The objective of this study was to explore the effect of (-)-guaiol on lung cancer using experimental validation, mRNA sequencing, and network pharmacology. Potential targets of (-)-guaiol and lung cancer were identified through SwissTargetPrediction, TCMSP, PharmMapper, OMIM, GeneCards, and DisGeNET databases. Common targets were analyzed using PPI network, topological screening, and functional enrichment using STRING, Cytoscape, and Metascape. Molecular docking with core targets was performed, along with molecular dynamics. In vitro assays (cell counting kit-8 assay, colony formation, wound healing, Transwell, western blot) and in vivo studies (subcutaneous xenograft modeling in nude mice, immunohistochemistry, mRNA sequencing) were conducted to validate the anti-tumor effects and mechanisms of (-)-guaiol compared with the control group. Through multi-database prediction, 153 (-)-guaiol targets and 91 common lung cancer targets were identified. Protein-protein interaction (PPI) network analysis screened 21 core targets (including ESR1, EGFR, etc.). GO and KEGG enrichment analyses revealed that these targets are involved in the regulation of pathways such as fatty acid metabolism. Molecular docking and molecular dynamics results demonstrated that (-)-guaiol possessed a favorable binding affinity toward the target proteins SRC, PTGS2, GSK3B, PPARG, ESR1, and HSP90AA1. mRNA sequencing indicated that the gene expression levels of both PPARG and CD36 were downregulated in lung cancer tissues of mice treated with (-)-guaiol compared with the control group. Combining the results of molecular docking, molecular dynamics, and mRNA sequencing, we selected the PPARG-related signaling pathway for subsequent experiments. Both in vivo and in vitro experiments validated that (-)-guaiol inhibits lung cancer cell proliferation, invasion, and xenograft tumor growth in mice by downregulating the PPARG pathway. To conclude, our results demonstrated that (-)-guaiol suppresses lung cancer progression through downregulation of the fatty acid oxidation-related pathway mediated by PPARG.

Lung cancer is the leading cause of cancer-related deaths globally. Prolonged targeted therapy use can lead to drug resistance and target mismatches, necessitating more effective and safer treatment strategies. Recent research has focused on the tumor microenvironment, which includes immune and stromal cells that play roles in tumor proliferation, metastasis, and neovascularization. Tumor-associated macrophages (TAMs) are key immune cells in the tumor microenvironment, promoting tumor invasion, metastasis, and immune escape. Their infiltration density in lung cancer tissue is a poor prognostic factor. Piperlongumine (PL), extracted from Piper longum, possesses antitumor and anti-inflammatory properties, inducing apoptosis and inhibiting invasion and metastasis in lung cancer cells. This study aims to elucidate the correlation between endoplasmic reticulum stress (ERS) in lung cancer cells and M2-type TAM polarization and the role of PL in regulating lung cancer progression. The network pharmacologic analysis revealed that Piperlongumine inhibits lung cancer progression by inducing endoplasmic reticulum stress. In vivo experiments demonstrated that Piperlongumine significantly reduced tumor volume and decreased the proportion of M2-type macrophages. Within the co-culture system, lung cancer cells were shown to promote macrophage M2-type polarization and enhance cancer cell migration. Piperlongumine effectively inhibited these effects by inducing endoplasmic reticulum stress in cancer cells, thereby reducing M2 polarization and cell migration. The addition of endoplasmic reticulum stress inhibitor 4-PBA counteracted Piperlongumine’s effects, further underscoring the crucial role of ERS in the treatment mechanism. Piperlongumine suppresses lung cancer growth by inducing endoplasmic reticulum stress, which inhibits macrophage M2-type polarization and reduces cell migration. These findings support Piperlongumine’s potential as a therapeutic agent and offer a foundation for targeting endoplasmic reticulum stress to modulate TAM function in lung cancer treatment.

