Cancer Grade Research Articles

A multimodal deep-learning model based on multichannel CT radiomics for predicting pathological grade of bladder cancer.

To construct a predictive model using deep-learning radiomics and clinical risk factors for assessing the preoperative histopathological grade of bladder cancer according to computed tomography (CT) images. A retrospective analysis was conducted involving 201 bladder cancer patients with definite pathological grading results after surgical excision at the organization between January 2019 and June 2023. The cohort was classified into a test set of 81 cases and a training set of 120 cases. Hand-crafted radiomics (HCR) and features derived from deep-learning (DL) were obtained from computed tomography (CT) images. The research builds a prediction model using 12 machine-learning classifiers, which integrate HCR, DL features, and clinical data. Model performance was estimated utilizing decision-curve analysis (DCA), the area under the curve (AUC), and calibration curves. Among the classifiers tested, the logistic regression model that combined DL and HCR characteristics demonstrated the finest performance. The AUC values were 0.912 (training set) and 0.777 (test set). The AUC values of clinical model achieved 0.850 (training set) and 0.804 (test set). The AUC values of the combined model were 0.933 (training set) and 0.824 (test set), outperforming both the clinical and HCR-only models. The CT-based combined model demonstrated considerable diagnostic capability in differentiating high-grade from low-grade bladder cancer, serving as a valuable noninvasive instrument for preoperative pathological evaluation.

18F]Fluorthanatrace PET in Ovarian Cancer: Comparison with [18F]FDG PET, Lesion Location, Tumor Grade, and Breast Cancer Gene Mutation Status.

Poly(adenosine diphosphate-ribose) polymerase-1 (PARP1) inhibitors have improved ovarian cancer treatment outcomes. However, clinical response remains heterogeneous. Existing biomarkers, mainly breast cancer susceptibility genes 1 and 2 (BRCA1/2), are suboptimal. New tools are needed to guide patient selection. In this study, [18F]fluorthanatrace ([18F]FTT), a PET radiotracer for imaging PARP1, was compared with [18F]FDG and tumor features commonly assessed in ovarian cancer. Methods: Subjects with epithelial ovarian cancer underwent both [18F]FTT and [18F]FDG PET before new oncologic treatment. The SUVmax of [18F]FTT and [18F]FDG was compared between lesions. [18F]FTT SUVmax was compared with tumor location, tumor grade, and germline or somatic BRCA1/2 status. Linear mixed models were fitted to identify subject-level differences. Results: Fifty-five lesions were identified in 14 subjects. No correlation was found between [18F]FTT SUVmax and [18F]FDG SUVmax per lesion, supporting distinct molecular targets. [18F]FTT uptake varied widely across lesions, with no significant differences between mean SUVmax and tumor location, grade, or BRCA1/2 status. Conclusion: Our findings suggest that [18F]FTT PET may provide unique information on ovarian cancer distinct from [18F]FDG PET and commonly assessed tumor features. Our results imply a wide range of PARP1 expression in the studied ovarian tumors not explained by [18F]FDG PET, location, grade, or mutational status.

Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images

Abstract Purpose: This study aimed to investigate the performance of multiparametric magnetic resonance imaging (mpMRI) radiomic feature-based machine learning (ML) models in classifying the Gleason grade group (GG) of prostate cancer. Methods: In this retrospective study, a total of 203 patients with histopathologically confirmed prostate cancer who underwent mpMRI before prostate biopsy were included. After manual segmentation, radiomic features (RFs) were extracted from T2-weighted, apparent diffusion coefficient, and high b-value diffusion-weighted magnetic resonance imaging (DWMRI). Patients were split into training sets and testing sets according to a ratio of 8:2. A pipeline considering combinations of two feature selection (FS) methods and six ML classifiers was developed and evaluated. The performance of models was assessed using the accuracy, sensitivity, precision, F1-measure, and the area under curve (AUC). Results: On high b-value DWMRI-derived features, a combination of FS method recursive feature elimination (RFE) and classifier random forest achieved the highest performance for classification of prostate cancer into five GGs, with 97.0% accuracy, 98.0% sensitivity, 98.0% precision, and 97.0% F1-measure. The method also achieved an average AUC for GG of 98%. Conclusion: Preoperative mpMRI radiomic analysis based on ML, as a noninvasive approach, showed good performance for classification of prostate cancer into five GGs. Advances in Knowledge: Herein, radiomic models based on preoperative mpMRI and ML were developed to classify prostate cancer into 5 GGs. Our study provides evidence that analysis of quantitative RFs extracted from high b-value DWMRI images based on a combination of FS method RFE and classifier random forest can be applied for multiclass grading of prostate cancer with an accuracy of 97.0%.

