Dynamic immunoediting by macrophages in homologous recombination deficiency-stratified pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease, notably resistant to existing therapies. Current research indicates that PDAC patients deficient in homologous recombination (HR) benefit from platinum-based treatments and poly-ADP-ribose polymerase inhibitors (PARPi). However, the effectiveness of PARPi in HR-deficient (HRD) PDAC is suboptimal, and significant challenges remain in fully understanding the distinct characteristics and implications of HRD-associated PDAC. We analyzed 16 PDAC patient-derived tissues, categorized by their homologous recombination deficiency (HRD) scores, and performed high-plex immunofluorescence analysis to define 20 cell phenotypes, thereby generating an in-situ PDAC tumor-immune landscape. Spatial phenotypic-transcriptomic profiling guided by regions-of-interest (ROIs) identified a crucial regulatory mechanism through localized tumor-adjacent macrophages, potentially in an HRD-dependent manner. Cellular neighborhood (CN) analysis further demonstrated the existence of macrophage-associated high-ordered cellular functional units in spatial contexts. Using our multi-omics spatial profiling strategy, we uncovered a dynamic macrophage-mediated regulatory axis linking HRD status with SIGLEC10 and CD52. These findings demonstrate the potential of targeting CD52 in combination with PARPi as a therapeutic intervention for PDAC.

Ursa: A Comprehensive Multiomics Toolbox for High-Throughput Single-Cell Analysis.

The burgeoning amount of single-cell data has been accompanied by revolutionary changes to computational methods to map, quantify, and analyze the outputs of these cutting-edge technologies. Many are still unable to reap the benefits of these advancements due to the lack of bioinformatics expertise. To address this issue, we present Ursa, an automated single-cell multiomics R package containing 6 automated single-cell omics and spatial transcriptomics workflows. Ursa allows scientists to carry out post-quantification single or multiomics analyses in genomics, transcriptomics, epigenetics, proteomics, and immunomics at the single-cell level. It serves as a 1-stop analytic solution by providing users with outcomes to quality control assessments, multidimensional analyses such as dimension reduction and clustering, and extended analyses such as pseudotime trajectory and gene-set enrichment analyses. Ursa aims bridge the gap between those with bioinformatics expertise and those without by providing an easy-to-use bioinformatics package for scientists in hoping to accelerate their research potential. Ursa is freely available at https://github.com/singlecellomics/ursa.

Antinuclear antibody (ANA) status predicts immune-related adverse events in liver cancer patients undergoing anti-PD-1 therapy.

Immune-related adverse events (irAEs) clinically resemble autoimmune diseases, indicating autoantibodies could be potential biomarkers for the prediction of irAEs. This study aimed to assess the predictive value of peripheral blood antinuclear antibody (ANA) status for irAEs, considering the time and severity of irAEs, as well as treatment outcome in liver cancer patients administered anti-PD-1 therapy. Ninety-three patients with advanced primary liver cancer administered anti-PD-1 treatment were analyzed retrospectively. They were divided into the ANA positive (ANA+, titer ≥ 1:100) and negative (ANA-, titer < 1:100) groups. Development of irAEs, progression-free survival (PFS), and overall survival (OS) were assessed. Compared with ANA- patients, ANA+ cases were more prone to develop irAEs (43.3% vs. 19.2%, P = 0.031). With the increase of ANA titers, the frequency of irAEs increased. The time interval between anti-PD-1 therapy and the onset of irAEs was significantly shorter in ANA+ patients compared with the ANA- group (median, 1.7 months vs. 5.0 months, P = 0.022). Moreover, the time between anti-PD-1 therapy and irAE occurrence decreased with increasing ANA titer. In addition, PFS and OS were decreased in ANA+ patients compared with the ANA- group (median PFS, 2.8 months vs. 4.2 months, P = 0.043; median OS, 21.1 months vs. not reached, P = 0.041). IrAEs occur at higher frequency in ANA+ liver cancer patients undergoing anti-PD-1 therapy. ANA titer could help predict irAE development and treatment outcome in these patients.

Deep dissection of stemness-related hierarchies in hepatocellular carcinoma.

