Translational cancer research最新文献

Background: The prognostic factors at mutational and transcriptional levels are not clear for stage IV colorectal cancer (CRC) patients with liver metastasis who undergo primary cancer palliative surgery with post-surgical adjuvant therapy. We aimed to establish and validate models for predicting the prognosis of these patients by combining mutational, transcriptional and clinicopathological information.

Methods: Data of 45 stage IV CRC patients with liver metastasis were downloaded from the cBioportal database as the training cohort. Another 30 patients from our hospital were retrospectively recruited as the external validation cohort. Patients were followed up to 4,699 days (median: 823 days) and 2,380 days (median: 980 days) for the overall survival (OS) in the training and validation cohort, respectively. Patients were followed up to 4,699 days (median: 264 days) and 2,259 days (median: 272 days) for the progression-free survival (PFS) in the training and validation cohort, respectively. Tissue samples of the primary CRC were collected and sequenced. Data were analyzed and figures were plotted using the R software.

Results: The mutational and transcriptional alterations and their features were characterized. APC, TP53, TTN, KRAS and SYNE1 were genes with the highest mutational frequency. Significantly upregulated and downregulated genes can be found in transcription. Age, APC2 mutations, and ADRB1, ASTL, MRPL23-AS1 and PDZK1 transcription significantly stratified patient OS, while the KRAS, PTPRF, FREM2, and CLOCK mutations and LY6H, TMEM163, RFX8, ARHDGDIG, TECTA and MYEOV transcriptions significantly stratified patient PFS. Multivariate analyses identified age, APC2 mutations and ADRB1 transcription as independent risk factors for OS. KRAS and PTPRF mutations and RFX8 and MYEOV transcriptions were independent risk factors for PFS. The above independent risk factors were used to establish prediction models for OS and PFS. For the OS model, the 1-, 3- and 5-year area under the curve (AUC) reached 0.858, 0.774 and 0.907 in internal validation, and reached 0.810, 0.778 and 0.924 in external validation, respectively. For the PFS model, the 6 months, 1-year and 1.5-year AUC reached 0.950, 0.803 and 0.847 in internal validation, and reached 0.919, 0.949 and 0.944 in external validation, respectively.

Conclusions: The prognostic factors for stage IV CRC patients with liver metastasis were identified. Models for predicting the OS and PFS were successfully established and validated. The models may help to establish the personalized therapeutic strategies before treatment.

Background: The occurrence rate of liver cancer is increasing in recent years. The significance of lactylation in tumor cells should not be neglected. This study aimed to discover a gene signature related to lactylation that can be used for diagnostic applications.

Methods: Data were downloaded from The Cancer Genome Atlas (TCGA), GSE14520, GSE84402, and GSE62232 datasets. Differential analysis and random forest analysis were performed in hepatocellular carcinoma (HCC). Receiver operating characteristic (ROC) curve was used to evaluate diagnostic efficiency, and logistic regression was established to obtain the risk score equation. Besides, the expression of genes in the signature was identified by TCGA database and verified by real-time quantitative polymerase chain reaction (RT-PCR). In order to analyze prognostic performance, patients diagnosed with HCC were stratified into either high risk score or low risk score categories based on the median risk score. In addition, immunotherapy and drug sensitivity of high risk score and low risk score groups were assessed.

Results: The prognostic performance of the diagnostic model was validated in the Gene Expression Omnibus (GEO) dataset. The patients with high risk score had worse outcomes than those with low risk score in the TCGA database. ROC curves showed the characteristics of lactylation-related gene signature with a good predictive capability. Furthermore, we developed a predictive nomogram for HCC patients, utilizing the data resources from TCGA. Finally, in terms of assessing the potency of immunotherapy in patients, the low risk score group had a lower Tumor Immune Dysfunction and Exclusion (TIDE) score, indicating a good response to immunotherapy.

Conclusions: Our identification of a gene model associated with lactylation presents a promising avenue for the development of targeted immunotherapeutic strategies.

Background: A lot of studies have shown a close relationship between cuproptosis and cancer. The main purpose of this study is to analyze the impact of cuproptosis on cervical cancer (CC).

