Diagnosis Of Cancer Research Articles

Orbital metastases from breast cancer: three case reports

Purpose: to describe rare cases of orbital metastases from breast cancer.Material and Methods. Three cases of Luminal A(1) and B(2) breast cancer with orbital metastases were identified and studied during the period from 01.01.2018 to 01.05.2024. Metastasis to the orbit was detected 5, 2 and 1.5 years after radical mastectomy for breast cancer. Patients received chemotherapy and radiation therapy.Results. Cases of orbital metastasis originating from breast cancer were found to have a low frequency, but may be associated with a long period between cancer diagnosis and initiation of treatment. Early detection of breast cancer and its active treatment with chemotherapy and radiation therapy are important factors in preventing orbital metastasis.Conclusion. A routine follow-up surveillance after radical treatment of breast cancer is important because there is an increase risk of developing orbital metastasis. Comprehensive strategies for treating breast cancer metastases can provide long-term disease control.

Multiomics biomarkers were not superior to clinical variables for pan-cancer screening

Abstract Background Cancer screening tests are considered pivotal for early diagnosis and survival. However, the efficacy of these tests for improving survival has recently been questioned. This study aims to test if cancer screening could be improved by biomarkers in peripheral blood based on multi-omics data. Methods We utilize multi-omics data from 500,000 participants in the UK Biobank. Machine learning is applied to search for proteins, metabolites, genetic variants, or clinical variables to diagnose cancers collectively and individually. Results Here we show that the overall performance of the potential blood biomarkers do not outperform clinical variables for collective diagnosis. However, we observe promising results for individual cancers in close proximity to peripheral blood, with an Area Under the Curve (AUC) greater than 0.8. Conclusions Our findings suggest that the identification of blood biomarkers for cancer might be complicated by variable overlap between molecular changes in tumor tissues and peripheral blood. This explanation is supported by local proteomics analyses of different tumors, which all show high AUCs, greater than 0.9. Thus, multi-omics biomarkers for the diagnosis of individual cancers may potentially be effective, but not for groups of cancers.

The Role of Urinary Extracellular Vesicles in Kidney Cancer: Diagnostic and Therapeutic Potential

Renal cancer ranks as the 14th most common cancer globally, with renal cell carcinoma (RCC) being the primary variant, arising from renal tubular epithelial cells; clear cell RCC constitutes about 80% of cases. Despite their limitations, surgery and targeted therapy remain the mainstays of RCC treatment. Regardless of advancements in RCC research, substantial obstacles continue to exist, such as delayed diagnosis, advanced distant metastasis, and drug resistance. As urine is an easily accessible biofluid, the identification of EVs has paved the way for novel biomarker research. Urinary extracellular vesicles (uEVs) are a novel source of biomarkers with potential applications in cancer detection and management, utilizing a less invasive approach. New data indicate that uEVs are crucial in several areas of RCC, containing tumor development, metastasis, immune evasion, and response to drugs. These vesicles facilitate intercellular communication by transporting a variety of bioactive substances, including RNA, DNA, proteins, and lipids, and are released into the extracellular space by the majority of cell types. uEVs RNAs and proteins are presently being investigated for their possible application as diagnostic biomarkers for different types of kidney cancer. This review summarizes the most recent research examining the potential of uEVs cargo as a biomarker for the diagnosis, prognosis, and treatment of renal cancer.

Abstract B037: Expression of MRTFA facilitates an immunosuppressive tumor microenvironment via upregulation of VSIR