BACKGROUND: Increasing evidence suggests that hepatocellular carcinoma (HCC) stem cells (LCSCs) play an essential part in HCC recurrence, metastasis, and chemotherapy and radiotherapy resistance. Multiple studies have demonstrated that stemness-related genes facilitate the progression of tumors. However, the mechanism by which stemness-related genes contribute to HCC is not well understood. Here, we aim to construct a stemness-related score (SRscores) model for deeper analysis of stemness-related genes, assisting with the prognosis and individualized treatment of HCC patients.Further, we found that the gene LPCAT1 was highly expressed in tumor tissues by immunohistochemistry, and sphere-forming assay revealed that knockdown of LPCAT1 inhibited the sphere-forming ability of hepatocellular carcinoma cells. METHODS: We used the TCGA-LIHC dataset to screen stemness-related genes of HCC from the MSigDB database. Prognosis, tumor microenvironment, immunological checkpoints, tumor immune dysfunction, rejection, treatment sensitivity, and putative biological pathways were examined. Random forest created the SRscores model. The anti-PD-1/anti-CTLA4 immunotherapy, tumor mutational burden, medication sensitivity, and cancer stem cell index were compared between the high- and low-risk score groups. We also examined risk scores for different cell types using single-cell RNA sequencing data and correlated transcription factor activity in cancer stem cells with SRscores genes. Finally, we tested core marker expression and biological functions. RESULTS: Patients can be divided into two subtypes (Cluster1 and Cluster2) based on the TCGA-LIHC dataset's identification of 11 stemness-related genes. Additionally, a SRscores was developed based on subtypes. Cluster2 and the group with the lowest SRscores had superior survival and immunotherapy response than Cluster1 and the group with the highest SRscores. The group with a high SRscores was significantly more enriched in classical tumor pathways than the group with a low SRscores. Multiple transcription factors and SRscores genes are correlated. The core gene LPCAT1 is highly expressed in rat liver cancer tissues and promotes tumor cell sphere formation. CONCLUSION: A SRscores model can be utilized to predict the prognosis of HCC patients as well as their response to immunotherapy.

[Comparison between macroscopic identification and DNA barcoding identification of Amomi Fructus].

This study aims to explore the consistency between macroscopic identification and DNA barcoding identification of Amomi Fructus. With the DNA barcoding identification results, we evaluated the reliability of identifying Amomi Fructus quality by combining macroscopic traits with main volatile chemical components. Thirteen batches of Amomi Fructus samples were collected for identification. Firstly, the morphological and sensory characteristics of each sample were observed and recorded according to the standard in Chinese Pharmacopoeia(2020 edition). The 100-fruit weight, longitudinal diameter, transverse diameter, and longitudinal diameter-to-transverse diameter ratio were measured, which correspond to large, solid, and full kernel representing good quality in the sensory evaluation. The odor value detected by electronic nose and major volatile components(borneol, camphor, limonene, and borneol acetate) correspond to the sensory evaluation of strong odor representing good quality. Secondly, DNA barcoding was employed to identify the 13 batches of samples. Finally, clustering analysis was performed for the main volatile components and macroscopic traits, and the identification results were compared with those of DNA barcoding. Except two batches of samples(No.6 and No.10), the macroscopic identification showed the results consistent with those of DNA barcoding, with an identification rate of 84.62%. The clustering results of the content of four volatile chemical components and macroscopic traits were also consistent with the DNA barcoding identification results. DNA barcoding can verify the results of macroscopic identification and provide a scientific basis for the inheritance and development of macroscopic identification. Moreover, the combination of macroscopic traits and chemical components demonstrates higher accuracy in the quality evaluation of Chinese medicinal materials.

Risk stratification of ST-segment elevation myocardial infarction (STEMI) patients using machine learning based on lipid profiles.

