Osteoarthritis (OA) is a degenerative joint disease marked by cartilage breakdown, chondrocyte apoptosis, and extracellular matrix (ECM) degradation. Non-coding RNAs, particularly circular RNAs (circRNAs), are increasingly recognized as key regulators of OA pathogenesis. This study reveals that circFN1 (hsa_circ_0058152) may act as a competing endogenous RNA, sponging miR-19b-3p and suppressing PTEN expression. This mechanism promotes chondrocyte apoptosis, enhances inflammatory responses, and accelerates ECM degradation, driving OA progression. We investigated the circFN1/miR-19b-3p/PTEN axis using in vitro experiments, including CCK-8 assays, qRT-PCR, Western blotting, ELISA, immunofluorescence, dual-luciferase reporter assays, and bioinformatics analyses to validate regulatory relationships and functional effects. Our study demonstrated that circFN1 suppresses chondrocyte proliferation and exacerbates the inflammatory microenvironment. We further revealed that circFN1 aggravates inflammation-induced chondrocyte injury in OA by repressing the miR-19b-3p/PTEN signaling axis. Collectively, we identified a novel circFN1/miR-19b-3p/PTEN pathway that amplifies IL-1β–induced osteoarthritis progression, highlighting its potential as a therapeutic target for OA intervention. This study identifies the circFN1/miR-19b-3p/PTEN axis as a novel regulator of OA progression. circFN1 may serve as a potential biomarker, and targeting this pathway could provide a basis for future therapeutic strategies.

PURPOSE:FLASH radiation therapy using high-energy x rays combines ultrahigh dose rate irradiation with the physical characteristics of high-energy x-ray beams, achieving a significant reduction in normal tissue biological damage while maintaining sufficient tissue penetration, thereby presenting great potential for clinical translation. However, the absence of a traceable absolute dosimetry method for FLASH x-ray beams remains a substantial limitation to its clinical implementation. This study aims to establish a quasi-adiabatic water-controlled probe-type graphite calorimeter for the absolute measurement of absorbed dose to water in the 10 MV FLASH x-ray beam, to address the current lack of a traceable dosimetry standard for x-ray FLASH radiation therapy. METHODS AND MATERIALS:A probe-type graphite calorimeter was developed, employing thermally stabilized water as the thermal control medium to precisely regulate the thermal equilibrium of the graphite core. This quasi-adiabatic system is designed to facilitate accurate absolute dose measurements under ultrahigh dose rate conditions. RESULTS:The results indicate that for a single irradiation with a total dose exceeding 2 Gy, the mean type A relative uncertainty, determined from 5 repeated measurements using the sample standard deviation, is less than 0.2%. By deriving the necessary correction factors for determining the absolute dose (ie, in Gy) of FLASH photon radiation therapy, the uncertainty in water absorbed dose measurement is determined to be 1.0% (k = 1). CONCLUSIONS:This study develops a probe-type graphite calorimeter for the absolute measurement of absorbed dose to water in 10 MV FLASH x-ray beams. The system is designed to address the current lack of a traceable dosimetric standard for x-ray FLASH radiation therapy, thereby supporting its clinical translation and application.

