Peroxiredoxin 4 (PRDX4) has been found to be upregulated and verified to play protective roles against oxidative stress in various tumors. However, its exact function and underlying molecular mechanisms in esophageal squamous cell carcinoma (ESCC) remain unclear. The aim of the present study was to evaluate the roles of PRDX4 in ferroptosis of ESCC cells and elucidate its potential molecular mechanisms. Bioinformatics analysis, western blotting and reverse transcription-quantitative PCR confirmed that PRDX4 was markedly upregulated in ESCC samples and cells. The close association of PRDX4 with lymph node metastasis and TNM staging was identified, and PRDX4 may be an independent prognostic marker for patients with ESCC. Furthermore, PRDX4 depletion inhibited the proliferation and invasion of ESCC cells, whereas PRDX4 overexpression had the opposite effect. Notably, PRDX4 knockdown promoted ferroptosis by increasing malondialdehyde and lipid peroxidation levels, and decreasing glutathione levels, coupled with decreased expression of glutathione peroxidase 4 and solute carrier family 7 member 11, and increased expression of prostaglandin-endoperoxide synthase 2. However, PRDX4 overexpression showed opposite effects, which were partly reversed by the ferroptosis inhibitor, ferrostatin-1 and the inducer erastin. Most crucially, PRDX4 depletion-mediated inactivation of the phosphoinositide 3-kinase (PI3K)/AKT signaling pathway could be rescued by 740 Y-P (a PI3K activator), whereas PRDX4 overexpression triggered the activation of the PI3K/AKT signaling pathway, which could be reversed by the PI3K inhibitor LY294002. Collectively, the data suggest that PRXD4 suppresses ferroptosis in ESCC cells by activating the PI3K/AKT signaling pathway, suggesting that targeting PRDX4 may be a novel strategy for treating patients with ESCC.

IntroductionThis study aimed to elucidate the function of NOTCH3 in pan-cancer and CRC progression, its impact on the tumor immune microenvironment, and its value as a therapeutic target and predictive biomarker.MethodsWe performed a multi-omics analysis of NOTCH3 alterations (expression, mutation, copy number variation, methylation) using data from The Cancer Genome Atlas (TCGA). Immune cell infiltration was assessed using multiple algorithms and single-cell RNA sequencing (scRNA-seq) data from CRC patients. In vitro functional experiments, including co-immunoprecipitation, chromatin immunoprecipitation (ChIP), luciferase reporter assays, and CD8+ T cell cytotoxicity co-cultures, were conducted in CRC cell line. An immune-competent mouse xenograft model was used to evaluate the efficacy of anti-NOTCH3 in combination with anti-PD-L1 therapy. Clinical validation was performed using independent immunotherapy-treated cohorts from the MSKCC database and our institutional cohort (102 patients) via immunohistochemistry and survival analysis.ResultsNOTCH3 is frequently altered across multiple cancers. In CRC, high NOTCH3 expression correlated with poor survival and fostered an immunosuppressive microenvironment. Mechanistically, NOTCH3 transcriptionally upregulates the immune checkpoint molecule PVR by binding to the transcription factor RBPJ; this process is abrogated by NOTCH3 mutations (e.g., R1669H). NOTCH3-mediated PVR upregulation suppressed CD8+ T cell cytotoxicity. scRNA-seq analysis revealed enhanced PVR-TIGIT interactions between cancer and immune cells in NOTCH3-high tumors. In vivo, NOTCH3 depletion synergized with anti-PD-L1 therapy to inhibit tumor growth and increase CD8+ T cell infiltration. Clinically, NOTCH3 mutation or low expression independently predicted improved survival in immunotherapy-treated CRC and pan-cancer cohorts.ConclusionNOTCH3 is a pivotal regulator of immune evasion in CRC via the RBPJ-PVR axis.

