Expression Levels Of Ki-67 Research Articles

Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data

BackgroundGliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patients diagnosed with malignant glioblastomas typically survive for less than a year. In light of this circumstance, genotyping is an effective means of categorizing gliomas. The Ki67 proliferation index, a widely used marker of cellular proliferation in clinical contexts, has demonstrated potential for predicting tumour classification and prognosis. In particular, magnetic resonance imaging (MRI) plays a vital role in the diagnosis of brain tumours. Using MRI to extract glioma-related features and construct a machine learning model offers a viable avenue to classify and predict the level of Ki67 expression. MethodsThis study retrospectively collected MRI data and postoperative immunohistochemical results from 613 glioma patients from the …… Hospital. Subsequently, we performed registration and skull stripping on the four MRI modalities: T1-weighted (T1), T2-weighted (T2), T1-weighted with contrast enhancement (T1CE), and Fluid Attenuated Inversion Recovery (FLAIR). Each modality's segmentation yielded three distinct tumour regions. Following segmentation, a comprehensive set of features encompassing texture, first-order, and shape attributes were extracted from these delineated regions. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) algorithm with subsequent sorting to identify the most important features. These selected features were further analysed using correlation analysis to finalise the selection for machine learning model development. Eight models: logistic regression (LR), naive bayes, decision tree, gradient boosting tree, and support vector classification (SVM), random forest (RF), XGBoost, and LightGBM were used to objectively classify Ki67 expression. ResultsIn total, 613 patients were enrolled in the study, and 24,455 radiomic features were extracted from each patient’s MRI. These features were eventually reduced to 36 after LASSO screening, RF importance ranking, and correlation analysis. Among all the tested machine learning models, LR and linear SVM exhibited superior performance. LR achieved the highest area under the curve score of 0.912 ± 0.036, while linear SVM obtained the top accuracy with a score of 0.884 ± 0.031. ConclusionsThis study introduced a novel approach for classifying Ki67 expression levels using MRI, which has been proven to be highly effective. With the LR model at its core, our method demonstrated its potential in signalling a promising avenue for future research. This innovative approach of predicting Ki67 expression based on MRI features not only enhances our understanding of cell activity but also represents a significant leap forward in brain glioma research. This underscores the potential of integrating machine learning with medical imaging to aid in the diagnosis and prognosis of complex diseases.

Deep learning model to predict Ki-67 expression of breast cancer using digital breast tomosynthesis.

Developing a deep learning (DL) model for digital breast tomosynthesis (DBT) images to predict Ki-67 expression. The institutional review board approved this retrospective study and waived the requirement for informed consent from the patients. Initially, 499 patients (mean age: 50.5years, range: 29-90years) referred to our hospital for breast cancer were participated, 126 patients with pathologically confirmed breast cancer were selected and their Ki-67 expression measured. The Xception architecture was used in the DL model to predict Ki-67 expression levels. The high Ki-67 vs low Ki-67 expression diagnostic performance of our DL model was assessed by accuracy, sensitivity, specificity, areas under the receiver operating characteristic curve (AUC), and by using sub-datasets divided by the radiological characteristics of breast cancer. The average accuracy, sensitivity, specificity, and AUC were 0.912, 0.629, 0.985, and 0.883, respectively. The AUC of the four subgroups separated by radiological findings for the mass, calcification, distortion, and focal asymmetric density sub-datasets were 0.890, 0.750, 0.870, and 0.660, respectively. Our results suggest the potential application of our DL model to predict the expression of Ki-67 using DBT, which may be useful for preoperatively determining the treatment strategy for breast cancer.

Prediction of Ki-67 expression in bladder cancer based on CT radiomics nomogram.

This study aimed to create and validate a radiomics nomogram for non-invasive preoperative Ki-67 expression level prediction in patients with bladder cancer (BCa) using contrast-enhanced CT radiomics features. A retrospective analysis of 135 patients was conducted, 79 of whom had high levels of Ki-67 expression and 56 of whom had low levels. For the dimensionality reduction analysis, the best features were chosen using the least absolute shrinkage selection operator and one-way analysis of variance. Then, a radiomics nomogram was created using multiple logistic regression analysis based on radiomics features and clinical independent risk factors. The performance of the model was assessed using the Akaike information criterion (AIC) value, the area under the curve (AUC) value, accuracy, sensitivity, and specificity. The clinical usefulness of the model was assessed using decision curve analysis (DCA). Finally, to establish a radiomics nomogram, the best 5 features were chosen and integrated with the independent clinical risk factors (T stage) and Rad-score. This radiomics nomogram demonstrated significant correction and discriminating performance in both the training and validation sets, with an AUC of 0.836 and 0.887, respectively. This radiomics nomogram had the lowest AIC value (AIC = 103.16), which was considered to be the best model. When compared to clinical factor model and radiomics signature, DCA demonstrated the more value of the radiomics nomogram. Enhanced CT-based radiomics nomogram can better predict Ki-67 expression in BCa patients and can be used for prognosis assessment and clinical decision making.