Purpose FLASH radiotherapy using high-energy X-rays combines ultra-high dose-rate irradiation with the physical characteristics of high-energy X-ray beams, achieving a significant reduction in normal tissue biological damage while maintaining sufficient tissue penetration, thereby presenting great potential for clinical translation. However, the absence of a traceable absolute dosimetry method for FLASH X-ray beams remains a substantial limitation to its clinical implementation. This study aims to establish a quasi-adiabatic water-controlled probe-type graphite calorimeter for the absolute measurement of absorbed dose to water in the 10 MV FLASH X-ray beam, to address the current lack of a traceable dosimetry standard for X-ray FLASH radiotherapy. Methods and Materials A probe-type graphite calorimeter was developed, employing thermally stabilised water as the thermal control medium to precisely regulate the thermal equilibrium of the graphite core. This quasi-adiabatic system is designed to facilitate accurate absolute dose measurements under ultra-high dose rate conditions. Results The results indicate that for a single irradiation with a total dose exceeding 2 Gy, the mean Type A relative uncertainty, determined from five repeated measurements using the sample standard deviation, is less than 0.2%. By deriving the necessary correction factors for determining the absolute dose (i.e., in Gy) of FLASH photon radiotherapy, the uncertainty in water-absorbed dose measurement is determined to be 1.0% (1σ). Conclusions This study develops a probe-type graphite calorimeter for the absolute measurement of absorbed dose to water in 10 MV FLASH X-ray beams. The system is designed to address the current lack of a traceable dosimetric standard for X-ray FLASH radiotherapy, thereby supporting its clinical translation and application.

OBJECTIVES:In this study, we aimed to examine the effects of cancer stem cells (CSCs) on residual hepatocellular carcinoma (HCC) after radiofrequency ablation (RFA), and how to reduce the frequency of carcinoma cells with the CSCs phenotype in residual tumors after RFA. MATERIALS & METHODS:Two HCC cell lines were exposed to 43 °C for 30 min in vitro using a water bath. Cell cycle, EdU assays and plate colony assays were performed to evaluate the proliferation of HCC cells. Cell migration was determined using wound healing and Transwell assays. Sphere formation and in vivo limiting dilution assays were performed to evaluate stemness. In vivo, two tumor-bearing mice were used to evaluate residual tumor growth, and treatments included an extracellular signal-regulated kinase (ERK) inhibitor U0126 and salinomycin (Sal). RESULTS:In vitro, sublethal heat accelerated cancer cell proliferation, migration, and stemness, and induced molecular changes of epithelial-mesenchymal transition (EMT), ERK, and the β-catenin pathway. ERK inhibitor and Sal inhibited the proliferation, migration, and stemness of heat-treated HCC cells. The results showed that, in vivo, the ERK inhibitor + Sal significantly inhibited the growth of residual tumors after incomplete RFA. Compared with incomplete RFA alone, EMT markers, ERK, and the β-catenin pathway were also significantly inhibited after treatment with ERK inhibitor + Sal. CONCLUSIONS:Incomplete RFA can accelerate cell proliferation, migration, and stemness in residual tumors. ERK inhibitor combined with Sal could inhibit cancer cell proliferation, migration, and stemness to synergically inhibit the progression of residual tumor.

Early identification of acute hematologic toxicity (HT) in locally advanced rectal cancer (LARC) patients undergoing radiotherapy is crucial for optimizing clinical outcomes. Here, we retrospectively collected multi-center LARC patients (n = 464, n = 56, and n = 79) with complete CT images, dose maps, hematologic biomarkers, and demographic information. A Transformer-based multimodal fusion model was constructed to combine the visual and non-visual representation features for HT prediction, and the study also testified to the modality-specific and region-specific contributions to HT. The multimodal fusion model achieved a state-of-the-art HT prediction performance in LARC patients: with an area under the curve (AUC) of 0.828 (95% confidence interval [CI]: 0.820-0.835), 0.757 (95% CI: 0.750-0.766), and 0.756 (95% CI: 0.752-0.762) in internal and two external testing datasets. The initial hematologic biomarkers were the best unimodal risk indicator, while the planning target volume served as the most sensitive region. The study confirms the sole and combined contributions of each modality to the radiotherapy-induced HT in LARC patients, and the multimodal fusion model shows promising interpretability and generalization for HT occurrence, which offers valuable insights to optimize personalized treatment plans for high-risk patients.