Background: The combination of immune checkpoint inhibitors (ICIs) with trastuzumab and chemotherapy has been shown to enhance treatment efficiency in locally advanced HER2‑positive gastric cancer (GC). Nevertheless, the specific beneficiaries and underlying mechanisms of this treatment regimen remain unclear. Methods: We conducted single‑cell RNA sequencing (scRNA‑seq) transcriptome analysis on paired samples who received trastuzumab in combination with SOX and programmed cell death protein 1 (PD‑1) inhibitors to investigate potential biomarkers for clinical application. These patients were enrolled in an investigator-initiated phase II randomized controlled trial of locally advanced HER2‑positive GC. Multiplex immunoffuorescence (mIF) staining (n=28) was performed to explore the association between CXCL13+VCAM1+CD8+T cells and tertiary lymphoid structures (TLS)–like aggregates. Furthermore, we validated the above results through in vitro functional assays, and in vivo humanized NSG mouse models. Results: A total of 31 patients received trastuzumab in combination with SOX and PD‑1 inhibitors. Ultimately, 29 patients underwent gastrectomy, resulting in a pathological complete response (pCR) rate of 31% (n=9). Five pairs of pre- and post-treatment tumor tissues underwent scRNA-seq, revealing that the upregulation of VCAM1 transcription in CXCL13⁺CD8⁺ T cells after neoadjuvant treatment correlated with favorable outcomes. Additionally, mIF further confirmed these findings, demonstrating that CXCL13+VCAM1+CD8+T cells were enriched in tertiary lymphoid structures (TLS)–like aggregates. In vitro, studies utilizing an NCI-N87 cell-T cell co-culture system indicated increased VCAM1 expression in T cells and a higher number of CXCL13+CD106+CD8+ T cells with the specified treatment of PD-1 inhibitor and trastuzumab. The number of CXCL13, CD4 T cells, and CD8 T cells within tumors significantly increased in humanized (huPBMC-NOG-dko) mouse models treated with PD-1 inhibitor, trastuzumab, and recombinant VCAM1. Communication analysis revealed that high interactions between CCL5 in CXCL13+CD8+T cells and ACKR1 in endothelial cells may promote the infiltration of CXCL13⁺CD8⁺T cells into TLS-like aggregates. Finally, these networks were further validated through mIF staining and in vivo co-culture experiments. Conclusion: The presence of VCAM1-high CXCL13⁺CD8⁺ T cells within TLS-like aggregates is pivotal for the efficacy of neoadjuvant treatment combining PD-1 inhibitors and trastuzumab in HER2-positive GC.

Depression and gastrointestinal disease are prevalent conditions that often coexist, significantly impacting daily life and productivity. Recent studies suggest a potential link between the intake of dietary live microbe and the alleviation of depressive symptoms. However, the relationship between live microbe consumption and depressive symptoms in patients suffering from gastrointestinal diseases remains unexplored. This study included participants with gastrointestinal diseases from the National Health and Nutrition Examination Survey (NHANES) spanning from 2005 to 2018. We utilized weighted multivariate logistic regression, subgroup analyses, and restricted cubic spline (RCS) analyses to investigate the association between live microbe consumption and depression. Additionally, the eXtreme Gradient Boosting (XGBoost) algorithm was implemented to develop a predictive model for depression based on individual characteristics. Of the 2,195 individuals, 472 (21.5

