Background Neoadjuvant immunotherapy has reshaped the treatment paradigm for esophageal squamous cell carcinoma (ESCC), yet mechanisms of resistance in non-responders remain poorly understood. Specifically, the spatial orchestration of the tumor microenvironment that limits effective antitumor immunity is not fully elucidated.Methods Integrating spatial transcriptomic analysis with large-scale tissue pathology, we mapped the spatial architecture of the immune landscape in immunotherapy-resistant ESCC to identify cellular and molecular determinants of treatment failure.Results We revealed a distinct immune exclusion phenotype in non-responders, characterized by peritumoral CD8+ T cell enrichment coupled with intratumoral depletion. At the invasive front, we identified COL11A1+ cancer-associated fibroblasts (CAFs) and SPP1+ tumor-associated macrophages as spatially correlated markers of immune exclusion, demarcating regions characterized by limited T cell infiltration. Mechanistically, a distinct subpopulation of LAMC2+ tumor cells localized at the tumor boundary acts as the master orchestrator of this barrier. These LAMC2+ cells exhibit aberrant lactate metabolism and elevated stemness, driving CAF activation via Semaphorin 3C secretion. Strikingly, this ‘LAMC2+ boundary tumor cell-immune-privileged niche’ axis exhibits pan-cancer correlations with immunotherapy resistance, positioning LAMC2 as a robust predictive biomarker.Conclusion In this study, we identify the tumor invasive margin as a critical determinant of immunotherapy resistance in ESCC. By defining the specific spatial markers and molecular architecture of an immune-privileged niche associated with the immune-exclusion barrier, our findings demonstrate the value of spatially resolved microenvironmental analysis. Moreover, we propose that therapeutic targeting of this boundary niche represents a promising strategy to overcome resistance in ESCC.

Lung adenocarcinoma (LUAD) is characterized by marked molecular heterogeneity, resulting in substantial variability in clinical outcomes. Aging is a major risk factor for LUAD and is closely associated with genomic instability, metabolic dysregulation, cellular senescence, and remodeling of the tumor microenvironment. However, the prognostic significance and biological relevance of aging-related genes in LUAD remain incompletely understood. This study aimed to systematically characterize aging-related molecular alterations in LUAD and develop a prognostic signature with potential biological and clinical relevance. Transcriptomic and clinical data for LUAD were obtained from The Cancer Genome Atlas (TCGA), and aging-related genes were collected from a public aging-associated gene set. Differentially expressed genes (DEGs) were identified by comparing tumor and normal tissues and intersected with genes from the GEO validation cohorts GSE37745 and GSE50081 to obtain common aging-related DEGs. Prognosis-associated genes were screened by univariate Cox regression, and molecular subtypes were identified by consensus clustering. An aging-related prognostic signature was then constructed using LASSO-Cox regression and validated in two independent GEO cohorts. Associations of the risk score with clinicopathological features, somatic mutation patterns, stemness, and the tumor immune microenvironment were further explored. In addition, single-cell RNA-seq data from GSE131907 were used to investigate the cellular distribution of a key model gene, while RT-qPCR analysis of clinical samples and in vitro experiments were performed for preliminary validation. A total of 274 aging-related DEGs were identified in LUAD, of which 52 were significantly associated with overall survival. Based on these genes, LUAD samples were classified into two molecular subtypes with distinct prognoses, immune infiltration patterns, and biological pathway enrichment characteristics. An eight-gene prognostic signature consisting of CD19, CYP17A1, TFAP2A, HMGA2, ABCC2, PCSK9, CLDN14, and INHA was subsequently established. This signature stratified patients into high-risk and low-risk groups with significantly different overall survival in the TCGA cohort, and its prognostic relevance was further evaluated in the GSE37745 and GSE50081 cohorts. The risk score was independently associated with prognosis and correlated with advanced clinicopathological features. High-risk tumors exhibited higher stemness, a higher somatic mutation frequency, and enrichment of proliferation-related pathways, whereas low-risk tumors displayed stronger immune-related characteristics and higher immune and stromal scores. Single-cell analysis showed that CLDN14 had relatively higher expression in B cells than in other annotated cell populations. RT-qPCR analysis in paired tumor and adjacent normal tissues from 10 LUAD patients further showed that CLDN14 was significantly upregulated in tumor tissues. In vitro assays suggested that CLDN14 knockdown impaired the proliferative, migratory, and invasive capacities of LUAD cell models. We established and validated an aging-related eight-gene prognostic signature for LUAD with independent prognostic value. This signature was associated with distinct clinical, molecular, stemness, mutational, and immune microenvironmental features. In addition, CLDN14 was identified as a candidate gene potentially associated with LUAD progression and was supported by preliminary experimental validation. These findings provide an initial aging-related framework for prognostic stratification and biological characterization in LUAD.

