Translational cancer research最新文献

Background: An accumulating number of studies show that CALD1 is associated with a variety of tumor microenvironments (TME) and is closely related to patients' survival. However, to the best of our knowledge, few studies examined the role of CALD1 in the immune microenvironment of glioma. The aim of this study is to investigate the potential correlation between CALD1 and the pathogenesis and progression of glioma, aiming to identify a novel therapeutic target.

Methods: We assessed the role of CALD1 in pan-cancer and investigated the correlation between CALD1 and TME of glioma by bioinformatic analysis and experimental verification.

Results: We found that CALD1 expression in glioma was associated with a variety of infiltrating immune cells. CALD1 can promote the development of glioma by affecting M2 macrophage infiltration. Also, we found that CALD1 was closely associated with tumor mutation burden, microsatellite instability, copy number variation, methylation, and stem cell index. Our clinical correlation study demonstrated that CALD1 was associated with overall survival, progression-free interval, and disease-specific survival in a variety of tumors. We verified the significantly high expression of CALD1 in glioma using quantitative real-time polymerase chain reaction (PCR) and Western blotting. Meanwhile, we also conducted relevant cell experiments to prove that CALD1 can affect the proliferation and migration ability of glioma cells in vitro.

Conclusions: Our results confirmed that CALD1 may be a prognostic marker for glioma and a potential target for immunotherapy in the future.

Background and objective: Radiation therapy (RT) is one of the important components of comprehensive treatment for breast cancer and has important value in improving the control rate of local areas, reducing the chance of recurrence and metastasis after breast cancer surgery, delaying disease progression, and improving the survival of breast cancer patients. The factors that affect the RT sensitivity of breast cancer are important. The above potential predictors of radiation efficacy can provide patients with a predictive method and therefore have significant value in clinical therapy. In this paper, we have summarised the predictive factors of radiotherapy sensitivity by reviewing recent research on breast cancer and focused on the following areas: tumor immune microenvironment (TIME), cancer stem cells, noncoding RNAs, signal transduction pathways, genes, etc. This review aims to provide theoretical basis and reference for improving the efficacy of radiotherapy and experimental individualized treatment of breast cancer.

Methods: We searched the Web of Science database to identify clinical studies published between 2010 and January 2024 that investigated radiotherapy sensitivity. The main findings of the validated studies were summarised.

Key content and findings: Improving the radiosensitivity of breast cancer is essential in the treatment of breast cancer. The radiosensitivity can be improved by modulating immune cells or immunomodulatory factors in the TIME, modulating signal transduction pathways, and other innovative combination therapy strategies. And we also summarized the predictive markers of breast cancer radiosensitivity.

Conclusions: In this paper, we reviewed the literature and summarized the newest research advances on the radiosensitivity of breast cancer patients. This review paper includes the following six aspects: the immune microenvironment, tumor stem cells, signaling pathways, regulation of gene/protein expression, small molecule drugs, and predictive markers for radiosensitivity.

Background: Bladder cancer (BC) is the sixth most common cancer and the ninth leading cause of cancer death among men in the world. Previous studies have shown that tumor hypoxia plays an important role in the occurrence and development of BC, but the role of tumor hypoxia in the prognosis and immune infiltration of BC remains unclear. Our aim was to perform a bioinformatics analysis combined with a clinical analysis to explore the roles of hypoxia in BC.

Methods: We acquired datasets (GSE13507, GSE5287, and GSE1827) containing mRNA expression information from BC cohorts from the Gene Expression Omnibus (GEO) and measured the Hypoxia score using the Gene Set Variation Analysis (GSVA). Then we used X-tile method and log-rank test and Pearson's correlation test to analyze the relation among the Hypoxia score and the clinicopathological and immunological characteristics of BC and used stepwise Cox regression analysis to establish a Prognostic model.

Results: Hypoxia was found to be closely associated with tumor grade, pathological type, invasion, and prognosis of BC in our study. Moreover, we determined that hypoxia was closely related to the infiltration abundance of multiple immune cells through a correlation analysis, and the tumor immune cell infiltration was further found to be significantly associated with the tumor grade and tumor type of BC. Furthermore, we constructed several models based on the Hypoxia score and tumor immune infiltration with C-indexes ranging from 0.703 and 0.888, which showed good performance in predicting the prognosis of BC.

