Blood metabolites and mitochondrial biological functions are associated with the occurrence and development of cancer, but the potential causal relationship between them and cervical cancer remains unknown. This bidirectional two-sample Mendelian randomization study aims to investigate the potential causal relationship between blood metabolites, mitochondrial biofunction and cervical cancer.

In this study, the blood metabolites and mitochondrial biofunction datasets were used as exposure factors, and the cervical cancer dataset from the Finnish dataset was used as the outcome. The study employed single nucleotide polymorphisms (SNPs) as instrumental variables and utilizes Inverse Variance Weighted (IVW), MR Egger, and Weighted Median methods for Mendelian randomization analysis. Sensitivity analysis was used to assess the reliability of the results. We also performed subgroup analysis based on the pathological types of cervical cancer. To further illuminate the possible metabolic mechanisms, MetaboAnalyst 6.0 tool was employed for metabolic pathway analysis.

The results indicated that 25 blood metabolites and 3 mitochondrial biofunctions have a potential causal relationship with cervical cancer. Among these blood metabolites, they can be categorized into fatty acid metabolites, bile acid metabolites, amino acid metabolites, hormone metabolites, caffeine metabolites, and others. Subgroup analysis reveals that different pathological types of cervical cancer have distinct potential risk and protective factors. Furthermore, metabolic pathway analysis suggests that linolenic acid metabolism exhibits strong anti-tumor potential. Specifically, Linolenate [alpha or gamma; (18:3n3 or 6)] showed an OR value of 0.81 (95% CI: 0.67-0.99), indicating a potential causal relationship between lipid metabolism and cervical cancer.

Blood metabolites and mitochondrial biofunction have potential causal relationships with cervical cancer, which provides new insights for further research on the etiology and treatment of cervical cancer.

This study aimed to develop a core outcome set (COS) for pituitary surgery to enhance the quality, efficiency and effectiveness of future pituitary adenoma surgery research.

Thirty-three outcomes were identified through a systematic review of pituitary adenoma surgery outcomes and a study on patient-reported measures. These were presented in an online survey to healthcare professionals (HCPs), patients and caregivers. In the first round, participants scored each outcome's importance on a 5-point scale (1-strongly disagree; 5-strongly agree) and could also suggest additional outcomes, which were reviewed and, if appropriate, added to existing domains. In the second round, participants re-scored the updated the list, considering group median and interquartile range scores from the previous round. Outcomes with a median score of 5 were included in the COS. A final live online consensus meeting discussed and voted on borderline outcomes (median scores 3-4).

The first round received 95 responses (52% HCPs, 48% patients/caregivers). Of the 33 outcomes, 16 received a median score of 5 (strongly agree), three received 4.5 and 14 received 4 (agree). Round two received 87 responses (52% HCPs, 48% patients and caregivers). Of the 33 outcomes, 14 received a median ranking of 5, two received 4.5, 15 received 4 and two received 3 (neutral). The live meeting (attended by 12 participants: 5 HCPs, 6 patients, 1 caregiver), reached consensus on the final COS, which includes 7 domains: short-term surgical outcomes; nasal outcomes; ophthalmic outcomes; endocrine outcomes; quality of life and psychological outcomes; other short-term outcomes; and disease control outcomes.

We advocate for use of the COS in future pituitary surgery research.

Higher expression of Complement C1q-like protein 4 (C1ql4) in breast cancer may be associated with increased tumor malignancy. As a protein involved in immune responses and inflammation, C1ql4 is expressed across all molecular subtypes of breast cancer. We conducted a retrospective analysis and follow-up study involving 111 patients who underwent breast cancer resection at the Department of Breast Surgery, Affiliated Hospital of Chengde Medical College, to investigate the relationship between C1ql4 expression and overall survival, as well as to evaluate the prognostic value of C1ql4 both independently and in combination with conventional clinicopathologic parameters. Chi-square analysis revealed a significant positive difference between C1ql4 protein expression and TNM staging, as well as molecular subtypes. Moreover, patients with high C1ql4 expression demonstrated significantly poorer prognoses. In conclusion, our findings suggest that C1ql4 expression may serve as a potential biomarker for elevated breast cancer malignancy.