The scarcity of reliable biomarkers and predictive models for platinum resistance in lung adenocarcinoma (LUAD) poses a significant clinical challenge. This study endeavors to identify molecular subtypes related to platinum resistance and construct a robust predictive model through multi-omics techniques. We performed integrative analysis of public datasets using advanced bioinformatics strategies, including spatial transcriptome deconvolution and consensus clustering. Bulk RNA deconvolution analysis was conducted to characterize tumor microenvironment heterogeneity. Feature selection was performed using the Supervised Principal Component (SuperPC) algorithm, followed by diagnostic model construction validated through receiver operating characteristic (ROC) analysis. Functional validation was performed through cytological experiments measuring cisplatin IC50 alterations following gene manipulation in LUAD cell lines. Consensus clustering revealed distinct LUAD subtypes, with Cluster1 demonstrating significant platinum resistance. We first subtyped the patients in the bulk transcriptome data based on consistency clustering, and then analyzed the differences between different platinum-resistant subtypes (Cluster 1 and Cluster 2), so as to screen 333 isotype-specific differentially expressed genes and 15 platinum resistance-related (PRR) genes were selected through machine learning. A refined 5-gene signature (ANKRD29/CACNA2D2/DSP/HSD17B6/SPP1) achieved exceptional predictive performance (AUC = 0.9639). Spatial transcriptomics demonstrated compartmentalized expression patterns: SPP1/DSP localized to tumor niches, HSD17B6/CACNA2D2 to epithelial regions, and ANKRD29 depletion in stromal areas. Cellular colocalization analysis revealed malignant epithelial PH proximity to myeloid and mast cells. Functional validation confirmed that ANKRD29/CACNA2D2 overexpression sensitized A549/DDP cells to cisplatin, while DSP/SPP1/HSD17B6 overexpression induced resistance. Experiments in nude mice have shown that these genes are closely related to cisplatin resistance in LUAD. This study identifies the Cluster1 subtype and malignant epithelial PH as crucial determinants of platinum resistance in LUAD. Our innovative 5-gene predictive model exhibits clinical-grade diagnostic accuracy, and spatial transcriptomic characterization offers mechanistic insights into the dynamics of the tumor microenvironment.

The integration of ferroptosis induction with cancer immunotherapy has emerged as a promising approach in oncology, offering dual mechanisms to overcome therapeutic resistance and tumor heterogeneity. Nevertheless, the dynamic and complicated crosstalk between ferroptosis processes and immune regulation in tumor microenvironments presents both opportunities and challenges. By inducing lipid peroxidation in tumor tissues, ferroptotic tumor cell death can stimulate immunogenicity. Nevertheless, excessive lipid peroxidation may paradoxically impair the functionality of multiple immune cells, thereby presenting crosstalk challenges in therapeutic strategies. To address these crosstalk challenges, several advanced drug delivery strategies have been proposed, such as immunostimulatory active pharmaceutical ingredients co-delivery, tumor-targeted delivery, and stimuli-responsive delivery. These drug delivery strategies demonstrate dual therapeutic efficacy by synergistically potentiating ferroptosis induction in malignant cells while concurrently mitigating immunotoxicity and even augmenting antitumor immunity. This review offers detailed insights into the crosstalk between ferroptosis and tumor immunity, along with a guiding overview of the three delivery strategies. The current obstacles and translational potential were thoroughly analyzed, providing valuable perspectives for future research.