BACKGROUND: Numerous studies have revealed the relationship between lipid expression and increased cardiovascular risk in ST-segment elevation myocardial infarction (STEMI) patients. Nevertheless, few investigations have focused on the risk stratification of STEMI patients using machine learning algorithms. METHODS: A total of 1355 STEMI patients who underwent percutaneous coronary intervention were enrolled in this study during 2015-2018. Unsupervised machine learning (consensus clustering) was applied to the present cohort to classify patients into different lipid expression phenogroups, without the guidance of clinical outcomes. Kaplan-Meier curves were implemented to show prognosis during a 904-day median follow-up (interquartile range: 587-1316). In the adjusted Cox model, the association of cluster membership with all adverse events including all-cause mortality, all-cause rehospitalization, and cardiac rehospitalization was evaluated. RESULTS: All patients were classified into three phenogroups, 1, 2, and 3. Patients in phenogroup 1 with the highest Lp(a) and the lowest HDL-C and apoA1 were recognized as the statin-modified cardiovascular risk group. Patients in phenogroup 2 had the highest HDL-C and apoA1 and the lowest TG, TC, LDL-C and apoB. Conversely, patients in phenogroup 3 had the highest TG, TC, LDL-C and apoB and the lowest Lp(a). Additionally, phenogroup 1 had the worst prognosis. Furthermore, a multivariate Cox analysis revealed that patients in phenogroup 1 were at significantly higher risk for all adverse outcomes. CONCLUSION: Machine learning-based cluster analysis indicated that STEMI patients with increased concentrations of Lp(a) and decreased concentrations of HDL-C and apoA1 are likely to have adverse clinical outcomes due to statin-modified cardiovascular risks. TRIAL REGISTRATION: ChiCTR1900028516 ( http://www.chictr.org.cn/index.aspx ).

Pan-cancer analysis of the CASP gene family in relation to survival, tumor-infiltrating immune cells and therapeutic targets.

The cysteinyl aspartate protease (caspase, or CASP) gene family plays a significant role in programmed cell death, inflammation and immunity. However, the correlation between CASP family members and prognosis and tumor-infiltrating lymphocytes in different tumors has not been determined. We investigated the role of CASP genes in cancer prognosis and their relationship with clinicopathological parameters. We also evaluated the correlation between the expression of CASP family members and cancer immune infiltration and evaluated whether these molecules can be used as targets for immunotherapy. The CASP1/2/4/5/7/9 genes may represent prognostic factors and therapeutic targets for breast cancer, hepatocellular carcinoma and pancreatic cancer. Another finding is that the CASP1/4/5 genes help to regulate innate immunity and T cell immunity and may also have an important effect on tumor checkpoint inhibition. These findings may elucidate the roles played by CASP family members in cancer progression and identify strategies to promote collaborative activities in the context of immunotherapy.

[High-speed identification of odor changes and substance basis of Myristicae Semen mildew by electronic nose and HS-GC-MS].

This study adopted headspace-gas chromatography-mass spectrometry(HS-GC-MS) and electronic nose to detect volatile components from Myristicae Semen samples with varying degrees of mildew, aiming at rapidly identifying odor changes and substance basis of Myristicae Semen mildew. The experimental data were analyzed by electronic nose and principal component analysis(PCA). The results showed that Myristicae Semen samples were divided into the following three categories by electronic nose and PCA: mildew-free samples, slightly mildewy samples, and mildewy samples. Myristicae Semen samples with different degrees of mildew greatly varied in volatile components. The volatile components in the samples were qualitatively and quantitatively detected by HS-GC-MS, and 59 compounds were obtained. There were significant differences in the composition and content in Myristicae Semen samples with different degrees of mildew. The PCA results were the same as those by electronic nose. Among them, 3-crene, D-limonene, and other terpenes were important indicators for the identification of mildew. Bicyclo[3.1.0]hexane, 4-methylene-1-(1-methylethyl)-, terpinen-4-ol, and other alcohols were key substances to distinguish the degree of mildew. In the later stage of mildew, Myristicae Semen produced a small amount of hydroxyl and aldehyde compounds such as acetaldehyde, 2-methyl-propionaldehyde, 2-methyl-butyraldehyde, and formic acid, which were deduced as the material basis of the mildew. The results are expected to provide a basis for the rapid identification of Myristicae Semen with different degrees of mildew, odor changes, and the substance basis of mildew.

