Translational cancer research最新文献

Background: Lung cancer is a major cause of cancer-related deaths worldwide. Unfortunately, non-small cell lung cancer (NSCLC) often lacks clear clinical symptoms and molecular markers for early diagnosis, which can hinder the initiation of timely treatments. In this study, we conducted an extensive bioinformatics analysis of copper-zinc superoxide dismutase (SOD1), a molecule linked to lung adenocarcinoma (LUAD) to enhance early detection and treatment approaches for this condition.

Methods: A bioinformatics analysis was conducted using a dataset from The Cancer Genome Atlas (TCGA) database. Several analytical methods, such as a differential expression analysis, a Kaplan-Meier survival analysis, a clinicopathological analysis, an enrichment analysis, protein-protein interaction (PPI) network construction using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and an immunoreactivity analysis of SOD1 expression in LUAD using TIMER were employed. We further validated the expression of SOD1 in LUAD through in vitro experiments using quantitative polymerase chain reaction (qPCR) and Western blot.

Results: Our findings indicate that LUAD tissues exhibited significantly higher expression levels of SOD1 than healthy tissues. The univariate Cox analysis showed that the elevated level was linked to unfavorable overall survival (OS) rates. Further, the Cox regression analysis of multiple variables suggested that elevated SOD1 expression levels acted as an autonomous prognosticator for unfavorable OS. We also conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, and a gene set enrichment analysis (GSEA) and observed differential pathway enrichment among patients with high SOD1 expression. In addition, a correlation between SOD1 and immune cell infiltration was found. The in vitro experiments confirmed that SOD1 expression was upregulated in LUAD.

Conclusions: SOD1 could serve as a reliable prognostic indicator in individuals diagnosed with LUAD. Our findings may prove valuable in the development of therapeutic and prognostic markers for LUAD. The potential clinical utility of SOD1 in LUAD requires further investigation.

Background: The brain serves as the primary site for metastasis in patients with both non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). The presence of lung cancer with brain metastasis (LCBM) is a debilitating condition associated with considerable morbidity and mortality. The objective of this study was to assess the incidence and survival rates of LCBM in the United States population.

Methods: We analyzed a total of 9,212 patients diagnosed with LCBM between 2010 and 2015, extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Our analysis assessed the incidence, relative survival, and conditional survival (CS) of LCBM. We utilized the Kaplan-Meier method to estimate overall survival and determine CS at year y+x after x years of survival, following the formula CS(y|x) = CS(y+x)/CS(x). Prognostic factor selection was performed using the least absolute shrinkage and selection operator (LASSO) regression approach, and multivariate Cox regression was employed to demonstrate the impact of these predictors on outcomes and construct a CS-based nomogram.

Results: The overall age-adjusted incidence rate of LCBM was 5.82 cases per 100,000, with a slight decline observed during our study period. Patient relative survival showed a continuous decline with increasing age. CS analysis revealed that the 5-year CS rate for patients initially diagnosed with LCBM adjusted from 3% to 13%, 28%, 52%, and 73% over successive years of survival (1-4 years). Identified predictors included age at diagnosis, sex, race, tumor size, tumor grade, surgery, radiotherapy, and chemotherapy. These predictors, along with the CS formula, were employed to develop a CS-based nomogram for real-time prognosis prediction. Calibration curve, area under the time-dependent receiver operating characteristic (ROC) curve, concordance index (c-index), and decision curve analysis (DCA) demonstrated the model's strong predictive capabilities.

Conclusions: This study deepened our understanding of LCBM patients, summarizing their epidemiological characteristics and CS patterns. We successfully developed a novel CS-based nomogram model for dynamic survival estimation, offering real-time and personalized prognostic information that is clinically valuable.