Breast cancer is a fatal malignancy facing human health, with most patients experiencing recurrence and resistance to chemotherapy. The immunosuppressive tumor microenvironment (TME) greatly limits the actual outcome of immunotherapy. This study aimed to develop a modality of theranostics nanoparticles for breast cancer based on a near-infrared light-triggered nanoparticle for the targeted delivery of ginsenoside Rg3 and immune adjuvants imiquimod (R837) for effective breast cancer immunotherapy. Folate-receptor (FA) targeting IR780-R837/ginsenoside Rg3-perfluorohexane (PFH) @ polyethylene glycol (PEG)-poly (lactide-co-glycolic acid) (PLGA) nanoparticles (FA-NPs) can be activated by near-infrared laser irradiation in tumors, which leads to rapid release of ginsenoside Rg3 and R837 in the regions with high expression of folate receptors and glucose transporter 1 (GLUT1). Meanwhile, the nanoparticles can be used as dual-mode contrast agents for photoacoustic and ultrasound imaging. This strategy provides a strong immune memory effect, which can prevent tumor recurrence after eliminating the initial tumor.

Hepatocellular carcinoma (HCC) stands as the prevailing manifestation of primary liver cancer and continues to pose a formidable challenge to human well-being and longevity, owing to its elevated incidence and mortality rates. Nevertheless, the quest for reliable predictive biomarkers for HCC remains ongoing. Recent research has demonstrated a close correlation between ferroptosis and disulfidptosis, two cellular processes, and cancer prognosis, suggesting their potential as predictive factors for HCC. In this study, we employed a combination of bioinformatics algorithms and machine learning techniques, leveraging RNA sequencing data, mutation profiles, and clinical data from HCC samples in The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) databases, to develop a risk prognosis model based on genes associated with ferroptosis and disulfidptosis. We conducted an unsupervised clustering analysis, calculating a risk score (RS) to predict the prognosis of HCC using these genes. Clustering analysis revealed two distinct HCC clusters, each characterized by significantly different prognostic and immune features. The median RS stratified HCC samples in the TCGA, GEO, and ICGC cohorts into high-and low-risk groups. Importantly, RS emerged as an independent prognostic factor in all three cohorts, with the high-risk group demonstrating poorer prognosis and a more active immunosuppressive microenvironment. Additionally, the high-risk group exhibited higher expression levels of tumor mutation burden (TMB), immune checkpoints (ICs), and human leukocyte antigen (HLA), suggesting a heightened responsiveness to immunotherapy. A cancer stem cell infiltration analysis revealed a higher similarity between tumor cells and stem cells in the high-risk group. Furthermore, drug sensitivity analysis highlighted significant differences in response to antitumor drugs between the two risk groups. In summary, our risk prognostic model, constructed based on ferroptosis-related genes associated with disulfidptosis, effectively predicts HCC prognosis. These findings hold potential implications for patient stratification and clinical decision-making, offering valuable theoretical insights in this field.

