scholarly journals Immune Subtypes Based on Immune-Related lncRNA: Differential Prognostic Mechanism of Pancreatic Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Qiyao Zhang ◽  
Zhihui Wang ◽  
Xiao Yu ◽  
Menggang Zhang ◽  
Qingyuan Zheng ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Immune Response ◽  
Prognostic Model ◽  
Effective Means ◽  
Prognostic Models ◽  
Functional Differences ◽  
Degree Of Malignancy ◽  
Prognostic Prediction ◽  
Cox Analysis

Pancreatic cancer consists one of tumors with the highest degree of malignancy and the worst prognosis. To date, immunotherapy has become an effective means to improve the prognosis of patients with pancreatic cancer. Long non-coding RNAs (lncRNAs) have also been associated with the immune response. However, the role of immune-related lncRNAs in the immune response of pancreatic cancer remains unclear. In this study, we identified immune-related lncRNA pairs through a new combinatorial algorithm, and then clustered and deeply analyzed the immune characteristics and functional differences between subtypes. Subsequently, the prognostic model of 3 candidate lncRNA pairs was determined by multivariate COX analysis. The results showed significant prognostic differences between the C1 and C2 subtypes, which may be due to the differential infiltration of CTL and NK cells and the activation of tumor-related pathways. The prognostic model of the 3 lncRNA pairs (AC244035.1_vs._AC063926.1, AC066612.1_vs._AC090124.1, and AC244035.1_vs._LINC01885) was established, which exhibits stable and effective prognostic prediction performance. These 3 lncRNA pairs may regulate the anti-tumor effect of immune cells through ion channel pathways. In conclusion, our research demonstrated the panoramic differences in immune characteristics between subtypes and stable prognostic models, and identified new potential targets for immunotherapy.

scholarly journals Haptoglobin and Its Related Protein, Zonulin—What Is Their Role in Spondyloarthropathy?

2021 ◽  
Vol 10 (5) ◽  
pp. 1131
Author(s):  
Magdalena Chmielińska ◽  
Marzena Olesińska ◽  
Katarzyna Romanowska-Próchnicka ◽  
Dariusz Szukiewicz
Keyword(s):  
Immune Response ◽  
Free Radicals ◽  
Immune System ◽  
Potential Role ◽  
Acute Phase Protein ◽  
Related Protein ◽  
Functional Differences ◽  
Phase Protein ◽  
Its Polymorphism

Haptoglobin (Hp) is an acute phase protein which supports the immune response and protects tissues from free radicals. Its concentration correlates with disease activity in spondyloarthropathies (SpAs). The Hp polymorphism determines the functional differences between Hp1 and Hp2 protein products. The role of the Hp polymorphism has been demonstrated in many diseases. In particular, the Hp 2-2 phenotype has been associated with the unfavorable course of some inflammatory and autoimmune disorders. Its potential role in modulating the immune system in SpA is still unknown. This article contains pathophysiological considerations on the potential relationship between Hp, its polymorphism and SpA.

scholarly journals Identification of Multi-omics Biomarkers and Construction of the Novel Prognostic Model for Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Xiaokai Yan ◽  
Chiying Xiao ◽  
Kunyan Yue ◽  
Min Chen ◽  
Hang Zhou
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Prognostic Model ◽  
Prognostic Models ◽  
Prognostic Indicators ◽  
Omics Data ◽  
Training Set ◽  
Test Set ◽  
Model Based ◽  
Cox Analysis