Conclusions: Our study showed that hypoxia plays an important role in the progression, prognosis, and tumor immune infiltration of BC. Our models based on hypoxia and tumor immune infiltration play a guiding role in the prognosis and treatment of BC patients.

Background: Hepatocellular carcinoma is a widespread cancer worldwide, ranking as the fifth most frequent cancer and the fourth leading cause of cancer-related deaths. According to comprehensive research, TLK2, a phosphorylated kinase, has been discovered to play a crucial role in promoting tumor development. However, the prognostic significance and influence of TLK2 on hepatocellular carcinoma tumor cells and the immune microenvironment remain unexplored, warranting further investigation. The aim of this study was to investigate the role of TLK2 in promoting the development of hepatocellular carcinoma.

Methods: The present study utilized The Cancer Genome Atlas (TCGA) database and other databases as training sets to examine the expression of TLK2 and its prognostic significance. The findings were subsequently validated through cell proliferation assays and cell colony assays. Gene set enrichment analysis (GSEA) was employed to investigate the tumor-related biological processes associated with TLK2 in hepatocellular carcinoma, while the relationship between TLK2 expression and Wnt/β-catenin signaling pathway was analyzed via TCGA dataset analysis. Western blotting and immunofluorescence assays were used to confirm the experimental results.

Results: TLK2 showed higher expression levels in tumor tissues than in normal tissues. Alpha fetoprotein (AFP), T stage, pathological stage, and histological grade were significantly associated with TLK2 expression. High TLK2 expression correlated with worse overall survival (OS) [hazard ratio (HR) =1.62, 95% confidence interval (CI): 1.14-2.29, P=0.007], progression-free survival (PFS) (HR =1.88, 95% CI: 1.40-2.52, P<0.001) and disease specific survival (DSS) (HR =1.86, 95% CI: 1.18-2.93, P=0.007) in the training and validation sets. Both univariate and multivariate analyses showed that TLK2 was an independent prognostic factor. GSEA showed that TLK2 was significantly enriched in tumor-related biological processes. TLK2 induced the activation of β-catenin signaling, resulting in sustained tumor growth. Methyl thiazolyl tetrazolium (MTT) and colony formation assays demonstrated that TLK2 could promote hepatocellular carcinoma progression. Furthermore, TLK2 showed a significant association with β-catenin in the Wnt pathway.

Conclusions: TLK2 represents an independent prognostic factor in hepatocellular carcinoma and can promote cancer progression via the β-catenin signaling pathway.

Background: In the context of head-and-neck squamous cell carcinoma (HNSCC), dendritic cells (DCs) assume pivotal responsibilities, acting as architects of antigen presentation and conductors of immune checkpoint modulation. In this study, we aimed to identify hub genes associated with DCs in HNSCC and explore their prognostic significance and implications for immunotherapy.

Methods: Integrated clinical datasets from The Cancer Genome Atlas (TCGA)-HNSCC and GSE65858 cohorts underwent meticulous analysis. Employing weighted gene co-expression network analysis (WGCNA), we delineated candidate genes pertinent to DCs. Through the application of random survival forest and least absolute shrinkage and selection operator (LASSO) Cox's regression, we derived key genes of significance. Lisa (epigenetic Landscape In Silico deletion Analysis and the second descendent of MARGE) highlighted transcription factors, with Dual-luciferase assays confirming their regulatory role. Furthermore, immunotherapeutic sensitivity was assessed utilizing the Tumor Immune Dysfunction and Exclusion online tool.

Results: This study illuminated the functional intricacies of HNSCC DC subsets to tailor innovative therapeutic strategies. We leveraged clinical data from the TCGA-HNSCC and GSE65858 cohorts. We subjected the data to advanced analysis, including WGCNA, which revealed 222 DC-related candidate genes. Following this, a discerning approach utilizing random survival forest analysis and LASSO Cox's regression unveiled seven genes associated with the prognostic impact of DCs, notably ACP2 and CPVL, associated with poor overall survival. Differential gene expression analysis between ACP2 ⁺ and ACP2 ^- DC cells revealed 208 differential expressed genes. Lisa analysis identified the top five significant transcription factors as STAT1, SPI1, SMAD1, CEBPB, and IRF1. The correlation between STAT1 and ACP2 was confirmed through quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Dual-luciferase assays in HEK293T cells. Additionally, TP53 and FAT1 mutations were more common in high-risk DC subgroups. Importantly, the sensitivity to immunotherapy differed among the risk clusters. The low-risk cohorts were anticipated to exhibit favorable responses to immunotherapy, marked by heightened expressions of immune system-related markers. In contrast, the high-risk group displayed augmented proportions of immunosuppressive cells, suggesting a less conducive environment for immunotherapeutic interventions.