BackgroundGastric cancer progression involves complex interactions among tumor cells, immune components, and stromal elements within the tumor microenvironment. However, a comprehensive understanding of cellular heterogeneity, spatial organization, and cell-cell communication in gastric cancer remains incomplete.MethodsSingle-cell RNA sequencing was performed on 252, 399 cells from six tissue types, spanning gastritis, intestinal metaplasia, primary tumors, adjacent normal tissue, and metastatic lesions. Integration with spatial transcriptomics enabled spatial mapping of cellular interactions. Pseudotime, cell-cell communication, and transcriptional heterogeneity analyses were conducted. Tumor stage-associated gene modules were identified using Weighted Gene Co-expression Network Analysis (WGCNA) of The Cancer Genome Atlas (TCGA) data. Finally, a deep learning-based prognostic model was developed and externally validated.ResultsOur analysis revealed dynamic remodeling of the tumor microenvironment during gastric cancer progression, characterized by the expansion of dysfunctional CD8+ T cells, pro-tumorigenic fibroblasts (e.g., ITGBL1+, PI16+, and ITLN1+), and altered myeloid populations. Stromal-immune crosstalk, particularly fibroblast-driven immunosuppressive signaling, was prominent. Spatial transcriptomics revealed the colocalization of immune and stromal cells, supporting spatially organized cellular interactions. WGCNA identified a gene module (657 genes) associated with T cell, myeloid, and stromal alterations, as well as tumor stage. A deep learning model based on this gene set accurately stratified patients according to survival in both TCGA and independent validation cohorts. Risk scores were correlated with clinical features, including tumor stage and therapeutic response.ConclusionsOur integrative single-cell, spatial, and computational analysis provides a high-resolution map of gastric cancer microenvironment remodeling. We identified key stromal and immune subpopulations, extensive cellular communication networks, and spatial structures that collectively drive tumor progression and metastasis. The derived gene signature and prognostic model have the potential for clinical risk stratification and therapeutic targeting in gastric cancer.

Background To establish and validate a dual-energy CT (DECT) radiomics model for predicting neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) across two vendors. Methods This was a secondary analysis drawn from a prospective cohort using DECT data of patients undergoing NAC followed by gastrectomy. Patients were stratified as responders (TRG 0/1) or non-responders (TRG 2/3) based on tumor regression grade (TRG). Radiomics features were extracted from polychromatic images at arterial/venous/delayed phases for building CECT model; Radiomics features extracted from polychromatic images, monochromatic (40 keV, 100 keV) and iodine maps were used to construct DECT model. Predictive features were selected via the least absolute shrinkage and selection operator regression method in the training cohort and tested in the validation cohort. Performances of models were evaluated using areas under the receiver operating characteristic curves (AUCs). Results In total, 317 patients were recruited: 221 at training dataset (59.9 ± 9.7 years, 37 females, 184 males) and 96 at validation dataset (61.5 ± 8.0 years, 18 females, 78 males). No clinical factors were found to be related with TRG status. The DECT model outperformed CECT model in the training dataset (AUC: 0.806 vs. 0.729, p = 0.041) and showed non-significant superiority in the validation dataset (AUC: 0.752 vs. 0.679, p = 0.225). High-risk patients defined by DECT model had significantly worse overall survival (HR = 1.996, p = 0.012) and disease-free survival (HR = 1.873, p = 0.037) than low-risk counterparts. Conclusion DECT radiomics demonstrates favorable performance in predicting NAC response and stratifying survival outcomes in LAGC, with cross-vendor generalizability supporting potential clinical utility.

Background:Esophageal squamous cell carcinoma (ESCC) presents significant health challenges due to its aggressive nature and poor prognosis from late-stage diagnosis. Despite these challenges, emerging therapies like immune checkpoint inhibitors offer hope. β1-adrenergic signaling has been implicated in T cell exhaustion, which weakens the immune response in ESCC. Blocking this pathway could restore T cell function. Recent advances in single-cell RNA sequencing (scRNA-seq) have enabled deeper insights into tumor heterogeneity and the immune landscape, opening the door for personalized treatment strategies that may improve survival and reduce resistance to therapy. Methods:We combined scRNA-seq with bulk RNA analysis to explore adrenergic receptor signaling in ESCC, focusing on changes before and after neoadjuvant therapy. We identified ADRB1+ T cells through data analysis and experimental validation. Copy number variation (CNV) analysis detected malignant cells within scRNA-seq data, while intercellular interaction analysis examined communication between cell populations. Deconvolution of TCGA data revealed key immune populations, which were integrated into a prognostic model based on the adrenergic receptor signaling pathway and differentially expressed genes. Results:The adrenergic receptor signaling pathway was found in various immune cells, including T cells. scRNA-seq analysis revealed increased ADRB1 expression in T cells after neoadjuvant therapy. Immunofluorescence confirmed colocalization of ADRB1 with T cells, and fluorescence-activated cell sorting (FACS) showed that ADRB1 expression was elevated alongside exhaustion markers, while immune function markers were reduced. CNV analysis highlighted malignant cells in the tumor microenvironment, and intercellular interaction analysis explored ADRB1+ T cells' role in immune support. Deconvolution of TCGA data identified ADRB1+ T cells, SPP1+ macrophages, and CD44+ malignant cells, all of which were prognostically significant. A prognostic model constructed from the intersection of the adrenergic receptor signaling pathway and differentially expressed genes following neoadjuvant therapy showed a significant prognostic effect. Conclusions:ADRB1 expression increases after neoadjuvant therapy in ESCC and correlates with poor prognosis. Our findings suggest ADRB1 as a potential prognostic biomarker and therapeutic target for post-neoadjuvant immunotherapy.

