scholarly journals Pancreatic cancer survival prediction via inflammatory serum markers

2022 ◽  
Author(s):  
Mira Lanki ◽  
Hanna Seppänen ◽  
Harri Mustonen ◽  
Aino Salmiheimo ◽  
Ulf-Håkan Stenman ◽  
...  
Keyword(s):  
Cox Regression ◽  
Reference Model ◽  
Serum Markers ◽  
Ductal Adenocarcinoma ◽  
Survival Prediction ◽  
High Survival ◽  
Survival Times ◽  
Survival Group ◽  
Preoperative Serum ◽  
Hs Crp

Abstract Background For prognostic evaluation of pancreatic ductal adenocarcinoma (PDAC), the only well-established serum marker is carbohydrate antigen CA19-9. To improve the accuracy of survival prediction, we tested the efficacy of inflammatory serum markers. Methods A preoperative serum panel comprising 48 cytokines plus high-sensitivity CRP (hs-CRP) was analyzed in 173 stage I–III PDAC patients. Analysis of the effect of serum markers on survival utilized the Cox regression model, with the most promising cytokines chosen with the aid of the lasso method. We formed a reference model comprising age, gender, tumor stage, adjuvant chemotherapy status, and CA19-9 level. Our prognostic study model incorporated these data plus hs-CRP and the cytokines. We constructed time-dependent ROC curves and calculated an integrated time-averaged area under the curve (iAUC) for both models from 1 to 10 years after surgery. Results Hs-CRP and the cytokines CTACK, MIF, IL-1β, IL-3, GRO-α, M-CSF, and SCF, were our choices for the prognostic study model, in which the iAUC was 0.837 (95% CI 0.796–0.902), compared to the reference model’s 0.759 (95% CI 0.691–0.836, NS). These models divided the patients into two groups based on the maximum value of Youden’s index at 7.5 years. In our study model, 60th percentile survival times were 4.5 (95% CI 3.7–NA) years (predicted high-survival group, n = 34) and 1.3 (95% CI 1.0–1.7) years (predicted low-survival group, n = 128), log rank p < 0.001. By the reference model, the 60th percentile survival times were 2.8 (95% CI 2.1–4.4) years (predicted high-survival group, n = 44) and 1.3 (95% CI 1.0–1.7) years (predicted low-survival group, n = 118), log rank p < 0.001. Conclusion Hs-CRP and the seven cytokines added to the reference model including CA19-9 are potential prognostic factors for improved survival prediction for PDAC patients.

scholarly journals Development and Validation of a 7-Gene Prognostic Signature to Improve Survival Prediction in Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Zengyu Feng ◽  
Hao Qian ◽  
Kexian Li ◽  
Jianyao Lou ◽  
Yulian Wu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Disease Free Survival ◽  
Roc Curves ◽  
Gene Signature ◽  
Ductal Adenocarcinoma ◽  
Survival Prediction ◽  
Cox Regression Analysis

Background: Previous prognostic signatures of pancreatic ductal adenocarcinoma (PDAC) are mainly constructed to predict the overall survival (OS), and their predictive accuracy needs to be improved. Gene signatures that efficaciously predict both OS and disease-free survival (DFS) are of great clinical significance but are rarely reported.Methods: Univariate Cox regression analysis was adopted to screen common genes that were significantly associated with both OS and DFS in three independent cohorts. Multivariate Cox regression analysis was subsequently performed on the identified genes to determine an optimal gene signature in the MTAB-6134 training cohort. The Kaplan–Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were employed to assess the predictive accuracy. Biological process and pathway enrichment analyses were conducted to elucidate the biological role of this signature.Results: Multivariate Cox regression analysis determined a 7-gene signature that contained ASPH, DDX10, NR0B2, BLOC1S3, FAM83A, SLAMF6, and PPM1H. The signature had the ability to stratify PDAC patients with different OS and DFS, both in the training and validation cohorts. ROC curves confirmed the moderate predictive accuracy of this signature. Mechanically, the signature was related to multiple cancer-related pathways.Conclusion: A novel OS and DFS prediction model was constructed in PDAC with multi-cohort and cross-platform compatibility. This signature might foster individualized therapy and appropriate management of PDAC patients.

scholarly journals Screening the genome for HCC-specific CpG methylation signatures as biomarkers for diagnosis and prognosis evaluation

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Rui-kun Zhang ◽  
Jia-lin Liu
Keyword(s):  
Cox Regression ◽  
Malignant Tumors ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Training Set ◽  
Cpg Sites ◽  
Diagnosis And Prognosis ◽  
Patient Prognosis ◽  
Prognosis Evaluation