Assessment of the expression of the immune checkpoint molecules PD-1, CTLA4, TIM-3 and LAG-3 across different cancers in relation to treatment response, tumor-infiltrating immune cells and survival.

Immune checkpoint molecules have been identified as crucial regulators of the immune response, which motivated the emergence of immune checkpoint-targeting therapeutic strategies. However, the prognostic significance of the immune checkpoint molecules PD-1, CTLA4, TIM-3 and LAG-3 remains controversial. The aim of our study was to conduct a systematic assessment of the expression of these immune checkpoint molecules across different cancers in relation to treatment response, tumor-infiltrating immune cells and survival. Oncomine and PrognoScan database analyses were used to investigate the expression levels and prognostic values of these immune checkpoint molecule genes across various cancers. Then, we used Kaplan-Meier plotter to validate the associations between the checkpoint molecules and cancer survival identified in the PrognoScan analysis. TIMER analysis was used to evaluate immune cell infiltration data from The Cancer Genome Atlas. Finally, we used Gene Expression Profiling Interactive Analysis to investigate the prognostic value of these four checkpoint molecules and assess the correlations between these four checkpoint molecules and genetic markers. These immune checkpoint molecules may potentially serve as prognostic factors and therapeutic targets in breast cancer, ovarian cancer and lung cancer. The prognostic roles of these checkpoint molecules varied greatly across cancers, which implied a noteworthy amount of heterogeneity among tumors, even within the same molecular subtype. In addition, the expression patterns of these checkpoint molecules were closely associated with treatment response and provided some useful direction when choosing chemotherapeutic drugs. These findings enhance our understanding of these checkpoints in cancer treatment and identify strategies to promote synergistic activities in the context of other immunotherapies.

Immune-related prognosis biomarkers associated with osteosarcoma microenvironment.

BACKGROUND: Osteosarcoma is a highly aggressive bone tumor that most commonly affects children and adolescents. Treatment and outcomes for osteosarcoma have remained unchanged over the past 30 years. The relationship between osteosarcoma and the immune microenvironment may represent a key to its undoing. METHODS: We calculated the immune and stromal scores of osteosarcoma cases from the Target database using the ESTIMATE algorithm. Then we used the CIBERSORT algorithm to explore the tumor microenvironment and analyze immune infiltration of osteosarcoma. Differentially expressed genes (DEGs) were identified based on immune scores and stromal scores. Search Tool for the Retrieval of Interacting Genes Database (STRING) was utilized to assess protein-protein interaction (PPI) information, and Molecular Complex Detection (MCODE) plugin was used to screen hub modules of PPI network in Cytoscape. The prognostic value of the gene signature was validated in an independent GSE39058 cohort. Gene set enrichment analysis (GSEA) was performed to study the hub genes in signaling pathways. RESULTS: From 83 samples of osteosarcoma obtained from the Target dataset, 137 DEGs were identified, including 134 upregulated genes and three downregulated genes. Functional enrichment analysis and PPI networks demonstrated that these genes were mainly involved in neutrophil degranulation and neutrophil activation involved in immune response, and participated in neuroactive ligand-receptor interaction and staphylococcus aureus infection. CONCLUSIONS: Our study established an immune-related gene signature to predict outcomes of osteosarcoma, which may be important targets for individual treatment.

Intervertebral disc degeneration associated with vertebral marrow fat, assessed using quantitative magnetic resonance imaging.