Abstract With the wider use of local ablation for hepatocellular carcinoma (HCC), patients who have large tumors close to major hepatic vessels remain a difficult group to treat. In these patients, local tumor progression (LTP) and treatment-related complications are both common. For many years, the evaluation and technical optimization of radiofrequency ablation (RFA) in perivascular HCC have been insufficient, and current guidelines give only general advice rather than clear recommendations. This article is a narrative review with a structured literature search. We searched PubMed, Web of Science, CNKI, and Wanfang from database inception to September 2024 using the keywords “hepatocellular carcinoma, radiofrequency ablation, perivascular tumor, large tumor, heat-sink effect, combined therapy, navigation, artificial intelligence, computational modeling". We mainly included phase I–III clinical studies, randomized controlled trials, observational cohorts, meta-analyses, translational experiments, and modeling studies. The available evidence shows that large perivascular tumors often have uneven heating due to the heat-sink effect. The ablation effect and the risk of vascular or biliary injury are determined by tumor size, vessel diameter and shape, electrode placement, and system power configuration. Perivascular HCC differs clearly from non-perivascular HCC in local control, recurrence pattern, and complication rate. Consequently, RFA alone is generally insufficient and should be considered only within multimodal treatment strategies. At present, the use of multimodal and individualized strategies for large perivascular HCC is still limited, and practical treatment algorithms are lacking. In this review, we discuss how large perivascular HCC should be managed within a multimodal framework, in which non-ablative therapies such as SBRT, transarterial approaches, and systemic treatment constitute the therapeutic backbone, while RFA-based strategies may serve selected adjunctive roles under specific clinical conditions.

Biomedical research data visualization faces several challenges, including insufficient expertise and fragmented methodologies, which severely limit research efficiency and result quality. FigureYa is a standardized visualization framework composed of 317 modular R/python scripts, rather than a standalone software or desktop application. It covers key domains such as expression profiling, immune analysis, survival analysis, and single-cell data visualization. Based on the concept of “replace data and use,” FigureYa significantly lowers the technical threshold, allowing researchers to generate high-quality charts without requiring an extensive programming background. Compared to generic online R code snippets, FigureYa offers rigorously developed, thoroughly validated, and biologically contextualized visualization modules originally written by the author team. Each script includes version-matched environments, example datasets, and detailed annotations, providing clear advantages in automation, reproducibility, and scientific professionalism, thereby providing a standardized visualization solution for complex biomedical data. This innovative tool optimizes research time allocation, promotes interdisciplinary collaboration, accelerates scientific discovery and clinical translation, and provides robust data visualization support for biomedical research.

Introduction This study explored a generative image synthesis method based on diffusion models, potentially providing a low-cost and high-efficiency training data augmentation strategy for medical artificial intelligence (AI) applications. Methods The MedMNIST v2 dataset was utilized as a small-volume training dataset under low-performance computing conditions. Based on the characteristics of existing samples, new medical images were synthesized using the proposed annotated diffusion model. In addition to observational assessment, quantitative evaluation was performed based on the gradient descent of the loss function during the generation process and the Fréchet Inception Distance (FID), using various loss functions and feature vector dimensions. Results Compared to the original data, the proposed diffusion model successfully generated medical images of similar styles but with dramatically varied anatomic details. The model trained with the Huber loss function achieved a higher FID of 15.2 at a feature vector dimension of 2048, compared with the model trained with the L2 loss function, which achieved the best FID of 0.85 at a feature vector dimension of 64. Discussion The use of the Huber loss enhanced model robustness, while FID values indicated acceptable similarity between generated and real images. Future work should explore the application of these models to more complex datasets and clinical scenarios. Conclusion This study demonstrated that diffusion model-based medical image synthesis is potentially applicable as an augmentation strategy for AI, particularly in situations where access to real clinical data is limited. Optimal training parameters were also proposed by evaluating the dimensionality of feature vectors in FID calculations and the complexity of loss functions.

