Translational cancer research最新文献

Background: In recent years, multi-omics models based on a variety of biomarkers have been continuously developed and increasingly applied in the field of oncology, especially in the early diagnosis of lung cancer. This study aimed to integrate computed tomography (CT) radiomics with seven lung cancer-associated autoantibodies (AABs) to develop multi-omics predictive models for pulmonary nodule (PN) characterization.

Methods: This retrospective study enrolled 179 patients with PNs measuring from 5 to 30 mm in diameter who underwent thoracic surgery at Zhongda Hospital, Southeast University between January 2020 and December 2024. The patients were pathologically categorized into lung cancer (n=87) and non-lung cancer (n=92) groups, and then randomly allocated into training and test sets at a ratio of 7 to 3. Least absolute shrinkage and selection operator (LASSO) regression was used for feature screening to construct a clinical model based on five clinical characteristics. A radiomics prediction model was constructed based on the radiomics features identified after delineating the regions of interest and extracting the radiomics features; the rad-score for each patient was calculated to develop a multi-analytic comprehensive model by combining different markers. The diagnostic performances of the models were compared using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value.

Results: The multi-omics model demonstrated superior diagnostic accuracy with an AUC of 0.902 [95% confidence interval (CI): 0.817-0.986], accuracy of 82.4%, sensitivity of 88.5%, and specificity of 80.0%, outperforming the clinical (AUC =0.848; 95% CI: 0.777-0.919) and radiomics (AUC =0.854; 95% CI: 0.786-0.922) models. Notably, the radiomics model exhibited high sensitivity (96.6%) but poor specificity (63.6%), while the multi-omics model resolved this trade-off via the synergistic integration of clinical-radiomic-biomarker features, achieving significant improvements in the PPV (81.5% vs. 72.7%) compared to the clinical model.

Conclusions: Integrating CT radiomics with seven lung cancer-AABs established a robust multi-omics framework for PN diagnosis. Compared to the standalone clinical or radiomics models, this comprehensive model demonstrated superior diagnostic performance.

Background: Papillary thyroid carcinoma (PTC) is among the most prevalent forms of thyroid cancer. Traditional Chinese medicine (TCM) has been widely employed in the management of PTC, with Oldenlandia diffusa (OD) demonstrating potential in cancer treatment. This study aimed to investigate the anti-PTC effects of OD and elucidate the underlying mechanisms.

Methods: The impact of OD on PTC cells was assessed using a variety of assays, including cell viability, colony formation, acridine orange/ethidium bromide (AO/EB) staining, and transwell assays. Potential targets and downstream pathways were explored through network pharmacology and molecular docking analyses. Protein and gene expression levels were determined using western blotting assays.

Results: OD demonstrated significant inhibitory effects on the biological functions of PTC cells. Through network analysis, 7 targets and 3 active compounds (stigmasterol, β-sitosterol, or poriferasterol) associated with OD's intervention in PTC were identified. Furthermore, correlation analysis revealed a significant positive association with the central gene involved in OD's anti-PTC effects. OD and its active compounds also modulated the phosphorylation of proteins related to the PI3K-AKT pathways, underscoring its anti-PTC efficacy.

Conclusions: OD and its active compounds suppress the biological functions of PTC by modulating the phosphorylation of proteins associated with the PI3K-AKT pathway. These findings suggest that OD may inhibit PTC progression by targeting the PI3K-AKT pathway, offering potential adjuvant therapeutic value for PTC.

Background: Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality, underscoring the urgent need for novel prognostic biomarkers and therapeutic targets. Although PKMYT1, a serine/threonine protein kinase, is implicated in cell cycle regulation, its comprehensive role and clinical significance in LUAD remain poorly defined. This study aims to investigate the oncogenic function, prognostic value, and immunomodulatory role of PKMYT1 in LUAD.

Methods: We performed integrated multi-omics analyses utilizing data from The Cancer Genome Atlas and Gene Expression Omnibus databases. Differential expression, survival, and immune cell infiltration analyses were conducted. Functional enrichment was assessed via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. Drug sensitivity was predicted in silico. In vitro functional validation included PKMYT1 knockdown in LUAD cell lines followed by assays for proliferation, migration/invasion, and apoptosis. Protein expression was confirmed in clinical LUAD tissues.

