Small extracellular vesicles in cancer

Extracellular vesicles (EV) are lipid-bilayer enclosed vesicles in submicron size that are released from cells. A variety of molecules, including proteins, DNA fragments, RNAs, lipids, and metabolites can be selectively encapsulated into EVs and delivered to nearby and distant recipient cells. In tumors, through such intercellular communication, EVs can regulate initiation, growth, metastasis and invasion of tumors. Recent studies have found that EVs exhibit specific expression patterns which mimic the parental cell, providing a fingerprint for early cancer diagnosis and prognosis as well as monitoring responses to treatment. Accordingly, various EV isolation and detection technologies have been developed for research and diagnostic purposes. Moreover, natural and engineered EVs have also been used as drug delivery nanocarriers, cancer vaccines, cell surface modulators, therapeutic agents and therapeutic targets. Overall, EVs are under intense investigation as they hold promise for pathophysiological and translational discoveries. This comprehensive review examines the latest EV research trends over the last five years, encompassing their roles in cancer pathophysiology, diagnostics and therapeutics. This review aims to examine the full spectrum of tumor-EV studies and provide a comprehensive foundation to enhance the field. The topics which are discussed and scrutinized in this review encompass isolation techniques and how these issues need to be overcome for EV-based diagnostics, EVs and their roles in cancer biology, biomarkers for diagnosis and monitoring, EVs as vaccines, therapeutic targets, and EVs as drug delivery systems. We will also examine the challenges involved in EV research and promote a framework for catalyzing scientific discovery and innovation for tumor-EV-focused research.

The role of lipid metabolic reprogramming in tumor microenvironment

Metabolic reprogramming is one of the most important hallmarks of malignant tumors. Specifically, lipid metabolic reprogramming has marked impacts on cancer progression and therapeutic response by remodeling the tumor microenvironment (TME). In the past few decades, immunotherapy has revolutionized the treatment landscape for advanced cancers. Lipid metabolic reprogramming plays pivotal role in regulating the immune microenvironment and response to cancer immunotherapy. Here, we systematically reviewed the characteristics, mechanism, and role of lipid metabolic reprogramming in tumor and immune cells in the TME, appraised the effects of various cell death modes (specifically ferroptosis) on lipid metabolism, and summarized the antitumor therapies targeting lipid metabolism. Overall, lipid metabolic reprogramming has profound effects on cancer immunotherapy by regulating the immune microenvironment; therefore, targeting lipid metabolic reprogramming may lead to the development of innovative clinical applications including sensitizing immunotherapy.

Cell-derived nanovesicles prepared by membrane extrusion are good substitutes for natural extracellular vesicles

Extracellular vesicles (EV) as drug delivery nanocarriers are under intense investigation. Although clinical-grade EVs have been produced on a large-scale, low yield and high production costs of natural EVs (nEV) limit the relevant industrial translation. Recent studies show that mechanical extrusion of cells can generate nEV-like cell-derived nanovesicles (CNV) which can also be used as drug nanocarriers. Moreover, in comparison with nEVs, CNVs have similar physicochemical properties. Nevertheless, a comprehensive comparison of cargo between nEVs and CNVs has not been investigated yet. Therefore, the aim of this study is to profile and compare CNVs to nEVs. Our results show that no significant difference was found in size, morphology, and classical markers between nEVs and CNVs derived from MDA-MB-231 cells. Protein sequencing data reveals the similarity of membrane proteins between the two groups was ~71%, while it was ~21% when pertaining to total protein cargo. Notably, a high similarity of membrane proteins was also found between nEVs and CNVs derived from eight additional cancer cell lines. Moreover, analysis of the top 1000 small RNAs with RNA sequencing showed a ~65% similarity between the two groups. Altogether, we infer from the high similarity of membrane proteins and small RNA cargo that CNVs can be a good substitute for nEVs. In brief, our findings support previous studies with a notion that CNVs yield comparable performance with nEVs and could pave the way for clinical implementation of CNV-based therapeutics in the future.