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common and invasive malignant tumors in the world. The change in DNA methylation is a key event in HCC. Methods Methylation datasets for HCC and 17 other types of cancer were downloaded from The Cancer Genome Atlas (TCGA). The CpG sites with large differences in methylation between tumor tissues and paracancerous tissues were identified. We used the HCC methylation dataset downloaded from the TCGA as the training set and removed the overlapping sites among all cancer datasets to ensure that only CpG sites specific to HCC remained. Logistic regression analysis was performed to select specific biomarkers that can be used to diagnose HCC, and two datasets—GSE157341 and GSE54503—downloaded from GEO as validation sets were used to validate our model. We also used a Cox regression model to select CpG sites related to patient prognosis. Results We identified 6 HCC-specific methylated CpG sites as biomarkers for HCC diagnosis. In the training set, the area under the receiver operating characteristic (ROC) curve (AUC) for the model containing all these sites was 0.971. The AUCs were 0.8802 and 0.9711 for the two validation sets from the GEO database. In addition, 3 other CpG sites were analyzed and used to create a risk scoring model for patient prognosis and survival prediction. Conclusions Through the analysis of HCC methylation datasets from the TCGA and Gene Expression Omnibus (GEO) databases, potential biomarkers for HCC diagnosis and prognosis evaluation were ascertained.

scholarly journals IMPDH2 and HPRT expression and a prognostic significance in preoperative and postoperative patients with osteosarcoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Parunya Chaiyawat ◽  
Areerak Phanphaisarn ◽  
Nutnicha Sirikaew ◽  
Jeerawan Klangjorhor ◽  
Viraporn Thepbundit ◽  
...  
Keyword(s):  
Overall Survival ◽  
Cox Regression ◽  
Expression Patterns ◽  
Prognostic Significance ◽  
Drug Repurposing ◽  
Survival Times ◽  
Metabolizing Enzymes ◽  
Hypoxanthine Guanine Phosphoribosyltransferase ◽  
High Grade Osteosarcoma ◽  
Enneking Stage

AbstractOsteosarcoma is one of the most aggressive bone tumors in children and adolescents. Development of effective therapeutic options is still lacking due to the complexity of the genomic background. In previous work, we applied a proteomics-guided drug repurposing to explore potential treatments for osteosarcoma. Our follow-up study revealed an FDA-approved immunosuppressant drug, mycophenolate mofetil (MMF) targeting inosine-5′-phosphate dehydrogenase (IMPDH) enzymes, has an anti-tumor effect that appeared promising for further investigation and clinical trials. Profiling of IMPDH2 and hypoxanthine–guanine phosphoribosyltransferase (HPRT), key purine-metabolizing enzymes, could deepen understanding of the importance of purine metabolism in osteosarcoma and provide evidence for expanded use of MMF in the clinic. In the present study, we investigated levels of IMPDH2, and HPRT in biopsy of 127 cases and post-chemotherapy tissues in 20 cases of high-grade osteosarcoma patients using immunohistochemical (IHC) analysis. Cox regression analyses were performed to determine prognostic significance of all enzymes. The results indicated that low levels of HPRT were significantly associated with a high Enneking stage (P = 0.023) and metastatic status (P = 0.024). Univariate and multivariate analyses revealed that patients with low HPRT expression have shorter overall survival times [HR 1.70 (1.01–2.84), P = 0.044]. Furthermore, high IMPDH2/HPRT ratios were similarly associated with shorter overall survival times [HR 1.67 (1.02–2.72), P = 0.039]. Levels of the enzymes were also examined in post-chemotherapy tissues. The results showed that high IMPDH2 expression was associated with shorter metastasis-free survival [HR 7.42 (1.22–45.06), P = 0.030]. These results suggest a prognostic value of expression patterns of purine-metabolizing enzymes for the pre- and post-chemotherapy period of osteosarcoma treatment.

A High C-Reactive Protein Level on Postoperative Day 7 is Associated with Poor Survival of Patients with Pancreatic Ductal Adenocarcinoma after Resection

2021 ◽  
pp. 000313482110234
Author(s):  
Masaji Tani ◽  
Hiroya Iida ◽  
Hiromitsu Maehira ◽  
Haruki Mori ◽  
Toru Miyake ◽  
...  
Keyword(s):  
Adjuvant Chemotherapy ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Median Overall Survival ◽  
Cox Regression ◽  
Ductal Adenocarcinoma ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Retrospective View ◽  
Node Metastases ◽  
Common Malignancy