Recent studies suggest that Visual Language Models (VLMs) hold great potential for tasks such as automated medical diagnosis. However, processing complex three-dimensional (3D) multimodal medical images poses significant challenges - specifically, the effective integration of complementary information and the occasional oversight of subtle yet critical pathological features. To address these issues, we present a novel two-stage fusion framework termed Hilbert-VLM. This framework leverages the HilbertMed-SAM module for precise lesion segmentation, with the generated multimodal enhanced prompts then guiding the VLM toward accurate disease classification. Our key innovation lies in the systematic redesign of the Segment Anything Model 2 (SAM2) architecture: we incorporate Hilbert space-filling curves into the scanning mechanism of the Mamba State Space Model (SSM) to maximize the preservation of spatial locality in 3D data, a property critical for medical image analysis. We also introduce a novel Hilbert-Mamba Cross-Attention (HMCA) mechanism and a scale-aware decoder to capture fine-grained details. Meanwhile, the prompt enhancement module unifies segmentation masks and their corresponding textual attributes into an information-dense prompt to support VLM inference. Extensive experiments were conducted to validate the effectiveness of the Hilbert-VLM model. On the BraTS2021 segmentation benchmark, it achieves a Dice score of 82.35 percent, with a diagnostic classification accuracy (ACC) of 78.85 percent. These results demonstrate that the proposed model offers substantial potential to improve the accuracy and reliability of medical VLM-based analysis.

Background:Motion management plays an important role in abdominal cancer radiotherapy. In this study, we investigated the motion measurement accuracy and tumor contrast in liver tumor using a fast volumetric four-dimensional magnetic resonance imaging (4D-MRI) technique using commercial sequence. Methods:Four volunteers and 34 patients with liver tumors were included in this study with institutional review board (IRB) approval, and all patients underwent routine MRI scans with additional 4D-MRI scan on a 3.0 Tesla MRI scanner. A fast-volumetric sequence [time-resolved imaging with stochastic trajectories-volumetric interpolated breath-hold examination (TWIST-VIBE)] was used to acquire 4D-MRI images. The temporal resolution of 4D-MRI was ~0.69 s per measurement. The 4D-MRI sequence was performed before and immediately after the injection of gadolinium contrast agent, termed as non-contrast 4D-MRI (in patient study) and contrast-enhanced 4D-MRI (4D-MRICE) respectively. The tumor average motion amplitude (AMA) and maximum motion amplitude (MMA) in the superior-inferior (SI), anterior-posterior (AP), and medium-lateral (ML) directions were measured in two sets of 4D-MRI and three sets of two-dimensional (2D) Cine magnetic resonance (MR) images. The tumor signal-to-noise ratio (SNR) and tumor-to-liver contrast-to-noise ratio (CNR) were also evaluated. Results:High temporal resolution (~0.69 s per measurement) and isotropic spatial resolution (~2.7 mm3 voxel size) were achieved with the fast volumetric 4D-MRI technique. Phantom experiments validated the motion measurement accuracy, showing that the average and MMAs in the SI, AP, and ML directions matched the programmed motions with errors below 3 mm. In patient studies, tumor motion amplitudes and trajectories measured by 4D-MRICE and non-contrast 4D-MRI (4D-MRINC) were comparable to conventional 2D Cine MRI in all three directions, with no statistically significant differences. Specifically, for AMA, the P values were 0.096, 0.019, and 0.009 for 4D-MRICE in the SI, AP, and ML directions, respectively, and 0.049, 0.008, and 0.016 for 4D-MRINC, respectively. For MMA, the corresponding P values were 0.054, 0.022, and 0.086 for 4D-MRICE, and 0.041, 0.007, and 0.016 for 4D-MRINC. Tumor SNR and tumor-to-liver CNR obtained with 4D-MRICE and 4D-MRINC were also comparable to those from standard diagnostic sequences (T1W, T2W) and Cine MRI, with P values <0.05, ensuring sufficient image quality for radiotherapy planning. Although minor motion artifacts were occasionally observed, the overall image quality and motion fidelity support the clinical feasibility of the method. Taken together, these results demonstrate that the TWIST-VIBE 4D-MRI technique provides accurate, high-quality volumetric motion assessment efficiently, without requiring specialized hardware or prolonged scanning times. Conclusions:Our preliminary results demonstrated that the commercially available TWIST-VIBE 4D-MRI sequence provides precise volumetric tumor motion tracking suitable for clinical abdominal radiotherapy. Its rapid acquisition and reliable image quality facilitate seamless workflow integration, potentially enhancing treatment precision and improving patient outcomes. The technique offers a practical alternative to existing imaging modalities by combining speed, spatial coverage, and soft tissue contrast without additional hardware requirements. Further efforts will target artifact minimization, optimization of spatial and temporal resolutions, improved clinical decision-making impact, and broader multi-center validation to ensure robust clinical implementation and generalizability across diverse patient populations.

