BMC Cancer最新文献

Background: Identifying effective predictive strategies to assess the response of immune checkpoint inhibitors (ICIs)-based combination therapy in advanced hepatocellular carcinoma (HCC) is crucial. This study presents a new longitudinal CT-based radiomics model to predict treatment response and prognosis in advanced HCC patients undergoing ICIs-based combination therapy.

Methods: Longitudinal CT images were collected before and during the treatment for HCC patients across three institutions from January 2019 to April 2022. A total of 1316 radiomic features were extracted from arterial and portal venous phase abdominal CT images for each patient. A model called Longitudinal Whole-liver CT-based Radiomics (LWCTR) was developed to categorize patients into responders or non-responders using radiomic features and clinical information through support vector machine (SVM) classifiers. The area under the curve (AUC) was used as the performance metric and subsequently applied for risk stratification and prognostic assessment. The Shapley Additive explanations (SHAP) method was used to calculate the Shapley value, which explains the contribution of each feature in the SVM model to the prediction.

Results: This study included 395 eligible participants, with a median age of 57 years (IQR 51-66), comprising 344 males and 51 females. The LWCTR model performed well in predicting treatment response, achieving an AUC of 0.883 (95% confidence interval [CI] 0.881-0.888) in the training cohort, 0.876 (0.858-0.895) in the internal validation cohort, and 0.875 (0.860-0.887) in the external test cohort. The Rad-Nomo model, integrating the LWCTR model's prediction score (Rad-score) with the modified Response Evaluation Criteria in Solid Tumors (mRECIST), demonstrated strong prognostic performance. It achieved time-dependent AUC values of 0.902, 0.823, and 0.850 at 1, 2, and 3 years in the internal validation cohort and 0.893, 0.848, and 0.762 at the same intervals in the external test cohort.

Conclusion: The proposed LWCTR model performs well in predicting treatment response and prognosis in patients with HCC receiving ICIs-based combination therapy, potentially contributing to personalized and timely treatment decisions.

Background: There is a low incidence of gastric neuroendocrine carcinoma (G-NEC), but it is associated with particularly aggressive biological behaviours and poor prognosis compared with other gastric neoplasms. Our study aimed to investigate the clinicopathologic traits and prognostic factors of patients with pure gastric neuroendocrine carcinoma treated with radical surgery.

Methods: We retrospectively analysed 60 patients with pure G-NEC who underwent radical gastrectomy between March 2010 and May 2019. 68 patient who underwent curative surgery for mixed gastric adenoneuroendocrine carcinoma (G-ANEC) from August 2012 to June 2022. The relationships between the clinicopathologic characteristics of pure G-NEC and overall survival (OS) and disease-free survival (DFS), as well as the comparison of pure-NEC with G-ANEC in terms of prognosis and treatment regimens, were evaluated using the Kaplan-Meier method and (or) Cox regression.

Results: The gastroesophageal junction (GEJ) was the predilection site for G-NEC. Tumor location, histology, and lymph node metastasis status were independent prognostic factors for OS (P < 0.05). Pathological T stage and the presence or absence of lymph node metastasis were independently associated variables with DFS (P = 0.019 and P = 0.041). Large cell neuroendocrine carcinoma (LCGNEC) did not differ statistically from the small cell neuroendocrine carcinoma (SCGNEC) (P = 0.314) for OS, while mixed type (MGNEC) vs. LCGNEC did differ significantly (P = 0.031). There were no significant differences in OS and DFS between etoposide and cisplatin (EP) and S-1 + oxaliplatin (SOX) / oxaliplatin + capecitabine (XELOX). The study of 106 patients found no significant impact of NEC proportion on OS (P = 0.438) or DFS (P = 0.079). Neoadjuvant/adjuvant chemotherapy targeting NEC versus adenocarcinoma showed no statistical difference in OS (P = 0.415, P = 0.350), but there was a trend toward longer survival with NEC-targeted regimen.

Conclusions: The LCGNEC did not differ statistically from the SCGNEC for OS, while the MGNEC vs. LCGNEC were different. The prognosis of G-NEC was related to the tumor location, histology, postoperative T stage, and lymph node metastasis. For gastric neuroendocrine carcinoma, prognosis does not differ statistically by NEC proportion. Chemotherapy regimens targeting lymph node metastases with an NEC component maybe better prognosis than those focusing on the adenocarcinoma component.

