Association of CCND1 rs9344 polymorphism with lung cancer susceptibility and clinical outcomes: a case-control study.

BACKGROUND: Cyclin D1 (CCND1) plays a pivotal role in cancer susceptibility and the platinum-based chemotherapy response. This study aims to assess the relationship between a common polymorphism (rs9344 G > A) in CCND1 gene with cancer susceptibility, platinum-based chemotherapy response, toxicities and prognosis of patients with lung cancer. METHODS: This study involved 498 lung cancer patients and 213 healthy controls. Among them, 467 patients received at least two cycles of platinum-based chemotherapy. Unconditional logistical regression analysis and meta-analysis were performed to evaluate the associations. RESULTS: The lung adenocarcinoma risk was significantly higher in patients with AA than GG + GA genotype (adjusted OR = 1.755, 95%CI = 1.057-2.912, P = 0.030). CCND1 rs9344 was significantly correlated with platinum-based therapy response in patients receiving PP regimen (additive model: adjusted OR = 1.926, 95%CI = 1.029-3.605, P = 0.040; recessive model: adjusted OR = 11.340, 95%CI = 1.428-90.100, P = 0.022) and in the ADC subgroups (recessive model: adjusted OR = 3.345, 95%CI = 1.276-8.765, P = 0.014). Furthermore, an increased risk of overall toxicity was found in NSCLC patients (additive model: adjusted OR = 1.395, 95%CI = 1.025-1.897, P = 0.034; recessive model: adjusted OR = 1.852, 95%CI = 1.088-3.152, P = 0.023), especially ADC subgroups (additive model: adjusted OR = 1.547, 95%CI = 1.015-2.359, P = 0.043; recessive model: adjusted OR = 2.030, 95%CI = 1.017-4.052, P = 0.045). Additionally, CCND1 rs9344 was associated with an increased risk of gastrointestinal toxicity in non-smokers (recessive model: adjusted OR = 2.620, 95%CI = 1.083-6.336, P = 0.035). Non-significant differences were observed in the 5-year overall survival rate between CCND1 rs9344 genotypes. A meta-analysis of 5432 cases and 6452 control samples did not find a significant association between lung cancer risk and CCND1 rs9344 polymorphism. CONCLUSION: This study suggests that in the Chinese population, CCND1 rs9344 could potentially serve as a candidate biomarker for cancer susceptibility and treatment outcomes in specific subgroups of patients.

Hsa_circ_0092856 Promoted the Proliferation, Migration, and Invasion of NSCLC Cells by Up-Regulating the Expression of eIF3a.

Circular RNA (circRNA) plays a very important regulatory role in a variety of human malignancies such as non-small-cell lung cancer (NSCLC). In the current study, we explored the role of hsa_circ_0092856 in the progression of NSCLC. We screened CircRNA from the eIF3a gene in the Circbase database. The biological functions of hsa_circ_0092856 in NSCLC were analyzed via qRT-PCR, a CCK-8 assay, a plate cloning experiment, scratch testing, a transwell chamber experiment, an RNA nuclear mass separation experiment, an RIP experiment, and a Western blot test. The results showed that hsa_circ_0092856 was highly expressed in NSCLC cells, and the knockdown of hsa_circ_0092856 could inhibit the proliferation, migration, and invasion of NSCLC cells. The overexpression of hsa_circ_0092856 has the opposite effect. The expression of eIF3a also changed with the change in hsa_circ_0092856. These results suggest that hsa_circ_0092856 may play a key role in the progression of NSCLC by regulating the expression of eIF3a.

Simultaneous Activation of Pyroptosis and cGAS-STING Pathway with Epigenetic/ Photodynamic Nanotheranostic for Enhanced Tumor Photoimmunotherapy.

