Translational cancer research最新文献

Background: Glioblastoma (GBM) is a highly lethal brain tumor with a complex tumor microenvironment (TME) and poor prognosis. This study aimed to develop and validate a novel immune-related prognostic model for GBM patients to enhance personalized prognosis prediction and develop effective therapeutic strategies.

Methods: RNA sequencing and clinical data for GBM patients were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) (GSE83300). Single-sample gene set enrichment analysis (ssGSEA) was performed using the gene set variation analysis (GSVA) package in R to classify the samples into high and low immune infiltration clusters based on 29 immune cell subtypes. Clustering validations included differential analysis of immune scores and comparison of human leukocyte antigen (HLA) family expression and immune cell subtypes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) analysis compared molecular mechanisms and cellular functions between clusters. Differentially expressed immune-related genes between the high and low immune infiltration clusters were screened out, and the prognostic immune-related genes (PIGs) were identified using univariate Cox regression. Co-expression analysis between PIGs and transcription factors (TFs) (Cistrome) was conducted, and a protein-protein interaction (PPI) network (STRING) was constructed. Least absolute shrinkage and selection operator (LASSO) regression constructed a prognostic model. Correlation analyses between PIGs, immune infiltrates, and GBM-related genes were performed. Tumor mutation burden (TMB) analysis and a nomogram incorporating age, gender, and risk score were developed for individualized prognosis prediction.

Results: A total of 312 differentially expressed immune-related genes were identified between high and low immune infiltration clusters. Of these, 28 genes were correlated with GBM prognosis. LASSO regression identified 10 genes (CLCF1, PTX3, TNFRSF14, SDC2, VGF, AREG, PLAUR, GRN, AQP9, and IGLV6-57) for the prognostic model. Patients were divided into high-risk and low-risk groups based on risk scores. Survival analysis showed significantly better overall survival (OS) for the low-risk group (P<0.05). The prognostic signature was validated as an independent prognostic factor. Correlation analyses demonstrated significant associations between the prognostic model, immune cell infiltrates, GBM-related genes, and immune checkpoint-related genes. A nomogram incorporating age, gender, and risk score was developed for personalized prognosis prediction.

Conclusions: In summary, our study provided a novel prognostic model based on ssGSEA for GBM patients and offered potential insights for understanding the tumor immune and molecular mechanisms of the disease.

Background: Phosphorylation-dephosphorylation is one of the most common and critical cellular activities. It is essential for cell cycle control and leads to large changes in protein conformation, which can alter protein function and coordinate multiple functions such as cell metabolism, gene transcription and translation, signaling, growth, differentiation, and apoptosis. Alterations in the phosphorylated proteome have been shown in many cancers. Many phosphatases that catalyze dephosphorylation have been described as oncogenes and tumor suppressors. Papillary renal cell carcinoma (PRCC) is the second most common subtype of kidney cancer, in which most patients diagnosed with PRCC are already in advanced stages with a poor prognosis. It is necessary to identify reliable predictors associated with early diagnosis and prognosis of PRCC. The study used PRCC patients data from The Cancer Genome Atlas (TCGA) database to evaluate dephosphorylation-related genes and build a panel of prognostic gene signatures which predicts accurately the outcome of PRCC patients.

Methods: The mutation data, and the fragments per kilobase of exon model per million mapped fragments (FPKM) data together with the corresponding clinical information were downloaded from TCGA database for 288 PRCC patients. Lasso regression algorithm (LASSO) and multivariate Cox regression analysis were performed to produce a panel of risk-related genetic signatures.

Results: We analyzed 417 dephosphorylation-associated genes and, finally, identified 9 genes (ADORA1, CDKN3, CRY2, PLPPR4, PPA2, PPP2R2B, PPP6R2, PTP4A1, TPTE2) and constructed a panel of signatures associated with prognosis. The area under the receiver operating characteristic curve (AUC) value was 0.833 for the prognostic risk score signature. It was confirmed that the risk score was an independent predictor of prognosis [hazard ratio (HR) =1.013, 95% confidence interval (CI): 1.002-1.024, P=0.02].