Background and objective: Obesity is an important risk factor for the onset of kidney cancer, and the mechanism of obesity leading to the occurrence and development of kidney cancer has been further studied and confirmed in the past decade. The emergence of the "obesity paradox" phenomenon has made the correlation between obesity and the prognosis of kidney cancer survival controversial. This review summarizes the association between obesity and the occurrence and development of kidney cancer based on newly discovered evidence in the past 10 years, in order to provide reference for follow-up research.

Methods: A comprehensive, non-systematic review of the latest literature was carried out in order to investigate the progress of the correlation between obesity and kidney cancer. PubMed, Web of Science and Embase were being examined and the last run was on July 15, 2024.

Key content and findings: The correlation between obesity and the occurrence and development of kidney cancer was discussed in this review, and the newly discovered evidence of epidemiology and related mechanisms in the past 10 years was summarized. The latest evidence suggests that obesity is an important risk factor for the development of kidney cancer. Perirenal fat plays an important role in promoting kidney cancer progression and prognosis.

Conclusions: Epidemiology shows that the high rates of kidney cancer and obesity coincide in terms of region and ethnicity. The underlying mechanisms associated with obesity in promoting the occurrence and development of kidney cancer mainly include: abnormal expression of adipocytokines, abnormal lipid metabolism, abnormalities in the insulin-like growth factor-I (IGF-I) axis and hyperinsulinemia/insulin resistance, hypoxia and inflammation. As adipose tissue is adjacent to the kidney, the effect of perirenal adipose tissue on the prognosis of kidney cancer is controversial, and some evidence supports the idea of the "obesity paradox".

Background: Hepatocellular carcinoma (HCC) is a common malignant tumor with high heterogeneity and poor prognosis, so early prediction and treatment are still difficult. Cuproptosis is a newly discovered type of programmed cell death that has been shown to be closely related to the occurrence and progression of HCC. Cancer morphology is influenced by genetic drivers, and computational pathology methods typically use tissue images such as entire slide images as input to predict clinical or genetic features. Therefore, the comprehensive analysis of pathological features and genomic data provides a feasible way to explore the potential mechanism of the tumor. The objective of this study was to develop a prediction model for HCC prognosis based on the pathomics signatures (PS) and the genomics signatures (GS).

Methods: A dataset comprising 315 HCC patients was randomly divided into a training set (n=200) and a validation set (n=115). Prognostic models related to PS and GS were constructed by univariate and multivariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO) regression analysis. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, univariate and multivariate Cox analyses, and nomogram were used to evaluate the predictive performance of the prognostic model. The prognostic value of the model was internally validated.

Results: A prognostic model incorporating clinical features, PS, and GS was developed using Cox regression analysis and LASSO regression analyses. Kaplan-Meier survival analysis revealed statistically significant differences in survival time between high-risk and low-risk subgroups in both the training and validation datasets (PS: P=0.003 and <0.001, respectively; GS: P=0.008 and 0.004, respectively). The time-dependent ROC curve showed favorable predictive value for survival in both the training and validation sets. The area under the ROC curves at 1, 3, and 5 years was 0.750, 0.830, and 0.870 in the training set, and 0.780, 0.810, and 0.760 in the validation set, respectively. A nomogram model based on the risk model score could effectively predict the survival probability of HCC patients. The calibration curves further demonstrated the good predictive capability of the nomogram model.

Conclusions: The prognostic model incorporating PS and GS could effectively predict the prognosis of HCC patients.

Background: In diffuse large B-cell lymphoma (DLBCL), bone marrow (BM) involvement includes two types that are concordant involvement and discordant involvement. It has been reported that concordant BM involvement has a worse prognosis than discordant involvement in previous studies. However, the prognostic effects of concordant or discordant BM involvement on DLBCL still need further research. In this work, DLBCL cases with BM involvement were collected and analyzed to better reflect the prognostic implications of concordant and discordant BM involvement.

Methods: We reviewed the cases with newly diagnosed DLBCL and BM involvement from April 2018 to April 2022 in Northern Jiangsu People's Hospital. Overall survival (OS) and progression-free survival (PFS) were accessed by the Kaplan-Meier method and compared between groups by the log-rank test. A multivariate regression analysis based on Cox proportional hazard model was used to test the independent effect of each variable on survival.