Abstract Breast cancer, the second highest cause of cancer-related deaths in women, accounts for 31% of female cancer diagnoses. African American women have lower survival rates than White American women, influenced by both biological factors (age, family history, genetic factors, tumor biology) and social determinants (socio-economic status, insurance status, health care access). However, precise biological determinants remain unclear. The primary reason for breast cancer mortality is metastasis to organs like the lung, liver, and brain, with transcription factors playing a key role in this process. Myocardin related transcription factor A/B (MRTFA/B) are co-activators of serum-response factor (SRF), and the MRTF-SRF complex promotes breast cancer cell migration, invasion, and metastasis by regulating genes involved in actin cytoskeleton remodeling. MRTFA also supports tumor growth by modulating cancer-associated fibroblasts (CAFs) and remodeling the extracellular matrix (ECM) in the tumor microenvironment. While the pro-metastatic role of MRTFA/B is established in vitro and in mice, their impact on human cancer progression and metastasis requires further investigation. Therefore, understanding MRTFA's role in breast cancer metastasis is crucial for improving patient outcomes, making it essential to investigate MRTFA/B expression patterns in both tumor cells and the tumor microenvironment (TME). To explore the clinical, demographic, tumor-intrinsic, and tumor microenvironmental patterns of MRTFA/B’s expression and activation, we utilized multiplex imaging methods on multiple racially diverse tissue microarrays (TMA) and bioinformatics analyses of The Cancer Genome Atlas (TCGA), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and available single-cell RNA sequencing datasets. Strikingly, MRTFA expression levels were higher in African American patients than in White American patients. Next, we investigated MRTFA expression patterns in the TME by using single-cell sequencing databases and found more abundant MRTFA expression in dendritic cells (DCs) within the TME compared to any other cell subtype. Moreover, our multiplex-imaging of tumor microarray (TMA) samples showed prominent MRTFA expression in HLA-DRA+CD45+ cells, indicative of antigen-presenting cells (APC). Notably, public single-cell RNA sequencing data indicated that breast cancer DCs strongly express the V-set immunoregulatory receptor (VSIR), an immune checkpoint protein, implying the immunosuppressive nature of the TME and we found that this increased VSIR expression is mediated by the MRTF-SRF complex. These findings suggest that DCs expressing MRTFA in breast cancer microenvironment contribute to creating the immunosuppressive TME via upregulation of VSIR expression, resulting in racial breast cancer disparities. Citation Format: Kihak Lee, Stephanie M. Wilk, Alexa M. Gajda, Mohamed Haloul, Virgilia Macias, Elizabeth L. Wiley, Zhengjia Chen, Xinyi Liu, Xiaowei Wang, Maria Sverdlov, Kent F. Hoskins, Ekrem Emrah Er. Expression of MRTFA facilitates an immunosuppressive tumor microenvironment via upregulation of VSIR [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr B037.

Enhanced breast cancer diagnosis through integration of computer vision with fusion based joint transfer learning using multi modality medical images

Enhanced breast cancer diagnosis through integration of computer vision with fusion based joint transfer learning using multi modality medical images

Abstract C032: The impact of neoadjuvant endocrine therapy (NET) in ER+ breast cancer tumor microenvironment and metastasis

