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Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related deaths. With the development of screening, patient selection and treatment strategies, patients' survival outcomes and living quality significantly improved. However, some patients still have local recurrence or residual tumors after receiving definitive therapies. Salvage surgery has been regarded as an effective option for recurrent or residual NSCLC, but its effectiveness remains undetermined. Furthermore, conversion surgery is a special type of salvage surgery for tumors converted from "initially unresectable" to "potentially resectable" status due to a favorable response to systemic treatments. Although conversion surgery is a promising curative procedure for advanced NSCLC, its concept and clinical value remain unfamiliar to clinicians. In this narrative review, we provided an overview of the safety and efficacy of salvage surgery, especially salvage surgery after sublobar resection in early-stage NSCLC. More importantly, we highlighted the concept and value of conversion surgery after systemic treatment in advanced NSCLC to gain some insights into its role in the treatment of lung cancer.
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Background: Lung adenocarcinoma (LUAD) is the primary form of lung cancer, yet the reliable biomarkers for early diagnosis remain insufficient. Thioredoxin reductase (TrxR) is strongly linked to the occurrence, development, and drug resistance of lung cancer, making it a potential biomarker. However, further research is required to assess its diagnostic value in LUAD. Methods: A retrospective analysis was performed on patients who underwent pulmonary nodule resection at our center from 2018 to 2022. Clinical data, including preoperative TrxR levels, imaging, and laboratory characteristics, were identified as study variables. Two prediction models were constructed using multiple logistic regression, and their prediction performance was evaluated comprehensively. Besides, bioinformatics analyses of TrxR coding genes including differential expression, functional enrichment, immune infiltration, drug sensitivity, and single-cell landscape were performed based on TCGA database, which were subsequently validated by Human Protein Atlas. Results: A total of 506 eligible patients (72 benign lesions, 77 AISs, 185 MIAs and 172 IACs) were identified in the clinical cohort. Two TrxR-based models were developed, which were able to distinguish between benign and malignant pulmonary nodules, as well as pathological subtypes of LUAD, respectively. The models exhibited good predictive ability with all AUC values ranging from 0.7 to 0.9. Based on calibration curves and clinical decision analysis, the nomogram models showed high reliability. Functional analysis indicated that TXNRD1 primarily participated in cell cycle and lipid metabolism. Immune infiltration analysis showed that TXNRD1 has a strong association with immune cells and could impact immunotherapy. Then, we identified small molecular compounds that inhibit TXNRD1 and confirmed TXNRD1 expression by single-cell landscape and immunohistochemistry. Conclusion: This study validated the diagnostic value of TrxR and TXNRD1 in clinical cohorts and transcriptional data, respectively. TrxR and TXNRD1 could be used in the risk diagnosis of early LUAD and facilitate personalized treatment strategies.
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Microbial-based therapies have the potential to combat immunotherapy resistance, extending the boundaries of oncological therapeutics. In a recent issue of Cell, Jia et al. demonstrates an example of microbial collaboration to produce a postbiotic that promotes the stemness program of CD8+ T cells to augment immunotherapy at the pan-cancer level.
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Linfócitos T CD8-Positivos , Imunoterapia , Neoplasias , Humanos , Imunoterapia/métodos , Neoplasias/terapia , Neoplasias/imunologia , Neoplasias/tratamento farmacológico , Linfócitos T CD8-Positivos/imunologia , Indóis , Animais , CamundongosRESUMO
Purpose: To develop an anthropomorphic diagnosis system of pulmonary nodules (PN) based on Deep learning (DL) that is trained by weak annotation data and has comparable performance to full-annotation based diagnosis systems. Methods: The proposed system uses deep learning (DL) models to classify PNs (benign vs. malignant) with weak annotations, which eliminates the need for time-consuming and labor-intensive manual annotations of PNs. Moreover, the PN classification networks, augmented with handcrafted shape features acquired through the ball-scale transform technique, demonstrate capability to differentiate PNs with diverse labels, including pure ground-glass opacities, part-solid nodules, and solid nodules. Results: The experiments were conducted on two lung CT datasets: (1) public LIDC-IDRI dataset with 1,018 subjects, (2) In-house dataset with 2740 subjects. Through 5-fold cross-validation on two datasets, the system achieved the following results: (1) an Area Under Curve (AUC) of 0.938 for PN localization and an AUC of 0.912 for PN differential diagnosis on the LIDC-IDRI dataset of 814 testing cases, (2) an AUC of 0.943 for PN localization and an AUC of 0.815 for PN differential diagnosis on the in-house dataset of 822 testing cases. These results demonstrate comparable performance to full annotation-based diagnosis systems. Conclusions: Our system can efficiently localize and differentially diagnose PNs even in resource-limited environments with good robustness across different grade and morphology sub-groups in the presence of deviations due to the size, shape, and texture of the nodule, indicating its potential for future clinical translation. Summary: An anthropomorphic diagnosis system of pulmonary nodules (PN) based on deep learning and weak annotation was found to achieve comparable performance to full-annotation dataset-based diagnosis systems, significantly reducing the time and the cost associated with the annotation. Key Points: A fully automatic system for the diagnosis of PN in CT scans using a suitable deep learning model and weak annotations was developed to achieve comparable performance (AUC = 0.938 for PN localization, AUC = 0.912 for PN differential diagnosis) with the full-annotation based deep learning models, reducing around 30%â¼80% of annotation time for the experts.The integration of the hand-crafted feature acquired from human experts (natural intelligence) into the deep learning networks and the fusion of the classification results of multi-scale networks can efficiently improve the PN classification performance across different diameters and sub-groups of the nodule.