Unravelling the natural history of localised prostate cancer in the post-prostate specific antigen era: implications for clinical management.

Management of localised prostate cancer (PCa) remains controversial in the era of prostate-specific antigen (PSA) testing. This study aims to describe the natural history of men with PCa being followed up expectantly to evaluate disease mortality. After exclusion, clinical data of 204 patients retrieved from a prospective large PCa registry were reviewed. Competing risk analysis were performed using the Fine-Gray model. The median age was 73 years old with a median follow up of 12.5 years. The median PSA was 8.85 ng/mL and the risk stratification were as follows: low (47.0%), intermediate (31.4%), and high risk (21.6%). There were 19 PCa (9.3%) and 84 non-PCa deaths (41.2%), with overall mortality at 50.5%. Multivariate analysis showed patients with high PSA values [subdistribution hazard ratio (sdHR) 7.13], poorer prostate cancer grade groups (PCGG) (sdHR 16.349), and therefore higher European Association of Urology risk group (sdHR 11.42) had greatly elevated prostate cancer mortality (PCM). Older patients greater than 75 years of age (sdHR 4.52) and high Charlson Comorbidity Index (CCI ≥6) scores had higher non-prostate cancer mortality (NPCM) (sdHR 7.87). Subgroup analysis of the high-risk group showed having a lower CCI score (≤3) had a greater risk of PCM than NPCM (sdHR 4.31 vs. 0.22) while the converse is observed for higher CCI scores (1.12 vs. 5.52). Overall PCM remains low in elderly men with conservatively treated PCa. Age and poorer CCI predict NPCM while PSA and PCGG predict PCM. In high-risk PCa group, CCI is a useful tool to determine which patients will benefit from radical treatment.

Correlation of Cytokine Profiles with Prostate-Specific Antigen and Disease Grade in Prostate Cancer.

BACKGROUND The development and progression of prostate cancer are multistep processes involving several growth factors, hormones, and cytokines. This study aimed to measure the serum concentrations of different cytokines and determine their correlation with prostate-specific antigen (PSA) levels and disease grade in patients with prostate adenocarcinoma. MATERIAL AND METHODS This cross-sectional study was conducted from March 2023 to March 2024 at the Clinic of Oncology of the University Hospital Center in Mostar, Bosnia and Herzegovina. Altogether, 50 male patients with prostate adenocarcinoma were included, of whom 28 had no proven metastases (PC group) and 22 had metastatic disease (MPC group). Serum concentrations of total (tPSA), free (fPSA), and complexed (cPSA) PSA were determined using a chemiluminescent microparticle immunoassay, whereas serum concentrations of cytokines were measured using a flow cytometry bead-based assay. RESULTS The MPC group had higher serum tPSA, fPSA, and cPSA levels than the PC group. The PC group had significantly higher serum levels of monocyte chemotactic protein (MCP)-1 than the MPC group (P=0.008). In the PC group, serum levels of interleukin (IL)-10 significantly correlated with cPSA. In the MPC group, serum concentrations of IL-1ß, tumor necrosis factor (TNF)-alpha, and IL-23 significantly correlated with disease grade. CONCLUSIONS Our study emphasizes the importance of MCP-1 in the development of prostate cancer, while IL-10 was the only cytokine whose serum level significantly correlated with cPSA. Serum concentrations of IL-1ß, TNF-alpha, and IL-23 may serve as potential biomarkers for disease grade.

Ki-67 and Histological Grade in Breast Cancer

AIMS: The aim of the study was to investigate the prognostic value of Ki-67 and histological grade in patients with estrogen receptor (ER) positive and human epidermal growth factor receptor 2 (HER2) negative early breast cancer. Although the proliferation activity of different tumors assessed with antigen Ki-67 was extensively studied in the last decade and showed that Ki67 expression was a useful prognostic factor in breast cancer, there is still a controversy about the utility value of Ki-67. METHODS: The retrospective study covered the period from 2016 to 2022. Year included 106 patients from Brcko District, with early estrogen-positive and HER2 receptors-negative breast cancer. The average patients’ age was 62.37 ± 11.65 years. Patients were divided into groups with high/low Ki-67 and high/low histological grade. Multiple linear regression analysis was performed for disease relapse and mortality. RESULTS: Patients with high Ki-67 were more frequent postmenopausal, had higher histological grade, cancer ˂2 cm, more frequent lymph nodes’ metastases, more frequently underwent to axillary surgery, and had a higher mortality rate. Patients with high histological grade were older, more frequent postmenopausal, had more frequent metastases to the lymph nodes, more frequent occurrence of high Ki-67, more frequent relapse of disease, and more frequent of death. CONCLUSION: Combination of high Ki-67 with high histological grade in ER positive and HER2-negative early breast cancer is an important prognostic factor.

