scholarly journals Assessment of Clinicopathological Characteristics and Development of an Individualized Prognostic Model for Patients With Hepatoid Adenocarcinoma of the Stomach

2021 ◽  
Vol 4 (10) ◽  
pp. e2128217
Author(s):  
Jian-Xian Lin ◽  
Zu-Kai Wang ◽  
Qing-Qi Hong ◽  
Peng Zhang ◽  
Zi-Zhen Zhang ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Clinicopathological Characteristics ◽  
Hepatoid Adenocarcinoma

Aberrant β-catenin activity in hepatoid adenocarcinoma of the stomach

2020 ◽  
Vol 20 ◽  
Author(s):  
Nan Wang ◽  
Rui Kong ◽  
Wei Han ◽  
Jie Lu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Pearson Correlation ◽  
Clinicopathological Characteristics ◽  
Cancer Tissue ◽  
Hepatoid Adenocarcinoma ◽  
Tumor Initiating Cells ◽  
Significant Difference ◽  
Diagnostic Confusion ◽  
Hematoxylin And Eosin ◽  
Cancer Tissues

Background: Hepatoid adenocarcinoma of the stomach (HAS) has been recognized as a rare primary gastric tumor characterized by hepatocellular carcinoma-like histology. HAS often causes diagnostic confusion with conventional gastric adenocarcinoma (CGA) due to the difficulty to detect hepatoid differentiation solely based on findings from hematoxylin and eosin (H&E) staining. Hence, HAS should be distinguished from solid-type CGA based on their different biological behaviors. β-catenin is highly expressed in hepatocellular carcinoma (HCC), which is involved in the maintenance of tumor initiating cells, drug resistance, tumor progression, and metastasis. Methods and Results: Given the dearth of HAS cases, systematic examination of the expression of β-catenin in HAS remains under-explored. In this study, 14 cases were subjected to immunostaining with with AFP, β-catenin, glypican3, hepar-1 and CerbB-2. In parallel, the clinicopathological characteristics of these patients were collected. We detected statistically significant difference in the expression of β-catenin (P = 0.000), glypican3 (P = 0.019), and hepar-1 (P = 0.007) between HAS cancer tissues and the adjacent non-cancerous tissues. Furthermore, a significant correlation was observed between the expression of β-catenin in HAS cancer tissue and adjacent tissue (Pearson correlation coefficient: 0.686, P = 0.007). Moreover, in cancer tissues, a significant correlation was observed between the expression of β-catenin and survival time (Spearman correlationcoefficient= - 0.482, P = 0.003). However, we found the expression of β-catenin did not correlate with the degree of tumor differentiation and tumor size, age, gender, serum AFP levels, microinvasion, and metastasis (P > 0.05). Conclusion: Our findings establish β-catenin as a useful marker that can distinguish HAS from CGA and may improve the early diagnosis to guide the appropriate and timely treatment of HAS patients.

scholarly journals Aerial View of the Association between m6A-Related LncRNAs and Clinicopathological Characteristics of Pancreatic Cancer

2021 ◽  
Author(s):  
Bowen Huang ◽  
Jun Lu ◽  
Dong Liu ◽  
Wenyan Gao ◽  
Li Zhou ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
High Risk ◽  
Prognostic Model ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Clinicopathological Characteristics ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Ductal Adenocarcinoma ◽  
Aerial View

Abstract Background There have been few reports on how long non-coding RNA (lncRNA) under the regulation of N6-methyladenosine (m6A) modification influences pancreatic cancer progression. In our study, the association between m6A-related lncRNAs and pancreatic ductal adenocarcinoma (PDAC) was comprehensively described for the first time based on the construction of a lncRNAs prognostic model. Methods The lncRNAs expression level and the prognostic value were investigated in 440 PDAC patients and 171 normal tissues from Genotype-Tissue Expression (GTEx), The Cancer Genome Atlas (TCGA), and International Cancer Genome Consortium (ICGC) databases. We implemented Pearson correlation analysis to explore the m6A-related lncRNAs, univariate Cox regression and Kaplan-Meier (K-M) methods were performed to screen the critical lncRNAs in PDAC patients. Then we used bioinformatic analysis and statistical analysis to illustrate the association between m6A-related lncRNAs and pancreatic cancer. Results Seven prognostic m6A-related lncRNAs were identified as prognostic lncRNAs, and they were inputted in the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to establish an m6A-related lncRNAs prognostic model in the TCGA database. Each patient has calculated a risk score and divided into low-risk and high-risk subgroups by the median value in two cohorts. Moreover, the model showed a robust prognostic ability in the stratification analysis of different risk subgroups, pathological grades, and recurrence events. The Cox regression demonstrated that the risk classification was an independent prognostic predictor. We established a competing endogenous RNA (ceRNA) network based on seven pivotal lncRNAs and twenty-six m6A regulators. Enrichment analysis indicated that malignancy-associated biological function and signaling pathways were enriched in the high-risk subgroup and m6A-related lncRNAs target mRNAs. We have even identified small molecule drugs that may affect the progression of pancreatic cancer. Conclusions In conclusion, we provide the first comprehensive aerial view between m6A-related lncRNAs and pancreatic cancer's clinicopathological characteristics.