OBJECTIVE: To investigate the potential clinical application of quantitative MRI in assessing the correlation between lumbar vertebrae bone marrow fat deposition and intervertebral disc degeneration. MATERIALS AND METHODS: A total of 104 chronic lower-back pain volunteers underwent 3.0-T MRI with T2-weighted imaging, T2 mapping, and iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL-IQ) between August 2018 and June 2019. Each disc was assessed with T2 value by T2 mapping, and the L1-S1 vertebral bone marrow fat fraction was assessed by IDEAL-IQ. The differences and relationship between T2 value and the adjacent vertebral bone marrow fat fraction values within the five Pfirrmann groups, five age groups, and five lumbar levels were statistically analyzed. RESULTS: The vertebral bone marrow fat fraction had a significant negative correlation with T2 values of nucleus pulposus' T2 values (p < 0.001). However, the significant negative correlation was only found between T2 values of nucleus pulposus and adjacent vertebral bone marrow fat in Pfirrmann II-III, L1/2-L5/S1 level, and 40-49 years' age groups. Pfirrmann grades of the intervertebral disc were positively correlated with adjacent vertebrae bone marrow fat fraction (p < 0.05). CONCLUSION: Lumbar bone marrow fat deposition significantly increases during the early stages of intervertebral disc degeneration. Quantitative measurements of bone marrow fat deposition and water content of intervertebral discs have a predictive value and are an important supplement to the qualitative traditional classification strategies for the early stages of intervertebral disc degeneration.

A single-cell pan-cancer analysis to show the variability of tumor-infiltrating myeloid cells in immune checkpoint blockade.

Myeloid cells are vital components of the immune system and have pivotal functions in orchestrating immune responses. Understanding their functions within the tumor microenvironment and their interactions with tumor-infiltrating lymphocytes presents formidable challenges across diverse cancer types, particularly with regards to cancer immunotherapies. Here, we explore tumor-infiltrating myeloid cells (TIMs) by conducting a pan-cancer analysis using single-cell transcriptomics across eight distinct cancer types, encompassing a total of 192 tumor samples from 129 patients. By examining gene expression patterns and transcriptional activities of TIMs in different cancer types, we discern notable alterations in abundance of TIMs and kinetic behaviors prior to and following immunotherapy. We also identify specific cell-cell interaction targets in immunotherapy; unique and shared regulatory profiles critical for treatment response; and TIMs associated with survival outcomes. Overall, our study illuminates the heterogeneity of TIMs and improves our understanding of tissue-specific and cancer-specific myeloid subsets within the context of tumor immunotherapies.

CENPL accelerates cell proliferation, cell cycle, apoptosis, and glycolysis via the MEK1/2-ERK1/2 pathway in hepatocellular carcinoma.

Centromere protein L (CENPL) is involved in the mitotic process of eukaryotic cells and the development of various types of cancer. However, its role in hepatocellular carcinoma (HCC) remains unclear. This study aimed to investigate the expression and clinical significance of CENPL in HCC, and explore its involvement in regulating HCC cell proliferation, apoptosis, cell cycle, and glycolysis both in vivo and in vitro. CENPL expression was analyzed in HCC and normal liver tissues using The Cancer Genome Atlas, Gene Expression Omnibus mining, real-time quantitative polymerase chain reaction, and immunohistochemistry. Functional assays were used to assess the role of CENPL in HCC cell proliferation, apoptosis, cell cycle, and glycolysis. The potential pathways underlying the regulatory effects of CENPL, as well as the expression of mitogen-activated protein kinase (MAPK) signaling pathway-related molecules and markers of proliferation and glycolysis were investigated. CENPL was significantly upregulated in HCC tissue and associated with multiple clinicopathological features and poor patient prognosis. Univariate and multivariate analyses demonstrated that CENPL may serve as an independent prognostic factor for HCC. Upregulation of CENPL in HCC regulated tumor proliferation and glycolytic processes. Mechanistic studies revealed that differentially expressed genes between the CENPL-overexpressing and control groups were mainly concentrated in the MAPK signaling pathway. Pathway inhibition analysis indicated that CENPL activated the MEK1/2-ERK1/2 signaling pathway to promote proliferation and glycolysis in HCC cells. This study elucidated the role of CENPL in regulating cell proliferation, apoptosis, cell cycle, and glycolysis in HCC. CENPL may represent a therapeutic target and prognostic biomarker for HCC.

Radiotherapy enhances efficacy of PD-1 inhibitors in advanced hepatocellular carcinoma: A propensity-matched real-world study.

