BMC Cancer最新文献

Background: Despite advances in treatment, the incidence of postoperative complications following pancreatectomy remains high, leading to frequent hospital readmissions. Therefore, this study aimed to investigate the relationship between preoperative exercise tolerance and the likelihood of unplanned readmission in patients with pancreatic ductal adenocarcinoma.

Methods: This retrospective analysis included 88 patients who underwent pancreatectomy at a single institution between July 2019 and September 2022 and focused on patients with pancreatic ductal adenocarcinoma. Patients' preoperative exercise tolerance was assessed using the 6-minute walk distance (6 MWD).

Results: The study found a 22.7% readmission rate within 1 year, with the median 6 MWD being significantly lower in readmitted patients than in those who were not readmitted (390.0 m versus 436.5 m; p < 0.01). A 6 MWD cut-off of 425 m was a strong predictor of readmission, with lower preoperative exercise tolerance associated with a higher risk of readmission.

Conclusions: Enhancing preoperative physical reserves through exercise therapy may reduce readmission rates and improve patient outcomes. Further research with larger sample sizes is required to confirm these findings.

Background: Breast cancer is the leading cause of cancer among women globally and the most common cancer among women in Sierra Leone. This study aimed to evaluate the patterns of clinical presentation, management and outcomes among breast cancer patients who presented at the Connaught Teaching Hospital Complex in Sierra Leone.

Method: A retrospective, cross-sectional study was conducted at the specialist outpatient clinic at the Connaught Hospital. The medical records of 253 breast cancer patients were selected through convenience sampling on the basis of the completeness of their data from 2018 to 2021. The patients' sociodemographics, obstetrics-gynecological history, clinicopathological characteristics, treatment modalities, and outcomes were analysed via SPSS version 24.

Results: Among the 253 patients with breast cancer, 246 (97.2%) were female, and the mean age of the patients was 47 ± 14 years. The mean age at menarche was 12 ± 3 years, and 35 (13.8%) patients had a family history of breast cancer. The left breast of 130 (51.2%) patients was the most commonly affected; approximately 119 (47%) patients presented with stage III B disease, and 35 (13.8%) presented with metastasis. With respect to treatment modalities, surgery, chemotherapy, and hormonal therapy were provided to patients - with 120 (48%) mastectomies, 73 (29.2%) neoadjuvant chemotherapy, 84 (33.6%) adjuvant chemotherapy, 98 (39.2%) hormonal therapy, 35 (14.0%) palliative care and approximately 38 (15.2%) refused or did not initiate any treatment. Among the outcomes of breast cancer patients, 19 (32.8%) was the highest mortality rate in 2019, and 10 (27.8%) was the lowest in 2018.

Conclusion: Our study revealed that over half of our breast cancer patients presented with advanced-stage disease, and mastectomy was the most common modality of treatment. Further studies are needed to understand the factors responsible for their late presentation and improve histological services and access to radiotherapy facilities for our patients.

Background and objectives: Accurate classification of lymphadenopathy is essential for determining the pathological nature of lymph nodes (LNs), which plays a crucial role in treatment selection. The biopsy method is invasive and carries the risk of sampling failure, while the utilization of non-invasive approaches such as ultrasound can minimize the probability of iatrogenic injury and infection. With the advancement of artificial intelligence (AI) and machine learning, the diagnostic efficiency of LNs is further enhanced. This study evaluates the performance of ultrasound-based AI applications in the classification of benign and malignant LNs.

Methods: The literature research was conducted using the PubMed, EMBASE, and Cochrane Library databases as of June 2024. The quality of the included studies was evaluated using the QUADAS-2 tool. The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated to assess the diagnostic efficacy of ultrasound-based AI in classifying benign and malignant LNs. Subgroup analyses were also conducted to identify potential sources of heterogeneity.

Results: A total of 1,355 studies were identified and reviewed. Among these studies, 19 studies met the inclusion criteria, and 2,354 cases were included in the analysis. The pooled sensitivity, specificity, and DOR of ultrasound-based machine learning in classifying benign and malignant LNs were 0.836 (95% CI [0.805, 0.863]), 0.850 (95% CI [0.805, 0.886]), and 33.331 (95% CI [22.873, 48.57]), respectively, indicating no publication bias (p = 0.12). Subgroup analyses may suggest that the location of lymph nodes, validation methods, and type of primary tumor are the sources of heterogeneity.