Engineered extracellular vesicles for concurrent Anti-PDL1 immunotherapy and chemotherapy

Immune checkpoint inhibitors (ICI) targeting PD-1/PD-L1 have been approved for the treatment of a variety of cancers. However, the efficacy of antibody-based ICIs could be further improved by mitigating anti-drug antibodies, proteolytic cleavage, and on-target off-tumor toxicity. One strategy for accomplishing this is through the use of extracellular vesicles (EVs), cell derived submicron vesicles with many unique properties. We constructed an engineered MDA-MB-231 cell line for harvesting EVs. This was accomplished by overexpressing a high-affinity variant human PD-1 protein (havPD-1), while simultaneously knocking out intrinsic PD-L1 and beta-2 microglobulin. The engineered havPD-1 EVs reduced PD-L1 overexpressing cancer cell proliferation and induced cellular apoptosis. Moreover, the EVs were shown to efficiently block PD-L1 mediated T cell suppression. Meanwhile antibody-dependent cellular cytotoxicity and complement-dependent cytotoxicity were not observed. The havPD-1 EVs treatment resulted in robust anti-tumor activity in both preventative co-implantation and therapeutic xenograft tumor models reconstituted with human T cells. The efficacy of the havPD-1 EVs was shown to be comparable to clinical anti-PD1 monoclonal antibodies. Additionally, loading the havPD-1 EVs with a potent PARP inhibitor was shown to further augment treatment efficacy. In brief, the engineered universal EVs harboring havPD-1 proteins can be used for cancer concurrent immunotherapy and chemotherapy.

Conferring receptors on recipient cells with extracellular vesicles for targeted drug delivery

Triple negative breast cancer (TNBC) is a heterogeneous subset of breast cancer characterized by its lack of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (HER2), which altogether prevents TNBC from being treated effectively. For many years, the treatment paradigms and overall survival of patients with TNBC have remained largely stagnant. Recent attempts to convert cold tumors to hot tumors by promoting antigen presentation have shown increased T cell infiltration and significantly induced immune responses for tumor killing. Inspired by this concept, the expression of specific targetable antigens on TNBC cells may further benefit relevant targeted drug delivery. In this study, we successfully conferred sufficient HER2 on the surface of TNBC MDA-MB-231 cells via simple EV-plasma membrane fusion with HER2+ extracellular vesicles (EV) derived from HER2 overexpressing BT-474 cells. Subsequently, anti-HER2 antibody conjugated paclitaxel-loaded liposomes were used for HER2-targeted drug delivery. Our findings demonstrated this HER2 grafting, in conjunction with targeted drug delivery, can improve the treatment efficacy in vitro and in vivo. This novel approach represents a facile method of altering cell membrane antigen presentation via convenient EVs uptake and may pave the way for the burgeoning wave of targeted therapy and/or immunotherapy.

HLA class II molecule HLA-DRA identifies immuno-hot tumors and predicts the therapeutic response to anti-PD-1 immunotherapy in NSCLC

BACKGROUND

Immune checkpoint blockade (ICB) only works well for a certain subset of patients with non-small cell lung cancer (NSCLC). Therefore, biomarkers for patient stratification are desired, which can suggest the most beneficial treatment.

METHODS

In this study, three datasets (GSE126044, GSE135222, and GSE136961) of immunotherapy from the Gene Expression Omnibus (GEO) database were analyzed, and seven intersected candidates were extracted as potential biomarkers for ICB followed by validation with The Cancer Genome Atlas (TCGA) dataset and the in-house cohort data.

RESULTS

Among these candidates, we found that human leukocyte antigen-DR alpha (HLA-DRA) was downregulated in NSCLC tissues and both tumor and immune cells expressed HLA-DRA. In addition, HLA-DRA was associated with an inflamed tumor microenvironment (TME) and could predict the response to ICB in NSCLC. Moreover, we validated the predictive value of HLA-DRA in immunotherapy using an in-house cohort. Furthermore, HLA-DRA was related to the features of inflamed TME in not only NSCLC but also in most cancer types.

CONCLUSION

Overall, HLA-DRA could be a promising biomarker for guiding ICB in NSCLC.

High B7-H3 expression with low PD-L1 expression identifies armored-cold tumors in triple-negative breast cancer

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.

Protocol to identify novel immunotherapy biomarkers based on transcriptomic data in human cancers

Immune checkpoint inhibitors have transformed the management of advanced cancers, but biomarkers for the prediction of therapeutic responses have not been fully uncovered. Here, we provide a step-by-step approach for the identification of novel biomarkers from public transcriptomic datasets. We comprehensively summarize the available transcriptomic datasets containing immunotherapy information and describe the necessary procedures to evaluate the effectiveness of a novel immunotherapy biomarker, which may accelerate the identification of novel immunotherapy biomarkers. For complete details on the use and execution of this protocol, please refer to Mei et al.1.