Matrine, a natural alkaloid with recognized anti-cancer potential, has been reported to induce cancer cell death. However, the mechanism remains incompletely defined. In this study, we investigated the pharmacological effect of matrine on colorectal cancer (CRC) using both in vivo tumor‑bearing mouse models and in vitro cell cultures. Our results demonstrated that matrine effectively suppressed colorectal cancer progression in mice, accompanied by an increased area of tumor necrosis and enhanced infiltration of anti-tumor immune cells, including CD4+ and CD8+ T-cells. Importantly, matrine treatment increased 4-hydroxynonenal (4-HNE) staining, a marker of ferroptosis, in tumor tissues. In vitro, matrine induced dose-dependent cell death in CRC cells, which was rescued by the ferroptosis inhibitors ferrostatin-1 (Fer-1) or liproxstatin-1 (Lip-1). Matrine promoted lipid peroxidation, as evidenced by increased BODIPY+ cells and elevated malondialdehyde (MDA) levels, alongside reduced glutathione (GSH). Mechanistically, matrine downregulated SLC7A11 and upregulated p53 expression. Further analysis revealed that matrine inhibited the phosphorylation of STAT3, a common upstream regulator of both SLC7A11 and p53. Rescue experiments showed that the antitumor effect of matrine depended on STAT3 inhibition, not on p53. Molecular docking showed that matrine could bind to the phosphotyrosine‑binding pocket of STAT3. Taken together, we revealed that matrine showed a significant inhibitory effect on CRC. The present findings identify a previously unrecognized mechanism through which matrine controls tumor cell fate by modulating ferroptosis, providing mechanistic insight into STAT3/SLC7A11-dependent regulation.

Recent years have witnessed growing interest in the interplay between B cells and non-small cell lung cancer (NSCLC) amid deepening investigations into cancer immunotherapy. B cells play pivotal roles in immunotherapeutic contexts by modulating tumor progression through immunoglobulin secretion, T-cell response activation, and direct tumoricidal activity. In this study, we employ bibliometric techniques to analyze research hotspots and evolutionary trends in NSCLC-associated B-cell investigations, aiming to inform future directions in this burgeoning field. We conducted a systematic retrieval of peer-reviewed documents (original articles, reviews, editorials) from the Web of Science Core Collection (2004–2024). Bibliometric networks were constructed using CiteSpace 6.1.R6 and VOSviewer 1.6.18. With temporal productivity patterns analyzed through Microsoft Excel 2021. This study analyzed 437 publications from 1,677 institutions across 156 countries/regions, encompassing 3,360 researchers. Annual publications showed sustained growth (2004–2021) followed by decline (2022–2024). China demonstrated highest productivity (n = 169), the United States dominated total citations (6,943). Keyword and highly cited literature analyses primarily focused on elucidating B cell heterogeneity and tertiary lymphoid structures, deciphering B cell-T cell interaction mechanisms, and exploring synergistic applications of B cells with other immunotherapeutic approaches. This study utilizes bibliometric analysis to explore research hotspots and developmental trends in B - cell - related studies of non - small cell lung cancer (NSCLC) over the past two decades. By elucidating the changing research landscape, this work aims to enhance researchers’ comprehension of the immunological milieu and advancements in NSCLC immunotherapy, promote scientific advancement in this field, and provide directional guidance for future research.

