Background: Liver cancer (LC) is one of China's most common malignant tumors, with a high mortality rate, ranking third leading cause of death after gastric and esophageal cancer. Recent patents propose the LncRNA FAM83H-AS1 has been verified to perform a crucial role in the progression of LC. LncRNA FAM83H-AS1 has been verified to perform a crucial role in the progression of LC. However, the concrete mechanism remains to be pending further investigation.
Objective: This study aimed to explore the embedding mechanism of FAM83H-AS1 molecules in terms of radio sensitivity of LC and provide potentially effective therapeutic targets for LC therapy.
Methods: Quantitative real-time PCR (qRT-PCR) was conducted to measure the transcription levels of genes. Proliferation was determined via CCK8 and colony formation assays. Western blot was carried out to detect the relative protein expression. A xenograft mouse model was constructed to investigate the effect of LncRNA FAM83H-AS1 on tumor growth and radio-sensitivity in vivo.
Results: The levels of lncRNA FAM83H-AS1 were remarkably increased in LC. Knockdown of FAM83H-AS1 inhibited LC cell proliferation and colony survival fraction. Deletion of FAM83H-AS1 increased the sensitivity of LC cells to 4 Gy of X-ray radiation. In the xenograft model, radiotherapy combined with FAM83H-AS1 silencing significantly reduced tumor volume and weight. Overexpression of FAM83H reversed the effects of FAM83H-AS1 deletion on proliferation and colony survival fraction in LC cells. Moreover, the over-expressing of FAM83H also restored the tumor volume and weight reduction caused by the knockdown of FAM83H-AS1 or radiation in the xenograft model.
Conclusion: Knockdown of lncRNA FAM83H-AS1 inhibited LC growth and enhanced radiosensitivity in LC. It has the potential to be a promising target for LC therapy.
Objective: We aimed to identify critical clinical features to develop an accurate webbased prediction model for estimating the overall survival (OS) of primary breast diffuse large Bcell lymphoma (PB-DLBCL) adult patients.
Methods: We first included all PB-DLBCL cases with available covariates retrieved from the Surveillance, Epidemiology, and End Results database. We sequentially performed univariate and multivariate Cox regression approaches to identify the predictors independently associated with prognosis, and all the predictors that passed these tests were then constructed to build a nomogram for predicting 3-, 5-, and 10-year survival rates of patients. The C-index and the receiver operating characteristic curve (ROC) were used to evaluate the prediction discrimination, and the calibration curve was applied to estimate the calibration.
Results: A total of PB-DLBCL adult patients were included (median age was 69 with the interquartile range [IQR] of 57-79 years), of which 466 (70%) were randomly allocated to the development cohort, and the remaining cases were collected for validation. Using three identified independent predictors (i.e., age, stage, and radiation), an accurate nomogram for predicting OS was developed and validated. The C-indices of our nomogram were both relatively acceptable, with 0.74 (95% CI: 0.71-0.78) and 0.72 (95% CI: 0.70-0.75) for the development and validation cohorts, respectively. The calibration curves also accurately predicted the prognosis of PB-DLBCL in all cases. In addition, ROC curves showed our nomogram to possess superior predictive ability compared to any single variable. To visually present this prediction model, a convenient webbased tool was implemented based on our prognostic nomogram.
Conclusion: For patients with PB-DLBCL, a more convenient and accurate web-based prediction model was developed and validated, which showed relatively good performances in both discrimination and calibration during model development and validation. External evaluation and validation are warranted by further independent studies.
Background: Gefitinib, an Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor (EGFR-TKI), frequently causes side effects when used to treat non-small cell lung cancer.
Objective: The purpose of this experiment was to investigate the side effect of gefitinib on the skin and colon of mice.