The clinical adaptation of 4D-MRI in respiratory motion management is limited by the low image quality and motion artifacts of 4D-MRI sequences. This study aims to develop a novel artifact Map-guided Nonlocal mean (AM-NLM) technique that can be integrated into the clinical 4D-MRI workflow to suppress motion artifacts and enhance image quality. The AM-NLM technique was developed and tested on 4D-MR images of 28 liver cancer patients. A multiphase motion field was computed on the frames with the minimum average localized gradient entropy for each phase to generate a full set of improved quality 4D-MR images. Artifact maps were calculated based on the local image sharpness to guide nonlocal averaging, and a set of denoised eight-phase 4D-MR images was finally generated. The 4D-MR images were evaluated for image quality and motion accuracy. Conventional 4D-MRI approaches were also evaluated for comparison. AM-NLM 4D-MR images have significant improvements in SNR and CNR compared to the original 4D-MR images. High motion accuracy was achieved for AM-NLM 4D-MR images because the average deviation in the diaphragm position from the mean value for each phase was at the subvoxel level. Both qualitative and quantitative results suggested that the 4D-MR images generated by the AM-NLM technique had high image quality while maintaining image sharpness and motion accuracy. The AM-NLM technique has shown capability of suppressing motion artifacts and enhancing image quality of clinically acquired 4D-MR images, making it a promising technique in applications of 4D-MRI in radiotherapy.

Accurate automatic medical image segmentation relies on high-quality, dense annotations, which are costly and time-consuming. Weakly supervised learning provides a more efficient alternative by leveraging sparse and coarse annotations instead of dense, precise ones. However, segmentation performance degradation and overfitting caused by sparse annotations remain key challenges. To address these issues, we propose CmFNet, a novel 3D weakly supervised cross-modal medical image segmentation approach. CmFNet consists of three main components: a modality-specific feature learning network, a cross-modal feature learning network, and a hybrid-supervised learning strategy. Specifically, the modality-specific feature learning network and the cross-modal feature learning network effectively integrate complementary information from multi-modal images, enhancing shared features across modalities to improve segmentation performance. Additionally, the hybrid-supervised learning strategy guides segmentation through scribble supervision, intra-modal regularization, and inter-modal consistency, modeling spatial and contextual relationships while promoting feature alignment. Our approach effectively mitigates overfitting, delivering robust segmentation results. It excels in segmenting both challenging small tumor regions and common anatomical structures. Extensive experiments on a clinical cross-modal nasopharyngeal carcinoma (NPC) dataset (including CT and MR imaging) and the publicly available CT Whole Abdominal Organ dataset (WORD) show that our approach outperforms state-of-the-art weakly supervised methods. In addition, our approach also outperforms fully supervised methods when full annotation is used. Our approach can facilitate clinical therapy and benefit various specialists, including physicists, radiologists, pathologists, and oncologists.