Background: Pharmacologically targeting the STING pathway offers a novel approach to cancer immunotherapy. However, small-molecule STING agonists face challenges such as poor tumor accumulation, rapid clearance, and short-lived effects within the tumor microenvironment, thus limiting their therapeutic potential. To address the challenges of poor specificity and inadequate targeting of STING in breast cancer treatment, herein, we report the design and development of a targeted liposomal delivery system modified with the tumor-targeting peptide iRGD (iRGD-STING-PFP@liposomes). With LIFU irradiation, the liposomal system exploits acoustic cavitation, where gas nuclei form and collapse within the hydrophobic region of the liposome lipid bilayer (transient pore formation), which leads to significantly enhanced drug release. Methods: Transmission electron microscopy (TEM) was used to investigate the physicochemical properties of the targeted liposomes. Encapsulation efficiency and in vitro release were assessed using the dialysis bag method, while the effects of iRGD on liposome targeting were evaluated through laser confocal microscopy. The CCK-8 assay was used to investigate the toxicity and cell growth effects of this system on 4T1 breast cancer cells and HUVEC vascular endothelial cells. A subcutaneous breast cancer tumor model was established to evaluate the tumor-killing effects and therapeutic mechanism of the newly developed liposomes. Results: The liposome carrier exhibited a regular morphology, with a particle size of 232.16 ± 19.82 nm, as indicated by dynamic light scattering (DLS), and demonstrated low toxicity to both HUVEC and 4T1 cells. With an encapsulation efficiency of 41.82 ± 5.67%, the carrier exhibited a slow release pattern in vitro after STING loading. Targeting results indicated that iRGD modification enhanced the system’s ability to target 4T1 cells. The iRGD-STING-PFP@liposomes group demonstrated significant tumor growth inhibition in the subcutaneous breast cancer mouse model with effective activation of the immune system, resulting in the highest populations of matured dendritic cells (71.2 ± 5.4%), increased presentation of tumor-related antigens, promoted CD8+ T cell infiltration at the tumor site, and enhanced NK cell activity. Conclusions: The iRGD-STING-PFP@liposomes targeted drug delivery system effectively targets breast cancer cells, providing a new strategy for breast cancer immunotherapy. These findings indicate that iRGD-STING-PFP@liposomes could successfully deliver STING agonists to tumor tissue, trigger the innate immune response, and may serve as a potential platform for targeted immunotherapy.

Non-small cell lung cancer (NSCLC) is a common malignancy whose prognosis and treatment outcome are influenced by many factors. Some studies have found that tertiary lymphoid structures (TLSs) in cancer may contribute to prognosis and the prediction of immunotherapy efficacy However, the combined role of TLSs in NSCLC remains unclear. We accessed The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to obtain mRNA sequencing data and clinical information as the TCGA cohort, and used our own sample of 53 advanced NSCLC as a study cohort. The samples were divided into TLS+ and TLS- groups by pathological tissue sections. Patients of the TLS+ group had a better OS (p = 0.022), PFS (p = 0.042), and DSS (p = 0.004) in the TCGA cohort, and the results were confirmed by the study cohort (PFS, p = 0.012). Furthermore, our result showed that the count and size of TLSs are closely associated with the efficacy of immunotherapy. In addition, the TLS+ group was associated with better immune status and lower tumor mutation load. In the tumor microenvironment (TME), the expression levels of CD4+ T cells and CD8+ T cells of different phenotypes were associated with TLSs. Overall, TLSs are a strong predictor of survival and immunotherapeutic efficacy in advanced NSCLC, and T cell-rich TLSs suggest a more ordered and active immune response site, which aids in the decision-making and application of immunotherapy in the clinic.

We conducted a retrospective study to evaluate the efficacy of immune checkpoint inhibitor (ICI) rechallenge in patients with advanced non-small cell lung cancer (NSCLC). The study included 111 patients who had previously received ICI therapy and experienced disease progression. The primary endpoints assessed were overall survival (OS), progression-free survival (PFS), and objective response rate (ORR). Our findings revealed that the ICI rechallenge showed promising results in improving patient outcomes. OS (r) is the time from rechallenging with immune checkpoint inhibitors to the last follow-up or death from any cause. The median OS (r) was 14.3 months (95% CI 11.3–17.3 months), with a median PFS (r) of 5.9 months (95% CI 4.1–7.7 months). The ORR was 17.1%; the DCR was 82.3%. Subgroup analysis demonstrated that patients without brain or liver metastases had a longer OS (r) compared to those with metastases (21.6 vs. 13.8 months, χ2 = 3.873, P = 0.046; 20.8 vs. 9.1 months, χ2 = 10.733, P = 0.001, respectively). Moreover, patients without driver gene mutations exhibited significantly longer OS than those with mutations or wild-type patients (22.9 vs. 16.1 vs. 7.5 months, χ2 = 10.710, P = 0.005). Notably, patients who switched to a different ICI during the rechallenge had shorter OS than those who did not change medications (10.4 vs. 21.1 months, χ2 = 9.014, P = 0.003). The incidence of immune-related adverse events did not significantly differ between the two treatment phases. These findings suggest that ICI rechallenge may be a viable therapeutic strategy for select NSCLC patients. Further prospective studies are needed to validate these results and guide treatment decisions for advanced NSCLC.