Abstract Background: Change in the genome plays a crucial role in cancerogenesis and many biomarkers can be used as effective prognostic indicators in diverse tumors. Currently, although many studies have constructed some predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance of which is unsatisfactory. To fill this shortcoming, we hope to construct a novel and accurate prognostic model with multi-omics data to guide prognostic assessments of HCC. Methods: The TCGA training set was used to identify crucial biomarkers and construct single-omic prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. Then the performances of single-omic models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), and receiver operating characteristic (ROC) curve, in the TCGA test set and external cohorts. Besides, a comprehensive model based on multi-omics data was constructed via multiple Cox analysis, and the performance of which was evaluated in the TCGA training set and TCGA test set. Results: We identified 16 key mRNAs, 20 key lncRNAs, 5 key miRNAs, 5 key CNV genes, and 7 key SNPs which were significantly associated with the prognosis of HCC, and constructed 5 single-omic models which showed relatively good performance in prognostic prediction with c-index ranged from 0.63 to 0.75 in the TCGA training set and test set. Besides, we validated the mRNA model and the SNP model in two independent external datasets respectively, and good discriminating abilities were observed through survival analysis (P < 0.05). Moreover, the multi-omics model based on mRNA, lncRNA, miRNA, CNV, and SNP information presented a quite strong predictive ability with c-index over 0.80 and all AUC values at 1,3,5-years more than 0.84.Conclusion: In this study, we identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC, and constructed five single-omic models and an integrated multi-omics model that may provide effective and reliable guides for prognosis assessment and treatment decision-making.

Abstract 2676: Role of the RON kinase in the regulation of the antitumor immune response in pancreatic cancer

2017 ◽  
Author(s):  
Alex Cazes ◽  
Michele L. Babicky ◽  
Jeffery Chakedis ◽  
Divya Sood ◽  
Dawn Jaquish ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Immune Response ◽  
Antitumor Immune Response

scholarly journals A Novel Autophagy-Related IncRNAs Signature for Prognostic Prediction and Clinical Value in Patients With Pancreatic Cancer

2020 ◽  
Vol 8 ◽  
Author(s):  
Zhengdong Deng ◽  
Xiangyu Li ◽  
Yuanxin Shi ◽  
Yun Lu ◽  
Wei Yao ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Risk Scores ◽  
Migration And Invasion ◽  
Characteristic Analysis ◽  
Cox Regression Analysis ◽  
Validation Set ◽  
Prognostic Prediction

Autophagy is an important bioprocess throughout the occurrence and development of cancer. However, the role of autophagy-related lncRNAs in pancreatic cancer (PC) remains obscure. In the study, we identified the autophagy-related lncRNAs (ARlncRNAs) and divided the PC patients from The Cancer Genome Atlas into training and validation set. Firstly, we constructed a signature in the training set by the least absolute shrinkage and selection operator penalized cox regression analysis and the multivariate cox regression analysis. Then, we validated the independent prognostic role of the risk signature in both training and validation set with survival analysis, receiver operating characteristic analysis, and Cox regression. The nomogram was established to demonstrate the predictive power of the signature. Moreover, high risk scores were significantly correlated to worse outcomes and severe clinical characteristics. The Pearson’s analysis between risk scores with immune cells infiltration, tumor mutation burden, and the expression level of chemotherapy target molecules indicated that the signature could predict efficacy of immunotherapy and targeted therapy. Next, we constructed an lncRNA–miRNA–mRNA regulatory network and identified several potential small molecule drugs in the Connectivity Map (CMap). What’s more, quantitative real-time PCR (qRT-PCR) analysis showed that serum LINC01559 could serve as a diagnostic biomarker. In vitro analysis showed inhibition of LINC01559 suppressed PC cell proliferation, migration, and invasion. Additionally, silencing LINC01559 suppressed gemcitabine-induced autophagy and promoted the sensitivity of PC cells to gemcitabine. In conclusion, we identified a novel ARlncRNAs signature with valuable clinical utility for reliable prognostic prediction and personalized treatment of PC patients. And inhibition of LINC01559 might be a novel strategy to overcome chemoresistance.

scholarly journals Identification of Multi-omics Biomarkers and Construction of the Novel Prognostic Model for Hepatocellular Carcinoma

2022 ◽  
Author(s):  
Xiaokai Yan ◽  
Chiying Xiao ◽  
Kunyan Yue ◽  
Min Chen ◽  
Hang Zhou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Prognostic Model ◽  
Prognostic Models ◽  
Prognostic Indicators ◽  
Omics Data ◽  
Training Set ◽  
Test Set ◽  
Model Based ◽  
Cox Analysis