Conclusion: AI can accurately differentiate benign from malignant LNs. Given the widespread use of ultrasonography in diagnosing malignant LNs in cancer patients, there is significant potential for integrating AI-based decision support systems into clinical practice to enhance the diagnostic accuracy.

Background: Colorectal cancer (CRC) is a prevalent malignancy worldwide, associated with significant morbidity and mortality. Cyclin-dependent kinase 1 (CDK1) plays a crucial role in cell cycle regulation and has been implicated in various cancers. This study aimed to evaluate the prognostic value of CDK1 in CRC and to identify traditional Chinese medicines (TCM) that can target CDK1 as potential treatments for CRC.

Methods: The expression and prognostic value of CDK1 were analyzed through TCGA, GEO, GEPIA, UALCAN and HPA databases. An ESTIMATE analysis was applied to estimate the proportions of stromal and immune cells in tumor samples. GO and KEGG enrichment analyses were performed to clarify the functional roles of CDK1-related genes. CCK-8, colony formation, cell migration, cell invasion, and wound healing assays were employed to explore tumor-promoting role of CDK1. Molecular docking, cellular thermal shift, and isothermal dose-response assays were employed to identify potential inhibitors of CDK1.

Results: CDK1 was highly expressed in CRC and associated with a poorer prognosis. The expression of CDK1 was also correlated with the levels of immune cells infiltration. CDK1-related genes were primarily involved in the cell cycle and the P53 signaling pathway. Knockdown of CDK1 inhibited the proliferation, migration, and invasion of CRC cells in vitro. Furthermore, Eriocitrin emerged as a potential inhibitor, exerting its anti-tumor effects by targeting and inhibiting CDK1 activity.

Conclusion: CDK1 plays a critical role in CRC prognosis. Eriocitrin, a potential CDK1 inhibitor derived from TCM, highlights a promising new therapeutic strategy for CRC treatment.

Background: Globally, estimates of cancer cases and deaths have increased since 2018, particularly in Latin America and Caribbean countries. In Antigua and Barbuda, understanding the burden of common cancers such as female breast, cervical, colorectal and prostate cancers is critical. This study aimed to assess the incidence, trends, and patterns of these four cancers from 2017 to 2021.

Methods: Using a retrospective observational study design, information on these cancers was abstracted from medical records at four key study sites in Antigua and Barbuda. Estimates of age-specific and age-standardized incidence were determined using direct standardization. The KeyFitz method was used to derive standard errors and confidence intervals. Derived estimates were employed to analyze trends and Joinpoint regression modeling was used to determine annual percentage change.

Results: Between 2017 and 2021, 391 cases of female breast (41.7%), cervical (10.2%), colorectal (20.2%) and prostate (27.9%) cancers were diagnosed. Overall mean age at presentation was 61.5 (± 12.9) years, ranging from 24 to 94 years, age-standardized incidence rate 65.2 (95% CI: 58.7-71.6) per 100,000 population. Age-standardized incidence rate for female breast cancer was 49.9 (95% CI: 42.2-57.8), annual percentage change in incidence a low of -0.2%. Prostate cancer had the second highest age-standardized incidence rate at 41.6 (95% CI: 33.8-49.4), annual percentage change showed a gradual but steady increase at 21.7%. Per cancer types, variations in age-standardized incidence rates were noted across age-groups, year-of-presentation, and parishes. Collectively, there was an 8.1% (95% CI: -14.9-37.6) annual percentage change increase in age-standardized incidence rates between 2017 and 2021. Incident cases, age-standardized incidence rates, and trends per cancer type are expected to gradually increase during 2022-2030 (average annual percentage increase is 3.4%).

Conclusions: This study is a first step in providing reasonable evidence on the incidence, trends, and patterns of four common cancers in Antigua and Barbuda. Female breast and prostate cancers were the dominant cancer types in terms of incidence, age-standardized incidence and predicted increasing incidence trends. Variableness in cancer-specific age-standardized rates across parishes and years of presentation were observed. Besides research, this study has importance for instituting cancer prevention and control measures, including surveillance and healthy lifestyles initiatives.

Background: Prescribing physical exercise as part of the clinical treatment of cancer patients has become an important strategy in the therapeutic arsenal available in the main health centers specialized in neoplastic diseases, but there is still uncertainty regarding the role of regular physical activity in modifying oxidative stress markers and antioxidants, as high levels of oxidative stress can lead to the development of carcinogens. Therefore, we assessed the effect of physical activity versus absence of physical activity on markers of oxidative stress and antioxidants in cancer patients.