A Study of Ki-67 Immunostaining in Prostate Carcinomas and Its Correlation with Gleason's Score and Prognosis: An Experience at a Tertiary Centre in the Himalayan Foothills.

Prostate cancer is a significant cause of cancer-related mortality among men worldwide, necessitating the exploration of prognostic biomarkers to aid in accurate risk assessment and treatment decision-making. This cross-sectional study aimed to comprehensively evaluate the role of Ki-67 as a prognostic marker in prostate cancer by examining its association with clinicopathological parameters. A total of 102 archived cases of prostate core biopsy specimens, histopathologically reported as prostate carcinoma, were included in this study. Histopathological grading was conducted using Gleason's scoring and grading system based on morphology. The statistical software "R" was utilized for data analysis. Kruskal-Wallis test and Fisher's exact test were employed to analyze the association between Ki-67 expression and clinicopathological parameters. The study revealed significant correlations between Ki-67 expression and various clinicopathological parameters in prostate cancer cases. High Ki-67 expression levels were associated with higher Gleason scores, increased incidence of perineural invasion, advanced T stages, lymph node metastasis, presence of distant metastasis, and higher prognostic stage groups. The findings of this cross-sectional study support the potential of Ki-67 as a prognostic marker in prostate cancer. The significant associations observed between Ki-67 expression and clinicopathological parameters indicate its usefulness in risk stratification and treatment decision-making. The incorporation of histopathological grading, including Gleason scoring, and analysis of perineural invasion strengthens the validity of the study. Ki-67, in combination with morphological assessments, provides valuable prognostic information for prostate cancer patients.

Inhibition of p38 MAPK/NF-κB p65 signaling pathway activity by rare ginsenosides ameliorates cyclophosphamide-induced premature ovarian failure and KGN cell injury

Ethnopharmacological relevancePanax ginseng C. A. Mey., one of the most used herbs in the world, shows effective treatment in reproductive injury. Recent studies have proven that the processed product, red ginseng, which is more active than ginseng itself. Therefore, it is speculated that its main functional component, rare ginsenosides (heat-transformed saponin, HTS), may be effective in treating premature ovarian failure (POF), but its efficacy has not yet been experimentally confirmed. Aim of the studyTo evaluate whether HTS could attenuate cyclophosphamide-induced inflammation and oxidative damage in POF model rats and the human granulosa-like KGN cell line and protect granulosa cell proliferation. Material and methodsHTS were isolated from ginsenosides and high performance liquid chromatography (HPLC) analysis was used to analyze the HTS components. Cyclophosphamide (CP) was used to establish a POF rat model and KGN cell injury model. Reactive oxygen species (ROS) and antioxidant enzyme production was determined using specific assays, while inflammatory cytokine secretion was measured by enzyme-linked immunosorbent assay (ELISA). The proliferative function of granulosa cells was assessed using high-content screening and immunohistochemistry to determine the Ki67 protein level. Protein expression in ovarian tissues and KGN cells was analyzed by Western blotting, quantitative real-time PCR (qRT-PCR) was used to determine the transcriptional changes in ovarian tissues and KGN cells. ResultsIn CP-treated POF model rats, HTS significantly decreased malondialdehyde (MDA), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) levels, increased glutathione oxidase (GSH) levels, and upregulated Ki67 expression in ovarian granulosa cells. In addition, HTS significantly increased cell survival and Ki67 expression levels in CP-treated cells, and superoxide dismutase (SOD) levels were significantly increased. HTS significantly downregulated IL-6, TNF-α, and interleukin-1β (IL-1β) mRNA expression and significantly inhibited nuclear factor kappa-B p65 (NF-κB p65) and p38 mitogen activated protein kinase (p38 MAPK) phosphorylation in POF model rats and KGN cells. Moreover, NF-κB p65 and p38 MAPK levels were significantly increased in ovarian granulosa cells. p65 and p38 protein and gene expression was significantly downregulated. ConclusionHTS ameliorated CP-induced POF and human granulosa cell injury, possibly by inhibiting inflammation and oxidative damage mediated by the p38 MAPK/NF-κB p65 signaling pathway.