Conclusions: We identified 9 genes associated with dephosphorylation differentially expressed in PRCC tumor tissues and established the first prognostic model based on dephosphorylation-associated genes in PRCC patients. It was shown to be a valid and reliable prognostic indicator that could predict the prognosis of PRCC patients accurately. This study has a lot of potential value for future studies.

Background: Colon cancer, a significant contributor to cancer-related mortality worldwide, exhibits a high recurrence rate in patients following surgical intervention, particularly when the disease has progressed to intermediate or advanced stages. This study undertakes a comprehensive analysis of the risk factors influencing postoperative recurrence in patients with middle- to late-stage colon cancer and subsequently develops a columnar graphical prediction model based on these findings. This model seeks to enhance the capability of identifying the risk of postoperative recurrence in patients with intermediate and advanced colon cancer, thereby providing a scientific foundation for the development of more personalized and effective prevention and management strategies.

Methods: An analysis was conducted on a cohort of 209 patients diagnosed with colon cancer and treated at our hospital between 2020 and 2021. Clinical data were gathered to compare recurrence rates of postoperative colon cancer among patients with different influencing factors. Logistic regression analysis was utilized to determine independent factors affecting the recurrence rate of postoperative colon cancer. A nomogram risk prediction model was developed and assessed for its effectiveness.

Results: The results of the regression analysis indicated that "Tumor stage" (stage IV), "Lymph node metastasis" (presence), "the level of C-reactive protein", and "the level of carcinoembryonic antigen" were identified as independent risk factors for postoperative colon cancer recurrence in patients. Additionally, "Differentiation degree" (medium/high), "Chemotherapy (have)", and "the level of serum albumin" were found to be associated with a decreased risk of recurrence. A nomogram prediction model was created using the mentioned risk factors, showing a link between higher scores and higher postoperative colon cancer recurrence rates. The model had a C-index of 0.834 [95% confidence interval (CI): 0.776-0.892] and was internally validated for strong and consistent performance.

Conclusions: This study developed a nomogram prediction model to forecast the recurrence rate of postoperative colon cancer by identifying independent influencing factors. The model demonstrates strong discrimination and consistency, offering valuable guidance in promptly assessing the likelihood of postoperative colon cancer recurrence in patients and implementing timely and effective preventive measures.

Background: The response of gastric cancer (GC) patients to first-line programmed cell death 1 (PD-1) blockade and S-1 plus oxaliplatin (SOX) chemotherapy varies considerably, and the underlying mechanisms driving this variability remain elusive. Exosomal microRNAs (miRNAs or miRs) have emerged as potential biomarkers for efficacy prediction due to their roles in GC biology and stable expression in serum. In this study, we aimed to identify biomarkers to predict patients' response to anti-PD-1 therapy and further elucidate the potential mechanisms by which these exosomal miRNAs modulate the immune response in GC.

Methods: Serum exosomes were extracted from 11 GC patients (five in the primary cohort and six in the validation cohort) treated with SOX and camrelizumab (a PD-1 inhibitor). High-throughput sequencing was performed to identify miRNA expression profiles, after which hierarchical clustering and a differential expression analysis were conducted. Functional enrichment analyses of the target genes for the significantly upregulated miRNAs were performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The validation of the candidate miRNAs was carried out by quantitative polymerase chain reaction (qPCR) in an independent cohort.

Results: MiRNA sequencing identified 3,083 miRNAs, of which 74 (42 upregulated and 32 downregulated) were differentially expressed between the responders and non-responders. The GO and KEGG pathway analyses of the top 20 upregulated miRNAs indicated that the target genes were significantly involved in transcription regulation, cytoplasmic processes, and protein binding, and that key pathways included the PI3K-AKT, MAPK, RAP1, and RAS signaling pathways. Consistent with the sequencing findings, the qPCR validation results showed significant differences in the expression levels of miRNA451a and miRNA142-5p between the responders and non-responders.