Results: PKMYT1 was significantly upregulated in LUAD tissues and high expression correlated with poor progression-free survival and overall survival. Multivariate analysis identified PKMYT1 as an independent prognostic factor. PKMYT1 expression was associated with an altered tumor immune microenvironment, specific immune cell infiltration patterns, tumor mutation burden, and immune checkpoint gene expression. High PKMYT1 expression correlated with reduced predicted sensitivity to chemotherapeutic agents. In vitro, PKMYT1 knockdown suppressed LUAD cell proliferation, migration, and invasion while promoting apoptosis, and reversed epithelial-mesenchymal transition.

Conclusions: This study establishes PKMYT1 as a critical oncogene, an independent prognostic biomarker, and a modulator of the tumor immune microenvironment in LUAD. These findings highlight PKMYT1's potential as a promising therapeutic target and a candidate biomarker for patient stratification, offering new insights for targeted therapy strategies in LUAD.

Background: Characterization of the molecular features of a brain tumor is a critical step for patient treatment. Tissue-based detection methods are limited by the location of brain tumors and high intratumor heterogeneity, which also preclude repeat sampling to monitor tumor progression. Cerebrospinal fluid (CSF)-based noninvasive methods may provide an opportunity to solve these problems, but efficient markers are lacking. This study aims to develop and validate a CSF-based liquid biopsy approach to investigate the molecular characterization and transcriptional regulation features of brain tumors.

Methods: In this study, we conducted genome wide analysis of CSF cell free DNA (cfDNA) data collected from Sonic hedgehog (SHH) pathway-activated medulloblastoma (MB) patients sourced from gene expression omnibus (GEO) database, to identify genome features that differed significantly between patients with MB and those with hydrocephalus (P<0.001) using the whole genome bisulfite sequencing (WGBS) dataset.

Results: A total of 397 differential cfDNA genomic loci were identified and verified by assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) as SHH-MB specific; 114 were located in promoter regions, and related to genes specifically expressed in SHH-MB and, combined with DNA methylation state in these regions, could be used to classify the SHH-MB subtype from 763 samples. Twelve of 283 non-promoter loci were identified as super-enhancers and binding sites for transcription factors related to brain tumors were also identified in associated genomic regions. Patients with SHH-MB were then classified using these CSF cfDNA derived transcription regulation features.

Conclusions: CSF cfDNA from patients with brain tumors was used to determine transcription regulation features, which could reflect the molecular characteristics of brain tumors. Further, these features represent biomarkers with potential to identify patients with tumors. Our study provides a new application for CSF cfDNA and extends its use for investigating tumor-specific gene transcription regulation.

Background: Depression plays a crucial role in lung adenocarcinoma (LUAD) occurrence, progression, and prognosis. However, the impact of depression-related genes (DRGs) on the prognosis of LUAD patients is unclear. Thus, a prognosis prediction model was constructed to assess the potential impact of depression on LUAD prognosis.

Methods: The gene expression profiles from The Cancer Genome Atlas (TCGA)-LUAD and GSE68465 were collected for model training and validation. By identifying the intersection of DRGs and differentially expressed genes (DEGs) in LUAD, a risk score model was constructed to stratify patient risk based on univariate and multivariate analyses. The immune infiltration status and therapeutic potential of different risk groups were further explored. The correlation between key genes and clinical outcomes was evaluated in Kaplan-Meier (KM) analysis. Finally, the expression and mechanism of key genes were verified by in vitro experiments.

Results: We identified 2,222 DEGs and 385 DRGs-DEGs, and DRGs-DEGs were closely related to nervous system function and cell signaling. Nine DRGs-DEGs were identified to construct the risk score model for risk stratification. The model's predictive accuracy for patient survival was confirmed by receiver operating characteristic (ROC) curve analysis. LUAD patients with high-risk had significantly higher levels of CD8 T cells, B cells memory, and macrophages M1, which may affect the prognosis of LUAD patients. Furthermore, low-risk patients responded better to immunotherapy. KM analysis revealed that ACSS3 was significantly associated with poor prognosis in LUAD patients. oe-ACSS3 inhibits LUAD cell proliferation, migration, and invasion, and also promotes apoptosis.

Conclusions: The nine-gene risk score model proposed in our study demonstrated promising prognostic performance, highlighting the significant role of depression in LUAD prognosis. ACSS3 was demonstrated to play a critical role in regulating LUAD progression and may be a potential therapeutic target for LUAD treatment.