Semi-supervised learning addresses the issue of limited annotations in medical images effectively, but its performance is often inadequate for complex backgrounds and challenging tasks. Multi-modal fusion methods can significantly improve the accuracy of medical image segmentation by providing complementary information. However, they face challenges in achieving significant improvements under semi-supervised conditions due to the challenge of effectively leveraging unlabeled data. There is a significant need to create an effective and reliable multi-modal learning strategy for leveraging unlabeled data in semi-supervised segmentation. To address these issues, we propose a novel semi-supervised multi-modal medical image segmentation approach, which leverages complementary multi-modal information to enhance performance with limited labeled data. Our approach employs a multi-stage multi-modal fusion and enhancement strategy to fully utilize complementary multi-modal information, while reducing feature discrepancies and enhancing feature sharing and alignment. Furthermore, we effectively introduce contrastive mutual learning to constrain prediction consistency across modalities, thereby facilitating the robustness of segmentation results in semi-supervised tasks. Experimental results on two multi-modal datasets demonstrate the superior performance and robustness of the proposed framework, establishing its valuable potential for solving medical image segmentation tasks in complex scenarios. The code is available at: https://github.com/DongdongMeng/SMMS .

Various deep learning auto-segmentation (DLAS) models have been proposed, some of which have been commercialized. However, the issue of performance degradation is notable when pretrained models are deployed in the clinic. This study aims to enhance precision of a popular commercial DLAS product in rectal cancer radiotherapy by localized fine-tuning, addressing challenges in practicality and generalizability in real-world clinical settings. A total of 120 Stage II/III mid-low rectal cancer patients were retrospectively enrolled and divided into three datasets: training (n = 60), external validation (ExVal, n = 30), and generalizability evaluation (GenEva, n = 30) datasets respectively. The patients in the training and ExVal dataset were acquired on the same CT simulator, while those in GenEva were on a different CT simulator. The commercial DLAS software was first localized fine-tuned (LFT) for clinical target volume (CTV) and organs-at-risk (OAR) using the training data, and then validated on ExVal and GenEva respectively. Performance evaluation involved comparing the LFT model with the vendor-provided pretrained model (VPM) against ground truth contours, using metrics like Dice similarity coefficient (DSC), 95th Hausdorff distance (95HD), sensitivity and specificity. LFT significantly improved CTV delineation accuracy (p < 0.05) with LFT outperforming VPM in target volume, DSC, 95HD and specificity. Both models exhibited adequate accuracy for bladder and femoral heads, and LFT demonstrated significant enhancement in segmenting the more complex small intestine. We did not identify performance degradation when LFT and VPM models were applied in the GenEva dataset. The necessity and potential benefits of LFT DLAS towards institution-specific model adaption is underscored. The commercial DLAS software exhibits superior accuracy once localized fine-tuned, and is highly robust to imaging equipment changes.

PurposeRepeated cone-beam CT (CBCT) scans for image-guided radiotherapy (IGRT) increase the health risk of radiation-induced malignancies. Patient-enrolled studies to optimize scan protocols are inadequate. We proposed a virtual clinical trial-based approach to evaluate projection-reduced low-dose CBCT for IGRT.Materials and methodsA total of 71 patients were virtually enrolled with 26 head, 23 thorax and 22 pelvis scans. Projection numbers of full-dose CBCT scans were reduced to 1/2, 1/4, and 1/8 of the original to simulate low-dose scans. Contrast-to-noise ratio (CNR) values in fat and muscle were measured in the full-dose and low-dose images. CBCT images were registered to planning CT to derive 6-degree-of-freedom couch shifts. Registration errors were statistically analyzed with the Wilcoxon paired signed-rank test.ResultsAs projection numbers were reduced, CNR values descended and the magnitude of registration errors increased. The mean CNR values of full-dose and half-dose CBCT were >3.0. For full-dose and low-dose CBCT (i.e. 1/2, 1/4 and 1/8 full-dose), the mean registration errors were< ± 0.4 mm in translational directions (LAT, LNG, VRT) and ±0.2 degree in rotational directions (Pitch, Roll, Yaw); the mean magnitude of registration errors were< 1 mm in translation and< 0.5 degree in rotation. The couch shift differences between full-dose and low-dose CBCT were not statistically significant (p>0.05) in all the directions.ConclusionThe results indicate that while the impact of dose-reduction on CBCT couch shifts is not significant, the impact on CNR values is significant. Further validation on optimizing CBCT imaging dose is required.