Cervical cancer is a significant global health issue affecting women worldwide, necessitating prompt detection and effective management. According to the World Health Organization (WHO), approximately 660,000 new cases of cervical cancer and 350,000 deaths were reported globally in 2022, with the majority occurring in low- and middle-income countries. These figures emphasize the critical need for effective prevention, early detection, and diagnostic strategies. Recent advancements in machine learning (ML) and deep learning (DL) have greatly enhanced the accuracy of cervical cancer cell classification and diagnosis in manual screening. However, traditional predictive approaches often lack interpretability, which is critical for building explainable AI systems in medicine. Integrating causal reasoning, causal inference, and causal discovery into diagnostic frameworks addresses these challenges by uncovering latent causal relationships rather than relying solely on observational correlations. This ensures greater consistency, comprehensibility, and transparency in medical decision-making. This study introduces CausalCervixNet, a Convolutional Neural Network with Causal Insight (CICNN) tailored for cervical cancer cell classification. By leveraging causality-based methodologies, CausalCervixNet uncovers hidden causal factors in cervical cell images, enhancing both diagnostic accuracy and efficiency. The approach was validated on three datasets: SIPaKMeD, Herlev, and our self-collected ShUCSEIT (Shiraz University-Computer Science, Engineering, and Information Technology) dataset, containing detailed cervical cell cytopathology images. The proposed framework achieved classification accuracies of 99.14%, 97.31%, and 99.09% on the SIPaKMeD, Herlev, and ShUCSEIT datasets, respectively. These results highlight the importance of integrating causal discovery, causal reasoning, and causal inference into diagnostic workflows. By merging causal perspectives with advanced DL models, this research offers an interpretable, reliable, and efficient framework for cervical cancer diagnosis, contributing to improved patient outcomes and advancements in cervical cancer treatment.

Objectives: In recent years, there has been a notable increase in the incidence and mortality rates of prostate cancer (PCa) in China, highlighting it as a significant public health issue. This study aimed to investigate the genetic association of PCa in China to better inform national disease management and medical resource allocation.

Methods: A systematic literature review was conducted using 5 English databases (Web of Science, PubMed, Embase, Cochrane, Scopus) and 1 Chinese database (CNKI) to identify articles published from database inception to October 8, 2022, which reported the genetic associations of PCa in China.

Results: Of the 11,195 articles retrieved, 41 were included in the review. A total of 116 different polymorphisms (including single nucleotide polymorphisms, deletions, insertions, and repeat lengths) in 58 genes were studied in Chinese populations. Among these, 37 out of 51 polymorphisms in 28 candidate genes such as BIRC5, C2orf43, COX-2, CYR61 (IGFBP10), DNMT1, DNMT3B, EXO1, FOXP4, and 7 unmapped SNPs were found to have either a positive or negative effect on PCa risk. However, 18 variants in 5 genes remain controversial across different studies. Additionally, 23 SNPs in 16 genes were reported to be associated with disease stage, Gleason score, PSA levels, PCa risk, and clinicopathological characteristics of PCa in China.

Conclusion: In Chinese populations, PCa risk and clinical features may result from individual genes, gene-gene interactions, and gene-environment interactions. These findings provide important insights into the relationship between genetic susceptibility and PCa risk in Chinese men.

Background: The tumor microenvironment of breast cancer encompasses a broad spectrum of immune cell populations. These cell populations are biologically/clinically relevant to varying degrees. The causal relationship between these immune cells and breast cancer remains uncertain despite their relevance.

Methods: Bi-directional two-sample Mendelian randomization (MR) analyses were conducted to investigate the causal relationship between 731 immune cell phenotypes and breast cancer, utilizing genome-wide association study (GWAS) statistics. The primary analytical methods employed were the weighted median (WM) and random effects inverse variance weighting (IVW). The MR-Egger method, MR-PRESSO and Cochran's Q-statistic were utilized to evaluate heterogeneity and pleiotropy among the instrumental variables.

Results: The study found a causal relationship between 27 immune cell traits and the onset of breast cancer using instrumental variables derived from GWAS data. Elevated levels of 13 immune cell populations and reduced levels of 14 immune cell populations were involved in triggering the development of breast cancer. Furthermore, the study revealed a causal relationship where breast cancer development had a causal effect on immune cell levels. Specifically, the onset of breast cancer may lead to elevated levels of 7 immune cell populations and reduced levels of 10 immune cell populations.

Conclusion: This study utilized genetic approaches to establish a causal relationship between immune cell traits and breast cancer. These findings offer potential novel targets for diagnosing and treating breast cancer.