Spectral CT for non-invasive evaluation of bladder cancer grade.

To investigate the potential role of dual-energy spectral computer tomography (CT) quantitative parameters in the definition of bladder cancer (BCa) pathological grading. This retrospective study evaluated the use of spectral CT imaging features for BCa. From 2021 to 2023, 63 patients with histologically-confirmed BCa diagnosis were examined at our Institution. The patients were pathologically divided, following international guidelines, into two groups: low-grade (n = 24) and high-grade urothelial carcinoma group (n = 39). The iodine concentrations (IC), the normalized iodine concentrations (NIC), and the slope of the spectrum curve (SLOPE) were calculated along with the measure of each lesion CT value on the monochromatic image from 40 to 120keV. The diagnostic performance was assessed by Receiver operator characteristic curve (ROC) analysis. The high-grade group showed significantly higher mean values of IC, SLOPE, and HU in 40 KeV monoenergetic images (VMI40 HU). AUC values for NIC, SLOPE, IC, and VMI40 HU were 0,677, 0,745, 0,745, and 0,755 respectively. In multivariate logistic regression models with backward stepwise, including all quantitative parameters, only VMI40 HU remained statistically significant to correlate with high-grade tumors. Preliminary data shows that quantitative parameters of dual-energy spectral CT can be helpful to characterize low-grade and high-grade urothelial bladder tumors. The prediction of high-grade BCa with non-invasive methods (e.g. dlCT) can aid in early detection of muscle-invasive and worse prognostic tumors that need more aggressive and timely treatments, personalizing the management on the risk of recurrence.

Predicting pan-cancer immune-checkpoint therapy and prognosis with a chromatin-accessibility-related alternative splicing signature: a retrospective study

Background: This study examines alternative splicing (AS) events in genes linked to chromatin accessibility in various cancers and their relation to the tumor immune microenvironment. Methods: Data from the Cancer Genome Atlas Database (TCGA) were used to identify independent prognostic factors for pan-cancer. We explored the correlation between differentially expressed genes and tumor immunity, including immune checkpoint genes, tumor development, and immune cells. A regulatory network diagram of alternative splicing-splicing factors (AS-SFs) was constructed to find potential immunotherapy targets. Results: IRF5 and E2F8 genes showed significant differential expression in pan-cancer. Age, cancer grade, primary tumor, cancer lymph nodes, and distant metastasis were independent prognostic factors. The risk model achieved good predictive performance, with AUC values of 0.705, 0.746, 0.743, and 0.743 for 1-year, 3-year, 5-year, and 10-year survival predictions, respectively. Positive correlations were found between IRF5/E2F8 and CD274/CTLA4 in certain cancers using TIMER and CIBERSORT software. Conclusions: AS events in chromatin accessibility genes (IRF5 and E2F8) have significant predictive value in pan-cancer prognosis. Our model assesses patient survival probability and highlights the synergistic impact of immune checkpoints and the AS-SF regulatory network on tumor immunotherapy.

Association of body mass index with pathologic agreement of preoperative and postoperative tumor grade in endometrial cancer.