Effects of different KRAS mutants and Ki67 expression on diagnosis and prognosis in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a prevalent form of non-small cell lung cancer with a rising incidence in recent years. Understanding the mutation characteristics of LUAD is crucial for effective treatment and prediction of this disease. Among the various mutations observed in LUAD, KRAS mutations are particularly common. Different subtypes of KRAS mutations can activate the Ras signaling pathway to varying degrees, potentially influencing the pathogenesis and prognosis of LUAD. This study aims to investigate the relationship between different KRAS mutation subtypes and the pathogenesis and prognosis of LUAD. A total of 63 clinical samples of LUAD were collected for this study. The samples were analyzed using targeted gene sequencing panels to obtain sequencing data. To complement the dataset, additional clinical and sequencing data were obtained from TCGA and MSK. The analysis revealed significantly higher Ki67 immunohistochemical scores in patients with missense mutations compared to controls. Moreover, the expression level of KRAS was found to be significantly correlated with Ki67 expression. Enrichment analysis indicated that KRAS missense mutations activated the SWEET_LUNG_CANCER_KRAS_DN and CREIGHTON_ENDOCRINE_THERAPY_RESISTANCE_2 pathways. Additionally, patients with KRAS missense mutations and high Ki67 IHC scores exhibited significantly higher tumor mutational burden levels compared to other groups, which suggests they are more likely to be responsive to ICIs. Based on the data from MSK and TCGA, it was observed that patients with KRAS missense mutations had shorter survival compared to controls, and Ki67 expression level could more accurately predict patient prognosis. In conclusion, when utilizing KRAS mutations as biomarkers for the treatment and prediction of LUAD, it is important to consider the specific KRAS mutant subtypes and Ki67 expression levels. These findings contribute to a better understanding of LUAD and have implications for personalized therapeutic approaches in the management of this disease.

Development and validation of a model for predicting the expression of Ki-67 in pancreatic ductal adenocarcinoma with radiological features and dual-energy computed tomography quantitative parameters

ObjectiveTo construct and validate a model based on the dual-energy computed tomography (DECT) quantitative parameters and radiological features to predict Ki-67 expression levels in pancreatic ductal adenocarcinoma (PDAC).Materials and methodsData from 143 PDAC patients were analysed. The variables of clinic, radiology and DECT were evaluated. In the arterial phase and portal venous phase (PVP), the normalized iodine concentration (NIC), normalized effective atomic number and slope of the spectral attenuation curves were measured. The extracellular volume fraction (ECVf) was measured in the equilibrium phase. Univariate analysis was used to screen independent risk factors to predict Ki-67 expression. The Radiology, DECT and DECT–Radiology models were constructed, and their diagnostic effectiveness and clinical applicability were obtained through area under the curve (AUC) and decision curve analysis, respectively. The nomogram was established based on the optimal model, and its goodness-of-fit was assessed by a calibration curve.ResultsComputed tomography reported regional lymph node status, NIC of PVP, and ECVf were independent predictors for Ki-67 expression prediction. The AUCs of the Radiology, DECT, and DECT–Radiology models were 0.705, 0.884, and 0.905, respectively, in the training cohort, and 0.669, 0.835, and 0.865, respectively, in the validation cohort. The DECT–Radiology nomogram was established based on the DECT–Radiology model, which showed the highest net benefit and satisfactory consistency.ConclusionsThe DECT–Radiology model shows favourable predictive efficacy for Ki-67 expression, which may be of value for clinical decision-making in PDAC patients.Critical relevance statementThe DECT–Radiology model could contribute to the preoperative and non-invasive assessment of Ki-67 expression of PDAC, which may help clinicians to screen out PDAC patients with high Ki-67 expression.Key points• Dual-energy computed tomography (DECT) can predict Ki-67 in pancreatic ductal adenocarcinoma (PDAC).• The DECT–Radiology model facilitates preoperative and non-invasive assessment of PDAC Ki-67 expression.• The nomogram may help screen out PDAC patients with high Ki-67 expression.Graphical

The influencing factors for tumor thrombus in patients with hepatocellular carcinoma.