Methods: Male Balb/c nu-nu nude mice aged 4-5 weeks were used as xenograft tumor models, and gefitinib at 150 mg/kg and 225 mg/kg was started at 9 days after the xenograft tumor grew out. The mice's weights and tumor volumes were tracked concurrently, and the mouse skin adverse reactions and diarrhea were observed during the treatment. The animal tissues were subjected to biochemical and pathological evaluations after 14 days.
Results: Gefitinib effectively decreased the size and weight of transplanted tumors in nude mice, while also lowering body weight and raising indexes of the liver and spleen. Gefitinib could cause skin adverse reactions and diarrhea in mice. Further pathological investigation revealed tight junction- related markers in the mice's skin and colon to be reduced and macrophages and neutrophils to be increased after gefitinib treatment.
Conclusion: The findings imply that gefitinib has negative effects on the skin and colon. Gefitinib- induced skin and colon adverse reactions in mice have been successfully modeled in this study.
Introduction: Nowadays, mounting evidence shows that variations in TGF-β signaling pathway-related components influence tumor development. Current research has patents describing the use of anti-TGF-β antibodies and checkpoint inhibitors for the treatment of proliferative diseases. Importantly, TGF-β signaling pathway is significant for lower-grade glioma (LGG) to evade host immunity. Loss of particular tumor antigens and shutdown of professional antigenpresenting cell activity may render the anti-tumor response ineffective in LGG patients. However, the prognostic significance of TGF-β related genes in LGG is still unknown.
Methods: We collected RNA-seq data from the GTEx database (normal cortical tissues), the Cancer Genome Atlas database (TCGA-LGG), and the Chinese Glioma Genome Atlas database (CGGA-693 and CGGA-325) for conducting our investigation.
Results: In addition, previous publications were explored for the 223 regulators of the TGF-β signaling pathway, and 30 regulators with abnormal expression in TCGA and GTEx database were identified. In order to identify hub prognostic regulators, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were used to screen from differentially expressed genes (DEGs). On the basis of 11 genes from LASSO-Cox regression analysis (NEDD8, CHRD, TGFBR1, TP53, BMP2, LRRC32, THBS2, ID1, NOG, TNF, and SERPINE1), TGF-β score was calculated. Multiple statistical approaches verified the predictive value of the TGF-β score for the training cohort and two external validation cohorts. Considering the importance of the TGF-β signaling pathway in immune regulation, we evaluated the prediction of the TGF-β score for immunological characteristics and the possible application of the immunotherapeutic response using six algorithms (TIMER, CIBERSORT, QUANTISEQ, MCP-counter, XCELL and EPIC) and three immunotherapy cohorts (GSE78820, Imvigor-210 and PRJEB23709). Notably, we compared our risk signature with the signature in ten publications in the meta-cohort (TCGA-LGG, CGGA-693 and CGGA-325), and the TGF-β score had the best predictive efficiency (C-index =0.812).
Conclusion: In conclusion, our findings suggest that TGF-β signaling pathway-related signatures are prognostic biomarkers in LGG and provide a novel tool for tumor microenvironment (TME) assessment.
Background: The purpose of this study was to explore the expression profiles of lipid metabolism-related genes in patients with Colorectal Cancer (CRC).
Methods: The lipid metabolism statuses of CRC patients from The Cancer Genome Atlas (TCGA) were analyzed. Risk characteristics were constructed by univariate Cox regression and minimum Absolute contraction and Selection Operator (LASSO) Cox regression. A histogram was constructed based on factors such as age, sex, TNM stage, T stage, N stage, and risk score to provide a visual tool for clinicians to predict the probability of 1-year, 3-year, and 5-year OS for CRC patients. By determining Area Under Curve (AUC) values, the time-dependent Receiver Operating characteristic Curve (ROC) was used to evaluate the efficiency of our model in predicting prognosis.
Results: A novel risk signal based on lipid metabolism-related genes was constructed to predict the survival of CRC patients. Risk characteristics were shown to be an independent prognostic factor in CRC patients (p <0.001). There were significant differences in the abundance and immune characteristics of tumor-filtering immune cells between high-risk and low-risk groups. The nomogram had a high potential for clinical application and the ROC AUC value was 0.827. Moreover, ROC analysis demonstrated that the nomogram model was more accurate to predict the survival of CRC patients than age, gender, stage and risk score.