Conclusions: Our research may yield a robust DC-based prognostic system for HNSCC; this will aid personalized treatment and improve clinical outcomes as the battle against this challenging cancer continues.

[This retracts the article DOI: 10.21037/tcr-20-2678.].

Background: The prognosis for patients with kidney renal clear cell carcinoma (KIRC) remains unfavorable, and the understanding of SRY-box transcription factor 11 (SOX11) in KIRC is still limited. The purpose of this paper is to explore the role of SOX11 in the prognosis of KIRC.

Methods: We analyzed SOX11 expression in KIRC and adjacent normal tissues using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Our study aims to establish a correlation between SOX11 expression and clinical pathological features. Differentially expressed genes (DEGs) were assessed using R software. Furthermore, we conducted Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and gene set enrichment analysis (GSEA). Integration of data from the Tumor Immune Estimation Resource (TIMER) and TCGA databases allowed us to assess the association between SOX11 expression and immune infiltration in KIRC. Additionally, we analyzed the association between SOX11 gene expression and N6-methyladenosine (m6A) modification in KIRC using TCGA and GEO data.

Results: Our findings revealed high SOX11 expression in KIRC, which showed a significant correlation with tumor staging and prognosis. GO/KEGG and GSEA analyses indicated that SOX11 was closely associated with sodium ion transport, synaptic vesicle circulation, and oxidative phosphorylation. Analysis of the TIMER and TCGA databases demonstrated correlations of SOX11 expression levels with the presence of CD8⁺ T lymphocytes, neutrophils, CD4⁺ T cells, as well as B cells. Moreover, both the TCGA and GEO datasets showed a substantial association between SOX11 and m6A modification-related genes, namely ZC3H13, FTO, METTL14, YTHDC1, IGF2BP1, and IGF2BP2.

Conclusions: SOX11 exhibits a correlation with m6A modification and immune infiltration, suggesting its potential as a prognostic biomarker for KIRC.

Background: Changes in gene expression are associated with malignancy. Analysis of gene expression data could be used to reveal cancer subtypes, key molecular drivers, and prognostic characteristics and to predict cancer susceptibility, treatment response, and mortality. It has been reported that inflammation plays an important role in the occurrence and development of tumors. Our aim was to establish a risk signature model of breast cancer with inflammation-related genes (IRGs) to evaluate their survival prognosis.

Methods: We downloaded 200 IRGs from the Molecular Signatures Database (MSigDB). The data of breast cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differential gene expression analysis, the least absolute shrinkage and selection operator (LASSO), Cox regression analysis, and overall survival (OS) analysis were used to construct a multiple-IRG risk signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out to annotate functions of the differentially expressed IRGs (DEIRGs) The predictive accuracy of the prognostic model was evaluated by time-dependent receiver operating characteristic (ROC) curves. Subsequently, nomograms were constructed to guide clinical application according to the univariate and multivariate Cox proportional hazards regression analyses. Eventually, we applied gene set variation analysis (GSVA), mutation analysis, immune infiltration analysis, and drug response analysis to compare the differences between high- and low-risk patients.

Results: Totally, 65 DEIRGs were obtained after comparing 1,092 breast cancer tissues with 113 paracancerous tissues in TCGA. Among them, 11 IRGs (IL18, IL12B, RASGRP1, HPN, CLEC5A, SCARF1, TACR3, VIP, CCL2, CALCRL, ABCA1) were screened with nonzero coefficient by LASSO regression analysis to construct the prognostic model, which was validated in GSE96058.The 11-gene IRGs risk signature model stratified patients into high- or low-risk groups, with those in the low-risk group having longer survival time and less deaths. Multivariate Cox analysis manifested that risk score, age, and stage were the three independent prognostic factors for breast cancer patients. There were 12 pathways with higher activities and 24 pathways with lower activities in the high-risk group compared with the low-risk group, yet no difference of gene mutation load was observed between the two groups. In immune infiltration analysis, we noted that the proportion of T cells showed a decreased trend according to the increase of risk score and most of the immune cells were enriched in the low-risk group. Inversely, macrophages M2 were more highly distributed in the high-risk group. We identified 67 approved drugs that showed a different effect between the high- and low-risk pa

Background: Ferroptosis is an iron-dependent cell death, which is distinct from the other types of regulated cell death. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in cervical cancer (CC) remains unclear. This study aims to explore the ferroptosis-related prognostic genes (FRPGs) expression profiles and their prognostic values in CC.

