Journal of Cancer Research and Clinical Oncology最新文献

Background: Breast cancer and heart failure are major public health issues globally, characterized by significant genetic heterogeneity and complex molecular mechanisms. This study aims to explore potential genetic targets in heart failure and breast cancer through combining genetic inference and single-cell expression analysis, and to assess the causal relationships of these targets using Mendelian randomization methods.

Methods: We first identified genetic variants associated with heart failure and breast cancer using genome-wide association study (GWAS) data. Subsequently, we analyzed the expression patterns of these genetic variants in different cell types through single-cell RNA sequencing technology. Furthermore, we utilized in vitro experiment to assess the effects of ITM2B on MCF7 using immunofluorescence.

Results: Our study identified multiple genetic variants associated with heart failure and breast cancer, showing specific expression patterns in heart and breast cells. The ITM2B gene is highly expressed in breast cancer. ITM2B shows the highest expression in T cells and the lowest expression in proliferating cells. Silencing the ITM2B gene can promote apoptosis and inhibit proliferation and migration of MCF7 breast cancer cells.

Conclusion: By integrating genetic inference and single-cell expression analysis, this study revealed potential genetic targets in heart failure and breast cancer, and validated the causal effects of these targets using Mendelian randomization methods.

Background: Women are predisposed to develop intolerance to cancer chemotherapy. Sarcopenia and chemotherapy are mutually related. Women are generally intolerable to chemotherapeutics such as 5-fluorouracil. Although adjuvant oxaliplatin-based chemotherapy, e.g. CAPOX is commonly used to treat colorectal cancer, its effects on patients in terms of sarcopenia and sex remain unknown. We investigated sex disparities in the impacts of CAPOX on body composition in this study.

Methods: We conducted a prospective study on diagnostic metrics used for sarcopenia in colorectal cancer patients receiving adjuvant CAPOX. Evaluations of the nutritional status by the Mini-Nutritional Assessment (MNA), gait speed, grip strength, skeletal muscle mass, fat mass, and bone mineral content using a body composition analyzer were performed in the first, fourth, and eighth cycles of CAPOX (first, second, and third measurements, respectively).

Results: Among 80 eligible patients, 61 completed four CAPOX cycles. The median differences in MNA, gait, grip strength, muscle mass, fat mass, and bone mineral content between the first and second measurements for men (n = 35) and women (n = 26) were + 10.5% and + 2.9% (p = 0.067), + 4.5% and - 2.6% (p = 0.16), + 1.8% and + 2.8% (p = 0.66), + 2.7% and + 1.3% (p = 0.021), + 4.5% and + 3.5% (p = 0.59), and + 3.3% and + 0.0% (p = 0.006), There were no sex differences in comparisons of the above metrics between the first and third measurements in 34 patients who completed eight CAPOX cycles (19 wen and 15 women).

Conclusions: Early cycles of adjuvant CAPOX may have a negative impact on the postoperative recovery of several metrics for diagnosing sarcopenia in women.

Background: Neoadjuvant chemoimmunotherapy (nCIT) for locally advanced esophageal squamous cell cancer (ESCC) has shown short-term benefits, but long-term survival outcomes are unclear. This study compares nCIT and neoadjuvant chemotherapy (nCT) in resectable ESCC.

Patients and methods: A retrospective analysis was conducted on ESCC patients who underwent nCT or nCIT followed by esophagectomy. Propensity score matching (PSM) with a caliper of 0.02 was employed to minimize bias. The primary endpoints included disease-free survival (DFS) and overall survival (OS).