Microvascular invasion (MVI) is considered to be an important factor in the early invasion and metastasis of liver cancer, and the survival rate of patients with MVI is much lower than that of patients without MVI. Therefore, it is crucial to accurately predict the independent predictors of tumor thrombus formation. This study aimed to assess the risk factors for tumor thrombus grades in patients with hepatocellular carcinoma (HCC). Between August 2011 and December 2022, the data of 231 patients diagnosed with HCC were collected and divided into the following three groups: an MVI-negative group, an MVI-positive group, and a portal vein tumor thrombus (PVTT) group. Univariate analysis was used to compare the differences between the three groups in terms of clinical features, pathology, and imaging features. Multiple logistic regression analysis was used to analyze the risk factors associated with tumor thrombus grades, and the cutoff value was finally calculated by using the receiver operating characteristic (ROC) curve. The incidence of MVI and PVTT in the patients with HCC were 10.0% and 6.1%, respectively; univariate analysis revealed statistically significant differences in tumor diameter, alpha fetoprotein level, Ki-67 expression level, gender, tumor quantity, arteriovenous fistula, peritumoral enhancement, and satellite nodules among the three groups (P<0.05). Multiple logistic regression analysis showed that Ki-67 expression level, tumor diameter, and peritumoral enhancement were independent risk factors for tumor thrombus grades (P<0.05). The area under the curve (AUC) of Ki-67 expression level and tumor diameter was 0.713 [95% confidence interval (CI): 0.626-0.800] and 0.753 (95% CI: 0.669-0.837), respectively, and the AUC of the combination analysis was 0.805 (95% CI: 0.723-0.888), with a cutoff value of 17.5% and 4.1 cm, respectively (P<0.05). Tumor diameter, Ki-67 expression level, and peritumoral enhancement can be used as independent predictors of tumor thrombus in patients with HCC. The combination of tumor diameter and Ki-67 expression level can further improve diagnostic efficacy.

Siah2 inhibitor and the metabolic antagonist Oxamate retard colon cancer progression and downregulate PD1 expression.

Solid tumors such as colon cancer are characterized by rapid and sustained cell proliferation, which ultimately results in hypoxia, induction of hypoxia-inducible factor-1α (HIF-1α), and activation of glycolysis to promote tumor survival and immune evasion. We hypothesized that a combinatorial approach of menadione (MEN) as an indirect HIF-1α inhibitor and sodium oxamate (OX) as a glycolysis inhibitor may be a promising treatment strategy for colon cancer. We investigated the potential efficacy of this combination for promoting an antitumor immune response and suppressing tumor growth in a rat model of colon cancer. Colon cancer was induced by once-weekly subcutaneous injection of 20 mg/kg dimethylhydrazine (DMH) for 16 weeks. Control rats received the vehicle and then no further treatment (negative control) or MEN plus OX for 4 weeks (drug control). Dimethylhydrazine-treated rats were then randomly allocated to four groups: DMH alone group and other groups treated with MEN, OX, and a combination of (MEN and OX) for 4 weeks. Serum samples were assayed for the tumor marker carbohydrate antigen (CA19.9), while expression levels of HIF-1α, caspase-3, PHD3, LDH, and PD1 were evaluated in colon tissue samples by immunoassay and qRT-PCR. Additionally, Ki-67 and Siah2 expression levels were examined by immunohistochemistry. The combination of MEN plus OX demonstrated a greater inhibitory effect on the expression levels of HIF-1α, Siah2, LDH, Ki-67, and PD1, and greater enhancement of caspase-3 and PHD3 expression in colon cancer tissues than either drug alone. Simultaneous targeting of hypoxia and glycolysis pathways by a combination of MEN and OX could be a promising therapy for inhibiting colon cancer cell growth and promoting antitumor immunity [1].

Characterizing Foxp3+ and Foxp3- T cells in the homeostatic state and after allo-activation: resting CD4+Foxp3+ Tregs have molecular characteristics of activated T cells.

Due to the intracellular expression of Foxp3 it is impossible to purify viable Foxp3+ cells on the basis of Foxp3 staining. Consequently CD4+Foxp3+ regulatory T cells (Tregs) in mice have mostly been characterized using CD4+CD25+ T cells or GFP-Foxp3 reporter T cells. However, these two populations cannot faithfully represent Tregs as the expression of CD25 and Foxp3 does not completely overlap and GFP+Foxp3+ reporter T cells have been reported to be functionally altered. The aim of this study was to characterize normal Tregs without separating Foxp3+ and Foxp3- cells for the expression of the main functional molecules and proliferation behaviors by flow cytometry and to examine their gene expression characteristics through differential gene expression. Our data showed that the expressions of Foxp3, CD25, CTLA-4 (both intracellular and cell surface) and PD-1 was mostly confined to CD4+ T cells and the expression of Foxp3 did not completely overlap with the expression of CD25, CTLA-4 or PD-1. Despite higher levels of expression of the T cell inhibitory molecules CTLA-4 and PD-1, Tregs maintained higher levels of Ki-67 expression in the homeostatic state and had greater proliferation in vivo after allo-activation than Tconv. Differential gene expression analysis revealed that resting Tregs exhibited immune activation markers characteristic of activated Tconv. This is consistent with the flow data that the T cell activation markers CD25, CTLA-4, PD-1, and Ki-67 were much more strongly expressed by Tregs than Tconv in the homeostatic state.