Introduction Pancreatic ductal adenocarcinoma (PDAC) is a common malignancy. While inflammation-related biomarkers influence patient survival after resection, it has not been known whether postoperative inflammations affect the survival of PDAC patients or not. Methods It was investigated whether the universal biomarkers on postoperative day (POD) 7 affect the survival of PDAC patients in the retrospective view, and univariate and multivariate analyses were performed via the Cox regression method. Results Overall, 108 consecutive patients underwent resection; 98 (90.7%) had T3 disease and 73 (67.6%) had lymph node metastases. Thirty-four patients (31.5%) experienced postoperative complications. Compared with preoperative values, the white blood cell count and C-reactive protein (CRP) level on POD 7 were significantly elevated ( P < .001 for both); conversely, the lymphocyte count was significantly reduced ( P < .001). Among 108 patients, 72 received adjuvant chemotherapy. The median overall survival was 21.0 months; the 5-year survival rate was 22.3%. On multivariate analysis, receiving adjuvant chemotherapy and low CRP levels on POD 7 (<7.6 mg/dL) were prognosticators of better survival. However, the CD classification was not a prognosticator of survival after resection. Conclusions Adjuvant chemotherapy and postoperative low CRP levels on POD 7 were prognosticators of better survival of PDAC patients after resection. Surgeons should be aware of managing postoperative infections because a high postoperative CRP level is related with unfavorable survival.

Identification of 6 gene markers for survival prediction in osteosarcoma cases based on multi-omics analysis

2021 ◽  
pp. 153537022199201
Author(s):  
Runmin Li ◽  
Guosheng Wang ◽  
ZhouJie Wu ◽  
HuaGuang Lu ◽  
Gen Li ◽  
...  
Keyword(s):  
Feature Selection ◽  
Cox Regression ◽  
Cancer Genomics ◽  
Predictive Performance ◽  
Training Group ◽  
Gene Expression Omnibus ◽  
Survival Prediction ◽  
Screening Process ◽  
Gene Markers ◽  
Validation Set

Multiple-omics sequencing information with high-throughput has laid a solid foundation to identify genes associated with cancer prognostic process. Multiomics information study is capable of revealing the cancer occurring and developing system according to several aspects. Currently, the prognosis of osteosarcoma is still poor, so a genetic marker is needed for predicting the clinically related overall survival result. First, Office of Cancer Genomics (OCG Target) provided RNASeq, copy amount variations information, and clinically related follow-up data. Genes associated with prognostic process and genes exhibiting copy amount difference were screened in the training group, and the mentioned genes were integrated for feature selection with least absolute shrinkage and selection operator (Lasso). Eventually, effective biomarkers received the screening process. Lastly, this study built and demonstrated one gene-associated prognosis mode according to the set of the test and gene expression omnibus validation set; 512 prognosis-related genes ( P < 0.01), 336 copies of amplified genes ( P < 0.05), and 36 copies of deleted genes ( P < 0.05) were obtained, and those genes of the mentioned genomic variants display close associations with tumor occurring and developing mechanisms. This study generated 10 genes for candidates through the integration of genomic variant genes as well as prognosis-related genes. Six typical genes (i.e. MYC, CHIC2, CCDC152, LYL1, GPR142, and MMP27) were obtained by Lasso feature selection and stepwise multivariate regression study, many of which are reported to show a relationship to tumor progressing process. The authors conducted Cox regression study for building 6-gene sign, i.e. one single prognosis-related element, in terms of cases carrying osteosarcoma. In addition, the samples were able to be risk stratified in the training group, test set, and externally validating set. The AUC of five-year survival according to the training group and validation set reached over 0.85, with superior predictive performance as opposed to the existing researches. Here, 6-gene sign was built to be new prognosis-related marking elements for assessing osteosarcoma cases’ surviving state.

scholarly journals Survival Rate of 1008 Short Dental Implants with 21 Months of Average Follow-Up: A Retrospective Study

2020 ◽  
Vol 9 (12) ◽  
pp. 3943
Author(s):  
João Caramês ◽  
Ana Catarina Pinto ◽  
Gonçalo Caramês ◽  
Helena Francisco ◽  
Joana Fialho ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Survival Rate ◽  
Dental Implants ◽  
Cox Regression ◽  
Survival Rates ◽  
High Survival ◽  
Implant Survival ◽  
Posterior Maxilla ◽  
Clinical Records