Introduction:Identifying predictive biomarkers for immune checkpoint inhibitor (ICI) treatment is critical for gastric cancer (GC) prognosis. C-X-C motif chemokine ligand 13(CXCL13) plays an important role in immune regulation by binding exclusively to its receptor CXCR5. However, its role, underlying mechanisms, and prognostic significance in ICI-treated GC patients remain controversial. Methods:This study investigated the clinical significance of CXCL13 and its potential immunomodulatory function in GC patients. A total of 144 GC patients from two cohorts, who received a combination of chemotherapy and anti-PD-1 antibody, were analyzed. The expression of CXCL13 was assessed using immunohistochemistry (IHC) and enzyme-linked immunosorbent assay. Associations between CXCL13, CXCR5, CD8, and CD4 were assessed by IHC and immunofluorescence. Survival analysis was performed using the Kaplan-Meier method and Cox proportional hazards model. The treatment response to CXCL13 and anti-PD-1 antibody was investigated using a subcutaneous xenograft tumor mouse model. Results:The results suggested that patients with high CXCL13 expression had prolonged survival. High CXCL13 expression exhibited increased infiltration of CXCR5+CD8+ T cells and was associated with better outcomes. The combined assessment of CXCL13, CXCR5, and CD8+ T cells served as an independent predictor of prognosis. Additionally, CXCR5 and CD8+ T cells were enriched in tertiary lymphoid structures (TLSs), which conferred a prognostic benefit in the presence of high CXCL13 expression. CXCL13, in combination with anti-PD-1 therapy, retarded tumor growth in vivo, resulting in increased infiltration of CXCR5+CD8+ T cells. Discussion:This study identified CXCL13 as a prognostic factor in GC patients receiving ICI therapy, emphasizing its critical role in the antitumor microenvironment via CXCR5+CD8+ T cells.

To longitudinally evaluate pathologic response outcomes after neoadjuvant immuno-chemotherapy (NICT) for patients with locally advanced gastric cancer (LAGC) using pre- and post-treatment dual-energy CT (DECT). Between Jan 2021 and Dec 2023, 115 patients who underwent NICT plus gastrectomy and triple-phase enhanced DECT scans before and after NICT were retrospectively enrolled. Pathologic tumor regression grade (TRG) was the reference standard, patients were labelled as responders (TRG = 0 + 1) and non-responders (TRG = 2 + 3) accordingly. A two-dimensional free-hand region of interest method was adopted to obtain the iodine concentration (IC) values. Pre- and post-NICT IC and normalized IC (nIC) were measured at arterial/venous/delay phase (AP/VP/DP), respectively; their changes [ΔIC (