Background: Immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1), such as atezolizumab (Ate) and pembrolizumab (Pem), have expanded therapeutic options for breast cancer (BC). However, the comparative efficacy of ICI-chemotherapy (ICI-CT) combinations with different CT backbones [e.g., solvent-based paclitaxel vs. nab-paclitaxel (nP)] and the impact of steroid premedication (immunosuppressive, often required for solvent-based taxanes) on ICIs efficacy remain underexplored-gaps that hinder precise regimen selection. This meta-analysis evaluates the effectiveness of these regimens, focusing on triple-negative BC (TNBC) and PD-L1-positive populations.

Methods: Searches were performed across PubMed, Embase, Cochrane and Web of Science to identify relevant studies comparing Ate- or Pem-based combinations (e.g., Ate + nP, Ate + CT, Pem + CT) with control regimens. Outcomes included overall survival (OS), progression-free survival (PFS), and subgroup analyses for TNBC. Meta-analyses were performed using random-effects models for pooled hazard ratios (HR) with corresponding 95% confidence intervals (CI). Heterogeneity was assessed using I² statistics, and publication bias was evaluated using Cochran's Q.

Results: A total of 12 studies involving 6,691 patients were included in the analysis. Ate combined with nP significantly improved OS (HR =0.75; 95% CI: 0.65-0.86) and PFS (HR =0.69; 95% CI: 0.62-0.78) compared to placebo + CT. Pem + CT also enhanced OS (HR =0.70; 95% CI: 0.57-0.86; I²=4.9%) and PFS (HR =0.75; 95% CI: 0.65-0.87; I²=44.6%). In TNBC, Ate-based regimens showed superior OS (HR =0.90; 95% CI: 0.78-1.04; I²=24.2%) and PFS (HR =0.83; 95% CI: 0.75-0.91; I²=49.0%), while Pem demonstrated stronger OS benefits (HR =0.70; 95% CI: 0.57-0.86). Subgroup analysis revealed Ate + nP outperformed Ate + CT in OS (HR =0.75 vs. 1.18) and PFS (HR =0.69 vs. 0.81), though CT combinations required further validation due to limited data. Heterogeneity was relatively low for OS (I²=49.2%) and PFS (I²=65.4%).

Conclusions: This is a relatively low-heterogeneity meta-analysis directly comparing Ate- and Pem-based combinations in BC. Ate + nP is the most effective regimen for PD-L1-positive TNBC, significantly improving survival. These findings support prioritizing Ate + nP for this population in clinical practice and underscore the need for biomarker-driven strategies to optimize immunotherapy efficacy. Future research should explore PD-L1 expression thresholds and CT sequencing to refine personalized treatment.

Background: Breast cancer (BRCA) is a common malignant tumor in women globally and has a poor prognosis. Molecular targeted therapy is a promising way for improving the treatment of BRCA. This study aimed to identify potential biomarkers for BRCA and construct a prognostic model.

Methods: The expression, mutation and survival data were obtained from The Cancer Genome Atlas database, and estrogen-related genes (ERGs) were extracted from a previous study. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses were used to determine hub genes. The risk model was evaluated by Kaplan-Meier and receiver operating characteristic (ROC) curves. Immune infiltration was analyzed by the Immuno-Oncology Biological Research package. Gene set enrichment analysis was used for the functional analysis. In vitro validation was finally performed.

Results: Totally 113 estrogen-related differentially expressed genes (ERDEGs) were identified. A risk model was constructed using four hub ERDEGs: BATF, CLDN7, TH and TFPI2. This model has a moderate predictive value, with area under curve (AUC) values over 0.69. In high-risk group, CD8 T cells, Tregs, NK cells and M1 macrophages were significantly decreased. BATF, CLDN7 and TH were up-regulated in BRCA, while TFPI2 was down-regulated in BRCA. Survival analysis exhibited that high expression of CLDN7 and TH was associated with poorer prognosis, while high expression of BATF and TFPI2 was associated with better prognosis. Additionally, CLDN7 overexpression significantly enhanced the invasion and migration of BRCA cells. It also inhibited the expression levels of p-STAT5, p-STAT3 and p-smad2/3 in BRCA cells.

Conclusions: We identified four hub genes closely related to the prognosis of BRCA, and a risk model constructed by these four genes may be useful for risk stratification and prognosis evaluation in BRCA patients.