Abstract Background: Change in the genome plays a crucial role in cancerogenesis and many biomarkers can be used as effective prognostic indicators in diverse tumors. Currently, although many studies have constructed some predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance of which is unsatisfactory. To fill this shortcoming, we hope to construct a novel and accurate prognostic model with multi-omics data to guide prognostic assessments of HCC. Methods: The TCGA training set was used to identify crucial biomarkers and construct single-omic prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. Then the performances of single-omic models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), and receiver operating characteristic (ROC) curve, in the TCGA test set and external cohorts. Besides, a comprehensive model based on multi-omics data was constructed via multiple Cox analysis, and the performance of which was evaluated in the TCGA training set and TCGA test set. Results: We identified 16 key mRNAs, 20 key lncRNAs, 5 key miRNAs, 5 key CNV genes, and 7 key SNPs which were significantly associated with the prognosis of HCC, and constructed 5 single-omic models which showed relatively good performance in prognostic prediction with c-index ranged from 0.63 to 0.75 in the TCGA training set and test set. Besides, we validated the mRNA model and the SNP model in two independent external datasets respectively, and good discriminating abilities were observed through survival analysis (P < 0.05). Moreover, the multi-omics model based on mRNA, lncRNA, miRNA, CNV, and SNP information presented a quite strong predictive ability with c-index over 0.80 and all AUC values at 1,3,5-years more than 0.84.Conclusion: In this study, we identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC, and constructed five single-omic models and an integrated multi-omics model that may provide effective and reliable guides for prognosis assessment and treatment decision-making.

scholarly journals Identification of Multi-omics Biomarkers and Construction of the Novel Prognostic Model for Hepatocellular Carcinoma

2022 ◽  
Author(s):  
Xiaokai Yan ◽  
Chiying Xiao ◽  
Kunyan Yue ◽  
Min Chen ◽  
Hang Zhou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Treatment Decision ◽  
Prognostic Models ◽  
Prognostic Indicators ◽  
The Novel ◽  
Decision Curve Analysis ◽  
Treatment Decision Making ◽  
Assessment And Treatment ◽  
Cox Analysis

Abstract Genome changes play a crucial role in carcinogenesis, and many biomarkers can be used as effective prognostic indicators in various tumours. Although previous studies have constructed many predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance is unsatisfactory. To fill this shortcoming, we hope to build a more accurate predictive model to guide prognostic assessments of HCC. We use the TCGA to identify crucial biomarkers and construct single-omic prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. The performances of single-omic models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), and receiver operating characteristic (ROC) curve. A multi-omics model was built and evaluated by decision curve analysis (DCA), C-index, and ROC analysis. Multiple mRNAs, lncRNAs, miRNAs, CNV genes, and SNPs were significantly associated with the prognosis of HCC. Five single-omic models were constructed, and the mRNA and lncRNA models showed good performance with c-indexes over 0.70. The multi-omics model presented a quite predictive solid ability with a c-index over 0.80. In this study, we identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC. In addition, we constructed multiple single-omic models and an integrated multi-omics model that may provide practical and reliable guides for prognosis assessment and treatment decision-making.

scholarly journals Development and Validation of a Ferroptosis-related Prognostic Model in Pancreatic Cancer

2021 ◽  
Author(s):  
Chen-jie Qiu ◽  
Xue-bing Wang ◽  
Zi-ruo Zheng ◽  
Chao-zhi Yang ◽  
Kai Lin ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
T Cell ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Risk Group ◽  
High Risk Group ◽  
Immune Cell Infiltration ◽  
Cox Analysis

Abstract Background: With the development of genomics, ferroptosis has been determined to be highly important in cancer. The purpose of this study was to identify ferroptosis-related genes (FRGs) associated with the prognosis of pancreatic cancer and to construct a prognostic model based on FRGs. Methods: Based on pancreatic cancer data obtained from The Cancer Genome Atlas (TCGA) database, we employed univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox analysis to establish the prognostic model from 232 FRGs. A nomogram was constructed by combining the prognostic model and clinicopathological features. Gene Expression Omnibus (GEO) datasets and tissue samples obtained from our center were utilized to validate the prognostic model. Relationship between risk score and immune cell infiltration was explored by CIBERSORT and TIMER.Results: The prognostic model was established based on four FRGs (ENPP2, ATG4D, SLC2A1 and MAP3K5) and can be an independent risk factor in pancreatic cancer (HR 1.648, 95% CI 1.335-2.035, p < 0.001). Based on the median risk score, patients were divided into a high-risk group and a low-risk group. The KM curve indicated that the overall survival (OS) of the low-risk group was significantly better than that of the high-risk group. The nomogram showed that the prognostic model was the most important element. Gene set enrichment analysis (GSEA) identified three key pathways, namely, TGFβ signaling, HIF signaling pathway and adherens junction. GSE57495, GSE62452 and 88 pancreatic cancer tissues from our center were utilized to validate the prognostic model. The prognostic model can also affect the immune cell infiltration, such as macrophages M0, M1, CD4+T cell and CD8+T cell. Conclusion: A ferroptosis-related prognostic model can be employed to predict the prognosis of pancreatic cancer. Ferroptosis can be an important marker and immunotherapy can be a potential therapeutic target for pancreatic cancer.