BACKGROUND: To address the need for immunotherapy in patients with advanced primary hepatocellular carcinoma (HCC), combination with radiotherapy (RT) has emerged as a promising strategy. In preclinical studies, irradiated tumors released tumor antigens to synergistically increase the antitumor effect of immunotherapy. Hence, we investigated whether RT enhances the efficacy of anti-programmed death receptor-1 (PD-1) inhibitors in advanced HCC in real-world practice. METHODS: Between August 2018 and June 2021, 172 patients with advanced primary HCC were enrolled in the tertiary center (Zhongshan Hospital of Fudan University); 95 were treated with a combination of RT and the inhibitor of PD-1 (RT-PD1 cohort), and 77 were administered anti-PD-1 therapy (PD1 cohort). The first cycle of PD-1 inhibitors was administered within 60 days or concurrently with RT. Propensity score matching for bias reduction was used to evaluate the clinical outcomes. RESULTS: Among 71 propensity-matched pairs, median progression-free survival was 5.7 months in the RT-PD1 cohort vs. 2.9 months in the PD1 cohort ( P  <0.001). Median overall survival was 20.9 months in the RT-PD1 cohort vs. 11.2 months in the PD1 cohort ( P  = 0.018). Compared with patients in the PD1 cohort, patients in the RT-PD1 cohort had significantly higher objective response rates (40.8%, 29/71 vs. 19.7%, 14/71, P  = 0.006) and disease control rates (62.0%, 44/71 vs. 31.0%, 22/71, P  <0.001). The incidences of toxic effects were not significantly different between the two cohorts. CONCLUSIONS: RT plus anti-PD-1 therapy is well tolerated. RT enhances the efficacy of anti-PD-1 therapy in patients with advanced primary HCC by improving survival outcomes without increased toxic effects.

Multimodal radiomics based on 18F-Prostate-specific membrane antigen-1007 PET/CT and multiparametric MRI for prostate cancer extracapsular extension prediction.

OBJECTIVES: To compare the performance of the multiparametric magnetic resonance imaging (mpMRI) radiomics and 18F-Prostate-specific membrane antigen (PSMA)-1007 PET/CT radiomics model in diagnosing extracapsular extension (EPE) in prostate cancer (PCa), and to evaluate the performance of a multimodal radiomics model combining mpMRI and PET/CT in predicting EPE. METHODS: We included 197 patients with PCa who underwent preoperative mpMRI and PET/CT before surgery. mpMRI and PET/CT images were segmented to delineate the regions of interest and extract radiomics features. PET/CT, mpMRI, and multimodal radiomics models were constructed based on maximum correlation, minimum redundancy, and logistic regression analyses. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and indices derived from the confusion matrix. RESULTS: AUC values for the mpMRI, PET/CT, and multimodal radiomics models were 0.85 (95% CI, 0.78-0.90), 0.73 (0.64-0.80), and 0.83 (0.75-0.89), respectively, in the training cohort and 0.74 (0.61-0.85), 0.62 (0.48-0.74), and 0.77 (0.64-0.87), respectively, in the testing cohort. The net reclassification improvement demonstrated that the mpMRI radiomics model outperformed the PET/CT one in predicting EPE, with better clinical benefits. The multimodal radiomics model performed better than the single PET/CT radiomics model (P < .05). CONCLUSION: The mpMRI and 18F-PSMA-PET/CT combination enhanced the predictive power of EPE in patients with PCa. The multimodal radiomics model will become a reliable and robust tool to assist urologists and radiologists in making preoperative decisions. ADVANCES IN KNOWLEDGE: This study presents the first application of multimodal radiomics based on PET/CT and MRI for predicting EPE.

Deciphering the immune modulation through deep transcriptomic profiling and therapeutic implications of DNA damage repair pattern in hepatocellular carcinoma.