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Accumulated evidence highlights that exercise can modulate multiple cytokines, influencing transcriptional pathways, and reprogramming certain metabolic processes, ultimately promoting antitumor immunity and enhancing the efficacy of immune checkpoint inhibitors in cancer patients. Exploring the mechanisms behind this will, for one thing, help us uncover key factors and pathways in exercise-assisted cancer immunotherapy, offering more possibilities for future treatment methods. For another, it will support the development of more personalized and effective exercise prescriptions, thereby improving the prognosis of cancer patients.
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BACKGROUND: Trauma is statistically a significant cause of mortality among patients across countries. Nevertheless, the precise correlation between genetic diagnostic markers and the intricate mechanism of trauma remains indistinct. METHODS: Our study exclusively centered on trauma patients and selected three trauma-related datasets from the Gene Expression Omnibus (GEO) database, all of which had blood samples collected within post-traumatic 12 h. Differential gene screening, the WGCNA and Cytoscape software were employed to analyze the two datasets, with a particular emphasis on the top 100 genes selected based on MCC algorithm scores. A logistic diagnostic model was constructed by analyzing the intersection genes in the third dataset, leading to the identification of diagnostic biomarkers with high efficiency. The global immune landscape of these patients was extensively investigated using a multidimensional approach. Meanwhile, the underlying pathological and physiological mechanisms associated with early trauma status are summarized by integrating existing literature. RESULTS: Out of these two GEO datasets, 21 overlapping genes were identified and incorporated into in the logistic diagnostic model constructed in the GSE36809 dataset. A panel of 9 genes was uncovered as a diagnostic biomarker, and their expression and correlation were subsequently verified. Additionally, by virtue of various algorithms, the findings revealed an upregulation of neutrophil expression and a downregulation of CD8+ T cell expression, indicating characteristic early trauma-induced inflammation activation and immune suppression. The correlation observed between the feature genes and immune cells serves to validate the exceptional diagnostic capability of these 9 genes in identifying trauma status and their promising potential for patients who could benefit from targeted immune interventions. Drawing from these findings, the discussion section offers insights into the underlying pathological and physiological mechanisms at play. CONCLUSION: Our research has discovered a novel diagnostic biomarker and unveiled its association with post-traumatic immune alterations. This breakthrough enables accurate and timely diagnosis of early trauma, facilitating the implementation of appropriate healthcare interventions.
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Biomarcadores , Inflamação , Ferimentos e Lesões , Humanos , Ferimentos e Lesões/imunologia , Ferimentos e Lesões/diagnóstico , Inflamação/imunologia , Perfilação da Expressão Gênica , Biologia Computacional/métodos , Transcriptoma , Bases de Dados Genéticas , Evasão da Resposta Imune , Redes Reguladoras de GenesRESUMO
BACKGROUND: The potential pathogenic mechanism of idiopathic pulmonary fibrosis is widely recognized to involve immune dysregulation. However, the current pool of studies has yet to establish a unanimous agreement regarding the correlation between various types of immune cells and IPF. METHODS: By conducting a two-sample Mendelian randomization analysis using publicly available genetic data, the study examined the causal relationship between IPF and 731 immune cells. To ensure the reliability of the results, combined sensitivity analyses and inverse Mendelian analyses were conducted. Moreover, within subgroups, multivariate Mendelian randomization analyses were utilized to investigate the autonomous causal connection between immune cell characteristics and IPF. RESULTS: After adjusting for false discovery rate, it was discovered that 20 immunophenotypes exhibited a significant association with IPF. After subgrouping for multivariate Mendelian randomization analysis, there were six immunophenotypes that remained significantly associated with IPF. These included CD33 + HLA DR + CD14dim (OR = 0.96, 95% CI 0.93-0.99, P = 0.033), HLA DR + NK (OR = 0.92, 95% CI 0.85-0.98, P = 0.017), CD39 + CD8 + T cell %T cell (OR = 0.93, 95% CI 0.88-0.99, P = 0.024), CD3 on activated & secreting Treg (OR = 0.91, 95% CI 0.84-0.98, P = 0.026), PDL-1 on CD14- CD16 + monocyte (OR = 0.89, 95% CI 0.84-0.95, P = 8 × 10-4), and CD45 on CD33 + HLA DR + CD14- (OR = 1.08, 95% CI 1.01-1.15, P = 0.011). CONCLUSION: Our study reveals a noteworthy association between IPF and various immune cells, providing valuable insights for clinical research and aiding the advancement of immunologically-based therapeutic strategies.