Although CRC incidence is declining overall, early-onset colorectal cancers are increasing. No prognostic models currently exist for predicting postoperative survival in Stage I–III early-onset colon or rectal cancer. Such tools are urgently needed to enable individualized risk assessment. We identified patients with early onset (EO) and late-onset (LO) colon or rectal cancer from the SEER database and randomly split them into training and test cohorts (7:3). External cohorts of early-onset colon and rectal cancer were collected from two Chinese hospitals. After LASSO-Cox feature selection, six models—RSF, LASSO-Cox, S-SVM, XGBSE, GBSA, and DeepSurv—were developed to predict cancer-specific survival (CSS). Performance was assessed using the C-index, Brier score, time-dependent AUC, calibration, and decision curves. SHAP was used for model interpretation. A risk stratification system and an online calculator were constructed based on the best-performing model. A total of 3,997 EO colon cancer, 2,016 EO rectal cancer, 30,621 LO colon cancer, and 8,667 LO rectal cancer patients from SEER, along with 205 EO colon cancer and 153 EO rectal cancer patients from Chinese institutions, were included in the study. Based on comprehensive evaluation across multiple datasets and metrics, the RSF model demonstrated the best and most stable performance, outperforming not only other machine learning models but also the traditional TNM staging system. In EO colon cancer, the RSF model achieved C-indices of 0.738 (test cohort) and 0.829 (external validation), mean AUCs of 0.765 and 0.889, and integrated Brier scores of 0.084 and 0.077, respectively. For EO rectal cancer, C-indices were 0.728 and 0.722, mean AUCs were 0.753 and 0.900, and integrated Brier scores were 0.106 and 0.095, respectively. The calibration and decision curves further confirmed the RSF model’s good calibration and clinical net benefit. The RSF model also showed robust performance in LOCRC cohorts. SHAP analysis was used to quantify the marginal contribution of each predictor within each cancer subtype. Based on the RSF model, we developed a CSS-based risk stratification framework and deployed an online prediction tool. In summary, we selected the RSF model for its outstanding predictive performance, naming it OncoE25, to support personalized health management for EO colon and rectal patients. We developed and validated OncoE25, an AI-driven RSF model for predicting postoperative overall survival (OS) in M0 early-onset colon and rectal cancer patients. OncoE25 showed superior and consistent performance in both the SEER database and a Chinese hospital cohort. SHAP analysis confirmed TNM stage as the most influential factor, with age, CEA levels, tumor deposits, and perineural invasion also contributing significantly. OncoE25 demonstrated robust risk stratification and individualized prediction capabilities. An online survival calculator was created to support real-time clinical application and personalized health management.

Gastric cancer (GC) remains a formidable global health issue with limited therapeutic options. ShenXia KuanZhong Decoction (SXKZD), a classical traditional Chinese medicine (TCM) formula, is used to manage GC; however, its anti-tumor mechanisms remain poorly understood. The anti-GC effects of SXKZD were investigated in a GC model using dose-weighted network pharmacology, molecular docking, molecular dynamics (MD) simulations, and pharmacokinetic profiling. Its impacts on tumor metabolism, immunity, and gut microbiota were assessed. A gut microbiota-substrate-metabolite (GM-S-M) network was constructed, and key targets and pathways were analyzed using computational and experimental methods. SXKZD treatment significantly alleviated tumor progression in GC models. Network analysis revealed upregulated TNF and IL6 expression in GC, which SXKZD reduced, alongside enrichment in IL-17 and TNF signaling pathways. Molecular docking and MD simulations confirmed stable binding of Ginsenoside Rh2 and 3-Indolepropionic acid to TNF, with binding energies of -147.63 kJ/mol and − 98.63 kJ/mol, respectively. Pharmacokinetic profiling showed 3-Indolepropionic acid’s high bioavailability, while GM-S-M analysis identified key microbial taxa (e.g., Lactobacillus plantarum, Akkermansia muciniphila) modulated by SXKZD, enhancing anti-tumor immunity and metabolism.To further confirm these computational predictions, in vitro CCK-8 assays revealed that GRh2 and IPA inhibited AGS cell growth in a concentration-dependent manner, with IC50 values of 68.74 ± 1.27 µg/mL and 780.60 ± 24.40 µg/mL at 24 hours, respectively. Western blot analysis demonstrated that GRh2 more effectively suppressed TNFα expression, whereas CETSA showed that IPA provided superior thermal stabilization of TNFα. SXKZD mitigates GC by modulating gut microbiota and inhibiting TNF signaling, offering a mechanistic basis for its therapeutic potential in GC management.

