In the era of immunotherapy, there lacks of a reliable genomic predictor to identify optimal patient populations in combined radiotherapy and immunotherapy (CRI). The purpose of this study is to investigate whether genomic scores defining radiosensitivity are associated with immune response. Genomic data from Merged Microarray-Acquired dataset (MMD) were established and the Cancer Genome Atlas (TCGA) were obtained. Based on rank-based regression model including 10 genes, radiosensitivity index (RSI) was calculated. A total of 12832 primary tumours across 11 major cancer types were analysed for the association with DNA repair, cellular stemness, macrophage polarisation, and immune subtypes. Additional 585 metastatic tissues were extracted from MET500. RSI was stratified into RSI-Low and RSI-High by a cutpoint of 0.46. Proteomic differential analysis was used to identify significant proteins according to RSI categories. Gene Set Variance Analysis (GSVA) was applied to measure the genomic pathway activity (18 genes for T-cell inflamed activity). Kaplan-Meier analysis was performed for survival analysis. RSI was significantly associated with homologous DNA repair, cancer stemness and immune-related molecular features. Lower RSI was associated with higher fraction of M1 macrophage. Differential proteomic analysis identified significantly higher TAP2 expression in RSI-Low colorectal tumours. In the TCGA cohort, dominant interferon-γ (IFN-γ) response was characterised by low RSI and predicted better response to programmed cell death 1 (PD-1) blockade. In conclusion, in addition to radiation response, our study identified RSI to be associated with various immune-related features and predicted response to PD-1 blockade, thus, highlighting its potential as a candidate biomarker for CRI.

This study aimed to compare the clinical outcomes of stereotactic ablative radiotherapy (SABR) and conventionally fractionated radiotherapy (CFRT) in hepatocellular carcinoma (HCC) patients with portal vein invasion (PVI). HCC patients with PVI treated with radiotherapy from 2007 to 2016 were analysed. CFRT was administered at a median dose of 51.5 Gy (interquartile range, 45–54 Gy) with 1.8–3 Gy per fraction. SABR was administered at a median dose of 45 Gy (interquartile range, 40–48 Gy) with 6–12.5 Gy per fraction. Treatment efficacy, toxicity, and associated predictors were assessed. Among the 104 evaluable patients (45 in the SABR group and 59 in the CFRT group), the overall response rate (ORR, complete and partial response) was significantly higher in the SABR group than the CFRT group (62.2% vs. 33.8%, p = 0.003). The 1-year overall survival (OS) rate (34.9% vs. 15.3%, p = 0.012) and in-field progression-free survival (IFPS) rate (69.6% vs. 32.2%, p = 0.007) were also significantly higher in the SABR vs. CFRT group. All 3 rates remained higher in the SABR group after propensity score matching. Multivariable analysis identified SABR and a biologically effective dose ≥65 Gy as favourable predicators of OS. There was no difference between treatment groups in the incidence of radiation-induced liver disease or increase of Child-Pugh score ≥ 2 within 3 months of radiotherapy. SABR was superior to CFRT in terms of ORR, OS, and IFPS. We suggest that SABR should be the preferred technique for HCC patients with PVI.

Cancer is a complex and heterogeneous group of diseases driven by genetic mutations and molecular changes. Identifying and characterizing cancer driver gene is crucial for understanding cancer biology and guiding precision oncology. Integrating multi-omics data can reveal the intricate molecular interactions underlying cancer progression and treatment responses. We developed a graph convolutional network (GCN) framework, DriverOmicsNet, that integrates multi-omics data using STRING protein–protein interaction networks and correlation-based weighted gene correlation network analysis (WGCNA). We applied this framework to 15 cancer types, analyzing 5555 tumor samples to predict cancer-related features such as homologous recombination deficiency, cancer stemness, immune clusters, tumor stage, and survival outcomes. DriverOmicsNet demonstrated superior predictive accuracy and model performance metrics across all target labels when compared with GCN models based on STRING network alone. Gene expression emerged as the most significant feature, reflecting the dynamic and functional state of cancer cells. The combined use of STRING PPI and WGCNA networks enhanced the identification of key driver genes and their interactions. Our study highlights the effectiveness of using GCNs to integrate multi-omics data for precision oncology. The integration of STRING PPI and WGCNA networks provides a comprehensive framework that improves predictive power and facilitates the understanding of cancer biology, paving the way for more tailored treatments.