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Fibrose Pulmonar Idiopática , Análise da Randomização Mendeliana , Humanos , Reprodutibilidade dos Testes , Fibrose Pulmonar Idiopática/genética , Linfócitos T CD8-Positivos , Antígenos HLA-DR , Estudo de Associação Genômica AmplaRESUMO
Microbiomes in the lung, gut, and oral cavity are correlated with lung cancer initiation and progression. While correlations have been preliminarily established in earlier studies, delving into microbe-mediated carcinogenic mechanisms will extend our understanding from correlation to causation. Building upon the causative relationships between microbiome and lung cancer, a novel concept of microbial biomarkers has emerged, mainly encompassing cancer-specific bacteria and circulating microbiome DNA. They might function as noninvasive liquid biopsy techniques for lung cancer early detection. Furthermore, potential microbial therapies have displayed initial efficacy in lung cancer treatment, providing multiple avenues for therapeutic intervention. Herein, we will discuss the molecular mechanisms and signaling pathways through which microbes influence lung cancer initiation and development. Additionally, we will summarize recent findings on microbial biomarkers as a member of tumor liquid biopsy techniques and provide an overview of the latest advances in various microbe-assisted/mediated therapeutic approaches for lung cancer.
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Microbioma Gastrointestinal , Neoplasias Pulmonares , Microbiota , Humanos , Neoplasias Pulmonares/diagnóstico , Neoplasias Pulmonares/etiologia , Neoplasias Pulmonares/terapia , Biomarcadores , Bactérias/genéticaRESUMO
The cancer mycobiome has recently become a research hotspot. While the intratumor mycobiota is implicated in cancer initiation and progression, the gut mycobiota functions as biomarkers for cancer diagnosis and treatment. In this forum article we highlight the involvement of the mycobiome in correlation-, causation-, and prediction-oriented cancer research and discuss the potential of this burgeoning field.
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Microbioma Gastrointestinal , Micobioma , Neoplasias , Humanos , Neoplasias/microbiologia , Biomarcadores Tumorais/metabolismo , Pesquisa BiomédicaAssuntos
Adenocarcinoma , Neoplasias Pulmonares , Nódulos Pulmonares Múltiplos , Humanos , Diagnóstico Diferencial , Adenocarcinoma/diagnóstico , Adenocarcinoma/genética , Adenocarcinoma/patologia , Nódulos Pulmonares Múltiplos/diagnóstico , Neoplasias Pulmonares/diagnóstico , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/patologiaRESUMO
Triple-negative breast cancer (TNBC) is generally regarded as the most aggressive subtype among breast cancers, but exhibits higher chemotherapeutic and immunotherapeutic responses due to its unique immunogenicity. Thus, appropriate discrimination of subtypes is critical for guiding therapeutic options in clinical practice. In this research, using multiple in-house and public cohorts, we investigated the expression features and immuno-correlations of B7-H3 in breast cancer and checked the anti-tumor effect of the B7-H3 monoclonal antibody in a mouse model. We also developed a novel classifier combining B7-H3 and PD-L1 expression in TNBC. B7-H3 was revealed to be related to immuno-cold features and accumulated collagen in TNBC. In addition, targeting B7-H3 using the monoclonal antibody significantly suppressed mouse TNBC growth, reversed the armored-cold phenotype, and also boosted anti-PD-1 immunotherapy. In addition, patients with B7-H3 high and PD-L1 low expression showed the lowest anti-tumor immune infiltration, the highest collagen level, and the lowest therapeutic responses to multiple therapies, which mostly belong to armored-cold tumors. Overall, this research provides a novel subtyping strategy based on the combination of B7-H3/PD-L1 expression, which leads to a novel approach for the management of TNBC.
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Accumulated research corroborates the feasibility of microbial antitumor therapies, enriching the diversity of cancer therapeutics. Recently, Ferrari et al. revealed the role of postbiotics in sensitizing cancer cells to immune checkpoint inhibitors (ICIs) and synergizing immunotherapy. Particularly, phytosphingosine induces upstream MYD88/NF-κB and downstream NLRC5 activation, enhances T cell effector responses, and inhibits tumor growth.