M2 macrophages play a pivotal role in promoting the growth and metastasis of lung cancer cells. Inhibiting M2 macrophage polarization represents an effective immunotherapeutic approach against tumors. Although (-)-Guaiol has been shown to exert potent inhibitory effects on M2 macrophage polarization, its underlying molecular mechanism remains unclear. This study aimed to elucidate the effects of (-)-Guaiol on M2 macrophage polarization and to explore the potential molecular mechanisms involved. Bone marrow-derived macrophages (BMDMs) from mice were polarized toward the M2 phenotype using IL-4 and M-CSF. Both ex vivo and in vivo experiments were performed to determine whether (-)-Guaiol suppresses M2 macrophage polarization through the PPAR-γ-related signaling pathway. PPAR-γ agonists and inhibitors were applied to confirm the involvement of PPAR-γ in the effects of (-)-Guaiol. A co-culture system of M2 macrophages and Lewis lung carcinoma (LLC) cells was established. Wound healing, Transwell invasion, and plate cloning experiments were performed to determine the effects of (-)-Guaiol on the metastasis and development of lung cancer cells. (-)-Guaiol markedly downregulated the expression of CD206, a characteristic surface marker of M2-polarized macrophages. It also significantly inhibited the proliferation, invasion, and metastatic potential of LLC cells co-cultured with M2 macrophages. Animal experiments revealed that (-)-Guaiol treatment significantly decreased the tumor volume and weight, as well as CD206 expression. Moreover, integrated in vitro and in vivo analyses revealed that (-)-Guaiol inhibited M2 macrophage polarization primarily by suppressing the PPAR-γ signaling pathway. These findings demonstrate that (-)-Guaiol inhibits M2 macrophage differentiation via targeted suppression of the PPAR-γ-related signaling pathway, thereby reducing the proliferation, migration, and invasion of lung cancer cells.

Stomach adenocarcinoma (STAD) is the most prevalent gastrointestinal malignancy and seriously threatens the life of the global population. Anoikis, a process of programmed cell death that occurs when cells detach from the extracellular matrix, is closely associated with tumor invasion and metastasis. In this study, we used the TCGA-STAD database to identify the expression patterns and prognostic relevance of anoikis-related genes (ARGs) in STAD. Functional enrichment analysis was used to explore the potential pathway. LASSO and Cox regression were used to construct anoikis-related prognostic signature. The anoikis risk score (ARS) incorporated 7 genes and stratified patients into highand low-risk subgroups by median value splitting. In addition, external validation was performed based on GSE66229, GSE15459, and GSE84437 cohorts. Nomograms were created based on risk characteristics in combination with clinical variants and the performance of the model was validated with time-dependent AUC, calibration curves, and decision curve analysis (DCA). The prognostic signature indicated that the low-risk subgroup had better outcomes and significant correlations with tumor microenvironment, immune landscape, immunotherapy response, and drug sensitivity. In addition, single-cell analysis displayed the cell types, the subcellular localization of prognostic genes, and the cellular interaction to reveal the potential molecular communication mechanism of anoikis resistance. Finally, in vitro experiments confirmed the critical role of CRABP2 in STAD. The results indicated that CRABP2 knockdown inhibited gastric cancer cell proliferation, migration and invasion, and promoted apoptosis. In summary, ARS can serve as a biomarker for predicting survival outcomes in STAD patients, providing new tools for personalized treatment decisions for STAD patients.

Ursolic Acid (UA) is a naturally occurring pentacyclic triterpenoid compound that is prevalent in various medicinal plants and fruits. It has garnered significant attention due to its broad spectrum of anticancer properties. In this study, we evaluated the antitumor effects of UA on Non-Small Cell Lung Cancer (NSCLC).UA significantly inhibited NSCLC viability and induced cell death in a time- and dose-dependent manner. Furthermore, the administration of UA resulted in an elevation of intracellular reactive oxygen species (ROS), lipid ROS, and ferrous iron levels, while concurrently suppressing the expression of SLC7A11, glutathione, and GPX4. Consequently, this led to an augmentation in the concentration of the lipid peroxidation substrate, malondialdehyde. All the changes were effectively attenuated by the ferroptosis inhibitor Ferrostatin-1(Fer-1) and Deferoxamine (DFO). Moreover, similar observations were made in animal experiments. The sequencing data indicate that UA influences ferroptosis by modulating Fatty Acid Desaturase-2 (FADS2). The reintroduction of FADS2 through ectopic expression restored the resistance to ferroptosis induced by UA in A549 cells, while the addition of exogenous oleic acid (OA) counteracted the impact of UA on the oxidative response. These results suggest that UA induces ferroptosis in NSCLC by affecting redox pathways and the FADS2-mediated synthesis of unsaturated fatty acids.These studies collectively underscore the promising role of UA in the development of effective anticancer therapies.