BackgroundGemcitabine is widely used in the treatment of various cancers. This study aims to evaluate gemcitabine-associated adverse events (AEs) using the Food and Drug Administration Adverse Event Reporting System (FAERS) database.Research design and methodsWe analyzed data spanning from January 2004 to June 2023. Employing reporting odds ratio (ROR) and Bayesian confidence propagation neural network (BCPNN) algorithms, we identified AEs with positive signals in patients administered gemcitabine.ResultsOut of 16,623,939 reports, 23,645 involved gemcitabine as the 'primary suspected (PS)' resulting in 74,306 AEs. Consistent with the reports in the specification and clinical trials, thrombocytopenia, pyrexia, neutropenia, and anemia were the most common AEs. Notably, our study identified some unexpected AEs such as abdominal pain, pleural effusion, ascites, and gastrointestinal hemorrhage, among others. The most significant SOC was 'Blood and lymphatic system disorders'. The median onset time for gemcitabine-related AEs was 24 days (interquartile range [IQR] 6-82 days), with most cases occurring within the initial 30 days following gemcitabine administration.ConclusionGemcitabine is associated with a broad spectrum of AEs affecting multiple organ systems, with a notable incidence of hospitalization. The study highlights both expected and unexpected AEs, which could enhance future clinical applications and safety of gemcitabine.

Dynactin subunit 2 (DCTN2) has been reported to play a role in progression of several tumours; however, the involvement of DCTN2 in potential mechanism or the tumour immune microenvironment among various cancers still remains largely unknown. Therefore, the objective of this study was to comprehensively investigate the expression status and potential function of DCTN2 in various malignancies through different database, such as The Cancer Genome Atlas, the Genotype-Tissue Expression and Gene Expression Omnimus databases. We discovered that DCTN2 expression was high in many type of tumours tissues compared to adjacent non-tumour ones. High DCTN2 signified poor prognosis for patients with tumours. Additionally, Gene Set Enrichment Analysis (GSEA) analysis revealed that DCTN2 was positively correlated with oncogenic pathways, including cell cycle, tumour metastasis-related pathway, while it was negatively with anti-tumour immune signalling pathway, such as INF-γ response. More importantly, we elucidated the functional impact of DCTN2 on hepatocellular carcinoma (HCC) progression and its underlying mechanisms. DCTN2 expression was much higher in HCC tissues than in adjacent non-tumour tissues. Silencing DCTN2 dramatically suppressed the proliferative and metastasis capacities of tumour cell in vitro. Mechanistically, DCTN2 exerted tumour-promoting effects by modulating the AKT signalling pathway. DCTN2 knockdown in HCC cells inhibited AKT phosphorylation and its downstream targets as well. Rescue experiments revealed that the anti-tumour effects of DCTN2 knockdown were partially reversed upon AKT pathway activation. Overall, DCTN2 may be a potent biomarker signifying tumour prognosis and a promising therapeutic target for tumour treatment, particularly in HCC.

BackgroundPlatelets can dynamically regulate tumor development and progression. Nevertheless, research on the predictive value and specific roles of platelets in gastric cancer (GC) is limited. This research aims to establish a predictive platelets-related gene signature in GC with prognostic and therapeutic implications.MethodsWe downloaded the transcriptome data and clinical materials of GC patients (n=378) from The Cancer Genome Atlas (TCGA) database. Prognostic platelets-related genes screened by univariate Cox regression were included in Least Absolute Shrinkage and Selection Operator (LASSO) analysis to construct a risk model. Kaplan-Meier curves and receiver operating characteristic curves (ROCs) were performed in the TCGA cohort and three independent validation cohorts. A nomogram integrating the risk score and clinicopathological features was constructed. Functional enrichment and tumor microenvironment (TME) analyses were performed. Drug sensitivity prediction was conducted through The Cancer Therapeutics Response Portal (CTRP) database. Finally, the expression of ten signature genes was validated by quantitative real-time PCR (qRT-PCR).ResultsA ten-gene (SERPINE1, ANXA5, DGKQ, PTPN6, F5, DGKB, PCDH7, GNG11, APOA1, and TF) predictive risk model was finally constructed. Patients were categorized as high- or low-risk using median risk score as the threshold. The area under the ROC curve (AUC) values for the 1-, 2-, and 3-year overall survival (OS) in the training cohort were 0.670, 0.695, and 0.707, respectively. Survival analysis showed a better OS in low-risk patients in the training and validation cohorts. The AUCs of the nomogram for predicting 1-, 2-, and 3-year OS were 0.708, 0.763, and 0.742, respectively. TME analyses revealed a higher M2 macrophage infiltration and an immunosuppressive TME in the high-risk group. Furthermore, High-risk patients tended to be more sensitive to thalidomide, MK-0752, and BRD-K17060750.ConclusionThe novel platelets-related genes signature we identified could be used for prognosis and treatment prediction in GC.