A gadoxetic acid-enhanced MRI-based model using LI-RADS v2018 features for preoperatively predicting Ki-67 expression in hepatocellular carcinoma

PurposeTo construct a gadoxetic acid-enhanced MRI (EOB-MRI) -based multivariable model to predict Ki-67 expression levels in hepatocellular carcinoma (HCC) using LI-RADS v2018 imaging features.MethodsA total of 121 patients with HCC who underwent EOB-MRI were enrolled in this study. The patients were divided into three groups according to Ki-67 cut-offs: Ki-67 ≥ 20% (n = 86) vs. Ki-67 < 20% (n = 35); Ki-67 ≥ 30% (n = 73) vs. Ki-67 < 30% (n = 48); Ki-67 ≥ 50% (n = 45) vs. Ki-67 < 50% (n = 76). MRI features were analyzed to be associated with high Ki-67 expression using logistic regression to construct multivariable models. The performance characteristic of the models for the prediction of high Ki-67 expression was assessed using receiver operating characteristic curves.ResultsThe presence of mosaic architecture (p = 0.045), the presence of infiltrative appearance (p = 0.039), and the absence of targetoid hepatobiliary phase (HBP, p = 0.035) were independent differential factors for the prediction of high Ki-67 status (≥ 50% vs. < 50%) in HCC patients, while no features could predict high Ki-67 status with thresholds of 20% (≥ 20% vs. < 20%) and 30% (≥ 30% vs. < 30%) (p > 0.05). Four models were constructed including model A (mosaic architecture and infiltrated appearance), model B (mosaic architecture and targetoid HBP), model C (infiltrated appearance and targetoid HBP), and model D (mosaic architecture, infiltrated appearance and targetoid HBP). The model D yielded better diagnostic performance than the model C (0.776 vs. 0.669, p = 0.002), but a comparable AUC than model A (0.776 vs. 0.781, p = 0.855) and model B (0.776 vs. 0.746, p = 0.076).ConclusionsMosaic architecture, infiltrated appearance and targetoid HBP were sensitive imaging features for predicting Ki-67 index ≥ 50% and EOB-MRI model based on LI-RADS v2018 features may be an effective imaging approach for the risk stratification of patients with HCC before surgery.

Apparent diffusion coefficient in intrahepatic cholangiocarcinoma diffusion-weighted magnetic resonance imaging noninvasively predicts Ki-67 expression.

Tumor Ki-67 expression reflects prognosis and cancer grade, and biopsy-based preoperative assessment of Ki-67 expression is key to treatment. Apparent diffusion coefficient (ADC) values obtained with this imaging may noninvasively predict Ki-67 by reflecting tumor cell density and limited water molecule movement from irregular alignment. This study aimed to investigate the ability of ADC values to predict Ki-67 expression in intrahepatic cholangiocarcinoma (ICC). We retrospectively analyzed 39 cases of ICC confirmed by surgical pathology. All patients had undergone magnetic resonance imaging, and ADC values (mean, minimum, and maximum) were calculated. Ki-67 expression was assessed by immunohistochemistry, and patients were divided into groups of high (n=18) and low (n=21) Ki-67 expression. To assess the diagnostic performance of the ADC values for Ki-67 expression, we used the receiver operating characteristic curve and compared the areas under the curve (AUC). The mean and minimum ADC values were significantly lower in the group with high Ki-67 expression. For predicting high Ki-67 expression, the AUC values were 0.701 for mean ADC, 0.818 for minimum ADC, and 0.571 for maximum ADC. The diagnostic sensitivity and specificity of the minimum ADC values were 88.9% and 76.2%, respectively. In addition, with ADC values combined, the AUC increased to 0.831. Apparent diffusion coefficient is a useful predictor of Ki-67 expression level in ICC. Apparent diffusion coefficient values, especially minimum ADC values, can noninvasively predict ICC associated with high Ki-67 expression.

Study on the correlation between different expression levels of Ki-67 and ultrasonic image characteristics in primary breast lymphoma

ObjectiveTo investigate the correlation between different expression levels of Ki-67 and ultrasonographic features in primary breast lymphoma (PBL), in order to guide clinical prediction of malignant degree and prognosis of PBL by noninvasive ultrasonography. MethodsThe ultrasonographic characteristics of PBL proved by pathology were retrospectively summarized and analyzed, and the general clinical data, pathological types and the expression level of Ki-67 in PBL were summarized, and the correlation between the ultrasonographic characteristics and different expression levels of Ki-67 was analyzed. ResultsIn this study,The main ultrasonic manifestations of PBL were as follows: the lesions were mostly located in the left breast, large size, irregular morphology, uneven echo, no burr sign, posterior echo enhancement effect, blood flow signal, parallel growth, and no calcification. All the cases had different levels of Ki-67 expression. Among them, in the high expression group (Ki-67 ≥ 80%), 3 cases had burr sign and 13 cases had clear boundary, in the low expression group (Ki-67 < 80%), 12 cases had burr sign and 4 cases had clear boundary, and the difference between the two groups was statistically significant (P < 0.05). In the rest, there was no statistically significant difference between the two groups (P > 0.05). ConclusionKi-67 was expressed in PBL to varying degrees, and the ultrasound images of PBL in the high expression group mostly showed no “burr sign” and “clear boundary”; on the contrary, the ultrasound images of PBL in the low expression group often showed “burr sign” and “unclear boundary”. Therefore, the ultrasonic images could predict the expression level of Ki-67, the malignant degree of PBL and its prognosis.