Methods: We looked for randomized controlled trials that included adult with any type of cancer performing some physical activity and compared them to a control group of cancer patients with no physical activity. The outcomes of interest collected were markers of oxidative stress and antioxidants. Six databases were used for the search (EMBASE, The Cochrane Library (CENTRAL), US National Library of Medicine (PubMed), Physiotherapy Evidence Database (PEDro), Cumulative Index of Nursing and Allied Health (CINAHL) and SPORTDiscus via EBSCO) until January 31st, 2024.

Results: In the end, 7 randomized controlled clinical trials were included, totaling 573 participants. It was possible to conduct a meta-analysis with demonstrated that physical activity potentially increases the antioxidant marker Trolox, Taoc, Gpx, (SMD = 1.23, CI: 0.13 to 2.34). However, these findings were classified as having very low to moderate GRADE evidence.

Conclusion: Cancer patients who participated in physical activity programs may have exhibited an increased concentration of antioxidants. Although the overall quality of the evidence in the studies was very low, these findings represent an important field of research that should be guided by studies with more participants and homogeneous methods of analysis of oxidant stress and antioxidant markers.

Trial registration: PROSPERO CRD42021257815.

Background: Metabolic pathways are known to significantly impact the development and advancement of lung cancer. This study sought to establish a signature related to butyrate metabolism that is specifically linked to lung adenocarcinoma (LUAD).

Methods: For the purpose of identifying butyrate metabolism-related differentially expressed genes (BMR-DEGs) in the TCGA-LUAD dataset, we introduced transcriptome data. This was followed by the implementation of the univariate Cox and LASSO analyses in order to construct a LUAD gene signature. We performed a comprehensive analysis of gene function enrichment between the two populations at risk, thoroughly examined their immune microenvironment characteristics, and assessed the effectiveness of immunotherapy. Finally, the function of CDKN3 in LUAD was verified by in vitro experiments.

Results: Through a comprehensive analysis of the TCGA-LUAD dataset, 51 significant BMR-DEGs were confirmed. Subsequently, five characteristic genes, CPS1, ABCC2, CDKN3, SLC2A1, and IGFBP1 were identified to create prognostic features for butyrate metabolism related outcomes in LUAD. Cox regression analysis determined that the pathological T stage, tumor stage, and RiskScore could serve as independent prognostic indicators. Analysis of the abundance of 22 immune infiltrating cells revealed that 15 immune cell types exhibited substantial differences and were strongly associated with risk ratings and prognosis. An important correlation exists between risk ratings and immunological checkpoints, which can be utilized to forecast the efficacy of treatment. In the high-risk group, there was an upregulation of the expression of PD-L2, PD-L1, and PD-1. Additionally, the risk score showed a positive correlation with TIDE and Exclusion score, while showing a negative correlation with Dysfunction score. Furthermore, the IC₅₀ values for cisplatin, paclitaxel, and docetaxel were notably elevated in the high-risk group, indicating that these medications could potentially provide therapeutic advantages for this particular group. Finally, we determined that knockdown CDKN3 inhibited the proliferation and metastasis of LUAD cells.

Conclusion: We identify and validate a novel BMR-related prognostic signature comprising 5 DEGs for LUAD patients. Our data might provide a new molecular target for LUAD treatment.

Background: Melanoma is a highly aggressive skin cancer, where early and accurate diagnosis is crucial to improve patient outcomes. Dermoscopy, a non-invasive imaging technique, aids in melanoma detection but can be limited by subjective interpretation. Recently, machine learning and deep learning techniques have shown promise in enhancing diagnostic precision by automating the analysis of dermoscopy images.

Methods: This systematic review examines recent advancements in machine learning (ML) and deep learning (DL) applications for melanoma diagnosis and prognosis using dermoscopy images. We conducted a thorough search across multiple databases, ultimately reviewing 34 studies published between 2016 and 2024. The review covers a range of model architectures, including DenseNet and ResNet, and discusses datasets, methodologies, and evaluation metrics used to validate model performance.

Results: Our results highlight that certain deep learning architectures, such as DenseNet and DCNN demonstrated outstanding performance, achieving over 95% accuracy on the HAM10000, ISIC and other datasets for melanoma detection from dermoscopy images. The review provides insights into the strengths, limitations, and future research directions of machine learning and deep learning methods in melanoma diagnosis and prognosis. It emphasizes the challenges related to data diversity, model interpretability, and computational resource requirements.