scholarly journals Aerial View of the Association Between m6A-Related LncRNAs and Clinicopathological Characteristics of Pancreatic Cancer

2022 ◽  
Vol 11 ◽  
Author(s):  
Bowen Huang ◽  
Jianzhou Liu ◽  
Jun Lu ◽  
Wenyan Gao ◽  
Li Zhou ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
High Risk ◽  
Cancer Progression ◽  
Prognostic Model ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Clinicopathological Characteristics ◽  
Survival Prognosis ◽  
Pancreatic Cell ◽  
Aerial View

Pancreatic cancer is a highly malignant tumor with a poor survival prognosis. We attempted to establish a robust prognostic model to elucidate the clinicopathological association between lncRNA, which may lead to poor prognosis by influencing m6A modification, and pancreatic cancer. We investigated the lncRNAs expression level and the prognostic value in 440 PDAC patients and 171 normal tissues from GTEx, TCGA, and ICGC databases. The bioinformatic analysis and statistical analysis were used to illustrate the relationship. We implemented Pearson correlation analysis to explore the m6A-related lncRNAs, univariate Cox regression and Kaplan-Meier methods were performed to identify the seven prognostic lncRNAs signatures. We inputted them in the LASSO Cox regression to establish a prognostic model in the TCGA database, verified in the ICGC database. The AUC of the ROC curve of the training set is 0.887, while the validation set is 0.711. Each patient has calculated a risk score and divided it into low-risk and high-risk subgroups by the median value. Moreover, the model showed a robust prognostic ability in the stratification analysis of different risk subgroups, pathological grades, and recurrence events. We established a ceRNA network between lncRNAs and m6A regulators. Enrichment analysis indicated that malignancy-associated biological function and signaling pathways were enriched in the high-risk subgroup and m6A-related lncRNAs target mRNA. We have even identified small molecule drugs, such as Thapsigargin, Mepacrine, and Ellipticine, that may affect pancreatic cancer progression. We found that seven lncRNAs were highly expressed in tumor patients in the GTEx-TCGA database, and LncRNA CASC19/UCA1/LINC01094/LINC02323 were confirmed in both pancreatic cell lines and FISH relative quantity. We provided a comprehensive aerial view between m6A-related lncRNAs and pancreatic cancer’s clinicopathological characteristics, and performed experiments to verify the robustness of the prognostic model.

scholarly journals Analysis of the prognostic significance and potential mechanisms of lncRNAs associated with m6A methylation in papillary thyroid carcinoma

2021 ◽  
Author(s):  
Yinde Huang ◽  
Xin Li ◽  
Wenbin Chen ◽  
Yuzhen He ◽  
Song Wu ◽  
...  
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Tumor Microenvironment ◽  
Thyroid Carcinoma ◽  
Immune Checkpoint ◽  
Prognostic Model ◽  
Prognostic Markers ◽  
Clinicopathological Characteristics ◽  
Papillary Thyroid ◽  
The Relationship ◽  
Pan Cancer

Abstract Background : m6A methylation-related long non-coding RNAs (lncRNAs) play a significant role in the progression of various tumors and can be used as prognostic markers. However, whether m6A-related lncRNAs also play the same function as prognostic markers in papillary thyroid carcinoma (PTC) remains unclear. Methods : Consensus cluster analysis was performed to divide PTC samples obtained from The Cancer Genome Atlas database into two clusters according to the expression of m6A-related lncRNAs. Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to create and verify a prognostic model. Furthermore, the relationship among risk scores, clusters, programmed death-ligand 1 (PD-L1), tumor microenvironment (TME), clinicopathological characteristics, immune infiltration, immune checkpoint, and tumor mutation burden (TMB) was analyzed. In addition, a nomogram was created, and subsequently, the drug sensitivity of lncRNAs in the prognostic model was analyzed. Finally, the relationship between these lncRNAs and prognosis in pan-cancer was investigated. Results: The prognosis, RAS, BRAF, M, and TME were found to be different in two clusters. The prognostic model included three lncRNAs: PSMG3-AS1 , BHLHE40-AS1 , and AC016747.3 . The risk score was associated with clusters, PD-L1, tumor microenvironment, clinicopathological characteristics, immune cell infiltration, immune checkpoint, and TMB, and thus, risk score was confirmed as useful prognostic indicators. Differentially expressed lncRNAs are involved in many malignancies and can be identified as cancer prognostic makers. Conclusion : According to our research, we can regard m6A-related lncRNAs involved in the procession of PTC as a biomarker of PFS for PTC patients, and pan-cancer.

scholarly journals Analysis of the prognostic significance and potential mechanisms of lncRNAs associated with m6A methylation in papillary thyroid carcinoma