Results: A total of 131 comparable pairs of ESCC patients receiving nCT and nCIT were selected for the final analysis. The nCIT had higher rates of pathological complete response (pCR) and major pathological response (mPR) compared to nCT. Additionally, nCIT led to significant tumor down-staging, higher rates of R0 resection, and increased lymph node clearance during surgery. Patients who received nCIT exhibited improved disease-free survival (DFS) and overall survival (OS) at the 3-year follow-up. The incidence of distant and mixed relapses was lower in the nCIT group compared to the nCT group. However, the risk of locoregional relapse was comparable between the two groups. Subgroup analyses showed that the benefits of nCIT were generally observed across most patient subgroups. Interestingly, in patients without pCR or mPR, nCIT still demonstrated better survival benefits than nCT.

Conclusion: nCIT demonstrated superior pathological response rates and improved 3-year DFS and OS compared to nCT alone in locally advanced ESCC, but long-term survival validation is needed.

Background: Currently, neoadjuvant chemoimmunotherapy (NCIT) is widely used in the perioperative treatment of esophageal squamous cell carcinoma (ESCC). However, the patterns of lymph node metastasis following this novel treatment approach remain poorly understood. The aim of this study was to elucidate the distribution and frequency of postoperative lymph node metastasis (LNM) after NCIT.

Methods: We retrospectively analyzed cases from March 2020 to March 2023 in our hospital and selected patients who underwent NCIT followed by R0 resection for esophageal cancer. A total of 257 patients with clinical stage T3N0 or T1-3N + thoracic esophageal cancer were included. The distribution and frequency of metastatic lesions in each lymph node station were recorded according to the Japan Esophageal Society (JES) staging system. Additionally, we analyzed the patterns of lymph node metastasis based on the location of the thoracic tumor.

Results: Among the 257 patients, 110 (42.8%) had pathologically positive lymph nodes postoperatively. Common sites of lymph node metastasis included station 107 (12.8%), station 106recR (11.7%), and station 7 (12.5%). The lymph node stations with lower metastasis rates were station 105, station 106tbL, and station 111, each with a metastasis rate of 2.3%. In upper thoracic (Ut) cases, station 106recR (23.7%) was the most common site of lymph node metastasis, while in middle thoracic (Mt) cases, station 107 (16.7%) had the highest metastasis rate, and in lower thoracic (Lt) cases, station 7 (17.6%) had the highest metastasis rate. Lymph node metastasis (LNM) was more likely to occur in station 101R in Ut and Mt cases than in Lt cases (13.2% and 8.6%; p < 0.01).

Conclusions: This study reveals the frequency and distribution patterns of lymph node metastasis following NCIT, highlighting the different patterns of lymph node metastasis based on tumor location. These findings can provide guidance for lymph node dissection during surgery.

Purpose: Colorectal cancer (CRC) remains a global health challenge with limited treatment success due to drug resistance. Recent research highlights the potential of small molecules to modulate CRC by targeting epigenetics or autophagy pathways. This systematic review explores the epigenetic effect of small molecules on autophagy in CRC, aiming to identify novel therapeutic strategies.

Methods: Following PRISMA guidelines, we systematically reviewed 508 studies from PubMed, Scopus, and Web of Science databases until August 13, 2023.

Results: Eight studies met inclusion criteria, examining the role of small molecules as epigenetic modulators (Histone acetylation/deacetylation, DNA methylation/demethylation and gene expression regulation by miRNAs) influencing the autophagy pathway in CRC. The studies encompassed in vitro and animal model in vivo studies. Small molecules exhibited diverse effects on autophagy in CRC. For instance, panobinostat promoted autophagy leading to CRC cell death, while aspirin inhibited autophagy flux, reducing aspirin-mediated CRC cell death. The epigenetic modulation of autophagy by various small molecules differently affects their anticancer effect, which underscores the complexity of therapeutic interventions.

Conclusion: Understanding the intricate dynamics among small molecules, epigenetic modifications, and autophagy in CRC is crucial for developing targeted therapeutic strategies. Considering the dual role of autophagy in tumorigenesis and tumor suppression, administration of these small molecules may differently affect the cancer cell fate and drug response or resistance based on their effect on the autophagy pathway. Therefore, recognition of the epigenetics mechanism of anticancer small molecules on autophagy may contribute to deciding how to prescribe them for better CRC treatment.