To compare the performance of spectral CT and diffusion-weighted imaging (DWI) for predicting pathologic response after neoadjuvant chemotherapy (NAC) in locally advanced gastric cancer (LAGC). This was a retrospective analysis drawn from a prospective dataset. Sixty-five patients who underwent baseline concurrent triple-phase enhanced spectral CT and DWI-MRI and standard NAC plus radical gastrectomy were enrolled, and those with poor images were excluded. The tumor regression grade (TRG) was the reference standard, and patients were classified as responders (TRG 0 + 1) or non-responders (TRG 2 + 3). Quantitative iodine concentration (IC), normalized IC (nIC), and apparent diffusion coefficient (ADC) were measured by placing a freehand region of interest manually on the maximal two-dimensional plane. Their differences between responders and non-responders were compared. The performances of significant parameters were evaluated by the receiver operating characteristic analysis. The correlations between parameters and TRG status were explored through Spearman correlation coefficient test. Kaplan–Meier survival analysis was adopted to analyze their relationship with patient survival. nICDP and ADC were associated with the TRG and yielded comparable performances for predicting TRG categories, with area under the curve (AUC) of 0.674 and 0.673, respectively. Their combination achieved a significantly increased AUC of 0.770 (p ; 0.05) and was associated with patient disease-free survival, with hazard ratio of 2.508 (1.043–6.029). Spectral CT and DWI were equally useful imaging techniques for predicting pathologic response to NAC in LAGC. The combination of nICDP and ADC gained significant incremental benefits and was related to patient disease-free survival. Spectral CT and DWI-based quantitative measurements are effective markers for predicting the pathologic regression outcomes of locally advanced gastric cancer patients after neoadjuvant chemotherapy. • The pathologic tumor regression grade, the standard criteria for treatment response after neoadjuvant chemotherapy in gastric cancer patients, is difficult to predict early. • The quantitative parameters of normalized iodine concentration at delay phase and apparent diffusion coefficients were correlated with pathologic response; their combination demonstrated incremental benefits and was associated with patient disease-free survival. • Spectral CT and DWI are equally useful imaging modalities for predicting tumor regression grade after neoadjuvant chemotherapy in patients with locally advanced gastric cancer.

BACKGROUND:The current standard of care for locally advanced gastric cancer (GC) involves neoadjuvant chemotherapy followed by radical surgery. Recently, neoadjuvant treatment for this condition has involved the exploration of immunotherapy plus chemotherapy as a potential approach. However, the efficacy remains uncertain. METHODS:A single-arm, phase 2 study was conducted to evaluate the efficacy and tolerability of neoadjuvant camrelizumab combined with mFOLFOX6 and identify potential biomarkers of response through multi-omics analysis in patients with resectable locally advanced GC. The primary endpoint was the pathological complete response (pCR) rate. Secondary endpoints included the R0 rate, near pCR rate, progression-free survival (PFS), disease-free survival (DFS), and overall survival (OS). Multi-omics analysis was assessed by whole-exome sequencing, transcriptome sequencing, and multiplex immunofluorescence (mIF) using biopsies pre- and post-neoadjuvant therapy. RESULTS:This study involved 60 patients, of which 55 underwent gastrectomy. Among these, five (9.1%) attained a pathological complete response (pCR), and 11 (20.0%) reached near pCR. No unexpected treatment-emergent adverse events or perioperative mortality were observed, and the regimen presented a manageable safety profile. Molecular changes identified through multi-omics analysis correlated with treatment response, highlighting associations between HER2-positive and CTNNB1 mutations with treatment sensitivity and a favourable prognosis. This finding was further supported by immune cell infiltration analysis and mIF. Expression data uncovered a risk model with four genes (RALYL, SCGN, CCKBR, NTS) linked to poor response. Additionally, post-treatment infiltration of CD8+ T lymphocytes positively correlates with pathological response. CONCLUSION:The findings suggest the combination of PD-1-inhibitor and mFOLFOX6 showed efficacy and acceptable toxicity for locally advanced GC. Extended follow-up is required to determine the duration of the response. This study lays essential groundwork for developing precise neoadjuvant regimens.