Non-small cell lung cancer (NSCLC) is a highly complex malignancy involving multiple molecular pathways including inflammatory responses, immune regulation and cell cycle dysregulation. Although previous studies have indicated the important role of inflammatory factors in NSCLC pathogenesis, the causal relationship between specific inflammatory factors and NSCLC risk, as well as their interactions with the immune microenvironment, has not been comprehensively elucidated. This study systematically evaluated the causal relationship between various inflammatory factors and NSCLC risk using Mendelian randomisation (MR) methodology. Through comprehensive transcriptomic analysis, network pharmacology approaches and protein-protein interaction network construction, we revealed molecular targets and key pathways in NSCLC. Additionally, we applied machine learning models to predict NSCLC and analysed the correlation between immune cell composition and cell cycle regulatory genes in NSCLC using flow cytometry. MR analysis showed that TGFB1 and CCL11 were positively correlated with NSCLC risk (OR = 1.173, p = 0.020; OR = 1.192, p = 0.003), while CD40 and CCL4 demonstrated protective effects (OR = 0.857, p = 0.015; OR = 0.896, p = 0.049). Bioinformatic analysis identified 74 overlapping drug-disease targets enriched in multiple inflammation-related signalling pathways. Machine learning models performed well in predicting NSCLC with AUC values of 0.723-0.763. Immune cell analysis revealed significantly increased CD8+ T cells and regulatory T cells (Tregs) in NSCLC samples, while naïve B cells were decreased. Complex correlations existed between cell cycle regulatory genes and immune cell composition, with CDK2 and CDK3 negatively correlated with Tregs (R = -0.8, p = 0.014; R = -0.72, p = 0.037), while CDK5 positively correlated with Tregs (R = 0.8, p = 0.014). This study revealed genetic associations between specific inflammatory factors and NSCLC risk, elucidating the complex interactions between inflammatory pathways and the immune microenvironment in NSCLC pathogenesis.

BackgroundLung cancer remains the leading cause of cancer-related deaths globally and represents the most common malignant tumor. While immunotherapy has significantly improved patient survival in recent years, the development of resistance limits its clinical efficacy. Currently, a systematic and comprehensive bibliometric analysis of drug resistance in immunotherapy for lung cancer is lacking. This study aims to address this gap by employing bibliometric methods to illuminate the knowledge structure and to identify key research hotspots in this critical area.MethodsWe retrieved publications concerning lung cancer immunotherapy drug resistance from the Web of Science Core Collection and PubMed databases, covering January 1, 2014, to December 31, 2024. NoteExpress was used for data integration, duplicate detection, and screening. Subsequently, we quantitatively and visually analyzed the characteristics of the selected literature, with an emphasis on country, institution, and keywords. This analysis was performed utilizing VOSviewer, CiteSpace, and the “bibliometrix” package in R.ResultThe annual publication output showed a marked upward trend, peaking in 2024. China produced the most publications, while the USA demonstrated higher citation impact. Analysis of keywords revealed a clear thematic evolution: from initial focus on clinical trials (e.g. Open-label) and specific drugs (e.g. Nivolumab), to immune checkpoints (e.g.PD-1/PD-L1), and more recently to underlying molecular mechanisms like the tumor microenvironment, autophagy, and ferroptosis.ConclusionsThis study offers a thorough overview of the most important research topics and emerging trends related to drug resistance and lung cancer immunotherapy. By integrating current knowledge, it enables researchers to swiftly identify pivotal research directions, thereby promoting in-depth development and innovation within the field and supporting the progression of clinical practice. For clinicians, this bibliometric insight provides a more scientific and precise basis for formulating treatment strategies, ultimately assisting lung cancer patients in deriving benefits from immunotherapy.