Background: Identifying candidates responsive to treatment is important in lupus nephritis (LN) at the renal flare (RF) because an effective treatment can lower the risk of progression to end-stage kidney disease. However, machine learning (ML)-based models that address this issue are lacking. Methods: Transcriptomic profiles based on DNA microarray data were extracted from the GSE32591 and GSE112943 datasets. Comprehensive bioinformatics analyses were performed to identify disease-defining genes (DDGs). Peripheral blood samples (GSE81622, GSE99967, and GSE72326) were used to evaluate the effect of DDGs. Single-sample gene set enrichment analysis (ssGSEA) scores of the DDGs were calculated and correlated with specific immunology genes listed in the nCounter panel. GSE60681 and GSE69438 were used to examine the ability of the DDGs to discriminate LN from other renal diseases. K-means clustering was used to obtain the separate gene sets. The clustering results were extended to data derived using the nCounter technique. The least absolute shrinkage and selection operator (LASSO) algorithm was used to identify genes with high predictive value for treatment response after the first RF in each cluster. LASSO models with tenfold validation were built in GSE200306 and assessed by receiver operating characteristic (ROC) analysis with area under curve (AUC). The models were validated by using an independent dataset (GSE113342). Results: Forty-five hub genes specific to LN were identified. Eight optimal disease-defining clusters (DDCs) were identified in this study. Th1 and Th2 cell differentiation pathway was significantly enriched in DDC-6. LCK in DDC-6, whose expression positively correlated with various subsets of T cell infiltrations, was found to be differentially expressed between responders and non-responders and was ranked high in regulatory network analysis. Based on DDC-6, the prediction model had the best performance (AUC: 0.75; 95% confidence interval: 0.44–1 in the testing set) and high precision (0.83), recall (0.71), and F1 score (0.77) in the validation dataset. Conclusions: Our study demonstrates that incorporating knowledge of biological phenotypes into the ML model is feasible for evaluating treatment response after the first RF in LN. This knowledge-based incorporation improves the model's transparency and performance. In addition, LCK may serve as a biomarker for T-cell infiltration and a therapeutic target in LN.

Acute pancreatitis (AP) is a serious inflammatory condition of the pancreas that can be associated with chylomicronemia syndrome (CS). Currently, no study has explored the differences between non-CS-associated AP and CS-associated AP in terms of gene expression. Transcriptomic profiles of blood samples from patients with AP were retrieved from GSE194331 (non-CS-associated) and GSE149607 (CS-associated). GSE31568 was used to examine the linkage between non-CS-associated AP and the expression of micro RNAs (miRNAs). Differentially expressed genes (DEGs) were identified, a gene regulatory network was constructed, and hub genes were defined. Subsequently, single-sample gene set enrichment analysis (ssGSEA) scores of hub genes were calculated to represent their regulatory-level activity. A total of 1851 shared DEGs were identified between non-CS-associated and CS-associated AP. Neutrophils were significantly enriched in both conditions. In non-CS-associated AP, miRNAs including hsa-miR-21, hsa-miR-146a, and hsa-miR-106a demonstrated a lower expression level as compared with the healthy control. Furthermore, the expression patterns and regulatory activities were largely opposite between non-CS-associated and CS-associated AP, with significantly lower estimated neutrophils in the latter case. In summary, we found that the regulation of neutrophils was altered in AP. There was a different gene expression pattern and lower estimated neutrophil infiltration in CS-associated AP. Whether these findings are clinically significant requires further investigation.

Few studies have focused on DNA methylation in endometrial cancer. The aim of our study is identify its role in endometrial cancer prognosis. A publicly available dataset was retrieved from The Cancer Genome Atlas. For validation of expression alteration due to methylation, RNA sequencing data were obtained from other independent cohorts. MethSurv was used to search for candidate CpG probes, which were then filtered by least absolute shrinkage and selection operator Cox regression and multivariate Cox regression analyses to identify final set of CpG probes for overall survival. A methylation-based risk model was developed and receiver operating characteristic analysis with area under curve was used for evaluation. Patients were divided into high- and low-risk groups using an optimal cut-off point. Comprehensive bioinformatic analyses were conducted to identify hub genes, key transcription factors, and enriched cancer-related pathways. Kaplan–Meier curve was used for survival analysis. A 5-CpG signature score was established. Its predictive value for 5-year overall survival was high, with area under curve of 0.828, 0.835 and 0.816 for the training, testing and entire cohorts. cg27487839 and cg12885678 had strong correlation with their gene expression, XKR6 and PTPRN2, and lower PTPRN2 expression was associated with poorer survival in both The Cancer Genome Atlas and the validation datasets. Low-risk group was associated with significantly better survival. Low-risk group harboured more mutations in hub genes and key transcription factors, and mutations in SP1 and MECP2 represented favourable outcome. We developed a methylation-based prognostic stratification system for endometrial cancer. Low-risk group was associated with better survival and harboured more mutations in the key regulatory genes.