We aim to study association of BMI of EC patients, with the level of agreement between preoperative and postoperative tumor grade. A retrospective study. We included patients with EC diagnosed in an outpatient clinic which had surgical staging as in our division. We categorized patients into BMI categories according to the World Health Organization; (BMI < 18.5kg/m2), (BMI 18.5-24.9kg/m2), (BMI 25-29.9kg/m2), (BMI 30-34.9kg/m2), (BMI 35-39.9kg/m2), and (BMI ≥ 40kg/m2). We further dichotomized the study population for obesity, defined as BMI ≥ 30.0. We analyzed agreement between preoperative and postoperative tumor grade, stratified by patient's BMI. Overall, 623 women met study inclusion criteria, with a median age of 64 [interquartile range (IQR) 57-72]. Among the study cohort, the median BMI was 30.7 [IQR 25.6-38.8], with 330 (53.0%) patients being obese. EC grade 1 was diagnosed preoperatively in 353 (56.7%), grade 3 in 148 (23.8%), and grade 2 in 122 (19.6%). Endometrioid histology was diagnosed in 463 (74.3%), serous in 78 (12.5%), mixed histotype in 51 (8.2%), clear cell in 20 (3.2%) and carcinosarcoma in 11 (1.8%). In 68.7% (n = 428), there was no change in postoperative grade, and in 24.9% (n = 155), there was upgrading of tumor, and in 6.4% (n = 40), there was a tumor downgrade. There were 3 (0.5%) cases in which no tumor was found on final pathology. The rate of no change was higher in preoperative grade 3 (89.9%) vs. grades 1 (63.5%) and grade 2 (58.2%), p < .001). There was no difference in grading agreement when obese patients were compared to non-obese, p = .248. There was no difference in grading agreement when comparing the various BMI categories, with no change proportion ranging between 58.2% in BMI 30.0-34.9 mg/kg2 and 79.7% in BMI 35.0-39.9 mg/kg2, p = .104. ROC analysis of BMI as predictor of no-change yielded an area under the curve of 0.466 (95% confidence interval 0.418-0.515) with a maximal performance at a BMI of 33.8 mg/kg2. The agreement between preoperative and postoperative tumor grade among all patients was kappa = 0.517. The agreement did not differ when compared between obese patients (kappa = 0.456) and non-obese (kappa = 0.575). Our study found no significant association between BMI and the agreement between preoperative and postoperative tumor grading in EC.

Chondrogenic Cancer Grading by Combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues

Raman spectroscopy (RS) is a promising tool for cancer diagnosis. In particular, in the last years several studies have demonstrated how the diagnostic performances of RS can be significantly improved by employing machine learning (ML) algorithms for the interpretation of Raman-based data. Recently, it has been demonstrated that RS can perform an accurate classification of chondrosarcoma tissues. Chondrosarcoma is a cancer of bones, that can occur in the soft tissues near the bones. It is normally characterized by three different malignant degrees and a benign counterpart, knows as enchondroma. In line with these findings, in this paper, we exploited ML algorithms to distinguish, as well as possible, between the three grades of chondrosarcoma and to distinguish between chondrosarcoma and enchondroma. We obtained a high level of accuracy of classification by analyzing a dataset composed of a relatively small number of Raman spectra, collected in a previous study by one of the authors of this paper. Such spectra were acquired from micrometric tissue sections with a confocal Raman microscope. We tested the classification performances of a support vector machine (SVM) and a random forest classifier (RFC), as representatives of ML algorithms, and two versions of the multi-layer perceptron (MLPC) as representatives of deep learning (DL). These models, especially RFC and MLPC, showed excellent classification performances, with accuracy reaching 99.7%. This outcome makes the aforementioned models a promising route for future improvements of diagnostic devices focused on detecting cancerous bone tissues. Alongside the diagnostic purpose, the aforementioned approach allowed us to identify characteristic molecules, i.e., amino acids, nucleic acids, and bioapatites, relevant for obtaining the final diagnostic response, through the use of a tool named by us Raman Band Identification (RBI). The method to evaluate RBI is the most important contribution of this paper, because RBI could represent a relevant parameter for the identification of biochemical processes on the basis of the tumor progression and associated malignant degree. In turn, the spectral bands highlighted by RBI could provide precious indicators in an attempt to restrict the spectral acquisition to specific Raman bands. This last objective could help to reduce the amount of experimental data needed to obtain an accurate final grading outcome, with a consequent reduction in the computational cost.

Analysis of Tumor Proliferation Markers in Early-Stage Luminal Breast Cancer: A Comprehensive Study Using Mitotic Activity Index, Ki-67, and Phosphohistone H3 Expression.