Lung cancer is characterized by high morbidity and mortality due to the lack of practical early diagnostic and prognostic tools. The present study uses machine learning algorithms to construct a clinical predictive model for non-small cell lung cancer (NSCLC) patients. Laboratory indices of the NSCLC patients at their initial visit were collected for quality control and exploratory analysis. By comparing the levels of the above indices between the survival and death groups, the statistically significant indices were selected for subsequent machine learning modeling. Ten machine learning algorithms were then employed to develop the predictive models with survival and recurrence as outcomes, respectively. Moreover, regression models were constructed using the random survival forest algorithm by incorporating the survival time dimension. Finally, critical variables in the optimal model were screened based on the interpretable algorithms to build a decision tree to facilitate clinical application. 682 patients were enrolled according to the inclusion and exclusion criteria. The preliminary comparison results revealed that except for fast blood glucose, CD3+T cell proportion, NK cell proportion, and CA72-4, there were significant statistical differences in other tumor markers, inflammation, metabolism, and immune-related indices between the survival and death groups (p < 0.01). Subsequently, indices with statistical differences were incorporated into machine learning modeling and evaluation. The results showed that among the ten prognostic models constructed using survival status as the outcome, the neural network model obtained the best predictive performance, with accuracy, sensitivity, specificity, AUC, and precision values of 0.993, 0.987, 1.000, 0.994, and 1.000, respectively. The corresponding SHAP16 algorithm revealed that the top five variables in terms of importance were interleukin6 (IL-6), soluble interleukin2 receptor (sIL-2R), cholesterol, CEA, and Cy211, respectively. The random survival forest model also confirmed the critical role of CEA, sIL-2R, and IL-6 in predicting the prognosis of NSCLC patients. A decision tree model with seven cut-off points based on the above three indices was eventually built for clinical application. The neural network model exhibited ideal predictive performance in the survival status of NSCLC patients, and the decision tree model constructed based on selected important variables was conducive to rapid bedside prognosis assessment and decision-making. There is a lack of highly sensitive, specific, and organ-specific biomarkers to predict the prognosis of lung cancer patients. Compared with traditional predictive models, the models constructed by machine learning methods have incredibly high predictive accuracy, sensitivity, and specificity. Both classification and regression algorithms confirmed the significant predictive value of IL-6, sIL-2R, and CEA on the prognosis of lung cancer patients. A decision tree prognostic model including IL-6, sIL-2R, and CEA with explicit cutoff values was further provided for rapid prognostic assessment and clinical decision-making.

To investigate whether the combination of chemotherapy with staged Chinese herbal medicine (CHM) therapy could enhance health-related quality of life (QoL) in non-small-cell lung cancer (NSCLC) patients and prolong the time before deterioration of lung cancer symptoms, in comparison to chemotherapy alone. A prospective, double-blind, randomized, controlled trial was conducted from December 14, 2017 to August 28, 2020. A total of 180 patients with stage I B–IIIA NSCLC from 5 hospitals in Shanghai were randomly divided into chemotherapy combined with CHM (chemo+CHM) group (120 cases) or chemotherapy combined with placebo (chemo+placebo) group (60 cases) using stratified blocking randomization. The European Organization for Research and Treatment of Cancer (EORTC) Quality-of-Life-Core 30 Scale (QLQ-C30) was used to evaluate the patient-reported outcomes (PROs) during postoperative adjuvant chemotherapy in patients with early-stage NSCLC. Adverse events (AEs) were assessed in the safety analysis. Out of the total 180 patients, 173 patients (116 in the chemo+CHM group and 57 in the chemo+placebo group) were included in the PRO analyses. The initial mean QLQ-C30 Global Health Status (GHS)/QoL scores at baseline were 57.16 ± 1.64 and 57.67 ± 2.25 for the two respective groups (P>0.05). Compared with baseline, the chemo+CHM group had an improvement in EORTC QLQ-C30 GHS/QoL score at week 18 [least squares mean (LSM) change 17.83, 95