Methods: Using The Cancer Genome Atlas (TCGA) public database, we analyzed the genetic correlation, expression, and prognostic value of 25 cuproptosis-related genes (CRGs) in CC. A least absolute shrinkage and selection operator (LASSO) risk regression model was constructed to compare the changes in associated pathways, prognosis, immune infiltration, and antibody programmed cell death-ligand 1 (anti-PD-L1) treatment response of the high- and low-risk groups. In addition, we collected CC tissue samples before and after radiotherapy for ribonucleic acid (RNA) sequencing, and analyzed the relationship between CRGs and radiotherapy.

Results: The results showed CRGs were differentially expressed and were associated with multiple metabolic pathways. High expression of COX7B, PIH1D2, NDUFA1, NDUFA2 and NDUFB1 indicated a better prognosis. CRGs signature could predict prognosis (P<0.001) and affect immune infiltration. The prognosis was better in the low-risk group, while the high-risk group was more correlated with PD-L1. SLC25A5 downregulated expression (P=0.001) and SLC6A3 upregulated (P=0.02) after radiotherapy. SLC25A5 was related to the degree of differentiation of CC; the worse the differentiation, the higher the expression.

Conclusions: CRGs may further affect patient prognosis and response to immunotherapy by influencing metabolic pathways and immune infiltration. Radiation could alter the expression of CRGs, which may have potential research value in evaluating the efficacy of radiotherapy.

Background: It is hypothesized that uric acid acts as an antioxidant and may prevent cancer. However, observational studies regarding the relationship between serum urate levels, gout, and breast cancer have provided discrepant evidence. Therefore, the objective of our study was to investigate the potential causal relationship between them.

Methods: This study included 12,451 participants from the National Health and Nutrition Examination Survey (NHANES) 2009-2018. Associations between urate levels, gout, and breast cancer were examined using multivariate logistic regression analysis. In addition, to assess the causal link among them, Mendelian randomization (MR) analysis was conducted, primarily using the inverse variance weighted (IVW) approach, supplemented by MR Egger and weighted median approaches, and a set of sensitivity analyses to test the robustness of the results, and finally, multivariate MR was used to adjust for confounders.

Results: In cross-sectional studies, urate levels [odds ratio (OR) 0.99, 95% confidence interval (CI): 0.89-1.09, P=0.80] and gout (OR 0.96, 95% CI: 0.53-1.76, P=0.90) were negatively associated with breast cancer risk after controlling for multiple confounders, although the P value was not significant. Two-sample MR analysis showed that serum urate levels were negatively associated with the estrogen receptor-negative (ER-) breast cancer (IVW, OR 0.916, 95% CI: 0.848-0.989, P=0.03) risk, but not significantly associated with overall and the estrogen receptor-positive (ER+) breast cancer (IVW, both P>0.05). In addition, gout was negatively associated with overall (IVW, OR 0.07, 95% CI: 0.008-0.594, P=0.02), ER+ (IVW, OR 0.062, 95% CI: 0.005-0.742, P=0.03), and ER- breast cancer (IVW, OR 0.041, 95% CI: 0.004-0.472, P=0.01) risk. These associations persisted after multivariate MR adjustment for smoking status, alcohol intake frequency, and body mass index (BMI).

Conclusions: Our study elucidated the relationship between uric acid, gout and breast cancer, and further studies are still needed in the future to clarify the mechanisms involved.

Background: The fat mass and obesity-associated protein (FTO) is implicated in various diseases and acts as a demethylase for the most abundant modification of mRNA, namely N6-methyladenosine (m⁶A) modification. It is known that FTO may play an oncogenic role or a tumor-suppressor role in different malignancies. The aim of this study was to investigate the functional roles of FTO in regulating biological processes related to hypopharyngeal squamous cell carcinoma (HSCC).

Methods: Using immunohistochemistry, quantitative real-time polymerase chain reaction (RT-qPCR), and Western blot analysis, we compared the expression levels of FTO in HSCC tissues to adjacent non-cancerous tissues. Furthermore, we evaluated the prognosis of patients with hypopharyngeal cancer in relation to FTO expression levels. In vitro, the Cell Counting Kit-8 (CCK8), wound healing assay, migration and invasion assays were used to identify roles of FTO in HSCC cells FaDu. Tumor xenografts in nude mice were used to disclose the effect of FTO in vivo. Then, transcriptome RNA sequencing (RNA-seq) assays were applied to screen for possible target genes. To confirm the specific site for modulating the expression of the target gene, we used the SRAMP database and methylated RNA immunoprecipitation PCR (MeRIP-PCR).

