measured. T-lymphocyte proliferation activity, Ig production by B cells, natural killer (NK) cell activity, and the proportion of cluster of differentiation 4 (CD4)/CD8 T cell subsets were detected. Expression of HOTAIR, ß-catenin, cyclinD1, GSK-3ß, and c-Myc in bone marrow was determined. It was revealed that the WBC number increased, while the PLT number along with the hemoglobin content in PB decreased with the upregulated HOTAIR. Additionally, elevated HOTAIR led to decreased levels of transforming growth factor-ß, interferon-γ, interleukin-10, and tumor necrosis factor-α in PB, proliferation activity in T-lymphocyte, and inhibited Ig production, NK cell activity, and the ratio of CD4/CD8 T cell subsets in B-lymphocyte. Furthermore, Wnt/ß-catenin was activated by overexpressing HOTAIR. Enhanced survival and proliferation were shown with increased expression of cyclinD1, GSK-3ß, and c-Myc in the bone marrow of mice induced by overexpressing HOTAIR. These results indicate that restored HOTAIR reduces the immunologic rejection of leukemia cells in mice by activating Wnt/ß-catenin pathway.

Shape-based virtual screen for the discovery of novel CDK8 inhibitor chemotypes.

With the aim of discovering novel cyclin-dependent kinase 8 (CDK8) inhibitors, a combined similarity search and molecular docking approach was employed, which led to 32 hits. Biological tests led to the discovery of several novel submicromolar inhibitors. In particular, compound C768-0769 (ZC0201) showed good CDK8 inhibitory activity, and compound ZC0201 effectively suppressed HCT-116 colorectal cancer cell proliferation by inducing G1/S transition arrest. Furthermore, modulation of phosphorylated signal transducer and activator of transcription 1 (Ser 727) (STAT1SER727), a pharmacodynamic biomarker of CDK8 activity, demonstrated that ZC0201 may cause G1/S transition arrest through CDK8 activity inhibition. Due to its good cellular activity, ZC0201 may be an ideal lead compound for further modification as a potential cancer therapeutic agent.

Comprehensive analysis of long noncoding RNA-associated competing endogenous RNA network in cholangiocarcinoma.

BACKGROUND: Long non-coding RNAs (lncRNAs) can interact with microRNAs (miRNAs) as a competing endogenous RNA (ceRNA) to regulate the expression of target genes, which can largely influence on tumorigenesis and tumor progression. However, the role of lncRNA-mediated ceRNAs in cholangiocarcinoma (CCA) remains unknown. This study aimed to develop novel lncRNAs as well as their action mechanisms in CCA. METHODS: The expression profiles of lncRNAs, miRNAs, and mRNAs of 36 CCA tissues and 9 non-tumor bile duct tissues were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed RNAs werre identified using the DESeq package in R. The ceRNA network was constructed in CCA based on bioinformatics generated from miRcode, miRTarBase, miRDB, and TargetScan. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using "DAVID 6.8" and R packages "Clusterprofile". Survival analysis was performed based on Kaplan-Meier curve analysis. RESULTS: We identified a total of 1411 differentially expressed lncRNAs, 3494 mRNAs, and 64 miRNAs between CCA and matched normal tissues. By combining the data predicted by databases with intersection RNAs, a lncRNA-miRNA-mRNA ceRNA network consisting of 116 lncRNAs, 14 miRNAs and 59 mRNAs was established. According to the survival analysis, we detected 11 DElncRNA to have a significant impact on the overall survival in patients with CCA (P < 0.05). CONCLUSIONS: Our study identified novel lncRNAs associated with CCA progression and prognosis and provided data to further understand lncRNA-mediated ceRNA regulatory mechanisms in the pathogenesis of CCA.

Nrf2 is a key factor in the reversal effect of curcumin on multidrug resistance in the HCT­8/5­Fu human colorectal cancer cell line.