Methods: The ferroptosis-related genes (FRGs) were obtained from The Cancer Genome Atlas (TCGA) and FerrDb databases. Core FRGs were determined by the Search Tool for the Retrieval of Interacting Genes (STRING) website. FRPGs were identified using univariate and multivariate Cox regressions, and the ferroptosis-related prognostic model was constructed. FRPGs were verified in clinical specimens. The relationship between FRPGs and tumor infiltrating immune cells were assessed through the CIBERSORT algorithm and the LM22 signature matrix. Bioinformatics functions of FRPGs were explored with the Database for Annotation, Visualization, and Integrated Discovery (DAVID).

Results: Thirty-three significantly up-regulated and 28 down-regulated FRGs were screened from databases [P<0.05; false discovery rate (FDR) <0.05; and |log₂ fold change (FC)| ≥2]. Twenty-four genes were found closely interacting with each other and regarded as hub genes (degree ≥3). Solute carrier family 2 member 1 (SLC2A1), carbonic anhydrases IX (CA9), and dual oxidase 1 (DUOX1) were identified as independent prognostic signatures for overall survival (OS) in a Cox regression. Time-dependent receiver operating characteristic (ROC) curves showed the predictive ability of the ferroptosis-related prognostic model, especially for 1-year OS [area under the curve (AUC) =0.76]. Consistent with the public data, our experiments demonstrated that the mRNA levels of SLC2A1 and DUOX1, and the protein levels of SLC2A1, DUOX1, and CA9 were significantly higher in the tumor tissues. Further analysis showed that there was a significant difference in the proportion of tumor infiltrating immune cells between the low- and high-risk group based on our prognostic model. The function enrichment of FRPGs was explored by applying Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses.

Conclusions: In this study, the features of FRPGs in CC were pictured. The results implicated that targeting ferroptosis may be a new reliable biomarker and an alternative therapy for CC.

Background: The relationship between lipid metabolism, immune response, and immunotherapy in prostate cancer (PCa) is closely intertwined, and targeted intervention in lipid metabolism may facilitate the success of anticancer immunotherapy. This research attempted to explore effective immunotherapy for PCa.

Methods: We obtained RNA sequencing (RNA-seq) data for PCa patients from the UCSC Xena platform. Data analysis of differentially expressed genes (DEGs) was performed using package limma in R. Then, DEGs were subjected to enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The Human Protein Atlas (HPA) database was conducted to validate the protein expression of the up-regulated lipid metabolism related genes (LMRGs) between PCa tissues and normal prostate tissues. And then we identified critical transcription factors (TFs), LMRGs and miRNA by constructing a regulatory network of TF-gene-miRNA. Furthermore, we determined the high and low groups based on the score of lipid metabolism enrichment. The hallmark gene sets were derived from gene expression profiles using the gene set variation analysis (GSVA) R package. Finally, we conducted immune infiltration analysis and drug sensitivity analysis.

Results: Immune response and lipid metabolism have undergone significant changes in PCa and paracancerous tissues compared to normal tissues. A total of 21 LMRGs were differentially up-regulated in PCa. The TF-gene-miRNA network showed that PLA2G7, TWIST1, and TRIB3 may be the key genes that elevated lipid metabolism in PCa. The high group had more infiltration of B cell memory, macrophage M0, macrophage M1, and myeloid dendritic cell resting, and the low group had more infiltration of B cell plasma, monocyte, myeloid dendritic cell activated, and mast cell resting. The majority of checkpoint genes exhibited high expression levels in the low group. Lipid metabolism was remarkedly correlated with drug sensitivity.

Conclusions: The analysis of lipid metabolism and related genes has revealed a complex regulatory mechanism that has a significant influence on immune response, immunotherapy, and medication guidance for patients with PCa.

Keywords: Prostate cancer (PCa); lipid metabolism; cancer immune; RNA sequencing (RNA-seq).