Prognostic significance of grade of malignancy based on histopathological differentiation and Ki-67 in pancreatic ductal adenocarcinoma.

Tumor cell malignancy is indicated by histopathological differentiation and cell proliferation. Ki-67, an indicator of cellular proliferation, has been used for tumor grading and classification in breast cancer and neuroendocrine tumors. However, its prognostic significance in pancreatic ductal adenocarcinoma (PDAC) remains uncertain. Patients who underwent radical pancreatectomy for PDAC were retrospectively enrolled, and relevant prognostic factors were examined. Grade of malignancy (GOM), a novel index based on histopathological differentiation and Ki-67, is proposed, and its clinical significance was evaluated. The optimal threshold for Ki-67 was determined to be 30%. Patients with a Ki-67 expression level > 30% rather than ≤ 30% had significantly shorter 5-year overall survival (OS) and recurrence-free survival (RFS). In multivariate analysis, both histopathological differentiation and Ki-67 were identified as independent prognostic factors for OS and RFS. The GOM was used to independently stratify OS and RFS into 3 tiers, regardless of TNM stage and other established prognostic factors. The tumor-node-metastasis-GOM stage was used to stratify survival into 5 distinct tiers, and surpassed the predictive performance of TNM stage for OS and RFS. Ki-67 is a valuable prognostic indicator for PDAC. Inclusion of the GOM in the TNM staging system may potentially enhance prognostic accuracy for PDAC.

Immunohistochemical markers Ki67 and P16 help predict prognosis in locally advanced cervical cancer

ObjectiveTo investigate the relationship between Ki-67 and P16 expression levels after neoadjuvant chemotherapy, and the clinicopathological characteristics and prognosis of patients with locally advanced cervical cancer. MethodsPatients with FIGO 2009 stage IB2 or IIA2 cervical cancer, who underwent neoadjuvant chemotherapy combined with radical hysterectomy at the First Affiliated Hospital of Chongqing Medical University between January 2015 and December 2019, were identified retrospectively to correlate postoperative Ki-67 and P16 expression levels with clinicopathological factors. The optimal threshold for predicting recurrence was analysed using receiver operating characteristic (ROC) curves for the Ki-67 index, and univariate and multi-factorial Cox regression analysis were used to investigate the association between clinicpathological features including Ki-67 and P16 and recurrence-free survival. ResultsIn total, 334 patients were included after screening. The cut-off value of Ki-67 for determining recurrence was 67.5 % according to the ROC curve. On multi-factorial Cox analysis, lymphatic vascular space (p = 0.003) and Ki-67 index (p = 0.005) were shown to increase the risk of recurrence, and were independent prognostic factors for recurrence, while the expression of P16 was not significantly associated with the risk of recurrence (p = 0.097, odds ratio = 0.319). Patients with cervical cancer in the high Ki-67 expression group (Ki-67 ≥ 67.5 %) had lower recurrence-free survival and overall survival than patients in the low Ki-67 expression group (Ki-67 < 67.5 %) (p = 0.001 and 0.036, respectively). ConclusionThe expression levels of Ki-67 and P16 after neoadjuvant chemotherapy for locally advanced cervical cancer correlated with tumour differentiation. High expression of Ki-67 (Ki-67 ≥ 67.5 %) may indicate poorer recurrence-free survival and overall survival.

Agaro-oligosaccharides mitigate deoxynivalenol-induced intestinal inflammation by regulating gut microbiota and enhancing intestinal barrier function in mice.

