Journal of Cancer Research and Clinical Oncology最新文献

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.

Background: Hepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide. Multiple observational studies demonstrated a negative association between the use of antithrombotic agents and the risk of HCC. However, the precise causal relationship between these factors remains uncertain. Therefore, our study used a two-sample Mendelian randomization (MR) analysis to assess the causal link between these two factors.

Method: The summary statistics of single nucleotide polymorphisms (SNPs) associated with the use of antithrombotic agents were acquired from a genome-wide association study (GWAS) performed on individuals of European descent. A two-sample MR analysis was performed using the inverse variance weighting (IVW), the weighted median estimate, the MR-Egger regression, and the weighted-mode estimate. Sensitivity analysis of the primary findings was performed using MR-PRESSO, MR-Egger regression, Cochran's Q test, and Leave-one-out analysis.

Results: Ten SNPs associated with the use of antithrombotic agents were selected as instrumental variables. The MR analysis performed using the four methods mentioned above revealed a negative causal association between the use of antithrombotic agents and HCC. Univariate MR estimates based on the inverse variance weighting (IVW) method suggested a negative causal association between the use of antithrombotic agents and HCC [odds ratio (OR) 0.444, 95% confidence interval (CI) 0.279 to 0.707, P = 0.001]. The other methods also produced similar results. No heterogeneity and horizontal pleiotropy were found.

Conclusion: Our findings suggested an inverse causal association of antithrombotic agents with the risk of HCC.

Purpose: Immune checkpoint blockade (ICB) therapies have shown efficacy in various tumors, but long-term responses in glioblastoma are less than 10%. Quantifying tumor in situ fluid circulating tumor DNA (TISF-ctDNA) and therapeutic dynamics may enable real-time GBM disease burden evaluation. This study explores the potential of tumor in situ fluid circulating tumor DNA (TISF-ctDNA) dynamics in predicting treatment efficacy.

Methods: TISF and peripheral blood samples were collected from patients with recurrent glioblastoma (rGBM) undergoing tislelizumab (a programmed death 1 inhibitor) combined with low-dose bevacizumab (an anti-vascular endothelial growth factor antibody) treatment before and during each immunotherapy cycle. Biomarkers evaluated included TISF-ctDNA, measured using Next Generation Sequencing (NGS), and host inflammation markers such as the platelet-to-lymphocyte ratio (PLR).

Results: All 32 patients received tislelizumab plus low-dose bevacizumab regularly. The median progression-free survival (PFS) was 4.0 months, and overall survival (OS) was 22.3 months. An analysis of 19 patients with continuous evaluable TISF showed baseline TISF-ctDNA abundance did not correlate with OS (p = 0.23) or PFS (p = 0.23). However, a change in TISF-ctDNA maximal Somatic Variant Allelic Frequency (MVAF) after six treatment cycles predicted both PFS (p = 0.02) and OS (p < 0.0001). Lower baseline PLR also correlated with better survival outcomes.

Conclusion: The combination of tislelizumab and low-dose bevacizumab therapy appears to be effective in extending both OS and PFS in rGBM patients. Continuous TISF-ctDNA testing shows potential utility in complementing radiological monitoring. The temporal change pattern of TISF MVAF is more predictive of immunotherapy response than imaging. PLR before immunotherapy can screen patients likely to benefit from tislelizumab plus low-dose bevacizumab therapy.

Trial registration: The trial registration number: NCT05502991; Date of registration: 2022-08-14.

The prognostic impact of clonal hematopoiesis (CH) in complete molecular remission (CMR) in acute myeloid leukemia (AML) remains controversial. Here, we explored the prognosis of CH-related gene mutations (CH-mutation) at CMR in patients with AML with NPM1 mutation (NPM1c AML). Ninety-one patients with de novo NPM1c AML were included between June 2018 and June 2023, including 32 patients with CH-related mutation at CMR and 59 patients without. A cutoff of ≥ 2.0% for variant allele frequency (VAF) of residual mutations was used to define CH-mutation at CMR. Thirty-two patients with CH-mutation at CMR had a greater median age and higher white blood cell (WBC) counts than those without (median age, 50.5 and 45 years, respectively; p = 0.028 and WBC count: 34.5 and 10 × 10⁹/l, respectively; p = 0.004). The incidence of DNMT3A and TET2 mutations before treatment was higher in the group with CH-mutations at CMR compared to the one without (71.9% vs. 13.6%, and 21.9% vs. 6.8%, respectively). Notably, all patients did not carry any CH of oncogenic potential (CHOP)-like mutations in CMR. There was no significant difference in event-free survival (EFS) or overall survival (OS) between the patients with and without CH-mutations at CMR or between the patients without allogeneic hematopoietic stem cell transplantation (allo-HSCT) of the two groups. In conclusion, our results suggested that CH-mutations probably did not have prognostic significance in patients with NPM1c AML who achieved CMR, and may be inappropriately for MRD monitoring.

Background: The advancements in artificial intelligence (AI) technology for image recognition were propelling molecular pathology research into a new era.

Objective: To summarize the hot spots and research trends in the field of molecular pathology image recognition.

Methods: Relevant articles from January 1st, 2010, to August 25th, 2023, were retrieved from the Web of Science Core Collection. Subsequently, CiteSpace was employed for bibliometric and visual analysis, generating diverse network diagrams illustrating keywords, highly cited references, hot topics, and research trends.

Results: A total of 110 relevant articles were extracted from a pool of 10,205 articles. The overall publication count exhibited a rising trend each year. The leading contributors in terms of institutions, countries, and authors were Maastricht University (11 articles), the United States (38 articles), and Kather Jacob Nicholas (9 articles), respectively. Half of the top ten research institutions, based on publication volume, were affiliated with Germany. The most frequently cited article was authored by Nicolas Coudray et al. accumulating 703 citations. The keyword "Deep learning" had the highest frequency in 2019. Notably, the highlighted keywords from 2022 to 2023 included "microsatellite instability", and there were 21 articles focusing on utilizing algorithms to recognize microsatellite instability (MSI) in colorectal cancer (CRC) pathological images.

Conclusion: The use of DL is expected to provide a new strategy to effectively solve the current problem of time-consuming and expensive molecular pathology detection. Therefore, further research is needed to address issues, such as data quality and standardization, model interpretability, and resource and infrastructure requirements.