PURPOSE:To propose a straightforward and time-efficient quality assurance (QA) approach of beam time delay for respiratory-gated radiotherapy and validate the proposed method on typical respiratory gating systems, Catalyst™ and AlignRT™. METHODS:The QA apparatus was composed of a motion platform and a Winston-Lutz cube phantom (WL3) embedded with metal balls. The apparatus was first scanned in CT-Sim and two types of QA plans specific for beam on and beam off time delay, respectively, were designed. Static reference images and motion testing images of the WL3 cube were acquired with EPID. By comparing the position differences of the embedded metal balls in the motion and reference images, beam time delays were determined. The proposed approach was validated on three linacs with either Catalyst™ or AlignRT™ respiratory gating systems. To investigate the impact of energy and dose rate on beam time delay, a range of QA plans with Eclipse (V15.7) were devised with varying energy and dose rates. RESULTS:For all energies, the beam on time delays in AlignRT™ V6.3.226, AlignRT™ V7.1.1, and Catalyst™ were 92.13 ± $ \pm $ 5.79 ms, 123.11 ± $ \pm $ 6.44 ms, and 303.44 ± $ \pm $ 4.28 ms, respectively. The beam off time delays in AlignRT™ V6.3.226, AlignRT™ V7.1.1, and Catalyst™ were 121.87 ± $ \pm $ 1.34 ms, 119.33 ± $ \pm $ 0.75 ms, and 97.69 ± $ \pm $ 2.02 ms, respectively. Furthermore, the beam on delays decreased slightly as dose rates increased for all gating systems, whereas the beam off delays remained unaffected. CONCLUSIONS:The validation results demonstrate the proposed QA approach of beam time delay for respiratory-gated radiotherapy was both reproducible and time-efficient to practice for institutions to customize accordingly.

Background:Lung cancer is the leading prevalent form of human cancer and has the highest mortality rate among all cancer types. The role and potential mechanism of the lung microbiome in lung cancer is still unknown. This study aims to investigate the microbiomes of lung cancer patients possessing different levels of infiltrated CD8+ T cells and programmed cell death-1 (PD-1) receptors, and further assess the correlation between specific microbes and the immune environment of lung tumor. Methods:We analyzed the microbiomes of lung cancer tissues from patients with different levels of infiltrated CD8+ T cells and PD-1 expression using 16S rRNA gene sequencing. The relative abundance of dominant phyla and genera was compared, and the correlation between microbial composition and immune markers was explored. Results:Our results showed that lung cancer tissues displayed similar microbiome profiles, including Proteobacteria, Bacteroidetes, and Actinobacteria as the dominant phyla; and Chryseobacterium, Triticum aestivum (bread wheat), and Acinetobacter as the dominant genera. We found that the relative abundance of Chryseobacterium was positively correlated with CD8+ T cell infiltration and the level of PD-1 expression, while the relative abundance of Acinetobacter was negatively associated with the PD-1 level. In addition, higher beta diversity was identified in samples with low CD8+ T cell infiltration, but no significant correlation between beta diversity and PD-1 expression was observed. Furthermore, the relative abundance of Cyanobacteria was significantly higher in both the CD8 high and PD-1 high groups. Conclusions:Our study indicated that the lung microbiota played an indispensable role in the CD8+ T cell-mediated tumor immune response. These findings shed light on valuable insights into the intricate interplay between the lung microbiome and the immune system in the progression of lung cancer, offing potential therapeutic strategies targeting the lung microbiome.