Multidrug resistance (MDR) is a major concern when using chemotherapy for the treatment of patients with colorectal cancer. MDR modulators are agents that can reverse MDR and, thus, enhance the chemosensitivity of tumor cells. The development of MDR modulators can improve the therapeutic efficacies of MDR in cancer. However, few effective MDR modulators have been identified so far. Curcumin has been reported to be an effective compound in the reversal of MDR in colorectal cancer cells. However, the mechanisms associated with the reversal effect of curcumin on MDR and its regulation of target factors in MDR cells remain to be fully elucidated. 3­(4,5­dimethyl­2­thiazol)­2,5­diphenyltetrazolium bromide assays, flow cytometer apoptosis assays as well as mRNA and protein expression assays were performed in the present study, and the results confirmed the reversal effect of curcumin on HCT­8/5­Fu cells and provided evidence that activated nuclear factor erythroid 2­related factor (Nrf2) deficiency induced by the curcumin altered the B­cell lymphoma 2 (Bcl­2) associated X protein/Bcl­2 expression ratio, which led to the induction of apoptosis in HCT­8/5­Fu cells. These results indicated that Nrf2 may have a functional in the reversal effect of curcumin and contribute, at least in part, to the outcomes of chemotherapy in patients with MDR.

Development and validation of a nomogram for predicting survival in patients with gastrointestinal stromal tumours.

BACKGROUND: This study aimed to develop and validate nomograms for predicting long-term overall survival (OS) and cancer-specific survival (CSS) in gastrointestinal stromal tumours (GISTs). METHODS: Patients diagnosed with GISTs between 2004 and 2015 were selected for the study from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly separated into the training set and the validation set. Multivariate analysis was used on the training set to obtain independent prognostic factors to build nomograms for predicting 3- and 5-year OS and CSS. The discrimination and calibration plots were used to evaluate the predictive accuracy of the nomograms. RESULTS: Data for a total of 5622 patients with GISTs were collected from the SEER database. Nomograms were established based on variables that were significantly associated with OS and CSS identified by the Cox regression model. The nomograms for predicting OS and CSS displayed better discrimination power than did the SEER stage and Tumour-Node-Metastasis (TNM) staging systems (7th edition) in the training set and validation set. Calibration plots of the nomograms indicated that OS and CSS closely corresponded to actual observation. CONCLUSIONS: The nomograms were able to more accurately predict 3- and 5-year OS and CSS of patients with GISTs than were existing models.

Development and Validation of a Nomogram for Predicting Overall Survival in Pancreatic NeuroendocrineTumors.

BACKGROUND: The objective of current study was to develop and validate a nomogram to predict overall survival in pancreatic neuroendocrine tumors (PNETs). METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was queried for patients with PNETs between 2004 and 2015. Patients were randomly separated into the training set and the validation set. Cox regression model was used in training set to obtain independent prognostic factors to develop a nomogram for predicting overall survival (OS). The discrimination and calibration plots were used to evaluate the predictive accuracy of the nomogram. RESULTS: A total of 3142 patients with PNETs were collected from the SEER database. Sex, age, marital status, primary site, TNM stage, tumor grade, and therapy were associated with OS in the multivariate models. A nomogram was constructed based on these variables. The nomogram for predicting OS displayed better discrimination power than the Tumor-Node-Metastasis (TNM) stage systems 7th edition in the training set and validation set. The calibration curve indicated that the nomogram was able to accurately predict 3- and 5-year OS. CONCLUSIONS: The nomogram which could predict 3- and 5-year OS were established in this study. Our nomogram showed a good performance, suggesting that it could be served as an effective tool for prognostic evaluation of patients with PNETs.

A nomogram to predict overall survival for biliary tract cancer.

BACKGROUND: The aim of the study was to develop and validate a nomogram to predict overall survival (OS) in biliary tract cancer (BTC). PATIENTS AND METHODS: Patients diagnosed with BTC between 2004 and 2014 were selected for the study from the Surveillance, Epidemiology, and End Results (SEER) database. All patients were randomly allocated to 2 sets, the training set (n = 8,869) and the validation set (n = 8,766), for the purposes of validation. The prognostic effects of each variable were examined using univariate and multivariate analyses. Cox regression models and a nomogram were developed based on significant prognostic factors. The predictive and discriminatory capacity of the nomogram was evaluated by Harrell's concordance index (C-index) and calibration plots. RESULTS: Data of 17,635 patients with BTC were collected from the SEER database. Age; race; tumor site; tumor grade; T, N, and M stage; marital status; and therapy were associated with survival in the multivariate models. All these factors were integrated to construct the nomogram. The nomogram for predicting OS displayed better discrimination power than the tumor-node-metastasis (TNM) stage system 6th edition in the training set and validation set. The calibration curve indicated that the nomogram was able to accurately predict 3- and 5-year OS. CONCLUSION: This predictive model has the potential to provide an individualized risk estimate of survival in patients with BTC.