基于"诸寒收引,皆属于肾"探讨从肾论治胃癌化学疗法(简称"化疗")后骨髓抑制.中医认为"诸寒收引,皆属于肾",肾为先天之本,主骨生髓,与脾胃之后天相互资生,是人体抵御外邪之根.研究发现,骨髓抑制的发生与肾密切相关.阐述以温补肾阳法纠正胃癌化疗后骨髓抑制的可行性,并举验案1则.

脾为"后天之本",机体内环境稳定是脾脏健运的重要特征.肿瘤微环境是肿瘤生长所必需的微环境,可能是防治肿瘤的潜在靶点.本文基于中医脾虚理论,并结合现代医学肿瘤微环境相关理论,探讨脾虚与肿瘤微环境的相关性,认为中医脾虚状态与肿瘤微环境中的慢性炎症、免疫抑制及缺氧状态等存在相关性.并从基础研究、临床研究两方面探讨脾虚理论,探讨健脾理论及健脾中药在调节肿瘤微环境中的作用,以期为调节肿瘤微环境、防治肿瘤提供相关证据支持,希冀丰富中医健脾理论的内涵,促进健脾中药在肿瘤疾病中的应用,为肿瘤研究及治疗提供新思路.

Objective:To explore the congenital Yunqi characteristics in patients with lung cancer and to explore the treatment strategies based on the Yunqi characteristics. Methods:A retrospective study including a total of 7030 lung cancer patients hospitalized in Shanghai Hospital of Traditional Chinese Medicine Affiliated to Shanghai University of Traditional Chinese Medicine in the past seven years were conducted to explore the differences in the distribution of heavenly stems,year evolutive phase,dominate-qi,guest-qi,Sitian period,Zaiquan period, and Yunqi combination. Moreover,the commonality of disease mechanisms and treatment strategies for lung cancer patients were investigated from the perspective of the five evolutive phases and six climatic factors theory. Results:The proportion of lung cancer patients born in the year of Gui or less fire was the highest among the heavenly stem and year evolutive phase. The difference of distribution of heavenly stems and year evolutive phase were statistically significant（P＜0.05）. The proportion of lung cancer patients born in the Yangming Zaojin period was the highest among the dominate-qi distribution. The difference of dominate-qi was statistically significant（P＜0.05）. The proportion of lung cancer patients born in the Taiyang Hanshui period was the highest among the guest-qi distribution There was no statistical difference in the distribution of lung cancer patients with different kinds of guest-qi（P ＞0.05）. The highest proportion of lung cancer patients was born in the Sitian period of Taiyang Hanshui and the Zaiquan period of Taiyin Shitu. In addition,the highest percentage of lung cancer patients were in years of disharmony and the lowest in years of Suihui. Conclusions:The congenital Yunqi characteristics of lung cancer patients were cold and dryness,so the main treatment method should be to tonify lung qi,supplemented by regulating qi,resolving phlegm,and moving fluid. Besides,the combination of acupuncture and herb could lead to better clinical efficacy.