The study investigates the correlation between CD3 T-cell expression levels and cervical cancer (CC) while developing a magnetic resonance (MR) imaging-based radiomics model for preoperative prediction of CD3 T-cell expression levels. Prognostic correlations between CD3D, CD3E, and CD3G gene expressions and various cancers were analyzed using the Cancer Genome Atlas (TCGA) database. Protein-protein interaction (PPI) analysis via the STRING database identified associations between these genes and T lymphocyte activity. Gene Set Enrichment Analysis (GSEA) revealed immune pathway enrichment by categorizing genes based on CD3D expression levels. Correlations between immune checkpoint molecules and CD3 complex genes were also assessed. The study retrospectively included 202 patients with pathologically confirmed early-stage CC who underwent preoperative MRI, divided into training and test groups. Radiomic features were extracted from the whole-lesion tumor region of interest (ROI_tumor) and from peritumoral regions with 3 mm and 5 mm margins (ROI_3mm and ROI_5mm, respectively). Various machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression, Random Forest, AdaBoost, and Decision Tree, were used to construct radiomics models based on different ROIs, and diagnostic performances were compared to identify the optimal approach. The best-performing algorithm was combined with intra- and peritumoral features and clinically relevant independent risk factors to develop a comprehensive predictive model. Analysis of the TCGA database demonstrated significant associations between CD3D, CD3E, and CD3G expressions and several cancers, including CC (p < 0.05). PPI analysis highlighted connections between these genes and T lymphocyte function, while GSEA indicated enrichment of immune-related pathways linked to CD3D. Immune checkpoint correlations showed positive associations with CD3 complex genes. Radiomics analysis selected 18 features from ROI_tumor and ROI_3mm across MRI sequences. The SVM algorithm achieved the highest predictive performance for CD3 T-cell expression status, with an area under the curve (AUC) of 0.93 in the training group and 0.92 in the test group. This MR-based radiomics model effectively predicts CD3 expression status in patients with early-stage CC, offering a non-invasive tool for preoperative assessment of CD3 expression, but its clinical utility needs further prospective validation.

It is well-documented that early recurrence of hepatocellular carcinoma following liver transplantation can markedly impact patient survival. Accurately identifying patients at risk for early recurrence, followed by timely interventions, could greatly improve the long-term efficacy of liver transplantation. The Milan criteria, the clinical gold standard for selecting patients with a low risk of post-transplant recurrence, fails to exclude high-risk patients with biologically aggressive hepatocellular carcinoma. Accordingly, there is an urgent need to develop and validate an improved model for predicting hepatocellular carcinoma post-liver transplantation. Herein, we established a new model to stratify the risk of early hepatocellular carcinoma recurrence following liver transplantation and facilitate decision-making regarding adjuvant therapy. Our newly established nomogram could predict early recurrence post-liver transplantation more effectively than the Milan criteria. Importantly, we found that adjuvant therapy could significantly benefit high-risk recipients but did not significantly affect low-risk recipients. Based on the new stratification criteria, adjuvant therapy should be actively considered for high-risk patients post-liver transplantation, whereas postoperative follow-up and observation are recommended for low-risk patients. Early recurrence of hepatocellular carcinoma (HCC) following liver transplantation (LT) can adversely affect long-term patient survival. The Milan criteria (MC) have limited capacity to predict early HCC recurrence, and no consensus regarding prophylactic adjuvant therapy (AT) after LT has been established. Herein, we developed an accurate model for predicting early HCC recurrence following LT to guide decision-making on AT. Overall, 364 patients with HCC from three transplantation centers in China were included and followed up for one-year post-LT. Baseline data were used to construct a nomogram, comparing performance with the MC. The efficacy of AT was compared between patients stratified into low- and high-risk subgroups based on nomogram scores.The nomogram included tumor burden score, alpha-fetoprotein level, platelet-to-lymphocyte ratio, pathological differentiation, and microvascular invasion as independent predictive factors. The concordance index and the area under the curve of the nomogram were 0·768 (95% confidence interval, 0·753-0·781) and 0·809, respectively, exceeding those of the MC. The results of the calibration curve and decision curve analysis were also satisfactory. Considering the high-risk subgroups, the AT group considerably outperformed the No-AT group in terms of 1-year recurrence-free survival (45·0 vs. 23·0%, P < 0·001). However, the low-risk AT and No-AT groups did not significantly differ (78·5 vs. 83·9%). In patients with HCC, the new nomogram predicted early recurrence post-LT more effectively than the MC. Based on the new stratification criteria, high-risk patients may benefit from AT, whereas AT is not recommended for low-risk patients.