Conclusions: This study identified specific plasma exosomal miRNAs in GC patients that differ between responders and non-responders to PD-1 monoclonal antibody therapy combined with chemotherapy. These miRNAs could serve as predictive biomarkers, paving the way for precision medicine and personalized therapy in the treatment of GC.

Background: Endometrial cancer (EC) is the most common gynecological malignancy in developed countries, with incidence rates continuing to rise globally. However, the precise mechanisms underlying EC pathogenesis remain largely unexplored. This study aims to prioritize genes associated with EC by leveraging multi-omics data through various bioinformatic methods.

Methods: We utilized the Open Targets Genetics (OTG) database to pinpoint potential causal variants and target genes for EC. To explore the pleiotropic effects of gene expression on EC, we applied the Summary-based Mendelian Randomization (SMR) using summary data from a genome-wide association study (GWAS) on EC and expression quantitative trait loci (eQTL) data from the Consortium for the Architecture of Gene Expression (CAGE). We also conducted a cross-tissue transcriptome-wide association study (TWAS) employing sparse canonical correlation analysis (sCCA). Results from the sCCA TWAS and single-tissue TWAS for 22 tissues were combined using the aggregated Cauchy association test (sCCA + ACAT) to identify genes with cis-regulated expression levels linked to EC.

Results: The OTG database recognized 15 genomic loci showing independent association with EC. Gene prioritization highlighted nine genes with relatively high locus-to-gene (L2G) scores (≥0.5), the majority of which aligned with those identified using the closest gene. Colocalization analysis identified 11 additional genes at these loci. Our SMR analysis revealed two genes, EVI2A and SRP14, exhibiting a significant pleiotropic association with EC. Cross-tissue TWAS identified 31 genes whose expression was significantly associated with EC after correction for multiple testing, with four genes (EIF2AK4, EVI2A, EVI2B, and NF1) also confirmed by gene colocalization in the OTG analysis.

Conclusions: We confirmed the involvement of EVI2A in the pathogenesis of EC and identified several other genes that may contribute to EC development. These findings offer new insights into the genetic mechanisms underlying EC and may inform future research and therapeutic strategies.

Background: The poor response of patients with gliomas to existing immunotherapy has resulted in negligible improvement in prognosis. It is widely acknowledged that HBXIP serves as a transcriptional activator implicated in tumorigenesis across various cancer types. However, its specific role within glioma remains unclear. The aim of this study was to determine the association between HBXIP expression and survival and tumor-infiltrating immune cells. In addition, to construct a prognostic model to predict the overall survival (OS) of patients with glioma.

Methods: Transcriptome sequencing data of 325 patients with glioma in the Chinese Glioma Genome Atlas (CGGA) database and 702 patients with glioma in The Cancer Genome Atlas (TCGA) were included for retrospective analysis and were used as the training group and the validation group, respectively. The expression of HBXIP in pancancer was detected in the database. A t-test and one-way analysis of variance were used to determine the differential expression levels of HBXIP across distinct subgroups of glioma. Functional annotations pertaining specifically to HBXIP's biological relevance underwent scrutiny via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The prognostic significance of HBXIP in glioma was ascertained through Kaplan-Meier curves and Cox regression models, a nomogram was used to establish a prognostic model, and the predictive power was evaluated with calibration curves and the concordance index. HBXIP's association with inhibitory immune checkpoints and tumor immune cell infiltration was examined using Pearson correlation coefficients via Tumor Immune Estimation Resource database.

Results: We found that HBXIP was upregulated in glioma, and that elevated HBXIP expression correlated significantly with adverse clinicopathological features and decreased OS. Multivariate analysis showed that HBXIP was an independent prognostic biomarker for glioma, and the established prognostic model could accurately predict the OS of patients. We also found that HBXIP expression was positively correlated with inhibitory immune checkpoint expression, HBXIP overexpression was associated with increased levels of tumor immunoinfiltrating cells in glioma that resulted in poor survival, and HBXIP demonstrated a positive correlation with the expression of immune cell marker genes.

Conclusions: HBXIP is closely related to the clinicopathologic factors in glioma and may function as an oncogene. Its high expression is associated with poor prognosis, which may potentially be linked to immune escape and immune cell infiltration. HBXIP is a potential biomarker of prognostic and immune infiltration in glioma.