The burden of colorectal cancer (CRC) varies substantially across different geographical locations. However, there was no further quantitative analysis of regional social development and the disease burden of CRC. In addition, the incidence of early- and late-onset CRC has increased rapidly in developed and developing regions. The main purpose of this study was to investigate the trends in CRC burden across different regions, in addition to the epidemiological differences between early and late-onset CRC and their risk factors. In this study, estimated annual percentage change (EAPC) was employed to quantify trends in age-standardized incidence rate (ASIR), mortality rate, and disability-adjusted life-years. Restricted cubic spline models were fitted to quantitatively analyze the relationship between trends in ASIR and Human Development Index (HDI). In addition, the epidemiological characteristics of early- and late-onset CRC were investigated using analyses stratified by age groups and regions. Specifically, meat consumption and antibiotic use were included to explore the differences in the risk factors for early- and late-onset CRC. The quantitative analysis showed that the ASIR of CRC was exponentially and positively correlated with the 2019 HDI in different regions. In addition, the growing trend of ASIR in recent years varied substantially across HDI regions. Specifically, the ASIR of CRC showed a significant increase in developing countries, while it remained stable or decreased in developed countries. Moreover, a linear correlation was found between the ASIR of CRC and meat consumption in different regions, especially in developing countries. Furthermore, a similar correlation was found between the ASIR and antibiotic use in all age groups, with different correlation coefficients for early-onset and late-onset CRC. It is worth mentioning that the early onset of CRC could be attributable to the unrestrained use of antibiotics among young people in developed countries. In summary, for better prevention and control of CRC, governments should pay attention to advocate self-testing and hospital visits among all age groups, especially among young people at high risk of CRC, and strictly control meat consumption and the usage of antibiotics.

目的:评价中药抗癌2号方联合高强度聚焦超声(HIFU)治疗中晚期胰腺癌的临床疗效及安全性.方法:将120例中晚期胰腺癌患者随机分为中药组、HIFU组和联合组,每组40例.所有患者均予基础治疗,中药组加予中药抗癌2号方口服,HIFU组加予HIFU治疗胰腺病灶,联合组加予中药抗癌2号方和HIFU治疗.比较3组患者的近期疗效、总生存期、临床获益反应、肿瘤标志物的变化情况,并监测不良反应.结果:联合组疾病控制率为78.4％(29/37),显著高于中药组的52.6％(20/38)(P＜0.05).与中药组及HIFU组比较,联合组在近期生存获益方面有显著优势(P＜0.05).联合组在体质量保持及疼痛程度缓解方面均优于HIFU组和中药组,临床获益显著,且有稳定肿瘤标志物的作用.3组患者血常规及肝肾功能均未出现显著的异常,治疗全程未见明显不良反应.结论:中药抗癌2号方联合HIFU可有效改善中晚期胰腺癌患者的临床症状,提升生活质量,并有助于提高患者生存率,疗效确切且安全性高.

目的 基于均匀设计法筛选并验证蠲瘤散结方治疗肺腺癌的主效应药物或药物组合.方法 构建Lewis肺癌细胞皮下移植瘤模型,以蠲瘤散结方中6味药物(白英、夏枯草、海藻、石见穿、石上柏、牡蛎)为考察因素,选用U12(1210)均匀设计表,每因素6个水平,重复1次,按组方设计所得12种不同药物组合进行给药干预,以小鼠瘤体体积及瘤体质量为结局指标,通过建立回归方程筛选主效应药物或药物组合.对筛选所得主效应药物,再通过上述均匀设计方法及动物实验进行药效学验证,探索最佳剂量配比和核心效应药物.结果 筛选实验表明,与对照组比较,第10组小鼠瘤体体积和质量显著下降(P<0.05).逐步回归分析结果表明,白英+石见穿和石见穿+石上柏药物组合可显著降低小鼠瘤体负荷(P<0.05),3味药物是蠲瘤散结方发挥抗肺腺癌作用的主效应药物.验证实验结果表明,3味主效应药物不同配比与蠲瘤散结方抑瘤作用相当,最佳药物配比为白英:石见穿:石上柏=3:2:1.白英是蠲瘤散结方发挥抗肺腺癌作用的核心药物.结论 以白英为主的清热解毒药组是蠲瘤散结方发挥抑瘤作用的主效应药物,其抗肺腺癌疗效与蠲瘤散结方相当.

本文总结王羲明教授从扶虚固真法论治急性髓细胞性白血病的临床经验.王教授认为,肾精虚损是急性髓细胞性白血病的关键病机,指出扶虚固真是其基本治法,应贯穿治疗始终.并强调分期论治:化疗前期治宜滋肾养阴、凉血解毒;化疗期间治宜健脾益肾、助运解毒;化疗后期治宜滋肾填精,兼清余毒.文末附验案一则以资佐证.