BACKGROUND:Patients with gastric cancer respond poorly to immunotherapy. There are still unknowns about the biomarkers associated with immunotherapy sensitivity and their underlying molecular mechanisms. METHODS:Gene expression data for gastric cancer were gathered from TCGA and GEO databases. DEGs associated with immunotherapy response came from ICBatlas. KEGG and GO analyses investigated pathways. Hub genes identification employed multiple machine algorithms. Associations between hub genes and signaling pathways, disease genes, immune cell infiltration, drug sensitivity, and prognostic predictions were explored via multi-omics analysis. Hub gene expression was validated through HPA and CCLE. Multiple algorithms pinpointed Cancer-Associated Fibroblasts genes (CAFs), with ten machine-learning methods generating CAFs scores for prognosis. Model gene expression was validated at the single-cell level using the TISCH database. RESULTS:We identified 201 upregulated and 935 downregulated DEGs. Three hub genes, namely CDH6, EGFLAM, and RASGRF2, were unveiled. These genes are implicated in diverse disease-related signaling pathways. Additionally, they exhibited significant correlations with disease-associated gene expression, immune cell infiltration, and drug sensitivity. Exploration of the HPA and CCLE databases exposed substantial expression variations across patients and cell lines for these genes. Subsequently, we identified CAFs-associated genes and established a robust prognostic model. The analysis in the TISCH database showed that the genes in this model were highly expressed in CAFs. CONCLUSIONS:The results unveil an association between CDH6, EGFLAM, and RASGRF2 and the immunotherapeutic response in gastric cancer. These genes hold potential as predictive biomarkers for gastric cancer immunotherapy resistance and prognostic assessment.

Lung adenocarcinoma (LUAD) is an essential pathological subtype of non-small cell lung cancer and offers a severe problem for worldwide public health. There is mounting proof that angiogenesis is a crucial player in LUAD progression. Consequently, the purpose of this research was to construct a novel LUAD risk assessment model based on genetic markers related to angiogenesis. We accessed The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases for LUAD mRNA sequencing data and clinical information. Based on machine algorithms and bioinformatics, angiogenic gene-related risk scores (RS) were calculated. Patients in the high-risk category had a worse prognosis ( p < 0.001) in the discovery TCGA cohort, and the results were confirmed by these three cohorts (validation TCGA cohort, total TCGA cohort, and GSE68465 cohort). Moreover, risk scores for genes involved in angiogenesis were independent risk factors for lung cancer in all four cohorts. The low-risk group was associated with better immune status and lower tumor mutational load. In addition, the somatic mutation study revealed that the low-risk group had a lower mutation frequency than the high-risk group. According to an analysis of tumor stem cell infiltration, HLA expression, and TIDE scores, the low-risk group had higher TIDE scores and HLA expression levels than the high-risk group, and the amount of tumor stem cell infiltration correlated with the risk score. In addition, high-risk groups may benefit from immune checkpoint inhibitors and targeted therapies. In conclusion, we developed an angiogenesis-related gene risk model to predict the prognosis of LUAD patients, which may aid in the classification of patients with LUAD and select medications for LUAD patients.

To remove redundant features and avoid the curse of dimensionality, the most important features should be selected for downstream tasks, including semi-supervised learning. Several semi-supervised constraint scores using pairwise constraints have been proposed to estimate the relevance of features. However, these methods evaluate the features individually and ignore the correlations between them. Thus, we propose a semi-supervised feature selection method called the iterative constraint score based on the hypothesis margin (HM-ICS), which uses forward sequential selection to select an optimal feature subset with a good ability to maintain the constraint structure of the data and distinguish samples that belong to different classes. HM-ICS iteratively modifies the classical constraint score method to measure the relevance between features and maintain the constraint structure of the data. By introducing the hypothesis margin, HM-ICS can ensure strong discriminative power of the optimal feature subset. Extensive experiments were conducted on nine UCI and five high-dimensional datasets, and the experimental results confirmed that HM-ICS can achieve better performance than state-of-the-art supervised and semi-supervised methods.