Results: The results showed that FTO was highly expressed in hypopharyngeal cancer tissues and was correlated with clinicopathology of patients. FTO promoted the proliferation, invasion and migration of hypopharyngeal cancer cells in vitro through its demethylase action. In vivo experiments showed that FTO promoted the growth of subcutaneously implanted tumors of hypopharyngeal cancer cells and their metastasis. Moreover, we revealed that FTO affected the malignant biological behavior of hypopharyngeal cancer cells by regulating the m⁶A modification level of SERPINE1 mRNA. FTO promoted epithelial-mesenchymal transformation (EMT) of hypopharyngeal cancer cells through the SERPINE1 signaling axis.

Conclusions: Our study highlighted the functional significance of the FTO/SERPINE1 axis in tumorigenesis of HSCC. Targeting FTO holds promise as a new therapeutic strategy for HSCC.

Background: The recurrent somatic mutations in genes such as Janus kinase 2 (JAK2) lead to cytokine-independent activation of the JAK-signal transducer and activator of transcription (STAT) pathway, a crucial factor in the development of classic myeloproliferative neoplasms (cMPNs). Protein tyrosine phosphatase 1B (PTP1B) is a significant regulator in this pathway, while the single nucleotide polymorphism (SNP) and promoter methylation profiles of the PTP1B gene in cMPN patients have largely remained unexplored. Therefore, to further explore the SNP and promoter methylation profiles of the PTP1B gene in cMPNs, we conducted a comprehensive SNP analysis of the PTP1B gene as well as the methylation status detection of the PTP1B promoter between cMPN patients and healthy controls.

Methods: Bone marrow (BM) biopsies were collected from a cohort comprising 96 cMPN patients and 50 healthy controls. SNP-specific extension primers were utilized to facilitate single base extension at the SNP site. A MALDI-TOF mass spectrometer and MassARRAY Typer software were used to detected the SNP. The incidence of SNPs within PTP1B were calculated in cMPN patients and healthy controls. The promoter region of the PTP1B gene were amplified and methylation Bisulfite amplicon sequencing (BSAS) analysis were performed, MethylKIT software was utilized to analyzed the methylation levels at each CpG site of PTP1B. Visualization of data was facilitated using the Methylation Plotter software. Statistical analysis of methylation was performed using the Kruskal-Wallis test. Differences of methylation at PTP1B gene sites were analyzed by Kruskal-Wallis test. P values <0.05 were considered to be statistically significant.

Results: Our findings revealed seven coding-region SNPs, including a novel variant (g.50579818T>A). Additionally, we identified aberrant hypermethylation and hypomethylation of several CpG islands within the PTP1B gene. Notably, the incidence of SNPs was significantly different between cMPN patients and healthy controls, and the methylation level of the PTP1B promoter was markedly elevated in cMPN samples compared to healthy controls.

Conclusions: In this study, we identified a novel SNP and observed differences in the frequency of seven SNPs and hypermethylation of PTP1B promoters between cMPN patients and normal controls. These results suggest that the PTP1B gene might play a critical role in the pathogenesis of cMPNs. Further research exploring more mechanism and larger sample is warranted to fully elucidate the specific role of PTP1B in cMPNs.

Background: Osteosarcoma (OS) is an aggressive and fast-growing malignant tumor associated with high mortality. Early diagnosis and prompt treatment can markedly enhance prognosis and increase survival rates. Constructing prognostic models can effectively predict OS progression, assist in patient diagnosis, and provide personalized treatment plans. In this study, we identified OS-related prognostic genes using the weighted gene co-expression network analysis (WGCNA) method to construct and validate a robust prognostic model, providing guidance for patient risk assessment and clinical treatment.

Methods: Clinical data for OS samples were collected from the Gene Expression Omnibus (GEO) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) databases. Statistical analyses, including enrichment analysis, cluster analysis, and model construction, were performed using the R programme.