Promoting innate immunity through pyroptosis induction or the cyclic GMP-AMP synthase-stimulator of interferon gene (cGAS-STING) pathway activation has emerged as a potent approach to counteract the immunosuppressive tumor microenvironment and elicit systemic antitumor immunity. However, current pyroptosis inducers and STING agonists often suffer from limitations including instability, unpredictable side effects, or inadequate intracellular expression of gasdermin and STING. Here, a tumor-specific nanotheranostic platform that combines photodynamic therapy (PDT) with epigenetic therapy to simultaneously activate pyroptosis and the cGAS-STING pathway in a light-controlled manner is constructed. This approach involves the development of oxidation-sensitive nanoparticles (NP1) loaded with the photosensitizer TBE, along with decitabine nanomicelles (NP2). NP2 enables the restoration of STING and gasdermin E (GSDME) expression, while NP1-mediated PDT facilitates the release of DNA fragments from damaged mitochondria to potentiate the cGAS-STING pathway, and promotes the activation of caspase-3 to cleave the upregulated GSDME into pore-forming GSDME-N terminal. Subsequently, the released inflammatory cytokines facilitate the maturation of antigen-presentation cells, triggering T cell-mediated antitumor immunity. Overall, this study presents an elaborate strategy for simultaneous photoactivation of pyroptosis and the cGAS-STING pathway, enabling targeted photoimmunotherapy in immunotolerant tumors. This innovative approach holds significant promise in overcoming the limitations associated with existing therapeutic modalities and represents a valuable avenue for future clinical applications.

Role of rs873601 Polymorphisms in Prognosis of Lung Cancer Patients Treated with Platinum-Based Chemotherapy.

BACKGROUND: Lung cancer is still the most lethal malignancy in the world, according to the report of Cancer Statistics in 2021. Platinum-based chemotherapy combined with immunotherapy is the first-line treatment in lung cancer patients. However, the 5-year survival rate is always affected by the adverse reactions and drug resistance caused by platinum-based chemotherapy. DNA damage and repair system is one of the important mechanisms that can affect the response to chemotherapy and clinical outcomes in lung cancer patients. OBJECTIVE: The objective of this study is to find the relationship between the polymorphisms of DNA repair genes with the prognosis of platinum-based chemotherapy in lung cancer patients. PATIENTS AND METHODS: We performed genotyping in 17 single nucleotide polymorphisms (SNPs) of Excision Repair Cross-Complementation group (ERCC) genes and X-ray Repair Cross-Complementing (XRCC) genes of 345 lung cancer patients via Sequenom MassARRAY. We used Cox proportional hazard models, state, and plink to analyze the associations between SNPs and the prognosis of lung cancer patients. RESULTS: We found that the ERCC5 rs873601 was associated with the overall survival time in lung cancer patients treated with platinum-based chemotherapy (p = 0.031). There were some polymorphisms that were related to the prognosis in specific subgroups of lung cancer. Rs873601 showed a great influence on the prognosis of patients more than 55 years, Small Cell Lung Cancer (SCLC), and smoking patients. Rs2444933 was associated with prognosis in age less than 55 years, SCLC, metastasis, and stage III/IV/ED patients. Rs3740051 played an important role in the prognosis of SCLC and metastasis patients. Rs1869641 was involved in the prognosis of SCLC patients. Rs1051685 was related to the prognosis in non-metastasis patients. CONCLUSION: The ERCC5 rs873601 (G>A) was a valuable biomarker for predicting the prognosis in lung cancer patients treated with platinum-based chemotherapy.

TRPV1 inhibition suppresses non-small cell lung cancer progression by inhibiting tumour growth and enhancing the immune response.

PURPOSE: TRPV1 is a nonselective Ca2+ channel protein that is widely expressed and plays an important role during the occurrence and development of many cancers. Activation of TRPV1 channels can affect tumour progression by regulating proliferation, apoptosis and migration. Some studies have also shown that activating TRPV1 can affect tumour progression by modulating tumour immunity. However, the effects of TRPV1 on the development of non-small cell lung cancer (NSCLC) have not been explored clearly. METHOD: The Cancer Genome Atlas (TCGA) database and spatial transcriptomics datasets from 10 × Genomics were used to analyze TRPV1 expression in various tumour tissues. Cell proliferation and apoptosis were examined by cell counting kit 8 (CCK8), colony formation, and flow cytometry. Immunohistochemistry, qPCR, and western blotting were used to determine the mRNA and protein expression levels of TRPV1 and other related molecules. Tumour xenografts in BALB/C and C57BL/6J mice were used to determine the effects of TRPV1 on NSCLC development in vivo. Neurotransmitter content was examined by LC-MS/MS, ELISA and Immunohistochemistry. Immune cell infiltration was assessed by flow cytometry. RESULTS: In this study, we found that TRPV1 expression was significantly upregulated in NSCLC and that patients with high TRPV1 expression had a poor prognosis. TRPV1 knockdown can significantly inhibit NSCLC proliferation and induce cell apoptosis through Ca2+-IGF1R signaling. In addition, TRPV1 knockdown resulted in increased infiltration of CD4+ T cells, CD8+ T cells, GZMB+CD8+ T cells and DCs and decreased infiltration of immunosuppressive MDSCs in NSCLC. In addition, TRPV1 knockout effectively decreased the expression of M2 macrophage markers CD163 and increased the expression of M1-associated, costimulatory markers CD86. Knockdown or knockout of TRPV1 significantly inhibit tumour growth and promoted an antitumour immune response through supressing γ-aminobutyric acid (GABA) secretion in NSCLC. CONCLUSION: Our study suggests that TRPV1 acts as a tumour promoter in NSCLC, mediating pro-proliferative and anti-apoptotic effects on NSCLC through IGF1R signaling and regulating GABA release to affect the tumour immune response.