Conclusion: This review underscores the potential of machine learning and deep learning methods to transform melanoma diagnosis through improved diagnostic accuracy and efficiency. Future research should focus on creating accessible, large datasets and enhancing model interpretability to increase clinical applicability. By addressing these areas, machine learning and deep learning models could play a central role in advancing melanoma diagnosis and patient care.

Background: Acute myeloid leukemia (AML) is an aggressive hematological neoplasm. Little improvement in survival rates has been achieved over the past few decades. Necroptosis has relationship with certain types of malignancies outcomes. Here, we evaluated the diagnostic ability, prognostic capacity of necroptosis-related genes (NRGs) and the effect of their copy number variations (CNVs) in AML.

Methods: Necroptosis-related differentially expressed genes (NRDEGs) were identified after intersecting differentially expressed genes (DEGs) from the Gene Expression Omnibus(GEO) database with NRGs from GeneCards, the Molecular Signatures Database (MSigDB) and literatures. Machine learning was applied to obtain hub-NRDEGs. The expression levels of the hub-NRDEGs were validated in vitro. The mRNA-miRNA and mRNA-TF interaction networks with the hub-NRDEGs were screened using Cytoscape^@. Single-sample gene set enrichment analysis (ssGSEA) was utilized to calculate correlations between the hub-NRDEGs and immune cells. CNV analysis of the hub-NRDEGs was carried out on the TCGA-LAML datasets from the TCGA database. Kaplan-Meier (K-M) survival analyses were utilized to evaluate the prognostic values along with Cox model.

Results: Six hub-NRDEGs (SLC25A5, PARP1, CTSS, ZNF217, NFKB1, and PYGL) were obtained and their expression changes derived from CNVs in AML were visualized. In total, 65 mRNA-miRNA and 80 mRNA-TF interaction networks with hub-NRDEGs were screened. The ssGSEA result showed the expression of RAPR1 was inversely related to CD56^dim natural killer cells and the expression of CTSS was positive related to Myeloid-derived suppressor cells (MDSCs) in AML. The K-M results demonstrated that ZNF217 had significant difference in the duration of survival in AML patients. Cox regression models revealed that the hub-NRDEGs had better predictive power at year-1 and year-5.

Conclusion: These screened NRDEGs can be exploited as clinical prognostic predictions in AML patients, as well as potential biomarkers for diagnosis and therapeutic targeting.

Introduction: Existing evidence suggests a lower uptake of cervical cancer screening among Indian women. Coverage is lower in rural than urban women, but such disparities are less explored. So, the present study was conducted to explore the self-reported coverage of cervical cancer screening in urban and rural areas stratified by socio-demographic characteristics, determine the spatial patterns and identify any regional variations, ascertain the factors contributing to urban-rural disparities and those influencing the likelihood of screening among women aged 30-49 years factors residing in urban, rural, and overall Indian settings.

Methods: We did a secondary analysis of the fifth round of the National Family Health Survey in India (2019-21) data with a sample size of 3,48,882 women. The coverage of cervical cancer screening was estimated using sampling weights. Urban-rural differences were compared using the chi-square test. Spatial patterns were analysed using aggregated district-level data, and the contribution of different independent variables to the urban-rural disparities was estimated using multivariate decomposition analysis. Multivariable logistic regression was conducted using STATA 17 to obtain the significant factors of reported screening in urban and rural areas.

Results: The nationwide coverage of cervical cancer screening was 2.0% (95% CI: 1.9-2.0). The urban (2.4%; 2.3-2.5) participants had higher screening coverage than their rural (1.8%; 1.7-1.8) counterparts. Moran's I statistic confirmed the presence of spatial dependence and geographical gradient. Decomposition analysis depicted small urban-rural differences in the screening coverage of 0.60% (0.4-0.8). Endowment and coefficient contributed to 88.15% and 11.85% of the disparities. Compositional changes were contributed majorly by regional differences, low education, scheduled tribes, and having living children > 2. Higher odds of having screening were associated with older age (AOR 1.45, 95% CI: 1.03-1.28), higher education (1.32; 1.13-1.55), higher age of first intercourse (1.60; 1.43-1.79), married (1.25; 1.08-1.45) and diabetic (1.39; 1.17-1.65) women, and those from South India (6.76; 5.90-7.75). The odds were lower among Muslims, scheduled tribes and participants using hormonal contraceptives.

Conclusion: There are significant urban-rural disparities in cervical cancer screening uptake that can be attributed to regional variation, educational inequalities, tribal groups, socio-economic inequalities and parity, necessitating the need to comprehensively design tailor-made advocacy initiatives and simultaneously address the broader determinants of health.