Background: This study aimed to evaluate the predictive efficacy of the prognostic nutritional index (PNI) in patients with intermediate and advanced hepatocellular carcinoma (HCC) treated with a regimen consisting of hepatic arterial infusion chemotherapy (HAIC), PD-(L)1 inhibitors, and molecular targeted therapies (MTTs).

Methods: A retrospective analysis was performed on the data of 88 HCC patients received triple therapy between January 2020 and August 2022 at three medical centers. Univariate and multivariable analyses were conducted to assess the relationship between PNI and survival outcomes.

Results: The median follow-up was 11.0 months (IQR: 8.0-17.0). The PNI cut-off value of 38.6 was determined using receiver operating characteristics (ROC) analysis. The median overall survival (OS) durations were 29.0 and 8.0 months in the high-PNI (≥ 38.6) and low-PNI (≤ 38.6) groups, respectively (HR = 0.306, 95% CI, 0.170-0.552, P < 0.001), and the median progression-free survival (PFS) durations were16.0 and 6.0 months, respectively (HR = 0.521, 95% CI, 0.303-0.896, P = 0.014). A higher complete response rate was observed in the high-PNI group (17.5% vs. 3.2%, P = 0.033). The univariate and multivariable analyses revealed that a PNI of ≥ 38.6 had an independent influence on both median OS (HR = 0.296; 95% CI, 0.159-0.551, P < 0.001) and median PFS (HR = 0.560; 95% CI, 0.318-0.987, P = 0.045).

Conclusion: The PNI is an objective and convenient tool that can potentially predict the prognosis of patients treated with HAIC-based triple therapy.

Background: Minimally invasive thermal therapies show great prospect in non-small cell lung cancer (NSCLC) treatment. However, changes in immune cell populations following microwave ablation (MWA) in NSCLC microenvironment are not fully revealed.

Objective: The present study was conducted to identify changes in immune cell populations and analyse dysregulated genes in immune cells after MWA in NSCLC microenvironment.

Methods: The patients received fractionated MWA in two treatments separated by 3 weeks. Tumor biopsy samples were obtained through core-needle biopsy before each fractionated MWA procedure at the same site and used for single-cell RNA sequencing with the 10x Genomics pipeline.

Results: A total of 9 major cell types were identified after MWA, which include neutrophils, T cells, B cells, monocytes, epithelial cells, chondrocytes, macrophages, tissue stem cells, and endothelial cells. After MWA, the tumor tissue exhibited an increased proportion of T cells. MWA altered gene expression in each cell cluster at the single-cell level. Cell trajectory analysis revealed that the cells at the starting point were most like T helper cells, naïve T cells, and regulatory T cells; they then developed into anergic T cells, T follicular cells, natural killer T cells, T memory cells, and exhausted T cells, and finally ended as γδ T cells and cytotoxic T cells. Moreover, after MWA, more interaction between monocytes and T cells (or B cells) were identified.

Conclusions: MWA increases local T-cell abundance and alters monocyte interactions, thereby reshaping the tumor microenvironment. This study lays a foundation for investigating dysregulated genes that may contribute to the MWA-induced immune response in NSCLC.

What is already known on this topic: Thermal ablation may change the immune profiles of patients by activating various steps in the cancer immunity cycle. However, changes in immune cell populations following MWA of NSCLC have not been fully reported.

What this study adds: After MWA, an increase in interactions between monocytes and T cells intratumorally was observed, which promoted antitumor immunity.

How this study might affect research, practice or policy: The current study illuminates the MWA-caused systemic immune response in NSCLC, which may help to identify the dysregulated genes involved in the MWA-caused immune response.

Background: Skull base chordoma is a rare and aggressive bone tumor with a poor prognosis. The basement membrane (BM) plays an pivotal role in tumor progression. However, the involvement of BM-related genes in assessing the prognosis and influencing the biological behavior of skull base chordomas remains unclear.

Methods: Patients with skull base chordoma undergoing endoscopic endonasal surgery were included in the study (77 patients for bulk transcriptome sequencing and 6 patients for single-cell RNA sequencing). A BM-related genes signature was established and validated using bulk transcriptome data. Additionally, we investigated the oncogenic potential of a key BM-related gene in chordoma cells in vitro.

Results: A prognostic signature consisting of five BM-related genes was identified through LASSO Cox regression analysis. The accuracy and reliability of this signature were validated by the validation cohort. Multivariate Cox analysis and a nomogram demonstrated that the risk score serves as an independent and reliable prognostic factor for skull base chordoma. Moreover, the BM-related gene signature was significantly associated with the immune microenvironment, immune checkpoint expression, and drug sensitivity. Single-cell RNA sequencing analysis revealed both the chordoma tumor cell and the fibroblast contributed to the overall BM signature. Finally, in vitro experiments demonstrated that the knockdown of ITGB3, the hub gene in the signature, inhibited the proliferation and migration of chordoma cells via the PI3K-Akt pathway.