SECTM1 is upregulated in immuno-hot tumors and predicts immunotherapeutic efficacy in multiple cancers

Immune checkpoint inhibitors (ICIs) have transformed the management of advanced cancers. However, many patients could not benefit from ICIs therapy, and thus several biomarkers for therapeutic prediction have been uncovered. In this research, more than ten public and in-house cohorts were used to explore the predictive value and immunological correlations of secreted and transmembrane 1 (SECTM1) in cancers. SECTM1 expression was enhanced in tumors from patients with well immunotherapeutic responses in multiple cancers. In addition, SECTM1 was immuno-correlated in pan-cancer and enhanced in immuno-hot tumors. In vitro assays revealed that SECTM1 was upregulated by the IFN-γ/STAT1 signaling. Moreover, analysis of in-house immunotherapy cohorts suggested both tumor-expressed and circulating SECTM1 are promising biomarkers to predict therapeutic responses. Overall, this study reveals that SECTM1 is a biomarker of benefit to ICIs in cancer patients. Further studies including large-scale patients are needed to establish its utilization as a biomarker of benefit to ICIs.

Epidermal Growth Factor Receptor Mutations in Resectable Non-Small Cell Lung Cancer Patients and their Potential Role in the Immune Landscape

BACKGROUND The epidermal growth factor receptor (EGFR) is a therapeutic target for non-small cell lung cancer (NSCLC), but knowledge on gene mutations that contribute to NSCLC development and persistence is lacking. In this study, we investigated genetic variations in EGFR and their association with the clinical and pathological factors of NSCLC. MATERIAL AND METHODS Clinical cases (331 patients) and The Cancer Genome Atlas (TCGA) cases (1040 patients) were selected and analyzed using the refractory mutation systems cBioPortal and the Tumor Immune Estimation Resource (TIMER). RESULTS EGFR mutation frequencies were 54.4% (180 of 331 patients) and 8.0% (83 of 1040 patients) in the clinical and TCGA cohorts, respectively. EGFR mutations were strongly associated with smoking and pathology (P≤0.05) in the clinical cohort, and with gender, smoking, and pathology (P=0.001, P<0.001, and P<0.001, respectively) in TCGA cohort. In cases of lung squamous carcinoma (LUSC), EGFR was overexpressed as a result of DNA amplification, but this amplified expression showed no association with the overall survival (OS) or progression-free survival of LUSC patients. EGFR gene alterations were, however, associated with worse OS in lung adenocarcinoma (LUAD) patients. Immune cell infiltrates from LUAD and LUSC tumors differed according to EGFR expression. EGFR mutations resulted in a decline of immune infiltration or a lack of infiltrating immune cells in the NSCLC microenvironment. CONCLUSIONS Mutational profiles of the EGFR in NSCLC patients provide useful information for the use of tyrosine kinase inhibitors for adjuvant or neoadjuvant therapy and immunotherapy.

Unveiling the new chapter in nanobody engineering: advances in traditional construction and AI-driven optimization

Nanobodies (Nbs), miniature antibodies consisting solely of the variable region of heavy chains, exhibit unique properties such as small size, high stability, and strong specificity, making them highly promising for disease diagnosis and treatment. The engineering production of Nbs has evolved into a mature process, involving library construction, screening, and expression purification. Different library types, including immune, naïve, and synthetic/semi-synthetic libraries, offer diverse options for various applications, while display platforms like phage display, cell surface display, and non-surface display provide efficient screening of target Nbs. Recent advancements in artificial intelligence (AI) have opened new avenues in Nb engineering. AI's exceptional performance in protein structure prediction and molecular interaction simulation has introduced novel perspectives and tools for Nb design and optimization. Integrating AI with traditional experimental methods is anticipated to enhance the efficiency and precision of Nb development, expediting the transition from basic research to clinical applications. This review comprehensively examines the latest progress in Nb engineering, emphasizing library construction strategies, display platform technologies, and AI applications. It evaluates the strengths and weaknesses of various libraries and display platforms and explores the potential and challenges of AI in predicting Nb structure, antigen-antibody interactions, and optimizing physicochemical properties.