Introduction and Aim: Routinely used proliferation markers such as mitotic activity index (MAI) and Ki-67 index show limited reproducibility due to high interobserver variability in breast cancer assessment. Phosphohistone H3 (PhH3), a novel proliferation marker, is gaining attention in breast cancer research. This study aimed to evaluate the inter-rater agreement among MAI, Ki-67, and PhH3 expressions in early-stage luminal breast cancer and assess the impact of replacing MAI with PhH3 index on tumor histological grading. Materials and Methods: Three pathologists assessed MAI, Ki-67, and PhH3 expressions in 66 early-stage luminal breast cancer specimens. Mitotic Activity Index was scored based on mitotic figures in an area of 2 mm2 while Ki-67 index utilized a 14% threshold for positively stained nuclei. Phosphohistone H3 expression cutoff was set at 13 positive cells per 2 mm2. The inter-rater agreement for the 3 variables was analyzed using Cohen kappa statistics. Results: Among the 3 parameters, the kappa score of the PhH3 expression reflected very strong agreement between the 3 observers (κ = 0.991, 0.907, and 0.916). Only moderate agreement was noted for MAI (κ = 0.898, 0.562, and 0.592) and substantial agreement for Ki-67 index (κ = 0.869, 0.673, and 0.678). Moreover, replacing MAI with PhH3 index led to upgrade of histological grade in 15% to 16% of patients. Conclusion: Our study demonstrated that PhH3 is a more reproducible proliferation marker than MAI and Ki-67. Incorporation of PhH3-based mitotic index in breast cancer grading might reduce the variation in the assessment of histological grade.

Prognostic impact of the newly revised IASLC proposed grading system for invasive lung adenocarcinoma: a systematic review and meta-analysis

BackgroundThis study aimed to evaluate the prognostic value of the newly revised International Association for the Study of Lung Cancer (IASLC) grading system (2020) on the 5-year overall survival (OS) and recurrence-free survival (RFS) in patients with lung adenocarcinoma (LADC).MethodsClinical studies that investigated the prognostic value of revised IASLC staging system in patients with LADC were retrieved from the PubMed, Web of Science, ScienceDirect, and Cochrane Library databases. This study was conducted in accordance to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and checklists.ResultsBased on inclusion and exclusion criteria, we included 12 studies for analysis. The grade of LADC was assessed by revised IASLC system, which included three grades. Compared to Grade 3 LADC, grade 1 (total [95% CI]: 1.38 [1.19, 1.60]) and grade 2 (total [95% CI]: 1.29 [1.15, 1.44]) LADC had higher 5-year OS rates. Similarly, Grade 1 (total [95% CI]: 1.76 [1.42, 2.18]) and Grade 2 (total [95% CI]: 1.51 [1.28, 1.77]) had higher 5-year RFS rates Grade 3 LADC. However, 5-year OS and RFS had no significant difference between Grade 1 and Grade 2 patients.ConclusionThis systematic review and meta-analysis provides evidence that the newly revised IASLC grading system is significantly associated with the prognosis of patients with LADC, where Grade 3 indicated unfavorable prognosis.

Abstract A025: RNA-Based Therapeutics to target the p53 pathway in high-grade serous ovarian cancer: TAp63-Regulated Oncogenic LncRNAs (TROLLs) as novel therapeutic targets in cancer

Abstract High-grade serous ovarian cancer (HGSOC) is the most common and malignant type of ovarian cancer with a high frequency of p53 mutation and hyperactivation of the AKT pathway. Even though roughly 85% of patients achieve remission after initial surgical debulking and chemotherapy, four out of five HGSOC patients will experience disease recurrence, resulting in a 5-year survival rate of 43%. We have identified and characterized two TAp63 Regulated Oncogenic Long non-coding RNAs (TROLL-2 and TROLL-3) whose transcription is induced by mutant p53 and high expression level correlate with a more aggressive ovarian cancer grade and phosphorylation of AKT. In-depth analysis demonstrated that TROLL-2 and TROLL-3 directly activate AKT in tumor cells, thus linking p53 alterations to AKT activity. The use of AKT inhibitors in the clinic is limited because of frequent adverse effects, likely due to the constitutive expression of AKT in all cells. TROLL-2 and TROLL-3 expression is limited to tumor cells and provides a more specific target for decreasing AKT activation, potentially minimizing off-target effects. We have optimized reduction of TROLL-2 and TROLL-3 expression using antisense oligonucleotides (ASOs) in an ex vivo perfusion platform for 3D culture of microtumors from primary patient derived tumors. Samples show maximum reduction of proliferation (EdU) and phosphorylation of AKT when TROLL-2 and TROLL-3 ASOs are used concurrently. We also performed phospho-proteomics and single cell RNAseq to compare individual ASO-mediated knockdown of each lncRNA to combined targeted ASO treatments. We have developed primary patient derived xenograft models using ovarian tumors orthotopically implanted into NSG mice generating a relevant preclinical model to determine the efficacy of ASO treatment. To reduce the rapid degradation and clearance of ASOs in circulation, ASOs were encapsulated into lipid nanoparticles and metal-organic frameworks (MOFs) and we are currently testing these in vivo. Since AKT activation is associated with increased staging and resistance to platinum and targeted therapies in ovarian cancer patients, our data provides preclinical rationale for the implementation of ASOs and the use of new delivery methods as a relevant translational therapy and novel method to exploit the specificity of TROLL-2 and TROLL-3 expression in HGSOC. Citation Format: Ashley Lui, Marco Napoli, Jaden R Baldwin, Christina L Carr, Angie Rivera, Duy Nguyen, W. Gregory Sawyer, Michael R Dunne, Erin George, Omar K Farha, Michelle Teplensky, Elsa R Flores. RNA-Based Therapeutics to target the p53 pathway in high-grade serous ovarian cancer: TAp63-Regulated Oncogenic LncRNAs (TROLLs) as novel therapeutic targets in cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr A025.