Background: Head and neck squamous cell carcinoma (HNSCC) has a poor prognosis due to late diagnosis and complex molecular mechanisms. Vascular endothelial growth factor C (VEGFC) is associated with angiogenesis and lymphangiogenesis. This study aimed to investigate VEGFC's prognostic value in HNSCC and its correlation with immune cell infiltration.

Methods: VEGFC gene expression was analyzed in HNSCC patients using Tumor Immune Estimation Resource 2.0 (TIMER2.0), Gene Expression Profiling Interactive Analysis (GEPIA), and University of ALabama at Birmingham CANcer data analysis Portal (UALCAN) databases, focusing on differential expression and clinical-pathological correlations. The impact of VEGFC on overall survival (OS) and disease-free survival (DFS) was assessed using GEPIA. RNA-seq profiles and clinical information from 503 HNSCC tumor tissues and 44 normal control tissues obtained from The Cancer Genome Atlas (TCGA) database were subjected to univariate and multivariate Cox regression analyses to develop a prognostic nomogram. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was used for a protein-protein interaction (PPI) network, while the Tumor-Immune System Interaction Database (TISIDB) for immune-related associations. Expression was further validated with the Gene Expression Omnibus dataset (GSE6631) and reverse transcription quantitative polymerase chain reaction (RT-qPCR).

Results: VEGFC was significantly upregulated in HNSCC and closely correlated with age, gender, race, and tumor stage (P<0.05). PPI and co-expression gene analysis identified ITGA3, NT5E, and PXN as highly associated with VEGFC (R>0.6, P<0.05), which are mainly enriched in PI3K/Akt, MAPK signaling pathway, and cancer-associated glycoproteins. High VEGFC expression predicted poor OS (P=0.003) and DFS (P=0.03). Univariate and multivariate Cox regression analyses confirmed VEGFC as an independent prognostic factor for HNSCC. The prognostic nomogram accurately predicted 1-, 3-, and 5-year survival and calibration curve was very close to ideal 45-degree diagonal line. VEGFC also correlated with immune cells infiltration, including B cells, CD4⁺ T cells, CD8⁺ T cells, as well as immune-related markers such as tumor-infiltrating lymphocytes (TILs) markers, immune modulators, and inflammatory chemokines (P<0.05).

Conclusions: VEGFC may serve as an independent prognostic factor and potential immunotherapeutic target in HNSCC, offering insights into patient risk stratification and personalized treatment strategies.

Background: Gastric cancer (GC) is a malignancy with a grim prognosis, ranking as the second most common cause of cancer-related deaths globally. Various investigations have demonstrated the substantial involvement of ferroptosis and pyroptosis in the advancement of tumors. Nevertheless, the precise molecular mechanisms by which distinct genes associated with ferroptosis and pyroptosis influence the tumor microenvironment (TME) in GC remain elusive. Therefore, this study aims to elucidate the role of ferroptosis and pyroptosis in GC and provide insigths for GC therapy and prognosis evaluation.

Methods: The data including gene expression, clinicopathological characteristics and survival information of GC samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts were collected, and the expression level of ferroptosis and pyroptosis genes (FPGs) in GC samples were analyzed. Consensus clustering analysis, Cox logistic regression, principal component analysis (PCA), and the "survival", "survminer", "limma", "ggplot2" and other packages in R were utilized to compare the differences among different groups. In the level of GC cells, cell viability experiments were conducted by Cell Counting Kit-8 (CCK-8) assay.

Results: Through the analysis of the expression level of FPGs in GC samples from the TCGA and GEO cohorts, twenty-three prognostic-related and differentially expressed FPGs were collected for further analysis. Through consensus clustering analysis, three distinct patterns of FPGs were identified and found to be correlated with clinicopathological characteristics, immune cell infiltration, and prognosis in patients with GC. Subsequently, 684 prognostic-related genes from 1,082 pattern-related differentially expressed genes (DEGs) were screened for constructing the FPG_Score system to quantify FPGs patterns in individual GC patients and predict the prognosis. The analysis indicated that GC patients with high FPG_Score exhibited improved survival rates, increased tumor mutation burden (TMB), higher microsatellite instability (MSI), and elevated programmed cell death protein ligand 1 (PD-L1) expression. These patients with high FPG_Score were more likely to benefit from immunotherapy and had a more favorable prognosis.