2021 ◽  
Author(s):  
Yinde Huang ◽  
Xin Li ◽  
Wenbin Chen ◽  
Yuzhen He ◽  
Song Wu ◽  
...  
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Tumor Microenvironment ◽  
Thyroid Carcinoma ◽  
Immune Checkpoint ◽  
Prognostic Model ◽  
Prognostic Markers ◽  
Clinicopathological Characteristics ◽  
Papillary Thyroid ◽  
The Relationship ◽  
Pan Cancer

Abstract Background : m6A methylation-related long non-coding RNAs (lncRNAs) play a significant role in the progression of various tumors and can be used as prognostic markers. However, whether m6A-related lncRNAs also play the same function as prognostic markers in papillary thyroid carcinoma (PTC) remains unclear. Methods : Consensus cluster analysis was performed to divide PTC samples obtained from The Cancer Genome Atlas database into two clusters according to the expression of m6A-related lncRNAs. Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to create and verify a prognostic model. Furthermore, the relationship among risk scores, clusters, programmed death-ligand 1 (PD-L1), tumor microenvironment (TME), clinicopathological characteristics, immune infiltration, immune checkpoint, and tumor mutation burden (TMB) was analyzed. In addition, a nomogram was created, and subsequently, the drug sensitivity of lncRNAs in the prognostic model was analyzed. Finally, the relationship between these lncRNAs and prognosis in pan-cancer was investigated. Results: The prognosis, RAS, BRAF, M, and TME were found to be different in two clusters. The prognostic model included three lncRNAs: PSMG3-AS1 , BHLHE40-AS1 , and AC016747.3 . The risk score was associated with clusters, PD-L1, tumor microenvironment, clinicopathological characteristics, immune cell infiltration, immune checkpoint, and TMB, and thus, risk score was confirmed as useful prognostic indicators. Differentially expressed lncRNAs are involved in many malignancies and can be identified as cancer prognostic makers. Conclusion : According to our research, we can regard m6A-related lncRNAs involved in the procession of PTC as a biomarker of PFS for PTC patients, and pan-cancer.

scholarly journals Clinicopathological Characteristics of Gastric Hepatoid Adenocarcinoma

2017 ◽  
Vol 17 (2) ◽  
pp. 88
Author(s):  
Jung Bin Yoon ◽  
Gwang Ha Kim ◽  
Do Youn Park ◽  
Young Geum Kim ◽  
Sung Ik Pyeon ◽  
...  
Keyword(s):  
Clinicopathological Characteristics ◽  
Hepatoid Adenocarcinoma

Clinicopathological Characteristics and Prognosis of Hepatoid Adenocarcinoma of the Stomach: Evaluation of a Pooled Case Series

2018 ◽  
Vol 38 (6) ◽  
pp. 1054-1061 ◽  
Author(s):  
Xiang-yu Zeng ◽  
Yu-ping Yin ◽  
Hua Xiao ◽  
Peng Zhang ◽  
Jun He ◽  
...  
Keyword(s):  
Case Series ◽  
Clinicopathological Characteristics ◽  
Hepatoid Adenocarcinoma

scholarly journals Integrated analysis identifies oxidative stress genes associated with progression and prognosis in gastric cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhengyuan Wu ◽  
Lin Wang ◽  
Zhenpei Wen ◽  
Jun Yao
Keyword(s):  
Oxidative Stress ◽  
Gastric Cancer ◽  
Prognostic Model ◽  
Clinical Information ◽  
Diagnostic Value ◽  
Clinicopathological Characteristics ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Stress Genes

AbstractOxidative stress (OS) reactions are reported to be associated with oncogenesis and tumor progression. However, little is known about the potential diagnostic value of OS in gastric cancer (GC). This study identified hub OS genes associated with the prognosis and progression of GC and illustrated the underlying mechanisms. The transcriptome data and corresponding GC clinical information were collected from The Cancer Genome Atlas (TCGA) database. Aberrantly expressed OS genes between tumors and adjacent normal tissues were screened, and 11 prognosis-associated genes were identified with a series of bioinformatic analyses and used to construct a prognostic model. These genes were validated in the Gene Expression Omnibus (GEO) database. Furthermore, weighted gene co-expression network analysis (WGCNA) was subsequently conducted to identify the most significant hub genes for the prediction of GC progression. Analysis revealed that a good prognostic model was constructed with a better diagnostic accuracy than other clinicopathological characteristics in both TCGA and GEO cohorts. The model was also significantly associated with the overall survival of patients with GC. Meanwhile, a nomogram based on the risk score was established, which displayed a favorable discriminating ability for GC. In the WGCNA analysis, 13 progression-associated hub OS genes were identified that were also significantly associated with the progression of GC. Furthermore, functional and gene ontology (GO) analyses were performed to reveal potential pathways enriched with these genes. These results provide novel insights into the potential applications of OS-associated genes in patients with GC.

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