Results: In total, 32 patients were included and 15 (46.9%) patients had concordant BM involvement and 17 (53.1%) patients had discordant BM involvement. Compared with the discordant group, the concordant group tended to be older and had elevated lactate dehydrogenase level. The outcome of patients with concordant BM involvement was worse than the discordant subset, including OS (P=0.04) and PFS (P=0.03). Furthermore, the discordant BM involvement was excluded to acquire a BM-adjusted International Prognostic Index (IPI) score. The significance of BM-adjusted IPI scores to predict OS was improved greatly compared with the previous IPI scores (P=0.053 vs. P=0.16). Multivariate analysis showed that the BM-adjusted IPI was an independent predictor for OS [hazard ratio =3.406; 95% confidence interval (CI): 1.145-10.127; P=0.03].

Conclusions: These results highlight the requirement for identifying BM infiltration type accurately and then adjusting the IPI score by excluding discordant BM involvement since concordant involvement can partly predict a poor prognosis of DLBCL with BM involvement other than discordant involvement.

Background: Many significant findings from recent studies have revealed the significance of histone deacetylase 1 (HDAC1) in the development of tumors and its strong association with tumor prognosis; these studies have mainly focused on one single cancer such as in lung cancer, breast cancer, and hepatocellular carcinoma (HCC). To date, there has been no comprehensive analysis and pan-analysis conducted from the overall perspective of cancer across all types. Hence, we analyzed public databases, conducted tube formation assay, and immunohistochemistry (IHC) staining of HDAC1 on six kinds of clinical samples to explore the prognostic and oncogenic effects of HDAC1 on 33 tumors for the first time. There currently remains a lack of efficient testing methods, therapies, and diagnostic and prognostic markers of tumor formation and development in different tumors.

Methods: Our initial objective was to investigate the possible cancer-causing functions of HDAC1 in 33 different types of tumors by utilizing The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and many different online websites, such as Tumor IMmune Estimation Resource 2 (TIMER2), Gene Expression Profiling Interactive Analysis 2 (GEPIA2), Genotype Tissue Expression (GTEx) database, Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset, and University of ALabama at Brimingham CANcer data analysis portal (UALCAN) tool, and so on. We even used small interfering RNA (siRNA) to knock down HDAC2 in HCC cell lines. IHC of HDAC1 was performed.

Results: HDAC1 exhibited high expression in numerous tumors, and strong correlations were observed between the messenger RNA (mRNA) levels of HDAC1 and the prognosis of individuals diagnosed with tumors. Human umbilical vein endothelial cells (HUVECs) tube formation and migration were significantly inhibited by conditioned media from HCC cells treated with siRNA of HDAC1. Several types of cancer have been found to exhibit elevated levels of phosphorylation at S421. Furthermore, as in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), and kidney renal papillary cell carcinoma (KIRP), HDAC1 expression was found to be correlated with inflammatory cell infiltration.

Conclusions: The levels of HDAC1 are expected to adapt to clinical adjuvant targeted therapy in most types of solid cancer.

Background: Methylsterol monooxygenase 1 (MSMO1) catalyzes C4-methylsterols demethylation in cholesterol biosynthesis pathway. MSMO1 is increased and up-regulation of MSMO1 is correlated with progression of some tumor. But the correlation of MSMO1 to cervical cancer is unknown. The current study aimed to explore the expression pattern of MSMO1 in cervical cancer and its correlation to clinical characteristics.

Methods: In this study, 306 cervical cancer cases and 13 non-tumor cases were included. We compared MSMO1 expression level in non-tumor cervical tissues and cervical cancer samples using the Wilcoxon rank sum test. Univariate regression was used to investigate the correlation between MSMO1 expression as well as other clinical characteristics and prognosis. Clinical characteristics associated with prognosis in univariate analysis were used as adjustments for multivariate analysis to further validate the relationship between MSMO1 expression and cervical cancer prognosis. Patients' survival in different subgroups was compared by Kaplan-Meier (KM) method. The potential protein interaction was analyzed. T cell infiltration level in MSMO1 high and low group patients was compared.