Roles of TRPM7 in ovarian cancer.

Ovarian cancer stands as the prevailing gynecologic malignancy, afflicting over 313,959 individuals annually worldwide, accompanied by more than 207,252 fatalities. Perturbations in calcium signaling contribute significantly to the pathogenesis of numerous cancers, including ovarian cancer, wherein alterations in calcium transporter expression have been reported. Overexpression of TRPM7, a prominent calcium transporter, has been linked to adverse prognostic outcomes in various cancer types. The focus of this comprehensive review centers around delineating the oncogenic role of TRPM7 in cancer development and exploring its therapeutic potential as a target in combating this disease. Notably, TRPM7 fosters cancer invasion, metastasis, and uncontrolled cell proliferation, thereby perpetuating the expansion and reinforcement of these malignant entities. Furthermore, this review takes ovarian cancer as an example and summarizes the "dual-mode" regulatory role of TRPM7 in cancer. Within the domain of ovarian cancer, TRPM7 assumes the role of a harsh tyrant, firmly controlling the calcium ion signaling pathway and metabolic reprogramming pathways.

Inference of disease-associated microbial gene modules based on metagenomic and metatranscriptomic data.

The identification of microbial characteristics associated with diseases is crucial for disease diagnosis and therapy. However, the presence of heterogeneity, high dimensionality, and large amounts of microbial data presents tremendous challenges in discovering key microbial features. In this paper, we present IDAM, a novel computational method for inferring disease-associated gene modules from metagenomic and metatranscriptomic data. This method integrates gene context conservation (uber-operons) and regulatory mechanisms (gene co-expression patterns) within a mathematical graph model to explore gene modules associated with specific diseases. It alleviates reliance on prior meta-data. We applied IDAM to publicly available datasets from inflammatory bowel disease, melanoma, type 1 diabetes mellitus, and irritable bowel syndrome. The results demonstrated the superior performance of IDAM in inferring disease-associated characteristics compared to existing popular tools. Furthermore, we showcased the high reproducibility of the gene modules inferred by IDAM using independent cohorts with inflammatory bowel disease. We believe that IDAM can be a highly advantageous method for exploring disease-associated microbial characteristics. The source code of IDAM is freely available at https://github.com/OSU-BMBL/IDAM, and the web server can be accessed at https://bmblx.bmi.osumc.edu/idam/.

Disruption of Iron Homeostasis to Induce Ferroptosis with Albumin-Encapsulated Pt(IV) Nanodrug for the Treatment of Non-Small Cell Lung Cancer.

Non-small cell lung cancer (NSCLC) is the most common pathological type of lung cancer , accounting for approximately 85% of lung cancers. For more than 40 years, platinum (Pt)-based drugs are still one of the most widely used anticancer drugs even in the era of precision medicine and immunotherapy. However, the clinical limitations of Pt-based drugs, such as serious side effects and drug resistance, have not been well solved. This study constructs a new albumin-encapsulated Pt(IV) nanodrug (HSA@Pt(IV)) based on the Pt(IV) drug and nanodelivery system. The characterization of nanodrug and biological experiments demonstrate its excellent drug delivery and antitumor effects. The multi-omics analysis of the transcriptome and the ionome reveals that nanodrug can activate ferroptosis by affecting intracellular iron homeostasis in NSCLC. This study provides experimental evidence to suggest the potential of HSA@Pt(IV) as a nanodrug with clinical application.

Early non-response as a predictor of later non-response to antipsychotics in schizophrenia: a randomized trial.