Perinuclear assembly of vimentin intermediate filaments induces cancer cell nuclear dysmorphia

Nuclear dysmorphia, characterized by crumpled or lobulated polymorphic nuclear shapes, has been used as an index for the malignant grades of certain cancers. The expression of vimentin, a type-III intermediate filament protein, is a hallmark of the epithelial-to-mesenchymal transition. However, it remains unclear whether vimentin is involved in cancer cell nuclear dysmorphia. In this study, we found that vimentin intermediate filaments (VIFs) frequently accumulated at the concave of dysmorphic nucleus in breast cancer MDA-MB-231cells. Depletion of vimentin apparently restored the nuclear shape of the cells, which was devastated by re-expression of vimentin, but not its assembly-defective Y117D mutant. Depletion of plectin, a cytoskeletal linker, partially prevented the perinuclear accumulation of VIFs and concomitantly restored the nuclear shape of the cells. In addition, depletion of vimentin in lung cancer A549cells largely prevented nuclear dysmorphia during the epithelial-to-mesenchymal transition induced by TGFβ. Moreover, we found that VIF-mediated nuclear dysmorphia led to defects in DNA repair. Together, our results unveil a novel role of VIFs in cancer cell nuclear dysmorphia, which is associated with genome instability.

CLINICOPATHOLOGICAL CORRELATION OF P53 EXPRESSION IN BENIGN PROSTATIC HYPERPLASIA AND PROSTATE ADENOCARCINOMA

Objective: To assess clinicopathological parameters in patients with benign prostatic hyperplasia and prostate adenocarcinoma, and evaluate their correlation with p53 overexpression in these prostatic conditions. Methods: The present ambispective study was conducted in the Department of Pathology in a tertiary care hospital of Northern India from 2022 to 2024. This study included prostatic trucut biopsies, transurethral prostatic resection (TURP) chips, and radical prostatectomy specimens from patients with benign prostatic hyperplasia (BPH) or prostatic adenocarcinoma. Tissue samples were processed, embedded in paraffin, sectioned, and stained with hematoxylin and eosin (H&amp;E) for histopathological evaluation. Immunohistochemical (IHC) analysis was performed to assess p53 expression using the GenomeMe antibody. Data on age, histological type and histological grade were collected. Statistical analysis included Chi-square test was conducted to evaluate associations between p53 overexpression and clinicopathological parameters. Results: The study analyzed 50 cases, revealing significantly higher p53 expression in prostatic adenocarcinoma compared to BPH. The majority of participants were aged between 61-70 years (46%). There were 50% patients with adenocarcinoma, 26% with BPH, 20% with BPH and chronic prostatitis, and 4% with BPH with prostatic intraepithelial neoplasia. There were strong associations between p53 overexpression and specific diagnoses, histological type and histological grade (Gleason scores) in prostate cancer. Conclusion: The study findings suggest that p53 overexpression is closely linked to malignant prostate conditions and could potentially serve as a valuable biomarker for distinguishing between benign and malignant prostatic diseases, as well as predicting tumor aggressiveness.

Discovery of RNA Biomarkers for Prostate Cancer Using Cross-Platform Transcriptomics.