Agarose-derived agaro-oligosaccharides (AgaroS) have been extensively studied in terms of structures and bioactivities; they reportedly possess antioxidant and anti-inflammatory activities that maintain intestinal homeostasis and host health. However, the protective effects of AgaroS on deoxynivalenol (DON)-induced intestinal dysfunction remain unclear. We investigated the effects of AgaroS on DON-induced intestinal dysfunction in mice and explored the underlying protective mechanisms. In total, 32 mice were randomly allocated to four treatments (n = 8 each) for 28 days. From day 1 to day 21, the control (CON) and DON groups received oral phosphate-buffered saline (200 μL per day); the AgaroS and AgaroS + DON groups received 200 mg AgaroS per kg body weight once daily by orogastric gavage. Experimental intestinal injury was induced by adding DON (4.8 mg per kg body weight) via gavage from day 21 to day 28. Phosphate-buffered saline was administered once daily by gavage in the CON and AgaroS groups. Herein, AgaroS supplementation led to a higher final body weight and smaller body weight loss and a lower concentration of plasma inflammatory cytokines, compared with the DON group. The DON group showed a significantly reduced ileal villus height and villus height/crypt depth, compared with the CON and AgaroS + DON groups. However, AgaroS supplementation improved DON-induced intestinal injury in mice. Compared with the DON group, ileal and colonic protein expression levels of claudin, occludin, Ki67, and mucin2 were significantly higher in the AgaroS supplementation group. Colonic levels of the anti-inflammatory cytokine IL-1β tended to be higher in the DON group than in the AgaroS + DON group. AgaroS altered the gut microbiota composition, accompanied by increased production of short-chain fatty acids in mice. In conclusion, our findings highlight a promising anti-mycotoxin approach whereby AgaroS alleviate DON-induced intestinal inflammation by modulating intestinal barrier functional integrity and gut microbiota in mice.

Tetramethylpyrazine regulates angiogenesis of endothelial cells in cerebral ischemic stroke injury via SIRT1/VEGFA signaling pathway

This study aims to investigate whether tetramethylpyrazine(TMP) can stimulate angiogenesis in cerebral microvascular endothelial cells and alleviate cerebral ischemic stroke(CIS) and to explore the underlying mechanisms. In the animal study, adult Sprague-Dawley rats(n=15) were assigned into sham surgery(sham), middle cerebral artery occlusion/reperfusion(MCAO/R), and MCAO/R+TMP(intraperitoneal injection of 20 mg·kg~(-1)) groups. The neurological function was evaluated by the Z-Longa method. The cerebral infarction volume was detected by TTC staining. Enzyme-linked immunosorbent assay(ELISA) was employed to detect the expression of vascular endothelial growth factor(VEGF), angiopoietin(Ang), and platelet-derived growth factor(PDGF). Immunofluorescence staining was employed to detect Ki67 and the expression of vascular endothelial growth factor A(VEGFA) and slient information regulator 1(SIRT1). Western blot was employed to determine the expression levels of VEGFA, SIRT1, angiopoietin-2(Ang-2), and platelet-derived growth factor B(PDGFB). In the cell study, mouse brain-derived endothelial cells(Bend.3) were cultured, and the optimal concentration of TMP was determined. Then, VEGF, Ang, and PDGF were detected by ELISA after the addition of cabozantinib. Western blot was employed to measure the expression of VEGFA, Ang-2, and PDGFB. Immunofluorescence staining was used to detect CD31, CD34, and Ki67, and the proliferation, migration, and tube formation ability of Bend.3 cells were observed in vitro. Western blot and immunofluorescence staining were performed to measure the expression of SIRT1 and VEGFA after addition of the SIRT1-specific inhibitor selisistat(EX-527). The results showed that compared with the sham group, the MCAO/R group had severe neurological function damage, increased infarction volume, up-regulated expression of VEGF, VEGFA, Ang, Ang-2, PDGF, and PDGFB, and down-regulated expression of Ki67 and SIRT1(P&lt;0.01). Compared with the MCAO/R group, the MCAO/R+TMP group presented alleviated neurological function damage, reduced infarction volume, and activated expression of VEGF, VEGFA, Ang, Ang-2, PDGF, PDGFB, Ki67, and SIRT1(P&lt;0.01). The cell experiments showed that compared with the normal group, Bend.3 cells were activated by oxygen glucose deprivation/reoxygenation(OGD/R) treatment(P&lt;0.05, P&lt;0.01). Compared with the OGD/R group, the OGD/R+TMP group upregulated the expression levels of VEGF, VEGFA, Ang, Ang-2, PDGF, PDGFB, SIRT1, Ki67, CD31, and CD34, enhanced the angiogenic ability of Bend.3 cells without being inhibited by BMS or EX-527(P&lt;0.05, P&lt;0.01, P&lt;0.001). The results suggest that TMP can activate the SIRT1/VEGFA signaling pathway to stimulate angiogenesis and alleviate CIS injury.