Results: MSMO1 expression level was up-regulated in cervical cancer (P<0.001). Patients who had stage III-IV diseases (P=0.04) and did not achieve complete response after primary treatment had higher MSMO1 expression (P<0.001). High MSMO1 expression patients showed a lower overall survival (OS) (P=0.004), disease-specific survival (DSS) (P=0.004) and progression-free survival (PFS) (P=0.002). High MSMO1 expression was a risk factor to OS (P=0.01), DSS (P=0.009) and PFS (P=0.009). Multiple variate analysis showed that high MSMO1 expression was an independent risk factor to OS [hazard ratio (HR) =1.902, 95% confidence interval (CI): 1.156-3.129, P=0.01], DSS (HR =2.172, 95% CI: 1.210-3.897, P=0.009) and PFS (HR =1.975, 95% CI: 1.189-3.282, P=0.009) in cervical squamous cell carcinoma (CESC). The prognostic value of high MSMO1 expression was further examined in other databases, including KM-plotter, Gene Expression Profiling Interactive Analysis (GEPIA) and Gene Expression Omnibus (GEO) database.

Conclusions: The current research showed that MSMO1 was increased and was associated with poor prognosis in CESC.

Background: Sialic acid-binding immunoglobulin-like lectin 8 (SIGLEC8) is involved in the progression of numerous diseases. This study aimed to examine the relationship between SIGLEC8 and the prognosis of patients with low-grade glioma (LGG) and the related mechanisms.

Methods: First, screening of the differentially expressed genes (DEGs) SIGLEC8 in The Cancer Genome Atlas (TCGA) database was performed. The expression was then correlated with the prognosis of patients with LGG and then verified using the Tumor Immune Estimation Resource (TIMER) and TCGA databases. Cox regression was employed to conduct multifactorial analysis and was followed by the construction of an internally validated nomogram based on these results. To investigate the possible mechanisms, we used gene set enrichment analysis (GSEA). We conducted a retrospective analysis of the clinical information of patients with LGG who were treated at Longgang Central Hospital of Shenzhen from January 2018 to December 2020 and from whom tumor and peritumoral tissues were taken during surgery. Expression of essential genes was identified by employing quantitative real-time polymerase chain reaction (qRT-PCR). Multivariate analysis, via Cox regression, was employed to determine the prognostic factors for patients with LGG.

Results: The transcriptional activity of SIGLEC8 was found to be elevated in LGG neoplastic tissues compared to neighboring nonneoplastic tissues. Overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) were improved in patients with LGG with reduced expression of SIGLEC8 as compared to those with increased expression of SIGLEC8. The nomogram's C-index is 0.804 (0.781-0.827). indicating good predictive accuracy. GSEA revealed that SIGLEC8 might influence LGG biological events by participating in the PD-1, IL3, JAK/STAT, and PI3KCI signal transduction pathways, as well as cytokine and inflammatory response, cell cycle, homeostasis, and extracellular matrix. This study included 72 patients with LGG. qRT-PCR showed upregulated SIGLEC8 expression in LGG tumor tissues, which was significantly associated with tumor number and metastasis to the lymph nodes (P<0.05). Multivariate analysis using Cox regression identified the high expression of SIGLEC8 as an independent risk factor in LGG prognosis (P<0.05).

Conclusions: For the prognosis of patients with LGG, the transcriptional activity of SIGLEC8 is increased in LGG tissues and is an independent risk factor. Interference with SIGLEC8 could promote tumor progression by regulating the JAK/STAT signaling pathway, indicating that SIGLEC8 may function as a distinctive predictive biomarker for patients with LGG.