The multi-omics analysis identifies a novel cuproptosis-anoikis-related gene signature in prognosis and immune infiltration characterization of lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) has emerged as one of the most aggressive lethal cancers. Anoikis serves as programmed apoptosis initiated by the detachment of cells from the extracel-lular matrix. Cuproptosis is distinct from traditional cell death modalities. The above two modes are both closely related to tumor progression, prognosis, and treatment. However, whether they have synergistic effects in LUAD deserves further investigation.

METHODS

The anoikis-related prognostic genes (ANRGs) co-expressed with cuproptosis-associated genes (CAGs) were screened using correlation analysis, analysis of variance, least absolute shrinkage, and selection operator (LASSO), and COX regression followed by functional analysis, and then LUAD risk score model was constructed. Using consensus clustering, the relationship between different subtypes and clinicopathological features, immune infiltration characteristics, and somatic mutations was analyzed. A nomogram was developed by incorporating clinical information, which provided a prediction of the survival of patients. Finally, a comprehensive analysis of ANRGs was performed and verified by the HPA database.

RESULTS

A total of 27 ANRGs associated with cuproptosis were obtained. On this basis, three distinct ANRGs subtypes were identified, and the differences between clinical prognosis and immune infiltration were observed. A risk score model has been constructed by incorporating seven ANRGs signatures (EIF2AK3, IKZF3, ITGAV, OGT, PLK1, TRAF2, XRCC5). A highly reliable nomogram was developed to help formulate treatment strategies based on risk score and the clinicopathological features of LUAD. The seven-gene signature was turned out to be strongly linked to immune cells and validated in single-cell data. Immunohistochemistry proved that all of them are highly expressed in LUAD tissues.

CONCLUSION

This study reveals the potential relationship between cuproptosis-related ANRGs and clinicopathological features, tumor microenvironment (TME), and mutation characteristics, which can be applied for predicting the prognosis of LUAD and help develop individualized treatment strategies.

Prognostic Value of GIMAP4 and Its Role in Promoting Immune Cell Infiltration into Tumor Microenvironment of Lung Adenocarcinoma

GIMAPs are recognized as an important regulator in the carcinogenesis and development of lung cancer, but the function of GIMAP4 in the tumor microenvironment (TME) of lung cancers is unclear. In this study, we investigated the expression and variation of GIMAP4 in lung adenocarcinoma (LUAD), to explore its association with infiltration of immune cells. The Cancer Genome Atlas (TCGA) data and Gene Expression Omnibus (GEO) data were analyzed. Infiltration of immune cells was identified with TIMER (Tumor Immune Estimation Resource) and TISIDB (an integrated repository portal for tumor-immune system interactions). GIMAP4 expression declined in non-small-cell lung cancer (NSCLC), correlated with a poor overall survival (OS) in LUAD, indicating that GIMAP4 was a promising prognostic biomarker in LUAD. GIMAP4 mutation frequency was 1.76% in TCGA cohort and was relevant to the expression of immune components. TIMER and CIBERSORT analysis further confirmed that high GIMAP4 expression possibly promoted immune cell infiltration into the TME, with low GIMAP4 impairing the efficacy of immunotherapies targeting common immune check point inhibitors (ICI). GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses were performed to provide insights into biological processes involved in LUAD. GIMAP4 was expected to be a prognostic biomarker in LUAD and provides potential adjuvant or neoadjuvant therapeutic strategies for targeting ICIs.

Crosstalk between Exercise and Immunotherapy: Current Understanding and Future Directions

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.

Immune checkpoint inhibitors for PD-1/PD-L1 axis in combination with other immunotherapies and targeted therapies for non-small cell lung cancer

It has been widely acknowledged that the use of immune checkpoint inhibitors (ICI) is an effective therapeutic treatment in many late-stage cancers. However, not all patients could benefit from ICI therapy. Several biomarkers, such as high expression of PD-L1, high mutational burden, and higher number of tumor infiltration lymphocytes have shown to predict clinical benefit from immune checkpoint therapies. One approach using ICI in combination with other immunotherapies and targeted therapies is now being investigated to enhance the efficacy of ICI alone. In this review, we summarized the use of other promising immunotherapies and targeted therapies in combination with ICI in treatment of lung cancers. The results from multiple animals and clinical trials were reviewed. We also briefly discussed the possible outlooks for future treatment.