Microarray and Single-Molecule Molecular Inversion Probe (smMIP)-based targeted RNA sequencing are two RNA profiling platforms for identifying disease-associated biomarkers. The microarray uses a GeneChip array with oligonucleotide probes to measure expression levels across thousands of genes, while smMIPs capture and quantify RNA transcripts and transcript variants via next-generation sequencing. To evaluate the strengths and weaknesses of both platforms, a comparative gene expression profiling study was conducted using RNA samples from 52 prostate tissues (normal, benign prostatic hyperplasia (BPH) and various prostate cancer (PCa) grades). Of all genes covered by both platforms, only 35% of the expression levels aligned, with 45% showing discrepancies. Both platforms identified the same 17 genes as potential PCa biomarkers. Microarray analysis identified an additional 253 genes that were not covered or not identified by smMIP technology, while smMIP technology identified eight markers not covered or not identified in the microarray core gene analysis, including fusion genes and splice variants. For high-grade prostate cancer (HG-PCa), the smMIP-method identified 8 markers, and the microarray identified 17 markers, with FOLH1, FAP and CLDN3 being common across both platforms. The choice of RNA expression analysis technology depends on research objectives; microarray technology is useful for the evaluation of a wide range of genes but has low throughput. In contrast, smMIP-based RNA sequencing enables sensitive analysis with minimal RNA in a medium- to high-throughput setting.

Sub-region based histogram analysis of amide proton transfer-weighted MRI for predicting tumor budding grade in rectal adenocarcinoma: a prospective study.

To explore the sub-regional histogram features of amide proton transfer-weighted (APTw) MRI, compared with those of diffusion-weighted imaging (DWI), in predicting the tumor budding (TB) grade of rectal cancer (RC). This study prospectively enrolled 74 patients with pathologically confirmed RC, who underwent APTw MRI before surgery from July 2022 to March 2023. Hematoxylin-eosin staining was used for TB scoring. K-means clustering (K = 4-6) was applied to obtain multiple sub-regions (n = 3-5), and corresponding histogram features (including mean, standard deviation, minimum, maximum, and 10th, 25th, 50th, 75th, and 90th quantile) of APT and apparent diffusion coefficient (ADC) maps were extracted and filtered using stepwise regression. When K = 5, the K-means clustering is four sub-regions, showing the best prediction for TB grade compared to K = 4 or 6. When K = 5, there were significantly higher histogram features of the APT map in sub-regions 3 and 4 in the high TB grade group compared to the low-intermediate TB grade group. Receiver operating characteristic (ROC) curve and internal validation suggested that the predictive efficiency of the model was highest when K = 5, with AUC, sensitivity, specificity, accuracy, and kappa values of 0.92, 93%, 71%, 87%, and 0.65, respectively. There were no significant differences in the histogram features of each sub-region in the ADC map (p > 0.05). The sub-regional histogram features of APTw images can help to distinguish the heterogeneous regions of RC, which can be used to predict the TB grade of RC. Question Can the sub-regional histogram features of APTw MRI predict the tumor budding (TB) grade of rectal cancer (RC)? Findings Differences exist in histogram features of APT map subregions between high and low-intermediate TB grade groups; subregions of the APT map have different predictive abilities. Clinical relevance APT-weighted imaging might outperform DWI in predicting TB grade in RC.

Effects of amide proton transfer imaging in diagnosis, grading and prognosis prediction of cervical cancer: A systematic review and meta-analysis

PurposeTo assess the effectiveness of Amide Proton Transfer (APT) imaging in predicting the histopathological characteristics of cervical cancer. MethodsA comprehensive literature search was conducted across multiple databases, covering studies until December 27, 2023. The meta-analysis was performed using Stata 15 and Review Manager 5.4 software. Key metrics analyzed included pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR), and summary receiver operating characteristic curves. The analysis focused on differentiating cervical cancer types, squamous carcinoma differentiation, and lymph node involvement. Meta-regression was employed to investigate heterogeneity. ResultsThirteen studies involving 868 patients were included in the meta-analysis. For differentiating adenocarcinoma from squamous carcinoma, the pooled sensitivity was 0.82 (95%CI: 0.71–0.90), specificity was 0.65 (95%CI: 0.48–0.79), and DOR was 9 (95%CI: 1.6–3.5). When distinguishing poorly differentiated from moderately/well-differentiated squamous carcinoma, the sensitivity was 0.74 (95%CI: 0.66–0.81), specificity was 0.83 (95%CI: 0.75–0.89), and DOR was 14 (95%CI: 8–23). For identifying lymph node involvement, the sensitivity was 0.87 (95%CI: 0.78–0.92), specificity was 0.66 (95%CI: 0.59–0.73), and DOR was 13 (95%CI: 7–26). No publication bias was detected. ConclusionsAPT imaging demonstrates high sensitivity and specificity in distinguishing between cervical cancer types, grading squamous carcinoma, and detecting lymph node involvement. It can be considered a reliable technique for predicting the pathological features of cervical cancer in clinical practice.

Prevalence and Significance of AGR2 Expression in Human Cancer.