Background Immune checkpoint inhibitors (ICIs) have made a breakthrough in the systemic treatment of patients with advanced tumors. However, little is known about their efficacy and safety in adjuvant settings after the resection of solid tumors. Methods We performed a meta-analysis on the efficacy and safety of programmed death 1 (PD1)/PD-1 ligand (PDL1) inhibitors in adjuvant therapy after tumor resection using Review Manager 5.3, based on published clinical studies. The outcomes included recurrence-free survival (RFS), disease-free survival (DFS), overall survival (OS), and adverse events (AEs). Results Eight randomized controlled trials (RCTs) were included in the analysis. The use of PD1/PDL1 inhibitors in adjuvant therapy significantly improved RFS (hazard ratio [HR] = 0.72; 95% confidence interval [CI] 0.67–0.78, p < 0.00001). However, there was no statistically significant difference in OS between PD1/PDL1 inhibitors and placebo (HR = 0.86; 95% CI 0.74–1.00, p = 0.05). Gender, age, and PDL1 status were independent predictors of RFS with PD1/PDL1 inhibitors. As for the safety analysis results, PD1/PDL1 inhibitors had a higher incidence of fatigue (risk ratio [RR] = 1.22; 95% CI 1.01–1.49, p = 0.04), nausea (RR = 1.47; 95% CI 1.11–1.94, p = 0.007), and pruritus (RR = 1.96; 95% CI 1.57–2.44, p < 0.00001). In addition, the incidence of any grade adverse events increased in the PD1/PDL1 inhibitor group (RR = 1.03; 95% CI 1.02–1.05, p < 0.0001). Conclusions This is the first meta-analysis on the efficacy and safety of PD1/PDL1 inhibitors in adjuvant therapy. The use of PD1/PDL1 inhibitors in adjuvant therapy could significantly reduce the recurrence rate after solid tumor resection. However, the incidence of fatigue, nausea, pruritus, and any grade AEs also increased, which should be monitored with vigilance.

Abstract PurposeIn view of the fact that peripheral blood parameters have been reported as predictors of immunotherapy to various cancers, this study aimed to determine the predictors of response to immune checkpoint inhibitors (ICIs) therapy in patients with upper gastrointestinal cancers from peripheral blood parameters.MethodsThe respective data of 93 patients with upper gastrointestinal tumor who received ICIs combination therapy were included in this study. The peripheral blood indexes of interest were neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), lactate dehydrogenase (LDH), and fibrinogen/albumin ratio (FAR). Survival probabilities of progression-free survival (PFS) was estimated using Kaplan–Meier curves and log-rank tests and the multivariate Cox proportional hazards model. Receiver-operating characteristic curve was used to determine a cutoff value for parameters and area under the curve.ResultsThe median PFS of all upper gastrointestinal cancers patients was 7.33 months. Baseline LDH0<194.5,NLR0<4.39,PLR0<218.7 and FAR0<0.13 were associated with longer PFS(13.4 VS 4.9 months; p < 0.001,10.8 VS 4.2 months; p<0.001,10.8 VS 6.7 months; p<0.05,8.4 VS 5.0 months; p<0.05,respectively).The 6 weeks after treatment value, LDH1<264.5,PLR1<117.2 were associated with longer PFS(7.5 VS 2.7 months; p < 0.05, 12.0 VS 6.2 months; p<0.05, respectively). The first disease progression value,LDH2<216.5 was associated with longer PFS(9.5 VS 4.2 months; p < 0.05). The NLR1 / NLR0 < 1.75 was associated with significant prolongation of PFS (7.5 VS 4.9 months, P <0.05).ConclusionsOur research illustrated that baseline LDH0,NLR0,PLR0,FAR0 and LDH1,LDH2,PLR1 at post-treatment are independent predictors for PFS in upper gastrointestinal tumor patients treated with ICIs.