BACKGROUND: It remains a challenge to predict the long-term response to antipsychotics in patients with schizophrenia who do not respond at an early stage. This study aimed to investigate the optimal predictive cut-off value for early non-response that would better predict later non-response to antipsychotics in patients with schizophrenia. METHODS: This multicenter, 8-week, open-label, randomized trial was conducted at 19 psychiatric centers throughout China. All enrolled participants were assigned to olanzapine, risperidone, amisulpride, or aripiprazole monotherapy for 8 weeks. The positive and negative syndrome scale (PANSS) was evaluated at baseline, week 2, week 4, and week 8. The main outcome was the prediction of nonresponse. Nonresponse is defined as a < 20% reduction in the total scores of PANSS from baseline to endpoint. Severity ratings of mild, moderate, and severe illness corresponded to baseline PANSS total scores of 58, 75, and 95, respectively. RESULTS: At week 2, a reduction of < 5% in the PANSS total score showed the highest total accuracy in the severe and mild schizophrenia patients (total accuracy, 75.0% and 80.8%, respectively), and patients who were treated with the risperidone and amisulpride groups (total accuracy, 82.4%, and 78.2%, respectively). A 10% decrease exhibited the best overall accuracy in the moderate schizophrenia patients (total accuracy, 84.0%), olanzapine (total accuracy, 79.2%), and aripiprazole group (total accuracy, 77.4%). At week 4, the best predictive cut-off value was < 20%, regardless of the antipsychotic or severity of illness (total accuracy ranging from 89.8 to 92.1%). CONCLUSIONS: Symptom reduction at week 2 has acceptable discrimination in predicting later non-response to antipsychotics in schizophrenia, and a more accurate predictive cut-off value should be determined according to the medication regimen and baseline illness severity. The response to treatment during the next 2 weeks after week 2 could be further assessed to determine whether there is a need to change antipsychotic medication during the first four weeks. TRIAL REGISTRATION: This study was registered on Clinicaltrials.gov (NCT03451734).

A Novel PDK1/MEK Dual Inhibitor Induces Cytoprotective Autophagy via the PDK1/Akt Signaling Pathway in Non-Small Cell Lung Cancer.

In a preliminary study, we synthesized a series of new PDK1/MEK dual inhibitors. Antitumor activity screening showed that Compound YZT exerts a strong inhibitory action in A549 cells. However, the specific mechanism of YZT against non-small cell lung cancer (NSCLC) is largely unknown. This work confirmed the anti-proliferation and pro-apoptosis effects of YZT in NSCLC cells. Furthermore, YZT promotes autophagy and provokes complete autophagic flux in NSCLC cells. Notably, compared with YZT alone, the combination of YZT with the autophagy inhibitor chloroquine (CQ) or 3-methyladenine (3-MA) markedly strengthened the anti-proliferative and pro-apoptotic actions, suggesting that YZT-induced autophagy is cytoprotective. We further found that YZT-induced autophagy may exert a cytoprotective function by preserving the integrity of mitochondria and decreasing mitochondrial apoptosis. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that PDK1 is an upstream protein of the Akt/mTOR axis and western blotting verified that YZT induces autophagy by the PDK1/Akt/mTOR signaling axis. Finally, YZT plus CQ significantly enhanced the anticancer activities compared to YZT alone in an animal study and immunohistochemistry showed that the level of LC3 was increased by YZT, which is in line with the in vitro results. In short, our study provides reliable experimental basis for developing Compound YZT as a new chemotherapeutic drug candidate and suggests that combined administration of YZT with CQ is a potential therapy against NSCLC.

A NIR-II Photoactivatable "ROS Bomb" with High-Density Cu2 O-Supported MoS2 Nanoflowers for Anticancer Therapy.

The fast conversion of hydrogen peroxide (H2 O2 ) into reactive oxygen species (ROS) at tumor sites is a promising anticancer strategy by manipulating nanomedicines with near-infrared light in the second region (NIR-II). However, this strategy is greatly compromised by the powerful antioxidant capacity of tumors and the limited ROS generation rate of nanomedicines. This dilemma mainly stems from the lack of an effective synthesis method to support high-density copper-based nanocatalysts on the surface of photothermal nanomaterials. Herein, a multifunctional nanoplatform (MCPQZ) with high-density cuprous (Cu2 O) supported molybdenum disulfide (MoS2 ) nanoflowers (MC NFs) is developed for the efficient killing of tumors via a potent ROS storm by an innovative method. Under NIR-II light irradiation, the ROS intensity and maximum reaction velocity (Vmax ) produced by MC NFs are 21.6 and 33.8 times that of the non-irradiation group in vitro, which is much higher than most current nanomedicines. Moreover, the strong ROS storm in cancer cells is efficiently formed by MCPQZ (increased by 27.8 times compared to the control), thanks to the fact that MCPQZ effectively pre-weakens the multiple antioxidant systems of cancer cells. This work provides a novel insight to solve the bottleneck of ROS-based cancer therapy.