This retrospective study evaluated the survival rate of short, sandblasted acid-etched surfaced implants with 6 and 8 mm lengths with at least 120 days of follow-up. Data concerning patient, implant and surgery characteristics were retrieved from clinical records. Sandblasted and acid-etched (SLA)-surfaced tissue-level 6 mm (TL6) or 8 mm (TL8) implants or bone-level tapered 8 mm (BLT8) implants were used. Absolute and relative frequency distributions were calculated for qualitative variables and mean values and standard deviations for quantitative variables. A Cox regression model was performed to verify whether type, length and/or width influence the implant survival. The cumulative implant survival rate was assessed by time-to-event analyses (Kaplan–Meier estimator). In all, 513 patients with a mean age of 58.00 ± 12.44 years received 1008 dental implants with a mean follow-up of 21.57 ± 10.77 months. Most implants (78.17%) presented a 4.1 mm diameter, and the most frequent indication was a partially edentulous arch (44.15%). The most frequent locations were the posterior mandible (53.97%) and the posterior maxilla (31.55%). No significant differences were found in survival rates between groups of type, length and width of implant with the cumulative rate being 97.7% ± 0.5%. Within the limitations of this study, the evaluated short implants are a predictable option with high survival rates during the follow-up without statistical differences between the appraised types, lengths and widths.

scholarly journals Different chest CT scoring systems in patients with COVID-19: could baseline CT be a helpful tool in predicting survival in patients with matched ages and co-morbid conditions?

2021 ◽  
pp. 028418512110063
Author(s):  
Okan Dilek ◽  
Emin Demirel ◽  
Hüseyin Akkaya ◽  
Mehmet Cenk Belibagli ◽  
Gokhan Soker ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Severity Score ◽  
Area Under The Curve ◽  
Scoring Systems ◽  
Survival Prediction ◽  
Success Rates ◽  
Survival Times ◽  
The Mean ◽  
Early Decision ◽  
Age Range

Background Computed tomography (CT) gives an idea about the prognosis in patients with COVID-19 lung infiltration. Purpose To evaluate the success rates of various scoring methods utilized in order to predict survival periods, on the basis of the imaging findings of COVID-19. Another purpose, on the other hand, was to evaluate the agreements among the evaluating radiologists. Material and Methods A total of 100 cases of known COVID-19 pneumonia, of which 50 were deceased and 50 were living, were included in the study. Pre-existing scoring systems, which were the Total Severity Score (TSS), Chest Computed Tomography Severity Score (CT-SS), and Total CT Score, were utilized, together with the Early Decision Severity Score (ED-SS), which was developed by our team, to evaluate the initial lung CT scans of the patients obtained at their initial admission to the hospital. The scans were evaluated retrospectively by two radiologists. Area under the curve (AUC) values were acquired for each scoring system, according to their performances in predicting survival times. Results The mean age of the patients was 61 ± 14.85 years (age range = 18–87 years). There was no difference in co-morbidities between the living and deceased patients. The survival predicted AUC values of ED-SS, CT-SS, TSS, and Total CT Score systems were 0.876, 0.823, 0.753, and 0.744, respectively. Conclusion Algorithms based on lung infiltration patterns of COVID-19 may be utilized for both survival prediction and therapy planning.

scholarly journals Prognostic Nomogram for Patients With Pancreatic Ductal Adenocarcinoma of Pancreatic Head After Pancreaticoduodenectomy

2021 ◽  
Vol 15 ◽  
pp. 117955492110241
Author(s):  
Hongkai Zhuang ◽  
Zixuan Zhou ◽  
Zuyi Ma ◽  
Shanzhou Huang ◽  
Yuanfeng Gong ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Perineural Invasion ◽  
Cox Regression ◽  
Area Under The Curve ◽  
Pancreatic Head ◽  
Ductal Adenocarcinoma ◽  
R0 Resection ◽  
Cox Regression Analysis ◽  
Ajcc Stage

Background: The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) of pancreatic head remains poor, even after potentially curative R0 resection. The aim of this study was to develop an accurate model to predict patients’ prognosis for PDAC of pancreatic head following pancreaticoduodenectomy. Methods: We retrospectively reviewed 112 patients with PDAC of pancreatic head after pancreaticoduodenectomy in Guangdong Provincial People’s Hospital between 2014 and 2018. Results: Five prognostic factors were identified using univariate Cox regression analysis, including age, histologic grade, American Joint Committee on Cancer (AJCC) Stage 8th, total bilirubin (TBIL), CA19-9. Using all subset analysis and multivariate Cox regression analysis, we developed a nomogram consisted of age, AJCC Stage 8th, perineural invasion, TBIL, and CA19-9, which had higher C-indexes for OS (0.73) and RFS (0.69) compared with AJCC Stage 8th alone (OS: 0.66; RFS: 0.67). The area under the curve (AUC) values of the receiver operating characteristic (ROC) curve for the nomogram for OS and RFS were significantly higher than other single parameter, which are AJCC Stage 8th, age, perineural invasion, TBIL, and CA19-9. Importantly, our nomogram displayed higher C-index for OS than previous reported models, indicating a better predictive value of our model. Conclusions: A simple and practical nomogram for patient prognosis in PDAC of pancreatic head following pancreaticoduodenectomy was established, which shows satisfactory predictive efficacy and deserves further evaluation in the future.

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