The incidence of brain tumors among children is second only to acute lymphoblastic leukemia, but the mortality rate of brain tumors has exceeded that of leukemia, making it the most common cause of death among children. Medulloblastoma (MB) is the most common type of brain tumor among children. Malignant brain tumors have strong invasion and metastasis capabilities, can spread through cerebrospinal fluid, and have a high mortality rate. In 2010, the World Health Organization first divided MB into four molecular subtypes based on molecular markers: WNT, Sonic hedgehog (SHH), Group 3, and Group 4. MB is a highly heterogeneous tumor. Different molecular subtypes of MB have significantly different clinical, pathological, and molecular characteristics. The prognosis of MB varies significantly among patients with different subtypes of this cancer. Thus, it is needed to study new diagnostic and therapeutic strategies. Metabolomics is an advanced analytical technology that uses various spectroscopic, electrochemical, and data analysis technologies to study and analyze the body's metabolites. By detecting changes in metabolite types and quantities in different types of samples, it can sensitively discover the physiological and pathological changes in the body. It has great potential for clinical application and personalized medicine. It is promising and can help develop personalized treatment strategies based on the metabolic profiles of individuals. It can unravel the unique metabolic profiles of MB, which may revolutionize our understanding of the disease and improve patients' outcomes.

Background: Breast cancer is a significant public health issue worldwide, being the most prevalent cancer among women and a leading cause of death related to this disease. The molecular processes that propel breast cancer progression are not fully elucidated, highlighting the intricate nature of the underlying biology and its crucial impact on global health. The objective of this research was to perform bioinformatics analyses on breast cancer-related datasets to gain a comprehensive understanding of the molecular mechanisms at play and to identify key genes associated with the disease.

Methods: The toolkit analyses involve techniques such as differential gene expression analysis, Gene Set Enrichment Analysis (GSEA), Weighted Co-Expression Network Analysis (WGCNA), and Machine Learning algorithms. Furthermore, in vitro cell experiments have demonstrated the impact of HSPB6 on cell migration, proliferation, and apoptosis.

Results: The study identified multiple genes that displayed differential expression in breast cancer, notably FHL1 and HSPB6. A machine learning model was developed in this study and specifically trained for breast cancer diagnosis using these genes, achieving high precision. Furthermore, analysis of immune cell infiltration revealed an enrichment of Tregs and M2 macrophages in the treated group, showcasing its significant impact on the tumor's immunological context. A temporal analysis of breast cancer cells using single-cell RNA sequencing provided insights into cellular developmental trajectories and highlighted changes in expression patterns across key genes during disease progression. The upregulation of HSPB6 in MCF7 cells significantly inhibited both cell migration and proliferation abilities, suggesting that promoting HSPB6 expression could induce ferroptosis in breast cancer cells.

Conclusion: Our findings have identified compelling molecular targets and distinctive diagnostic markers for the clinical management of breast cancer. This data will serve as crucial guidance for further research in the field.

Background: Breast cancer remains the leading malignant neoplasm among women globally, posing significant challenges in terms of treatment and prognostic evaluation. The metabolic pathway of polyamines is crucial in breast cancer progression, with a strong association to the increased capabilities of tumor cells for proliferation, invasion, and metastasis.

Methods: We used a multi-omics approach combining bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) to study polyamine metabolism. Data from The Cancer Genome Atlas, Gene Expression Omnibus, and Genotype-Tissue Expression identified 286 differentially expressed genes linked to polyamine pathways in breast cancer. These genes were analyzed using univariate COX and machine learning algorithms to develop a prognostic scoring algorithm. Single-cell RNA sequencing validated the model by examining gene expression heterogeneity at the cellular level.