Results: The WGCNA method was used to identify genes which were important to OS development and progression, screening for those relevant to prognosis to build a reliable and widely applicable model. To enhance the model's applicability to diverse OS patient populations, we initially conducted a clustering analysis based on the identified prognostic-related key genes. We then identified differentially expressed genes (DEGs) between clusters and used these genes to subtype OS patients, assessing their ability to distinguish among different patient populations. Subsequently, we selected prognostic-related DEGs to establish the prognostic model, resulting in a risk scoring method utilizing the expression of creatine kinase, mitochondrial 2 (CKMT2) and cell growth regulator with EF-hand domain 1 (CGREF1). We validated the predictive capability of the constructed prognostic model, confirming its robust predictive performance. Finally, based on our prognostic model, we analyzed the immune infiltration and drug sensitivity of OS patients, aiding in evaluating responses to immunotherapy and optimizing treatment plans.

Conclusions: A predictive model based on OS-related prognostic genes was constructed to accurately evaluate risk and guide treatment in OS patients, and CKMT2 and CGREF1 were identified as potential therapeutic targets.

Background: The presence of fatty liver (FL) has been suggested to influence the incidence of colorectal cancer (CRC). This study aimed to evaluate the predictive utility of six alternative indices of FL-namely, liver fat percentage (PLF), lipid accumulation product (LAP), hepatic steatosis index (HSI), United States fatty liver index (USFLI), fatty liver index (FLI), and Zhejiang University index (ZJU)-in assessing the risk of CRC. We aimed to determine their effectiveness in predicting CRC risk by comparing these surrogate indices.

Methods: Data for this study were derived from the National Health and Nutrition Examination Survey (NHANES) conducted between 2003 and 2018, focusing on adults over 20 years old. The six FLIs were calculated using established methodologies outlined in prior research. To identify key variables, the Boruta algorithm was employed. The relationships between FLIs and CRC risk were assessed using multivariable logistic regression, generalized linear models (GLMs), and restricted cubic spline (RCS) models. Additionally, subgroup analyses were performed to investigate the effects of potential confounders.

Results: Among the 16,250 individuals surveyed, 96 were diagnosed with CRC. Those with CRC exhibited significantly higher levels of PLF (4.65 vs. 3.31, P=0.004), LAP (55.63 vs. 42.34, P=0.04), USFLI (23.22 vs. 17.83, P<0.001), and FLI (58.16 vs. 50.86, P=0.048) compared to individuals without CRC. Multivariate logistic regression and RCS analyses indicated that, of the six indices, only USFLI was significantly associated with an increased risk of CRC. Notably, further stratification of USFLI revealed that this association was consistently stronger in individuals aged over 65 years [odds ratio (OR) =1.023; 95% confidence interval (CI): 1.005-1.041; P=0.01] and among non-smokers (OR =1.018; 95% CI: 1.003-1.033; P=0.02) after adjusting for multiple confounders.

Conclusions: The USFLI index demonstrated a more significant association with the risk of CRC compared to the other five alternative FLIs, highlighting its potential utility in predicting CRC risk in clinical settings.

Background: Perineural invasion (PNI) in colon cancer (CC) is widely associated with poor prognosis. In this study, we aimed to develop a predictive model for PNI and to assess its prognostic value in CC patients.

Methods: Data for CC patients with or without PNI were obtained from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Potential features were selected by stepwise logistic regression, and multivariate logistic regression was used to develop the nomogram. Nomogram performance was assessed based on its calibration curve, discrimination ability and clinical utility. The prognostic value of PNI was assessed using Kaplan-Meier analysis, a competing risk model, and a Fine-Gray multivariable regression model.

Results: A total of 51,826 subjects were included in the study. The nomogram consisted of 11 features was constructed, which provided good calibration and discrimination with area under the curve values of 0.787 vs. 0.781 (development cohort vs. validation cohort). Patients with PNI had worse CC-specific survival (P<0.001) and a higher CC-specific death rate (Gray's test, P<0.001) than patients without PNI. Fine-Gray multivariable regression analysis showed that patients with PNI had a higher CC-specific death rate than patients without PNI [hazard ratio (HR) =1.243; 95% confidence interval (CI): 1.183-1.305; P<0.001]. Pathologic stage T4 (pT4) CC patients without PNI treated with chemotherapy (ChemT) plus radiotherapy (RT) had a lower CC-specific death rate than ChemT-treated or non-therapy patients.

Conclusions: The nomogram developed herein has certain clinical application value for predicting PNI risk in CC patients. PNI is a survival predictor for CC patients. pT4 patients without PNI might benefit from combined ChemT and RT.