Yanlu Xiong

This study reports a Bi ³⁺ -doped Cs ₂ ZrCl ₆ NCs for luminescencebased thermometry. The doping improved uniformity, photoluminescence and blue-shifted emission. The NCs showed strong temperature-dependent luminescence (293K-433K) and good cycling stability, enabling...

Radiomics holds great potential for the noninvasive evaluation of lung cancer mutations; however, challenges related to data imbalance and model robustness limit its current efficacy and safety. This multicenter study aimed to develop and validate a model utilizing the Synthetic Minority Over-sampling Technique (SMOTE) and clustering methods to address the challenges associated with small sample sizes and imbalanced radiomics features between negative and positive samples. A total of 230 patients with lung adenocarcinoma who underwent positron emission tomography/computed tomography and genetic testing for anaplastic lymphoma kinase (ALK), epidermal growth factor receptor (EGFR), or Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations were included. Multi-layer perceptron (MLP) models were developed for each genetic mutation group based on 2 different approaches for processing radiomics features. The MLP model incorporating SMOTE and clustering demonstrated potentially improved performance, with AUC values of 0.591 for ALK, 0.750 for EGFR, and 0.789 for KRAS in the external test set. These findings suggest that the MLP model combined with SMOTE and clustering has potential for predicting ALK, EGFR, and KRAS mutations in lung adenocarcinoma.

Targeted therapy and immunotherapy represent major innovations in the treatment of lung cancer. However, in patients with driver gene-positive tumors, the emergence of acquired resistance to targeted drugs is inevitable. As for immunotherapy, its efficacy in early-stage lung cancer patients remains uncertain due to strong immune heterogeneity. In advanced and locally advanced patients, high tumor mutational burden leads to significant genomic instability and tumor progression, and resistance still inevitably develops even with standard chemotherapy combined with immunotherapy. Cancer vaccines, as an approach that activates the antitumor immune cycle from its origin, offer advantages such as the ability to target multiple antigens, minimal off-target effects, a wide therapeutic window, and low toxicity. Furthermore, such vaccines can induce long-lasting immune memory and possess a certain capacity to remodel the tumor immune microenvironment, which helps prevent cancer recurrence, demonstrating broad prospects in lung cancer treatment. Currently, various types of tumor vaccines (including those based on microorganisms, peptides, proteins, exosomes, and DNA) have been developed, yet they often face limitations in safety, insufficient personalization, and immature production pipelines. In contrast, messenger RNA (mRNA)-based vaccines offer distinct advantages, including the efficient generation of protective immune responses, relatively low side effects, and lower acquisition costs, making them a forefront option for novel lung cancer therapies. This review summarizes the current research status of lung cancer vaccines, clarifies the unique therapeutic advantages of mRNA vaccines compared to traditional vaccine modalities, and highlights existing challenges associated with mRNA vaccines. It also provides an overview of current clinical trials of mRNA vaccines for lung cancer and proposes rational design and clinical application strategies for personalized mRNA vaccines within the framework of precision oncology, based on evidence-based medicine.

Backgroud: The benefit of adjuvant chemotherapy for patients with stage IB lung adenocarcinoma remains unclear. We compared adjuvant chemotherapy outcomes in Asian and non-Asian stage IB lung adenocarcinoma patients using multi-source clinical data. Methods: Patients with stage IB lung adenocarcinoma from the 2004–2015 Surveillance, Epidemiology, and End Results (SEER) database and the 2020–2021 thoracic surgery department of three Chinese medical centers were included. Patients were divided into adjuvant chemotherapy and observation groups. Propensity score matching (PSM) was performed to reduce confounding bias. Survival curves were plotted and compared using the Kaplan–Meier method and log-rank test, respectively. Results: In the SEER data, 3408 patients met the inclusion criteria, overall survival (OS) was significantly better in the adjuvant chemotherapy group than in the observation group. In the non-Asian population, OS was significantly better in the adjuvant chemotherapy group, both before and after PSM. In the Asian population, OS did not significantly differ between the two groups before or after PSM. Among 690 patients from the three centers, disease-free survival (DFS) and OS in the adjuvant chemotherapy group were not better than those in the observation group, before or after PSM and in patients with high pathological grade or visceral pleural infiltration (VPI). Conclusions: Adjuvant chemotherapy can significantly improve the prognosis of non-Asian patients with stage IB lung adenocarcinoma. In Asian patients, adjuvant chemotherapy does not improve prognosis, even in patients with high pathological grade or VPI.

Background: Centromere protein F (CENP‐F) is an important nuclear matrix protein that regulates mitosis and the cell cycle, and plays a crucial role in recruiting spindle checkpoint proteins to maintain the accuracy of chromosome segregation. Studies have shown that CENP‐F is closely involved in the pathogenesis of various diseases, particularly in the development and progression of malignant tumors, where it exhibits significant oncogenic activity.Objective: This review aims to systematically summarize the molecular structure, subcellular localization, expression regulation, intracellular transport mechanisms, biological functions, and carcinogenic mechanisms of CENP‐F, as well as explore its potential value in cancer diagnosis and therapy.Methods: A comprehensive review and analysis of domestic and international research literature related to CENP‐F were conducted, focusing on its role in tumorigenesis, development, and as a therapeutic target.Results: CENP‐F acts as an oncogene and can maintain or promote the malignant phenotype of tumor cells through multiple mechanisms, including regulating signaling pathways related to cell proliferation and apoptosis, promoting metabolic reprogramming, angiogenesis, and tumor cell invasion and metastasis. Additionally, it plays an important role in the immune microenvironment and drug resistance regulation.Conclusion: CENP‐F plays a key, multidimensional role in tumor biology and is a promising therapeutic target for cancer treatment. Further exploration of the core pathways through which CENP‐F regulates tumorigenesis and its potential for clinical translation is needed.