CircRNAs in tumor immunity and immunotherapy: Perspectives from innate and adaptive immunity.

Tumor immunotherapy is a new therapeutic approach that has been evolving in the last decade and has dramatically changed the treatment options for cancer. Circular RNAs (circRNAs) are non-coding RNAs (ncRNAs) with high stability, tissue-specific and cell-specific expression. There is growing evidence that circRNAs are involved in the regulation of both adaptive and innate immunity. They play important roles in tumor immunotherapy by affecting macrophage, NK and T cell function. The high stability and tissue specificity make them ideal candidate biomarkers for therapeutic effects. CircRNAs also represent one of promising targets or adjuvant for immunotherapy. Investigations in this field progress rapidly and provide essential support for the diagnosis, prognosis and treatment guidance of cancers in the future. In this review, we summarize the role of circRNAs on tumor immunity from the viewpoint of innate and adaptive immunity, and explore the role of circRNAs in tumor immunotherapy.

Proteomic profiling of eIF3a conditional knockout mice.

Eukaryotic translation initiation factor 3 subunit A (eIF3a) is the largest subunit of the eukaryotic translation initiation factor 3 (eIF3). eIF3a plays an integral role in protein biosynthesis, hence impacting the onset, development, and treatment of tumors. The proteins regulated by eIF3a are still being explored in vivo. In this study, a Cre-loxP system was used to generate eIF3a conditional knockout mice. Tandem mass tag (TMT) labeling with LC-MS/MS analysis was used to identify differentially expressed proteins (DEPs) in fat, lungs, skin, and spleen tissue of the eIF3a knockout mice and controls. Bioinformatics analysis was then used to explore the functions and molecular signaling pathways of these protein landscapes. It was observed that eIF3a is essential for life sustenance. Abnormal tissue pathology was found in the lungs, fat, skin, spleen, and thymus. In total, 588, 210, 324, and 944 DEPs were quantified in the lungs, fat, skin, and spleen, respectively, of the eIF3a knockout mice as compared to the control. The quantified differentially expressed proteins were tissue-specific, except for eight proteins shared by the four tissues. A broad range of functions for eIF3a, including cellular signaling pathway, immune response, metabolism, defense response, phagocytes, and DNA replication, has been revealed using bioinformatics analysis. Herein, several pathways related to oxidative stress in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, including nitrogen metabolism, peroxisome, cytochrome P450 drug metabolism, pyruvate metabolism, PPAR signaling pathway, phospholipase D signaling pathway, B-cell receptor signaling pathway, ferroptosis, and focal adhesion, have been identified. Collectively, this study shows that eIF3a is an essential gene for sustaining life, and its downstream proteins are involved in diverse novel functions beyond mRNA translational regulation.

eIF3a sustains non-small cell lung cancer stem cell-like properties by promoting YY1-mediated transcriptional activation of ß-catenin.

Cancer stem cells (CSCs) are the leading cause of recurrence and poor prognosis in non-small cell lung cancer (NSCLC). Eukaryotic translation initiation factor 3a (eIF3a) participates in many tumor development processes, such as metastasis, therapy resistance, and glycolysis, all of which are closely associated with the presence of CSCs. However, whether eIF3a maintains NSCLC-CSC-like properties remains to be elucidated. In this study, eIF3a was highly expressed in lung cancer tissues and was linked to poor prognosis. eIF3a was also highly expressed in CSC-enriched spheres compared with adherent monolayer cells. Moreover, eIF3a is required for NSCLC stem cell-like traits maintenance in vitro and in vivo. Mechanistically, eIF3a activates the Wnt/ß-catenin signaling pathway, promoting the transcription of cancer stem cell markers. Specifically, eIF3a promotes the transcriptional activation of ß-catenin and mediates its nuclear accumulation to form a complex with T cell factor 4 (TCF4). However, eIF3a has no significant effect on protein stability and translation. Proteomics analysis revealed that the candidate transcription factor, Yin Yang 1 (YY1), mediates the activated effect of eIF3a on ß-catenin. Overall, the findings of this study implied that eIF3a contributes to the maintenance of NSCLC stem cell-like characteristics through the Wnt/ß-catenin pathway. eIF3a is a potential target for the treatment and prognosis of NSCLC.