Anterior gradient 2 (AGR2) is a resident endoplasmic reticulum (ER) protein with a vital role in embryonal development, mucus maturation, tissue regeneration, and wound healing. To determine the prevalence and clinical significance of AGR2 expression in cancer, a tissue microarray containing 14,966 tumors from 134 different tumor types and subtypes as well as 608 samples of 76 different normal tissue types was analyzed by immunohistochemistry (IHC). AGR2 positivity was found in 103 of 134 tumor categories, and 83 tumor categories contained at least one strongly positive case. AGR2 expression was most frequently seen in tumors of the female genital tract, particularly adenocarcinomas (up to 100%), various breast cancer subtypes (57.1%-100%), urothelial carcinoma (74.6%-100%), adenocarcinomas of the upper and lower gastrointestinal tract (93.6%-99.6%), and pancreaticobiliary cancers (65.2%-98.2%). AGR2 positivity was slightly less common in squamous cell carcinomas (46.4%-77.3%) and mainly absent in mesenchymal and lymphoid tumors. While AGR2 expression was only weak or absent in the normal thyroid, it was moderate to strong in 46.0% of adenomas, 52.8% of follicular carcinomas, and 81.8% of papillary carcinomas of the thyroid. High AGR2 expression was strongly linked to poor ISUP (p < 0.0001), Fuhrman (p < 0.0001), and Thoenes (p < 0.0001) grades as well as advanced pT stage (p = 0.0035) in clear cell renal cell carcinoma (ccRCC). Low AGR2 expression was associated with high BRE grade in breast cancer (p = 0.0049), nodal metastasis (p = 0.0275) and RAS mutation (p = 0.0136) in colorectal cancer, nodal metastasis (p = 0.0482) in endometrioid endometrial carcinoma, high grade in noninvasive urothelial carcinoma (p = 0.0003), and invasive tumor growth in urothelial carcinoma (p < 0.0001). It is concluded that AGR2 expression occurs in a broad range of different tumor entities and that AGR2 assessment may serve as a diagnostic aid for the distinction of thyroidal neoplasms and as a prognostic marker in various cancer types.

Ayurvedic perspective of dietary risk factors of colorectal cancers - A hospital-based case control study

BackgroundThe rising prevalence of colorectal cancer (CRC) may be attributed to various nutritional and behavioural factors, making both factors as important topics for discussion to the layman and the oncology community. ObjectiveTo explore additional dietary risk factors, other than those already known, according to the Ayurvedic perspective in CRC patients. MethodsDetail dietary data was collected from 420 patients of CRC and 116 healthy volunteers registered at our institute with the help of a food frequency questionnaire. Descriptive analysis was done by plotting radar charts, whereas the logistic regression models were used to calculate the adjusted Odd Ratios (ORs) associated with consumption of green chilli, red chilli powder and curd, individually and in combinations. ResultsThe controls in the present study were younger, had more males, the majority of them belonged to middle- and lower-income groups and had a family history of cancer as compared with cohort of case studies. Green chilli consumption was found to be the maximum within the lower-income group (n = 18, 66.67%), while that of red chilli (n = 150, 48.23%), and curd (n = 107, 34.04%) within the middle class. Maximum consumption of green chillies (Males n = 48, 29.27%; Females n = 36, 21.95%), red chillies (Males n = 40, 29.85%; Females n = 16, 24.61%) and curd (Males n = 31, 28.97%) was observed in the age group 46 to 60 irrespective of sex. Tobacco was found to be the most common addiction in all groups. The maximum number of patients frequently consuming these three dietary items presented majorly with rectal cancer, and liver metastasis and were in advanced grade and stage of cancer. Curd and curd with red chilli powder had a significant association with the development of CRC with an OR of 2.7280 (95% CI 1.6346 to 4.5531) and 5.0806 (95% CI 2.4015 to 10.7485), respectively, which was highly significant (p < 0.0001). Green chilli was notably an associated risk with an OR of 2.0095 (95% CI 1.3258 to 3.0458), which was also statistically significant (p = 0.001). Red chilli powder and green chilli with curd had ORs as 1.6917 (95% CI 1.1105 to 2.5771) and 2.1778 (95% CI 1.1591 to 4.0918) with p = 0.0144 and 0.0156, respectively. ConclusionIn the present study, green chilli, red chilli, and curd are identified as additional dietary risk factors for colorectal cancers, owing to their ability to produce chronic inflammation leading to various inflammatory conditions including cancer.