This study was aimed at differentiating brain metastases (BMs) from non-small cell lung cancer (NSCLC) vs. small cell lung cancer (SCLC), and the adenocarcinoma (AD) vs. non-adenocarcinoma (NAD) subtypes, according to radiomics features derived from multiparametric magnetic resonance imaging (MRI). A total of 276 patients with BMs, including 98 with SCLC and 178 with NSCLC, were randomly divided into training (193 cases) and test (83 cases) datasets in a 7:3 ratio. Of the 178 patients with NSCLC, 155 had primary AD, and 23 had NAD; those patients were also randomly divided into training (124 cases) and test (54 cases) datasets. Logistic regression analysis was used to construct classification models based on the radiomics features extracted from contrast-enhanced T1-weighted imaging (T1CE), T2-fluid-attenuated inversion recovery (T2-FLAIR), and diffusion-weighted imaging (DWI) images. Diagnostic efficiency was evaluated with the area under the receiver operating characteristic curve (AUC) through Delong's test, calibration curves through the Hosmer-Lemeshow test and Brier score, precision-recall curves, and decision curve analysis. Compared with radiomics features derived from a single sequence, multiparametric combined-sequence MRI radiomics features based on T1CE, T2-FLAIR, and DWI images exhibited greater specificity in distinguishing BMs originating from various lung cancer subtypes. In the training and test datasets, the AUCs of the model for the classification of SCLC and NSCLC BMs were 0.765 (95% CI 0.711, 0.822) and 0.762 (95% CI 0.671, 0.845), respectively, whereas the AUCs of the prediction models combining the three sequences in differentiating AD from NAD BMs were 0.861 (95% CI 0.756, 0.951) and 0.851 (95% CI 0.649, 0.984), respectively. The radiomics classification method based on the combination of multiple MRI sequences can be used for differentiating various lung cancer BMs.

Prostate cancer (PCa) is the second most common cause of cancer related deaths in men in the UK. A national screening programme for PCa does not exist due to the unsuitability of the total prostate specific antigen (tPSA) test which is not specific for PCa and has a high false positive rate. Serum tPSA was measured in n = 25,356 male Randox Health clients. A biomarker-based (tPSA, EGF, MCP-1, IL-8) prostate cancer risk score (PCRS) was then applied to a retrospective cohort (n = 1,142/25,356) of individuals to assess PCa risk. A comparative analysis between tPSA and PCRS indicated that 90.5% of the cohort were assigned low risk of PCa. Of those with an elevated PCRS, 67.8% (78/115) had a normal tPSA value based on tPSA age-adjusted cut-offs. In addition, we observed a significant negative correlation between increasing body mass index (BMI) in men with high BMI (≥ 30) and tPSA levels. No correlation was observed between BMI and PCRS. The tPSA test is potentially unsuitable for use in males with BMI ≥ 30. Use of PCRS could provide more accurate PCa risk stratification for males with BMI ≥ 30. Future assessment of the clinical utility of PCRS is warranted.

The migrasome, a recently identified cellular organelle, is closely associated with cell migration and plays a critical role in various physiological and pathological processes. The relationship between migrasomes and the prognosis of kidney cancer patients remains unclear. We utilized least absolute shrinkage and selection operator (LASSO) Cox regression analysis on data from The Cancer Genome Atlas- kidney renal clear cell carcinoma (TCGA-KIRC) dataset to identify migrasome-related long non-coding RNAs (lncRNAs) with prognostic significance in KIRC. We then developed and validated a prognostic risk model based on these lncRNAs. In addition, to identify potential immunotherapeutic agents, we analyzed tumor immune dysfunction and exclusion scores; we also conducted immune cell infiltration profiling. Risk score analysis identified 12 migrasome-related lncRNAs significantly associated with the prognosis of TCGA-KIRC patients. Kaplan-Meier survival analysis demonstrated that the prognostic model effectively stratified patients into high- and low-risk groups; the high-risk group showed a significantly worse prognosis relative to the low-risk group. Importantly, higher risk scores were associated with increased immune cell infiltration. This prognostic model underscores the importance of migrasome-related lncRNAs in KIRC and provides a novel tool for predicting patient prognosis and immune response in KIRC.