Lung adenocarcinoma is the most prevalent type of lung cancer, with invasive lung adenocarcinoma being the most common subtype. Screening and early treatment of high-risk individuals have improved survival; however, significant differences in prognosis still exist among patients at the same stage, especially in the early stages. Invasive lung adenocarcinoma has different histological morphologies and biological characteristics that can distinguish its prognosis. Notably, several studies have found that the pathological subtypes of invasive lung adenocarcinoma are closely associated with clinical treatment. This review summarised the distribution of various pathological subtypes of invasive lung adenocarcinoma in the population and their relationship with sex, smoking, imaging features, and other histological characteristics. We comprehensively analysed the genetic characteristics and biomarkers of the different pathological subtypes of invasive lung adenocarcinoma. Understanding the interaction between the pathological subtypes of invasive lung adenocarcinoma and the tumour microenvironment helps to reveal new therapeutic targets for lung adenocarcinoma. We also extensively reviewed the prognosis of various pathological subtypes and their effects on selecting surgical methods and adjuvant therapy and explored future treatment strategies.

Background and Objective: Pulmonary nodules (PNs) are small (≤3 cm) radiographic opacities within lung parenchyma. The use of low-dose computed tomography (LDCT) has led to a significant increase in the identification of solitary nodules. Malignant lung nodules comprise only 5% of all nodules, with management differing greatly from benign cases. Despite diagnostic advancements, there is heterogeneity in prognosis, which can result in undertreatment of high-risk patients and inappropriate treatment for low-risk patients. Therefore, accurately distinguishing benign from malignant nodules and effectively stratifying the risk of malignant nodules is a pressing clinical challenge requiring urgent resolution. The main objectives of this review were to explore the research progress in the clinical management of malignant PNs, including early detection, individualized treatment, and prognosis prediction, in order to shed light on precision medicine for patients with PNs. Methods: The review examined various approaches for the identification and prognosis prediction of early lung cancer characterized by lung nodules, including the use of classical clinicopathological features, liquid biopsy, and artificial intelligence. Key Content and Findings: The detection rate of early lung cancer characterized by lung nodules is increasing annually, and accurate identification and prognosis prediction are critical for appropriate therapeutic strategies and precise postoperative management. Classical clinicopathological features, such as demographic and radiological features, play an important role in the diagnosis and prognosis assessment of early lung cancer, but liquid biopsy and artificial intelligence are also promising due to their obvious convenience and accuracy. Conclusions: The review highlights the importance of precision medicine in the clinical management of malignant lung nodules. The use of classical clinicopathological features, liquid biopsy, and artificial intelligence can contribute to the early detection, individualized treatment, and accurate prognosis prediction for patients with lung nodules, ultimately improving their clinical outcomes.

The development and growth of tumors remains an important and ongoing threat to human life around the world. While advanced therapeutic strategies such as immune checkpoint therapy and CAR-T have achieved astonishing progress in the treatment of both solid and hematological malignancies, the malignant initiation and progression of cancer remains a controversial issue, and further research is urgently required. The experimental animal model not only has great advantages in simulating the occurrence, development, and malignant transformation mechanisms of tumors, but also can be used to evaluate the therapeutic effects of a diverse array of clinical interventions, gradually becoming an indispensable method for cancer research. In this paper, we have reviewed recent research progress in relation to mouse and rat models, focusing on spontaneous, induced, transgenic, and transplantable tumor models, to help guide the future study of malignant mechanisms and tumor prevention.

Background: Lung adenocarcinoma (LUAD) with pathological ipsilateral mediastinal lymph node (LN) involvement (pN2) exhibits strong biological and clinical heterogeneity. Thus, it is necessary to classify the biomolecular characteristics that lead to the prognostic heterogeneity of pN2-LUAD. Methods: The clinical characteristics and bulk RNA sequencing (RNA-seq) data of 75 patients with pN2-LUAD obtained from The Cancer Genome Atlas (TCGA) database were collected as the training set. The disease-free survival (DFS) and overall survival (OS) of patients with different molecular classifications were evaluated. Next, differentially expressed genes (DEGs), biology, and immune cell infiltration in the microenvironment were analysed. Finally, DEGs in the pN2-A and pN2-B groups were included using a least absolute shrinkage and selection operator (LASSO) model, and gene signatures were selected for pN2-A/B type classification. The RNA-seq and single-nucleus RNA sequencing (snRNA-seq) data from our center (n=58) and the GSE68465 dataset (n=53) were used as the validation data sets. Results: Patients with pN2 LUAD were classified into two distinct molecular categories (pN2-A and pN2-B) based on transcriptome information, pN2-A and pN2-B represent low-risk and high-risk patients, respectively. The survival analysis showed that pN2-A patients had significantly better DFS (P=0.0162) and OS (P=0.0105) compared to pN2-B patients. Multivariate analysis confirmed that molecular classification was an independent factor affecting the prognosis of pN2 LUAD (P=0.0038, and P=0.0024). Next, we found that compared with pN2-A stage patients, pN2-B stage patients had a higher frequency of canonical oncogenic pathway mutations and enrichments. At the single-cell level, we also found that the increase of endothelial cells and the decrease of cytotoxic T/natural killer (NK) cells led to a worse prognosis for pN2-B patients compared to pN2-A patients. Moreover, we established a reasonable gene prediction model of 18 differentially expressed genes (DEGs) to classify the pN2-A and pN2-B patients. Finally, the key above-mentioned results were confirmed using our data and the GES68645 dataset. Conclusions: The molecular classification of pN2 LUAD is expected to be a powerful supplement to pN2 substaging. Driver gene status and the immune microenvironment mediate different molecular types of LUAD and provide evidence for individualized treatment strategies.