Conclusion: This study explored the critical role of BM-related genes in skull base chordoma, which affected postoperative recurrence and maligant behavior of chordoma via the PI3K-Akt signaling pathway.

Objectives: The Node Reporting and Data System (Node-RADS) offers a reliable framework for lymph node assessment, but its prognostic significance remains unexplored. This study aims to investigate the added prognostic value of Node-RADS in patients with locally advanced gastric cancer (LAGC) undergoing neoadjuvant chemotherapy (NAC) followed by gastrectomy.

Materials and methods: This single-center retrospective study included 118 patients with LAGC underwent NAC and gastrectomy. The maximum Node-RADS score and the number of metastatic lymph node stations (defined as LNM-Station) were evaluated on pretreatment CT. The pretreatment Node-RADS-CT and Node-RADS-integrated models were developed using Cox regression to predict overall survival (OS) and disease-free survival (DFS). The pretreatment cN-CT models, cN-integrated models, as well as post-NAC pathological models were also developed in comparison. The performance of the models was assessed in terms of discrimination, calibration and clinical applicability.

Results: The LNM-Station was significantly associated with OS and DFS (all p < 0.05). The Node-RADS-CT model showed higher Harrell's consistency index (C-index) than cN-CT model (0.755 vs. 0.693 for OS, p = 0.017; 0.759 vs. 0.706 for DFS, p = 0.018). The Node-RADS-integrated model also achieved higher C-index than cN-integrated model (0.771 vs. 0.731 for OS, p = 0.091; 0.773 vs. 0.733 for DFS, p = 0.053). The net reclassification improvement (NRI) of the Node-RADS-integrated model at 5 years was 0.379 for OS and 0.364 for DFS (all p < 0.05). The integrated discrimination improvement (IDI) of the Node-RADS-integrated model was 0.103 for OS and 0.107 for DFS (all p < 0.05). The C-indices (OS: 0.745; DFS: 0.746) of pathological models were slightly lower than those of Node-RADS-based models (all p > 0.05).

Conclusion: The baseline Node-RADS score and LNM-Station were effective prognostic indicators for LAGC. The pretreatment CT Node-RADS-based models can offer added prognostic value for LAGC, compared with clinical N stage.

Background: Follow-up is essential especially for patients who are at a high risk of recurrence after radical resection of hepatocellular carcinoma (HCC). The aim of this study was to develop a predictive model aimed at identifying the risk factors associated with being lost to follow-up (LTFU) in high-risk patients for recurrence following radical resection of HCC.

Methods: The retrospective study was conducted at our institution between October 2018 to May 2023. The patients who underwent radical liver resection for HCC and had high-risk factors for recurrence were categorized into an LTFU group and a control group. Multivariate logistic regression analysis was utilized to determine risk factors and construct a nomogram predictive model.

Results: A total of 352 patients were included and subsequently classified into two distinct groups: the LTFU group (n = 123, 34.94%) and the control group (n = 229, 65.06%). Logistic regression analysis was then conducted to explore the potential associations between various factors and the occurrence of LTFU. The findings identified several independent risk factors for LTFU, including smoking (odds ratio, OR = 1.823, 95% confidence interval, CI 1.086-3.060, p = 0.023); residing more than 200 km away from the hospital (OR = 1.857, 95% CI 1.105-3.121, p = 0.019); having an unstable profession (OR = 1.918, 95% CI 1.112-3.311, p = 0.019); and lacking medical insurance (OR = 5.921, 95% CI 1.747-20.071, p = 0.004); the presence of liver cirrhosis (OR = 2.161, 95% CI 1.153-4.048, p = 0.016); an operation time less than 240 min (OR = 2.138, 95% CI 1.240-3.688, p = 0.006); and the absence of postoperative adjuvant therapy (OR = 2.641, 95% CI 1.504-4.637, p = 0.001). Based on these seven significant factors, a main effects model was established, designated as the Wei-LTFU model, which achieved an area under the curve value of 0.744 (95% CI 0.691-0.798) in predicting the likelihood of LTFU.

Conclusion: A main effects model, namely the Wei-LTFU model, incorporating the seven significant factors was formulated to predict the likelihood of LTFU occurrence, ultimately aiming to assist healthcare workers in developing effective strategies to improve follow-up outcomes for patients.