Conclusions: Our study innovatively provided a comprehensive analysis of FPGs in GC, and constructed the FPG_Score system for stratification of individual patients, so as to predict its benefit from immunotherapy and prognosis.

Background: Head and neck squamous cell carcinomas (HNSCs) are a diverse collection of tumors that originate in the oral cavity, pharynx, and larynx and pose a severe threat to human health, contributing to a fast-rising burden of cancer morbidity and mortality. The search for prognostic biomarkers of HNSC has been a hot topic. Spindle and kinetochore-associated (SKA) complex, including three members SKA1/2/3, which stabilize the spindle microtubules at the kinetosite during mitosis metaphase, has been demonstrated to be associated with poor prognosis of different cancers. The function of SKA1/2/3 in HNSC remains to be investigated. We used a vast variety of public datasets and web-based technologies to investigate SKA complex expression and its link to patient prognosis, and discovered multiple pathways by which the SKA complex is regulated in HNSC.

Methods: The Cancer Genome Atlas (TCGA) database was used to determine SKA1/2/3 expression level in HNSC. SKA1/2/3-related proteins level and immune cells infiltration level were identified. Metascape was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. GSE31056 from Gene Expression Omnibus (GEO) database was used as an external dataset for data validation. A nomogram containing SKA1/2/3 for the prognosis of HNSC patients was established.

Results: SKA1/2/3 were highly expressed in HNSC. High expression of SKA2 was significantly related to the poor overall survival (OS) and poor disease-free survival (DFS) of HNSC patients. SKA1/2/3 and the related proteins were enriched in cell division, chromosome segregation, and mitotic cell cycle. SKA1/2/3 expression was obviously positively correlated to several immune cells' infiltration. The expression values of SKA1/2/3 were higher in tumors than in healthy tissues in GSE31056.

Conclusions: SKA1/2/3 were shown to be related to the prognosis and immune cell infiltration of HNSC, which could be used as biological markers and therapeutic targets for HNSC.

Background: Ovarian cancer accounts for 3% of all malignancies in women and kills about 140,000 women worldwide each year, representing the fifth leading cause of cancer-related death in women. At diagnosis, 70% of patients with ovarian cancer are already at stage III or IV disease, with a 5-year survival rate of less than 45%. Studies have found that solute carrier family 1 member 3 (SLC1A3) is highly expressed in various cancers and is associated with the poor prognosis of these cancers. However, the role of SLC1A3 in ovarian cancer remains unknown. The purpose of this study was to investigate the role of the SLC1A3 gene in the proliferation, apoptosis, migration, and outcomes of ovarian cancer.

Methods: The expression level of SLC1A3 was measured via quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR). Knockdown experiments were performed with small interfering RNA targeting SLC1A3 in ovarian cancer cells. After the knockdown of SLC1A3, proliferation was evaluated with Cell Counting Kit 8 (CCK8) assay, apoptosis was measured by flow cytometry, and migration was evaluated via wound-healing assay. Kaplan-Meier method was used to analyze the effect of SLC1A3 expression on the prognosis of patients with ovarian cancer.

Results: High expression of SLC1A3 was associated with poor prognosis in ovarian cancer patients, and the expression of SLC1A3 in ovarian cancer cells was higher than that in ovarian epithelial cells. In vitro experiments demonstrated that knockdown of SLC1A3 restrained the proliferation activity of ovarian cancer cells, enhanced cell apoptosis, and inhibited cell migration.

Conclusions: High expression of SLC1A3 is linked to poor prognosis in ovarian cancer patients. SLC1A3 activity impedes apoptosis while enhancing the proliferation and migration of ovarian cancer cells, suggesting its potential as a therapeutic target for drug development.