Conclusion: In this study, we demonstrated a lipid metabolism-related genes prognosis biomarker associated with the tumor immune micro-environment in patients with CRC.
Background: Although epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) have been proven a long-lasting treatment effect in pulmonary adenocarcinoma, most patients still progressed within one year due to the acquired resistance. Complex mutations of rare rare sites after acquiring resistance are rarely reported in pulmonary adenocarcinoma.
Case presentation: A 62-year-old woman was diagnosed with pulmonary adenocarcinoma with stage IV. Genetic testing at the initial treatment showed EGFR L858R positive. After being treated with gefitinib, persistent 2 years disease progression occurred due to drug resistance. The genetic testing showed that EGFR L858R was eliminated, while a rare rare complex mutation of L861Q/G719X appeared. After 160 mg furmonertinib was treated for 1 month, the primary tumor regressed and the intracranial lesions disappeared. The patient has achieved progression-free survival (PFS) for more than 20 months.
Conclusion: Pulmonary adenocarcinoma with rare rare complex mutations in EGFR induced by gefitinib resistance and disease progression might benefit from furmonertinib treatment.
Background: Breast cancer accounts for over 1.8 million new cases worldwide annually, and prompt diagnosis and treatment are imperative to prevent mortality. Necroptosis, a form of programmed cell death, is thought to be a critical pathway for cancer cell apoptosis, yet, its relationship with breast cancer progression and molecular mechanisms remains largely unexplored.
Objectives: Our study aims to investigate the molecular characteristics and clinical prognostic value of necroptosis-related genes in breast cancer using a comprehensive approach that involves integrated bioinformatics analysis along with drug sensitivity assessment.
Methods: Transcriptional, clinical, and tumor mutation burden (TMB) data related to breast cancer from the TCGA and GEO databases were integrated, and the necroptosis gene set was downloaded from the GSEA website for analysis. The screening conditions were set as adjusted p < 0.05 and |log2FC(fold change)| > 0.585 to screen for differential expression genes related to breast cancer necroptosis. Survival prognosis analysis was conducted on breast cancer necroptosis genes. Further analysis was conducted on prognosis-related necroptosis genes, including immune infiltration analysis and GO/KEGG enrichment analysis, to explore the potential biological functions and signaling pathway mechanisms of breast cancer necroptosis genes. Drug sensitivity screening was conducted on the prognosis-related necroptosis to identify potential drugs that target the promotion of necroptosis gene expression, and ultimately, single-gene analysis was performed on the core target genes obtained.
Results: Through integrated bioinformatics analysis, 29 differentially expressed mRNAs related to BRCA-Necroptosis were identified, including 18 upregulated mRNAs and 11 downregulated mRNAs. In addition, single-factor analysis of differential genes showed that the expression of HSPA4, PLK1, TNFRSF1B, FLT3, and LEF1 was closely related to BRCA survival prognosis. Based on the expression of these genes, a breast cancer prognosis model was constructed, and it was found that the area under the curve (AUC) of the curve of the risk genes for necroptosis was the largest, indicating that these genes have a certain clinical predictive significance for the occurrence and prognosis of BRCA. Additionally, there were significant differences in clinical characteristics of BRCA patients with different necroptosis gene expressions. Furthermore, GSVA and immune infiltration analysis revealed that Necroptosis-DEGs mainly affect the occurrence and progression of BRCA by participating in immune functions such as APC co-inhibition, APC costimulation, CCR, checkpoint, as well as infiltrating immune cells such as B cells naive, plasma cells, and T cells CD8. Moreover, the necroptosis gene group column chart indicated a 1-year survival rate of 0.979, a 3-year survival rate of 0.883, and a
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：