Prognostic evaluation of patients with glioblastoma using a new score prediction model.

Despite the wide reportage of prognostic factors for glioblastoma (GBM), it is difficult to determine how these factors interact to affect patients' survival. To determine the combination of prognostic factors, we retrospectively analyzed the clinic data of 248 IDH wild-type GBM patients and built a novel prediction model. The survival variables of patients were identified via univariate and multivariate analyses. In addition, the score prediction models were constructed by combining classification and regression tree (CART) analysis with Cox regression analysis. Finally, the prediction model was internally validated using the bootstrap method. Patients were followed for a median of 34.4 (interquartile range, 26.1-46.0) months. Multivariate analysis identified gross total resection (GTR) (HR 0.50, 95% CI: 0.38-0.67), unopened ventricles (HR 0.75 [0.57-0.99]), and MGMT methylation (HR 0.56 [0.41-0.76]) as favorable independent prognostic factors for PFS. GTR (HR 0.67 [0.49-0.92]), unopened ventricles (HR 0.60 [0.44-0.82]), and MGMT methylation (HR 0.54 [0.38-0.76]) were favorable independent prognostic factors for OS. In the process of building the model, we incorporated GTR, ventricular opening, MGMT methylation status, and age. The model had six and five terminal nodules in PFS and OS respectively. We grouped terminal nodes with similar hazard ratios together to form three sub-groups with different PFS and OS (P < 0.001). After the internal verification of bootstrap method, the model had a good fitting and calibration. GTR, unopened ventricles, and MGMT methylation were independently associated with more satisfactory survival. The novel score prediction model which we construct can provide a prognostic reference for GBM.

Improved GNNs for Log D7.4 Prediction by Transferring Knowledge from Low-Fidelity Data.

The n-octanol/buffer solution distribution coefficient at pH = 7.4 (log D7.4) is an indicator of lipophilicity, and it influences a wide variety of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and druggability of compounds. In log D7.4 prediction, graph neural networks (GNNs) can uncover subtle structure-property relationships (SPRs) by automatically extracting features from molecular graphs that facilitate the learning of SPRs, but their performances are often limited by the small size of available datasets. Herein, we present a transfer learning strategy called pretraining on computational data and then fine-tuning on experimental data (PCFE) to fully exploit the predictive potential of GNNs. PCFE works by pretraining a GNN model on 1.71 million computational log D data (low-fidelity data) and then fine-tuning it on 19,155 experimental log D7.4 data (high-fidelity data). The experiments for three GNN architectures (graph convolutional network (GCN), graph attention network (GAT), and Attentive FP) demonstrated the effectiveness of PCFE in improving GNNs for log D7.4 predictions. Moreover, the optimal PCFE-trained GNN model (cx-Attentive FP, Rtest2 = 0.909) outperformed four excellent descriptor-based models (random forest (RF), gradient boosting (GB), support vector machine (SVM), and extreme gradient boosting (XGBoost)). The robustness of the cx-Attentive FP model was also confirmed by evaluating the models with different training data sizes and dataset splitting strategies. Therefore, we developed a webserver and defined the applicability domain for this model. The webserver (http://tools.scbdd.com/chemlogd/) provides free log D7.4 prediction services. In addition, the important descriptors for log D7.4 were detected by the Shapley additive explanations (SHAP) method, and the most relevant substructures of log D7.4 were identified by the attention mechanism. Finally, the matched molecular pair analysis (MMPA) was performed to summarize the contributions of common chemical substituents to log D7.4, including a variety of hydrocarbon groups, halogen groups, heteroatoms, and polar groups. In conclusion, we believe that the cx-Attentive FP model can serve as a reliable tool to predict log D7.4 and hope that pretraining on low-fidelity data can help GNNs make accurate predictions of other endpoints in drug discovery.

Association of variations in the CAT and prognosis in lung cancer patients with platinum-based chemotherapy.