We sought to investigate the factors influencing liver regeneration after hepatectomy for hepatocellular carcinoma and the relationship between liver regeneration and prognosis. This retrospective cohort study enrolled 92 hepatocellular carcinoma (HCC) patients undergoing hemihepatectomy at Qingdao University Affiliated Hospital (2014-2020) with complete CT imaging (postoperative day 3 and month 1) and clinical records. Using Hisense CAS software, we performed three-dimensional liver reconstruction to quantify standardized residual liver volume (SRLV) and calculate hepatic regeneration rate (HRR) at 1-month postoperation. Patients were stratified into high and low-regeneration groups based on median HRR. Univariate analysis and multivariate logistic regression were applied to identify factors influencing regeneration. Kaplan-Meier survival curves with log-rank tests analyzed tumor-free survival (TFS) and overall survival (OS) outcomes in relation to regeneration capacity. The cohort comprised 61 right and 31 left hemihepatectomies. Median 1-month HRR was 17.6% overall, with significant disparity between right (20.29%) and left hepatectomy subgroups (12.2%). Univariate analysis identified age, sex, alcohol history, hepatitis B status, cirrhosis severity, and SRLV as significant regeneration predictors (all P < 0.05). Multivariate modeling established cirrhosis severity (OR = 0.217, 95% CI:0.064-0.732, P = 0.014) and SRLV (OR = 0.989, 95% CI:0.982-0.995, P < 0.001) as independent determinants.Prognostically, high-regeneration patients exhibited extended median TFS (16 vs. 5 months, P<0.05) compared to low-regeneration counterparts, though no significant OS difference was observed (P>0.05). Cirrhosis severity and standardized residual liver volume (SRLV) independently predict post-hemihepatectomy liver regeneration in HCC patients. Preoperative 3D reconstruction-guided SRLV assessment combined with cirrhosis evaluation optimizes surgical planning. Enhanced hepatic regeneration correlates with shorter tumor-free survival (median 16 vs 5 months, P＜0.05), necessitating intensified surveillance in high-regeneration cohorts to mitigate recurrence risks.

This study primarily focuses on the potential sources of autofluorescence, including keratins (KRT) encoded by KRT1, KRT7, and KRT8, to investigate their contributions to the differences in autofluorescence between cancerous tissues and adjacent non-tumor tissues, as well as their potential for real-time diagnosis of RCC. First, the autofluorescence of renal cell carcinoma (RCC) tissues under 488 nm laser excitation was observed and compared with the autofluorescence of neighboring non-tumor tissues. Then, the effect on the autofluorescence intensity was analyzed by knocking down the KRT1/KRT7 gene. In addition, autofluorescence data were collected from 174 pairs of tumor and adjacent non-tumor tissue samples (from 60 RCC patients). Diagnostic performance was evaluated using ROC analysis to determine the threshold value for tumor autofluorescence intensity. Under 488 nm laser excitation, the intensity of green autofluorescence in cancerous tissues of RCC patients was significantly lower than that in non-tumor tissues. Further analysis showed that KRT1 knockdown resulted in a 73% reduction in autofluorescence intensity, suggesting that KRT1 plays a key role in the reduced autofluorescence observed in tumor tissues. In addition, analysis of autofluorescence data from 174 tumor and adjacent non-tumor tissue samples showed an AUC of 0.880 for ROC analysis, a diagnostic sensitivity and specificity of 0.843 and 0.835, respectively, and a threshold value of 27.45 for using tumor autofluorescence intensity. KRT1 is a major contributor to the tumor autofluorescence observed in RCC. An autofluorescence-based diagnostic model facilitates real-time assessment of surgical margins during partial nephrectomy, thereby potentially improving surgical success rates.