BACKGROUND:To establish a spectral CT-based nomogram for predicting early neoadjuvant chemotherapy (NAC) response for locally advanced gastric cancer (LAGC). METHODS:This study prospectively recruited 222 cases (177 male and 45 female patients, 9.59 ± 9.54 years) receiving NAC and radical gastrectomy. Triple enhanced spectral CT scans were performed before NAC initiation. According to post-operative tumor regression grade (TRG), patients were classified into responders (TRG = 0 + 1) or non-responders (TRG = 2 + 3), and split into a primary (156) and validation (66) dataset at 7:3 ratio chronologically. We compared clinicopathological data, follow-up information, iodine concentration (IC), normalized ICs (nICs) in arterial/venous/delayed phases (AP/VP/DP) between responders and non-responders. Independent risk factors of response were screened by multivariable logistic regression and adopted for model construction. Model was visualized by nomograms and its capability was determined through receiver operating characteristic (ROC) curves. Log-rank survival analysis was conducted to explore associations between TRG, nomogram and patients' survival. RESULTS:This work identified Borrmann classification, ICDP, and nICDP were independent risk factors of response outcomes. A spectral CT-based nomogram was built accordingly and achieved an area under the curve (AUC) of 0.797 (0.692-0.879) and 0.741(0.661-0.811) for the primary and validation dataset, respectively, higher than AUC of individual parameters alone. The nomogram was related to disease-free survival in the validation dataset (Hazard ratio (HR): 5.19 [1.18-12.93], P = 0.02). CONCLUSIONS:The spectral CT-based nomogram provides an efficient tool for predicting the pathologic response outcomes of GC after NAC and disease-free survival risk stratification.

The study focuses on the association between serum carotenoids and cancer-related death. Using data from the National Health and Nutrition Examination Survey (2001-2006 and 2017-2018), the study encompasses 10,277 participants older than age 20 years, with recorded baseline characteristics and serum carotenoid concentrations (including α-carotene, trans-β-carotene, cis-β-carotene, β-cryptoxanthin, trans-lycopene, and lutein/zeaxanthin). We hypothesized that serum carotenoid concentrations were negatively associated with cancer-related death. The weighted chi-square analyses indicate significant negative correlations between higher serum concentrations of α-carotene, β-cryptoxanthin, trans-lycopene, and total carotenoids, and the risk of cancer-related deaths. Using weighted Cox regression analysis, this study confirms that α-carotene, β-cryptoxanthin, trans-lycopene, and total carotenoids, as continuous or categorical variables, are inversely related to cancer mortality (P < .0001). Furthermore, considering competitive risk events, lower concentrations of serum β-cryptoxanthin (Fine-Gray P = 1.12e-04), trans-lycopene (P = 5.68e-14), and total carotenoids (P = .03) are associated with an increased risk of cancer-related deaths. The research reveals a crucial inverse relationship between serum carotenoid concentrations and cancer-related death.