An anthropomorphic diagnosis system of pulmonary nodules using weak annotation-based deep learning

The accurate categorization of lung nodules in CT scans is an essential aspect in the prompt detection and diagnosis of lung cancer. The categorization of grade and texture for nodules is particularly significant since it can aid radiologists and clinicians to make better-informed decisions concerning the management of nodules. However, currently existing nodule classification techniques have a singular function of nodule classification and rely on an extensive amount of high-quality annotation data, which does not meet the requirements of clinical practice. To address this issue, we 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. The proposed system uses 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. 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. In summary, our system demonstrates efficient localization and differential diagnosis of PNs in a resource limited environment, and thus could be translated into clinical use in the future.

Recurrent lung adenocarcinoma benefits from microwave ablation following multidisciplinary treatments: A case with long-term survival

Lung cancer has become the leading cause of cancer death all over the world. Nowadays, there is a consensus that the treatment of non-small cell lung cancer (NSCLC) prefers a combination of multidisciplinary comprehensive treatment and individualized treatment, which can significantly improve the prognosis of patients. Here, we report a female patient with recurrence-prone NSCLC. She had a decade-long disease course, during which the lesion recurred twice and finally cured with Multi-Disciplinary Treatment (MDT). An elderly female patient was admitted to the hospital after diagnosis of lung cancer, and treated with surgery and postoperative adjuvant chemotherapy. Five years later, suspicious lesions were found by computed tomography (CT) reexamination, and then confirmed tumor recurrence by puncture biopsy. Based on the genetic test results, gefitinib was used for subsequent targeted therapy, and the lesion gradually shrunk to disappear. However, the lesion appeared again two years later, after consultation the microwave ablation was adopted and the curative effect was excellent. At last, regular reexamination showed no abnormality, the patient has survived so far. The case proves the great benefit of multidisciplinary comprehensive treatment, especially microwave ablation for patient with recurrence-prone NSCLC. And the effect of systemic anti-tumor immune response induced by microwave ablation on lung cancer also needs to be further explored.

Decoding the causal association between immune cells and three chronic respiratory diseases: Insights from a bi-directional Mendelian randomization study

BACKGROUND

Numerous studies have indicated the correlations of immune traits and chronic respiratory diseases (CRDs). Whereas, causality is still implicative. Hence, our study was designed to investigate the causal relations utilizing bidirectional Mendelian randomization (MR) and to identify the immune traits of potential significance.

METHODS

Using GWAS datasets, we performed Mendelian randomization (MR) analyses to examine 731 immune traits associated with three CRDs: asthma, bronchiectasis and chronic obstructive pulmonary disease (COPD). Six widely applied MR approaches, along with Bayesian weighted Mendelian randomization analysis, were utilized to assess causality. Through extensive sensitivity assessments, heterogeneity and pleiotropy have been examined. For integrity, leave-one-out analysis was implemented as the final step.

RESULTS

Our study reveals 13 immune traits that may have a genetic basis for predicting the occurrence of CRDs, which include two risk traits (CD62L- myeloid dendritic cell (DC) absolute count (AC), CD8 on CD28+ CD45RA- CD8+ T cell) and four protective traits (CD39+ CD8+ %T cell, CD4 on CD39+ activated CD4 regulatory T (Treg) cell, herpes virus entry mediator (HVEM) on Central Memory (CM) CD8+ T cell, CD16 on CD14+ CD16+ monocyte) in COPD, three protective traits (IgD- CD27- %B cell, CD3 on CM CD8+ T cell, CD16 on CD14+ CD16+ monocyte) and one risk trait (CD62L- %DC) in bronchiectasis. Additionally, two risk traits (CD14- CD16- AC monocyte, CD19 on IgD+ CD38+ B cell) and one protective trait (HVEM on CD45RA- CD4+ T cell) were identified in asthma. Sensitivity analyses showed no indications of pleiotropy or signs of heterogeneity. The inverse MR assessment results gave no evidence of reverse causations, ultimately validating the soundness of the findings.

CONCLUSIONS

Our investigation identifies latent correlations of immune traits and three major CRDs, offering novel perspectives on the preventive and therapeutical strategies for CRDs.