AIMS: DNA damage repair (DDR) plays a pivotal role in hepatocellular carcinoma (HCC), driving oncogenesis, progression, and therapeutic response. However, the mechanisms of DDR mediated immune cells and immuno-modulatory pathways in HCC are yet ill-defined. METHODS: Our study introduces an innovative deep machine learning framework for precise DDR assessment, utilizing single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data. Single-cell RNA sequencing data were obtained and in total 85,628 cells of primary or post-immunotherapy cases were analyzed. Large-scale HCC datasets, including 1027 patients in house together with public datasets, were used for 101 machine-learning models and a novel DDR feature was derived at single-cell resolution (DDRscore). Druggable targets were predicted using the reverse phase protein array (RPPA) proteomic profiling of 169 HCC patients and RNA-seq data from 22 liver cancer cell lines. RESULTS: Our investigation reveals a dynamic interplay of DDR with natural killer cells and B cells in the primary HCC microenvironment, shaping a tumor-promoting immune milieu through metabolic programming. Analysis of HCC post-immunotherapy demonstrates elevated DDR levels that induces epithelial-mesenchymal transition and fibroblast-like transformation, reshaping the fibrotic tumor microenvironment. Conversely, attenuated DDR promotes antigen cross-presentation by dendritic cells and CD8+ T cells, modulating the inflammatory tumor microenvironment. Regulatory network analysis identifies the CXCL10-CXCR3 axis as a key determinant of immunotherapeutic response in low DDR HCC, potentially regulated by transcription factors GATA3, REL, and TBX21. Using machine learning techniques by combining bulk RNA-seq data in house together with public datasets, we introduce DDRscore, a robust consensus DDR scoring system to predict overall survival and resistance to PD-1 therapy in HCC patients. Finally, we identify BRAF as a potential therapeutic target for high DDRscore patients. CONCLUSION: Our comprehensive findings advance our understanding of DDR and the tumor microenvironment in HCC, providing insights into immune regulatory mechanisms mediated via DDR pathways.

A dynamic online nomogram predicting prostate cancer short-term prognosis based on 18F-PSMA-1007 PET/CT of periprostatic adipose tissue: a multicenter study.

BACKGROUND: Rising prostate-specific antigen (PSA) levels following radical prostatectomy are indicative of a poor prognosis, which may associate with periprostatic adipose tissue (PPAT). Accordingly, we aimed to construct a dynamic online nomogram to predict tumor short-term prognosis based on 18F-PSMA-1007 PET/CT of PPAT. METHODS: Data from 268 prostate cancer (PCa) patients who underwent 18F-PSMA-1007 PET/CT before prostatectomy were analyzed retrospectively for model construction and validation (training cohort: n = 156; internal validation cohort: n = 65; external validation cohort: n = 47). Radiomics features (RFs) from PET and CT were extracted. Then, the Rad-score was constructed using logistic regression analysis based on the 25 optimal RFs selected through maximal relevance and minimal redundancy, as well as the least absolute shrinkage and selection operator. A nomogram was constructed to predict short-term prognosis which determined by persistent PSA. RESULTS: The Rad-score consisting of 25 RFs showed good discrimination for classifying persistent PSA in all cohorts (all P < 0.05). Based on the logistic analysis, the radiomics-clinical combined model, which contained the optimal RFs and the predictive clinical variables, demonstrated optimal performance at an AUC of 0.85 (95% CI: 0.78-0.91), 0.77 (95% CI: 0.62-0.91) and 0.84 (95% CI: 0.70-0.93) in the training, internal validation and external validation cohorts. In all cohorts, the calibration curve was well-calibrated. Analysis of decision curves revealed greater clinical utility for the radiomics-clinical combined nomogram. CONCLUSION: The radiomics-clinical combined nomogram serves as a novel tool for preoperative individualized prediction of short-term prognosis among PCa patients.

Mechanism of immune activation mediated by genomic instability and its implication in radiotherapy combined with immune checkpoint inhibitors.

Various genetic and epigenetic changes associated with genomic instability (GI), including DNA damage repair defects, chromosomal instability, and mitochondrial GI, contribute to development and progression of cancer. These alterations not only result in DNA leakage into the cytoplasm, either directly or through micronuclei, but also trigger downstream inflammatory signals, such as the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) signaling pathway. Apart from directly inducing DNA damage to eliminate cancer cells, radiotherapy (RT) exerts its antitumor effects through intracellular DNA damage sensing mechanisms, leading to the activation of downstream inflammatory signaling pathways. This not only enables local tumor control but also reshapes the immune microenvironment, triggering systemic immune responses. The combination of RT and immunotherapy has emerged as a promising approach to increase the probability of abscopal effects, where distant tumors respond to treatment due to the systemic immunomodulatory effects. This review emphasizes the importance of GI in cancer biology and elucidates the mechanisms by which RT induces GI remodeling of the immune microenvironment. By elucidating the mechanisms of GI and RT-induced immune responses, we aim to emphasize the crucial importance of this approach in modern oncology. Understanding the impact of GI on tumor biological behavior and therapeutic response, as well as the possibility of activating systemic anti-tumor immunity through RT, will pave the way for the development of new treatment strategies and improve prognosis for patients.