Background: Anoikis resistance and epithelial-mesenchymal transition (EMT) are crucial factors in tumor invasiveness and metastasis in lung adenocarcinoma (LUAD). Identifying anoikis-EMT-related genes could be beneficial for predicting prognosis and immunotherapeutic efficacy in patients with LUAD. This study aims to establish and validate a novel prognostic signature based on anoikis-EMT-related genes for LUAD and to identify the potential biomarkers encapsulated within it.

Methods: Anoikis-related genes and EMT-related genes were retrieved from the GeneCards and dbEMT 2.0 databases. Univariate Cox regression analysis and principal component analysis (PCA) were conducted to define anoikis and EMT levels. Gene expression and clinical information of patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Univariate Cox regression and multivariate Cox regression analyses were conducted to construct a risk score model. Immune correlation and drug sensitivity analyses were performed to investigate the association of the risk score with the immune profile and antitumor treatment. Three essential genes in the model were examined for messenger RNA (mRNA) expression by reverse transcription-polymerase chain reaction (RT-PCR) and for protein levels via the Human Protein Atlas (HPA) database.

Results: LUAD patients demonstrating low Anoikis Potential Index (API) combined with high EMT Potential Index (EPI) exhibited the poorest overall survival (OS). We further constructed a nine-gene prognostic risk model that combines anoikis and EMT. High-risk patients demonstrated significantly shorter survival duration. The clinical-prognostic nomogram accurately predicted outcomes at 1, 3, and 5 years. In addition, patients in low-risk group demonstrated superior immune responses to treatment and were more sensitive to commonly used chemotherapy drugs. Our validation studies confirmed upregulated expression of ANGPTL4, SLC2A1, and BIRC5 in LUAD, observed at both transcriptional and translational levels.

Conclusions: The anoikis-EMT-based risk model effectively forecasts both OS and immunotherapy response in LUAD patients, accelerating the identification of groundbreaking molecular biomarkers and prospective molecular targets.

Background: Although endometriosis-associated ovarian cancer (EAOC) is considered a separate clinical entity, no specific prognostic biomarkers aid in its management. This has, therefore, been among the factors hindering the development of tailored treatments. We aim to develop a robust, histotype-aware biomarker for EAOC through an integrative computational approach to explain its association with the tumor immune microenvironment.

Methods: A multi-stage bioinformatics approach using multiple independent Gene Expression Omnibus (GEO) cohorts was employed. We extracted consensus differentially expressed genes (DEGs) from three discovery datasets (EAOC vs. non-malignant tissue). These DEGs were further distilled into high-confidence hub genes using two machine learning algorithms. The pan-cancer prognostic potential was assessed via meta-analysis and tested for validity in an independent, EAOC-enriched cohort (GSE65986). The derived immune context was assessed using CIBERSORTx deconvolution in a pure EAOC cohort (GSE226870), while the cellular origin of our candidate was determined using an independent ovarian clear cell carcinoma (OCCC) single-cell RNA sequencing (scRNA-seq) dataset (GSE224334).

Results: From our analysis, we identified 75 consensus DEGs distilled into five hub genes. Among these, B4GALNT3 was the key candidate. While the pan-ovarian cancer meta-analysis showed a non-significant protective trend, we confirmed in our EAOC-enriched validation cohort that high B4GALNT3 expression was significantly associated with improved overall survival [hazard ratio (HR) =0.350, P=0.04]. It showed robust diagnostic potential with an overall area under the curve (AUC) of 0.962 [95% confidence interval (CI): 0.923-0.993] in leave-one-dataset-out cross-validation among discovery datasets. Immune deconvolution revealed that B4GALNT3 expression correlated with an anti-tumor microenvironment composed of increased levels of plasma B cells, memory B cells, and activated dendritic cells, with decreased regulatory T cells and M2 macrophages. Finally, scRNA-seq analysis confirmed that B4GALNT3 was intrinsically highly expressed in malignant and epithelial cells, with low expression in immune lineages.

Conclusions: B4GALNT3 is a novel, subtype-specific protective biomarker in EAOC. Our findings support a mechanism by which tumor-cell-intrinsic expression of B4GALNT3 drives protection from immune microenvironments. This work identifies B4GALNT3 as a promising prognostic factor and potential target for further mechanistic studies and protein-level validation in EAOC.