NUPR1 contributes to activate TFE3-dependent autophagy leading to cervical cancer proliferation

Cervical cancer is a malignant tumor that occurs in the cervix of women and endangers their lives. In this study, we aimed to assess the roles of NUPR1 and TFE3 in cervical cancer. The Cancer Genome Atlas (TCGA) database was used to assess the correlation between NUPR1 and TFE3 expression in cervical cancer. By silencing NUPR1 and TFE3, and through 3-MA treatment, we determined whether their silencing could lead to lysosomal dysfunction, thereby inhibiting autophagy and cervical cancer cell proliferation. Their roles were further analyzed using molecular biological methods. Silencing NUPR1 and TFE3 inhibited cell proliferation and decreased the expression levels of autophagy-related genes, p62 and LC3B. By tracing lysosomes within cells, NUPR1 and TFE3 knockdown were found to induce lysosomal dysfunction, thereby inhibiting autophagy. In vivo experimental studies have shown that knockdown of NUPR1 and TFE3 can inhibit tumor growth, while reducing the ki67, p62, and LC3B expression levels and promoting apoptosis. Furthermore, the expression levels of lamp1 and lamp2, and the phosphorylation of PI3K (p-PI3K) and Akt (p-Akt) were significantly reduced after NUPR1 and TFE3 knockdown. However, treatment with 3-MA and overexpression of TFE3 could partially reverse the effect of silencing NUPR1. Overall, silencing NUPR1 reduced autophagy by inhibiting TFE3 in cervical cancer. Our results supply new evidence for the use of NUPR1 as a therapeutic target in cervical cancer.

Co-Expression Network Analysis and Molecular Docking Demonstrate That Diosgenin Inhibits Gastric Cancer Progression via SLC1A5/mTORC1 Pathway.

Tumor-Node-Metastasis (TNM) stage of gastric cancer (GC) is one of the main factors affecting clinical outcome. The aim of this study was to explore the targets related to TNM stage of GC, and screening natural bioactive drug. RNA sequencing data of the TCGA-STAD cohort were downloaded from UCSC database. Genes associated with TNM staging were identified by weighted gene co-expression network analysis (WGCNA). Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (Xgboost), random forest (RF) and cytohubba plug-in of cytoscope were applied to screen hub genes. Natural bioactive ingredients were available from the HERB database. Molecular docking was used to evaluate the binding activity of active ingredients to the hub protein. CCK-8, flow cytometry, transwell and Western blot assays were used to analyze the effects of diosgenin on GC cells. 898 TNM-related genes were screened out through WGCNA. Three genes associated with GC progression/prognosis were identified, including nuclear receptor subfamily 3 group C member 2 (NR3C2), solute carrier family 1 member 5 (SLC1A5) and FAT atypical cadherin 1 (FAT1) based on the machine learning algorithms and hub co-expression network analysis. Diosgenin had good binding activity with SLC1A5. SLC1A5 was highly expressed in GC and was closely associated with tumor stage, overall survival and immune infiltration of GC patients. Diosgenin could inhibit cell viability and invasive ability, promote apoptosis and induce cell cycle arrest in G0/G1 phase. In addition, diosgenin promoted cleaved caspase 3 expression and inhibited Ki67, cyclin D1, p-S6K1, and SLC1A5 expression levels, while the mTORC1 activator (MHY1485) reversed this phenomenon. For the first time, this work reports diosgenin may inhibit the activation of mTORC1 signaling through targeting SLC1A5, thereby inhibiting the malignant behaviors of GC cells.

MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma.

To investigate the value of interpretable machine learning model and nomogram based on clinical factors, MRI imaging features, and radiomic features to predict Ki-67 expression in primary central nervous system lymphomas (PCNSL). MRI images and clinical information of 92 PCNSL patients were retrospectively collected, which were divided into 53 cases in the training set and 39 cases in the external validation set according to different medical centers. A 3D brain tumor segmentation model was trained based on nnU-NetV2, and two prediction models, interpretable Random Forest (RF) incorporating the SHapley Additive exPlanations (SHAP) method and nomogram based on multivariate logistic regression, were proposed for the task of Ki-67 expression status prediction. The mean dice Similarity Coefficient (DSC) score of the 3D segmentation model on the validation set was 0.85. On the Ki-67 expression prediction task, the AUC of the interpretable RF model on the validation set was 0.84 (95% CI:0.81, 0.86; p < 0.001), which was a 3% improvement compared to the AUC of the nomogram. The Delong test showed that the z statistic for the difference between the two models was 1.901, corresponding to a p value of 0.057. In addition, SHAP analysis showed that the Rad-Score made a significant contribution to the model decision. In this study, we developed a 3D brain tumor segmentation model and used an interpretable machine learning model and nomogram for preoperative prediction of Ki-67 expression status in PCNSL patients, which improved the prediction of this medical task. Ki-67 represents the degree of active cell proliferation and is an important prognostic parameter associated with clinical outcomes. Non-invasive and accurate prediction of Ki-67 expression level preoperatively plays an important role in targeting treatment selection and patient stratification management for PCNSL thereby improving prognosis.