Results: Our single-cell analyses revealed distinct subpopulations with different expressions of genes related to polyamine metabolism, highlighting the heterogeneity of the tumor microenvironment. The SuperPC model (a constructed risk score) demonstrated high accuracy when predicting patient outcomes. The immune profiling and functional enrichment analyses revealed that the genes identified play a crucial role in cell cycle control and immune modulation. Single-cell validation confirmed that polyamine metabolism genes were present in specific cell clusters. This highlights their potential as therapeutic targets.

Conclusions: This study integrates single cell omics with machine-learning to develop a robust scoring model for breast cancer based on polyamine metabolic pathways. Our findings offer new insights into tumor heterogeneity, and a novel framework to personalize prognosis. Single-cell technologies are being used in this context to enhance our understanding of the complex molecular terrain of breast cancer and support more effective clinical management.

Purpose: The aim of this study was to set up reliable and reproducible culture conditions for 3D tumoroids derived from non-small cell lung cancer (NSCLC) cell lines to enable greater opportunity for successful cultivation of patient-derived samples.

Methods: Four NSCLC cell lines, two adenocarcinomas (A549, NCI-H1975) and two squamous cell carcinomas (HCC-95, HCC-1588), were first cultured in traditional 2D settings. Their expected expression profiles concerning TTF-1, CK7, CK5, and p40 status were confirmed by immunohistochemistry (IHC) before the generation of 3D cultures. Tumoroids were established in the hydrogel GrowDex^®-T, Nunclon™ Sphera™ flasks, BIOFLOAT™ plates, and Corning^® Elplasia^® plates. Western blot was used to verify antigen protein expression. Hematoxylin-eosin staining was used to evaluate the cell morphology in the 2D and 3D cultures. Mutational analysis of KRAS and EGFR by PCR on extracted DNA from 3D tumoroids generated from cells with known mutations (A549; KRAS G12S mutation, NCI-H1975; EGFR L858R/T790M mutations).

Results: We successfully established 3D cultures from A549, NCI-H1975, HCC-95, and HCC-1588 with all four used cultivation methods. The adenocarcinomas (A549, NCI-H1975) maintained their original IHC features in the tumoroids, while the squamous cell carcinomas (HCC-95, HCC-1588) lost their unique markers in the cultures. PCR analysis confirmed persistent genetic changes where expected.

Conclusion: The establishment of tumoroids from lung cancer cell lines is feasible with various methodologies, which is promising for future tumoroid growth from clinical lung cancer samples. However, analysis of relevant markers is a prerequisite and may need to be validated for each model and cell type.

Purpose: To establish an explainable ¹⁸F-FDG PET/CT-derived prediction model to identify EGFR mutation status and subtypes (EGFR wild, EGFR-E19, and EGFR-E21) in lung adenocarcinoma (LUAD).

Methods: Baseline ¹⁸F-FDG PET/CT images of 478 patients with LUAD from 2 hospitals were collected. Data from hospital A (n = 390) was randomly split into a training group (n = 312) and an internal test group (n = 78), with data from hospital B (n = 88) utilized for external test. Further, a total of 4,760 handcrafted radiomics features (HRFs) were extracted from PET/CT scans. Candidates for the prediction model were constructed by cross-combinations of 11 feature selection methods and 7 classifiers. The optimal model was determined by combining the results of cross-center data validation and model visualization (Yellowbrick). The predictive performance was assessed via receiver operating characteristic curve, confusion matrix and classification report. Four explainable artificial intelligence technologies were used for optimal model interpretation.

Results: Sex and SUV_max were selected as clinical risk factors, which were then combined with 8 robust PET/CT HRFs to establish the models. The optimal performance was obtained by combining a light gradient boosting machine classifier with random forest feature selection method achieving an optimal performance with a macro-average AUC of 0.75 in the internal test group and 0.81 in the external test group.

Conclusion: The explainable EGFR mutation status prediction model have certain clinical practicability and good generalization performance, which may help in the timely selection of treatment options and prognosis prediction in patients with LUAD.