Establishment and validation of nomograms for predicting survival of lung invasive adenocarcinoma based on the level of pathological differentiation: a SEER cohort-based analysis

BACKGROUND

The pathological differentiation of invasive adenocarcinoma (IAC) has been linked closely with epidemiological characteristics and clinical prognosis. However, the current models cannot accurately predict IAC outcomes and the role of pathological differentiation is confused. This study aimed to establish differentiation-specific nomograms to explore the effect of IAC pathological differentiation on overall survival (OS) and cancer-specific survival (CSS).

METHODS

The data of eligible IAC patients between 1975 and 2019 were collected from the Surveillance, Epidemiology, and End Results (SEER) database, and randomly divided in a ratio of 7:3 into a training cohort and a validation cohort. The associations between pathological differentiation and other clinical characteristics were evaluated using chi-squared test. The OS and CSS analyses were performed using the Kaplan-Meier estimator, and the log-rank test was used for nonparametric group comparisons. Multivariate survival analysis was performed using a Cox proportional hazards regression model. The discrimination, calibration, and clinical performance of nomograms were assessed by area under receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA).

RESULTS

A total of 4,418 IAC patients (1,001 high-differentiation, 1,866 moderate-differentiation, and 1,551 low-differentiation) were identified. Seven risk factors [age, sex, race, tumor-node-metastasis (TNM) stage, tumor size, marital status, and surgery] were screened to construct differentiation-specific nomograms. Subgroup analyses showed that disparate pathological differentiation played distinct roles in prognosis, especially in patients with older age, white race, and higher TNM stage. The AUC of nomograms for OS and CSS in the training cohort were 0.817 and 0.835, while in the validation cohort were 0.784 and 0.813. The calibration curves showed good conformity between the prediction of the nomograms and the actual observations. DCA results indicated that these nomogram models could be used as a supplement to the prediction of the TNM stage.

CONCLUSIONS

Pathological differentiation should be considered as an independent risk factor for OS and CSS of IAC. Differentiation-specific nomogram models with good discrimination and calibration capacity were developed in the study to predict the OS and CSS in 1-, 3- and 5-year, which could be used predict prognosis and select appropriate treatment options.

Multi-level insights into the immuno-oncology-microbiome axis: From biotechnology to novel therapies

The multifaceted interactions among the immune system, cancer cells and microbial components have established a novel concept of the immuno-oncology-microbiome (IOM) axis. Microbiome sequencing technologies have played a pivotal role in not only analyzing how gut microbiota affect local and distant tumors, but also providing unprecedented insights into the intratumor host-microbe interactions. Herein, we discuss the emerging trends of transiting from bulk-level to single cell- and spatial-level analyses. Moving forward with advances in biotechnology, microbial therapies, including microbiota-based therapies and bioengineering-inspired microbes, will add diversity to the current oncotherapy paradigm.

Statins Lower Lipid Synthesis But Promote Secretion of Cholesterol-Enriched Extracellular Vesicles and Particles

Lipid droplets are lipid-rich cytosolic organelles that play roles in cell signaling, membrane trafficking, and many other cellular activities. Recent studies revealed that lipid droplets in cancer cells have various biological functions, such as energy production, membrane synthesis, and chemoresistance, thereby fostering cancer progression. Accordingly, the administration of antilipemic agents could improve anti-cancer treatment efficacy given hydrophobic chemotherapeutic drugs could be encapsulated into lipid droplets and then expelled to extracellular space. In this study, we investigated whether statins could promote treatment efficacy of lipid droplet-rich ovarian SKOV-3 cells and the potential influences on generation and composition of cell-derived extracellular vesicles and particles (EVP). Our studies indicate that statins can significantly lower lipid biosynthesis. Moreover, statins can inhibit proliferation, migration, and invasion of SKOV-3 cells and enhance chemosensitivity in vitro and in vivo. Furthermore, statins can lower EVP secretion but enforce the release of cholesterol-enriched EVPs, which can further lower lipid contents in parental cells. It is the first time that the influence of statins on EVP generation and EVP-lipid composition is observed. Overall, we demonstrated that statins could inhibit lipid production, expel cholesterol to extracellular space via EVPs, and improve chemosensitivity.

The diagnostic potential role of thioredoxin reductase and TXNRD1 in early lung adenocarcinoma: A cohort study

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.