PURPOSE: To explore the relationship between ATM, ATR and CAT polymorphisms and prognosis of lung cancer patients received platinum-based chemotherapy. METHODS: 404 patients with lung cancer who received platinum-chemotherapy were enrolled and DNA typing was performed. Cox regression analysis and stratification analyses was performed to assess relationships between OS and PFS with SNPs genotypes. The prognosis of lung adenocarcinomaand squamous cell carcinomapatients was analyzed with The Cancer Genome Atlas (TCGA) database according to the grouping of CAT expression. RESULTS: CAT rs769217 was significantly related to PFS of patients with lung cancer who received platinum-chemotherapy. In the Additive model, rs769217 was associated with PFS (HR = 0.747, 95% CI = 0.581-0.960, p = 0.023). In the Dominant model, CT and TT genotypes led to lung cancer progression 0.738 times more than CC genotype. In stratification analyses of association between CAT rs769217 polymorphisms and PFS, the HR of patients at stage IV in additive model was 0.73, and HR was 0.745 (p = 0.034) in dominant model. For OS analyses, HR was 0.672 in the older lung cancer patients (>55 years old) in additive model. Meanwhile, in the Dominant model, it was found that the older patients with CT and TT genotypes had better prognosis, and the risk of death after receiving platinum-based chemotherapy was 0.692 times that of patients with CC genotype (p = 0.037). TCGA data shows that LUAD patients with high CAT expression have longer OS (p = 0.020). CONCLUSION: CAT rs769217 is significantly related to PSF of platinum-based chemotherapy in lung cancer patients and may be a biomarker for predicting the prognosis of lung cancer patients with platinum-based chemotherapy.

Comprehensive bioinformatic analysis constructs a CXCL model for predicting survival and immunotherapy effectiveness in ovarian cancer.

Background: Immunotherapy has limited effectiveness in ovarian cancer (OC) patients, highlighting the need for reliable biomarkers to predict the effectiveness of these treatments. The C-X-C motif chemokine ligands (CXCLs) have been shown to be associated with survival outcomes and immunotherapy efficacy in cancer patients. In this study, we aimed to evaluate the predictive value of 16 CXCLs in OC patients. Methods: We analyzed RNA-seq data from The Cancer Genome Atlas, Gene Expression Omnibus, and UCSC Xena database and conducted survival analysis. Consensus cluster analysis was used to group patients into distinct clusters based on their expression patterns. Biological pathway alterations and immune infiltration patterns were examined across these clusters using gene set variation analysis and single-sample gene set enrichment analysis. We also developed a CXCL scoring model using principal component analysis and evaluated its effectiveness in predicting immunotherapy response by assessing tumor microenvironment cell infiltration, tumor mutational burden estimation, PD-L1/CTLA4 expression, and immunophenoscore analysis (IPS). Results: Most CXCL family genes were overexpressed in OC tissues compared to normal ovarian tissues. Patients were grouped into three distinct CXCL clusters based on their CXCL expression pattern. Additionally, using differentially expressed genes among the CXCL clusters, patients could also be grouped into three gene clusters. The CXCL and gene subtypes effectively predicted survival and immune cell infiltration levels for OC patients. Furthermore, patients with high CXCL scores had significantly better survival outcomes, higher levels of immune cell infiltration, higher IPS, and higher expression of PD-L1/CTLA4 than those with low CXCL scores. Conclusion: The CXCL score has the potential to be a promising biomarker to guide immunotherapy in individual OC patients and predict their clinical outcomes and immunotherapy responses.

Establishing a Prediction Model for the Efficacy of Platinum-Based Chemotherapy in NSCLC Based on a Two Cohorts GWAS Study.

Platinum drugs combined with other agents have been the first-line treatment for non-small cell lung cancer (NSCLC) in the past decades. To better evaluate the efficacy of platinum-based chemotherapy in NSCLC, we establish a platinum chemotherapy response prediction model. Here, a total of 217 samples from Xiangya Hospital of Central South University were selected as the discovery cohort for a genome-wide association analysis (GWAS) to select SNPs. Another 216 samples were genotyped as a validation cohort. In the discovery cohort, using linkage disequilibrium (LD) pruning, we extract a subset that does not contain correlated SNPs. The SNPs with p < 10-3 and p < 10-4 are selected for modeling. Subsequently, we validate our model in the validation cohort. Finally, clinical factors are incorporated into the model. The final model includes four SNPs (rs7463048, rs17176196, rs527646, and rs11134542) as well as two clinical factors that contributed to the efficacy of platinum chemotherapy in NSCLC, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.726.