Role of intratumoral tertiary lymphoid structures as key modulators in the antitumor immune response in pancreatic cancer

Pancreatology ◽  
2020 ◽  
Vol 20 ◽  
pp. S125
Author(s):  
C. Mota Reyes ◽  
R. Istvanffy ◽  
O. Safak ◽  
B. Konukiewitz ◽  
A. Muckenhuber ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Immune Response ◽  
Antitumor Immune Response ◽  
Tertiary Lymphoid Structures ◽  
Lymphoid Structures

Prognostic model for survival in patients with advanced pancreatic cancer receiving palliative chemotherapy.

2015 ◽  
Vol 33 (3_suppl) ◽  
pp. 248-248
Author(s):  
Yu Uneno ◽  
Tadayuki Kou ◽  
Masashi Kanai ◽  
Michio Yamamoto ◽  
Peng Xue ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Poor Prognosis ◽  
Prognostic Model ◽  
Palliative Chemotherapy ◽  
Cox Regression ◽  
Advanced Pancreatic Cancer ◽  
Prognostic Index ◽  
Prognostic Models

248 Background: The prognosis of patients with advanced pancreatic cancer (APC) is extremely poor. Several clinical and laboratory factors have been known to be associated with prognosis of APC patients. However, there are few clinically available prognostic models predicting survival in APC patients receiving palliative chemotherapy. Methods: To construct a prognostic model to predict survival in APC patients receiving palliative chemotherapy, we analyzed the clinical data from 306 consecutive patients with pathologically confirmed APC who received palliative chemotherapy. We selected six independent prognostic factors which remained independent prognostic factors after multivariate analysis. Thereafter, we rounded the regression coefficient (β) for each independent prognostic factor derived from the Cox regression equation (HR = eβ) and developed a prognostic index (PI). Results: Developed prognostic index (PI) was as follows: PI = 2 (if performance status score 2–3) + 1 (if metastatic disease) + 1 (if initially unresectable disease) + 1 (if carcinoembryonic antigen level ≥5.0 ng/ml) + 1 (if carbohydrate antigen 19-9 level ≥1000 U/ml) + 2 (if neutrophil–lymphocyte ratio ≥5). The patients were classified into three prognostic groups: favorable (PI 0–1, n = 73), intermediate (PI 2–3, n = 145), and poor prognosis (PI 4–8, n = 88). The median overall survival for each prognostic group was 16.5, 12.3 and 6.2 months, respectively, and the 1-year survival rates were 67.3%, 51.3%, and 19.1%, respectively (P < 0.01). The c index of the model was 0.658. This model was well calibrated to predict 1-year survival, in which overestimation (2.4% and 0.2% in the favorable and poor prognosis groups, respectively) and underestimation (3.6% in the intermediate prognosis group) were observed. Conclusions: This prognostic model based on readily available clinical factors would help clinicians in estimating the overall survival in APC patients receiving palliative chemotherapy.

scholarly journals Role of different immune cells and metabolic pathways in modulating the immune response in pancreatic cancer (Review)

2020 ◽  
Vol 22 (6) ◽  
pp. 4981-4991
Author(s):  
Nnenna Elebo ◽  
Pascaline Fru ◽  
Jones Omoshoro‑Jones ◽  
Geoffrey Patrick Candy ◽  
Ekene Nweke
Keyword(s):  
Pancreatic Cancer ◽  
Immune Response ◽  
Immune Cells ◽  
Metabolic Pathways ◽  
Cancer Review
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