RECQL4 Inhibits Radiation-Induced Tumor Immune Awakening via Suppressing the cGAS-STING Pathway in Hepatocellular Carcinoma.

Many patients with hepatocellular carcinoma (HCC) respond poorly to radiotherapy despite remarkable advances in treatment. A deeper insight into the mechanism of sensitivity of HCC to this therapy is urgently required. It is demonstrated that RECQL4 is upregulated in the malignant cells of patients with HCC. Elevated RECQL4 levels reduce the sensitivity of HCC to radiotherapy by repairing radiation-induced double-stranded DNA (dsDNA) fragments. Mechanistically, the inhibitory effect of RECQL4 on radiotherapy is due to the reduced recruitment of dendritic cells and CD8+ T cells in the tumor microenvironment (TME). RECQL4 disrupts the radiation-induced transformation of the TME into a tumoricidal niche by inhibiting the cGAS-STING pathway in dendritic cells. Knocking out STING in dendritic cells can block the impact of RECQL4 on HCC radiosensitivity. Notably, high RECQL4 expressions in HCC is significantly associated with poor prognosis in multiple independent cohorts. In conclusion, this study highlights how HCC-derived RECQL4 disrupts cGAS-STING pathway activation in dendritic cells through DNA repair, thus reducing the radiosensitivity of HCC. These findings provide new perspectives on the clinical treatment of HCC.

Anoikis resistance of small airway epithelium is involved in the progression of chronic obstructive pulmonary disease.

Background: Anoikis resistance is recognized as a crucial step in the metastasis of cancer cells. Most epithelial tumors are distinguished by the ability of epithelial cells to abscond anoikis when detached from the extracellular matrix. However, no study has investigated the involvement of anoikis in the small airway epithelium (SAE) of chronic obstructive pulmonary disease (COPD). Methods: Anoikis-related genes (ANRGs) exhibiting differential expression in COPD were identified using microarray datasets obtained from the Gene Expression Omnibus (GEO) database. Unsupervised clustering was performed to classify COPD patients into anoikis-related subtypes. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were used to annotate the functions between different subtypes. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were leveraged to identify key molecules. The relative proportion of infiltrating immune cells in the SAE was quantified using the CIBERSORT and ssGSEA computational algorithms, and the correlation between key molecules and immune cell abundance was analyzed. The expression of key molecules in BEAS-2B cells exposed to cigarette smoke extract (CSE) was validated using qRT-PCR. Results: A total of 25 ANRGs exhibited differential expression in the SAE of COPD patients, based on which two subtypes of COPD patients with distinct anoikis patterns were identified. COPD patients with anoikis resistance had more advanced GOLD stages and cigarette consumption. Functional annotations revealed a different immune status between COPD patients with pro-anoikis and anoikis resistance. Tenomodulin (TNMD) and long intergenic non-protein coding RNA 656 (LINC00656) were subsequently identified as key molecules involved in this process, and a close correlation between TNMD and the infiltrating immune cells was observed, such as activated CD4+ memory T cells, M1 macrophages, and activated NK cells. Further enrichment analyses clarified the relationship between TNMD and the inflammatory and apoptotic signaling pathway as the potential mechanism for regulating anoikis. In vitro experiments showed a dramatic upregulation of TNMD and LINC00656 in BEAS-2B cells when exposed to 3% CSE for 48 hours. Conclusion: TNMD contributes to the progression of COPD by inducing anoikis resistance in SAE, which is intimately associated with the immune microenvironment.