This work focused on developing and validating the spectral CT-based nomogram to preoperatively predict perineural invasion (PNI) for locally advanced gastric cancer (LAGC). This work prospectively included 196 surgically resected LAGC patients (139 males, 57 females, 59.55 ± 11.97 years) undergoing triple enhanced spectral CT scans. Patients were labeled as perineural invasion (PNI) positive and negative according to pathologic reports, then further split into primary (n = 130) and validation cohort (n = 66). We extracted clinicopathological information, follow-up data, iodine concentration (IC), and normalized IC values against to aorta (nICs) at arterial/venous/delayed phases (AP/VP/DP). Clinicopathological features and IC values between PNI positive and negative groups were compared. Multivariable logistic regression was performed to screen independent risk factors of PNI. Then, a nomogram was established, and its capability was determined by ROC curves. Its clinical use was evaluated by decision curve analysis. The correlations of PNI and the nomogram with patients’ survival were explored by log-rank survival analysis. Borrmann classification, tumor thickness, and nICDP were independent predictors of PNI and used to build the nomogram. The nomogram yielded higher AUCs of 0.853 (0.744–0.928) and 0.782 (0.701–0.850) in primary and validation cohorts than any other parameters (p < 0.05). Both PNI and the nomogram were related to post-surgical treatment planning. Only PNI was associated with disease-free survival in the primary cohort (p < 0.05). This work prospectively established a spectral CT-based nomogram, which can effectively predict PNI preoperatively and potentially guide post-surgical treatment strategy in LAGC. • The present prospective study established a spectral CT-based nomogram for preoperative prediction of perineural invasion in LAGC. • The proposed nomogram, including morphological features and the quantitative iodine concentration values from spectral CT, had the potential to predict PNI for LAGC before surgery, along with guide post-surgical treatment planning. • Normalized iodine concentration at the delayed phase was the most valuable quantitative parameter, suggesting the importance of delayed enhancement in gastric CT.

To investigate the potential of intravoxel incoherent motion diffusion-weighted imaging (IVIM) for preoperative prediction of lymphovascular invasion (LVI) in gastric cancer (GC). This study prospectively enrolled 90 patients (62 males, 28 females, 60.79 ± 9.99 years old) who received radical gastrostomy. Abdominal MRI examinations including IVIM were performed within 1 week before surgery. Patients were divided into LVI-positive and -negative group according to pathological diagnosis after surgery. The apparent diffusion coefficient (ADC) and IVIM parameters, including true diffusion coefficient (D), pseudodiffusion coefficient (D*), and pseudodiffusion fraction (f), were compared between the two groups. The relationship between MRI parameters and LVI was studied by Spearman’s correlation analysis. Multivariable logistic regression analysis was used to screen independent predictors of LVI. Receiver-operating characteristic curve analyses were applied to evaluate the efficacy. The ADC, D in LVI-positive group were lower, whereas tumor thickness and f parameter in LVI-positive group were higher than those in LVI-negative group, and they were statistically correlated with LVI (p < 0.05). D, f and tumor thickness were independent risk factors of LVI. The area under the curve of ADC, D, f, thickness, and the combined parameter (D + f + thickness) were 0.667, 0.754, 0.695, 0.792, and 0.876, respectively. The combined parameter demonstrated higher efficacy than any other parameters (p < 0.05). The ADC, D, and f can effectively distinguish LVI status of GC. The D, f and thickness were independent predictors. The combination of the three predictors further improved the efficacy.

Background While the tumor microenvironment (TME) affects immune checkpoint blockade (ICB) efficacy, ICB also reshapes the characteristics of TME. Thus far, studies have focused on the TME evolution during neoadjuvant or adjuvant ICB therapy in gastric cancer (GC). However, the interaction between TME characteristics and neoadjuvant immunotherapy plus chemotherapy remains to be elucidated. Methods We performed single-cell RNA sequencing on ten GC specimens pre- and post-neoadjuvant camrelizumab plus mFOLFOX6 to determine the impact of the TME on the efficacy of the combination therapy and the remodeling of TME by the therapy. Results A high baseline interferon gamma (IFN-γ) signature in CD8+ T cells predicts better responses to the combination therapy. We also observed that the IFN-γ signature significantly decreased in multiple cell types, and the exhausted signature of CD8+ T cells was significantly suppressed during the neoadjuvant therapy. Conclusions Our data reveal interactions between the TME and neoadjuvant immunotherapy plus chemotherapy in GC. Importantly, it also highlights the signature of CD8+ T cells in predicting response to the combination therapy in GC.