Abstract Breast cancer is the most common cancer in women worldwide and the second leading cause of death. The Estrogen Receptor positive (ER+) subtype constitutes approximately 80% of the yearly 250,000 breast cancer diagnoses in the US. Anti-estrogen endocrine therapy, administered either pre- or post-operatively, is the cornerstone treatment for all stages of breast cancer. Pre- operative or neoadjuvant endocrine therapy (NET) reduces pre-surgical tumor burden, improves surgical outcome, and provides prognostic information. Although the treatment often has a favorable initial response, about 20% of patients eventually develop resistance and recurrent disease, even decades after the primary diagnosis.We hypothesized that resistance in NET is mediated through pro-metastatic changes in the tumor microenvironment affecting either (i) dissemination machinery or (ii) cancer cell phenotype.The main mode of breast cancer hematogenous dissemination to distant sites is through intravasation portals called Tumor Microenvironment of Metastasis (TMEM) doorways. Each TMEM doorway consists of a tumor cell overexpressing the actin-regulatory protein MENA, a Tie2high/ VEGFAhigh macrophage and an endothelial cell, all in direct contact. TMEM doorway activity leads to vascular opening events, during which tumor cells located in areas around TMEM doorways intravasate and disseminate to secondary sites. Therefore, to investigate the impact of NET on cancer cell dissemination, we evaluated its effect on (i) TMEM doorway score (the number of TMEM doorways per tissue area), (ii) TMEM doorway opening, (iii) the number of circulating tumor cells and (iv) the number of disseminated tumor cells. We used the orthotopic MCF7 breast cancer xenograft model grown in NOD/SCID mice and tested two drugs widely used in NET, Fulvestrant (a Selective Estrogen Receptor Degrader) and Tamoxifen (a Selective Estrogen Receptor Modulator). NET did not affect the TMEM doorway score, TMEM doorway activity, the number of CTCs or the number of DTCs. We are now focused on evaluating the effect of NET on the density of cancer cells which are upregulated in programs of invasion, stemness, and dormancy. This “Triple Threat Phenotype (TTP)” gives tumor cells an enhanced ability to disseminate to and colonize distant organs. To this effect we will stain primary MCF7 tumors from NET and vehicle-treated mice for markers of invasiveness (MENA), stemness (SOX9) and dormancy (NR2F1). Detecting any changes in the phenotype of metastatic tumor cells following NET will help us understand underlying mechanisms of endocrine therapy resistance and may lead to new therapeutic targets and improve patient outcomes. Citation Format: Anastasia Aristoteleia Chondronikola, Dimitra Anastasiadou, Aliona Zintiridou, Camille L. Duran, Nicole Barth, Burcu Karadal-Ferrena, Erika Pereira-Zambalde, Lina Ariyan, Suryansh Shukla, George S. Karagiannis, David Entenberg, John S. Condeelis, Maja H. Oktay. The impact of neoadjuvant endocrine therapy (NET) in ER+ breast cancer tumor microenvironment and metastasis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr C032.

Abstract PR004: Age-induced chronic accumulation of glucocorticoids drives therapy resistance in lung cancer

Abstract Non-small cell lung cancer (NSCLC), a highly aggressive cancer, is the leading cause of cancer- related deaths in the U.S. While chemotherapy and targeted therapy are the main treatment modalities in the clinic, many patients do not respond to these treatments. Strikingly, the median age for lung cancer diagnosis is 71. Yet, preclinical and clinical research have largely been skewed towards young mice and patients. Here, we address this gap in knowledge and evaluate if old age affects the response to standard of care NSCLC therapies in KRAS-driven NSCLC. Using human sera from young and old healthy subjects to treat NSCLC cell lines, we show that age-driven circulatory changes are sufficient to drive a state of profound resistance to both chemotherapies as well as KRASG12C targeted therapies. We confirmed the relevance of our findings in vivo, by transplanting cancer cells derived from the K-rasLSL-G12D/+; p53fl/fl genetically engineered mouse model of lung cancer into young and old C57BL/6 mice. Using this model we showed that unlike young lung tumor-bearing mice on chemotherapy, old tumor-bearing mice do not respond to treatment and have a poor prognosis. Mechanistically, we found that old age drives a transcriptional reprogramming that favors drug resistance and identified the glucocorticoid receptor as a major driver of these transcriptional changes. We traced this to an age-induced chronic accumulation of glucocorticoids in circulation and within the tumor interstitial fluid. Chronic treatment with age-relevant concentrations of cortisol alone was sufficient to drive glucocorticoid receptor activation and induce resistance to therapies mirroring the effects of old age. Further supporting a driving role for glucocorticoids in driving age- induced drug resistance, blocking the glucocorticoid receptor genetically or pharmacologically rescues the drug resistance state imposed by old age in NSCLC cells. Lastly, using TCGA patient data we show an inverse correlation between glucocorticoid receptor activation (GR score) and progression-free survival of lung cancer patients. Together, our work demonstrates a role for age-induced glucocorticoids in circulation in promoting therapy-resistance in NSCLC patients and supports the use of therapeutic agents that block the glucocorticoid receptor as a strategy to increase therapy efficacy in NSCLC patients. Citation Format: Devesh Raizada, Felicia Lazure, Stanislav Drapela, Nadir Sarigul, Joanne Tejero, Didem Ilter, Ana Da Silva Gomes. Age-induced chronic accumulation of glucocorticoids drives therapy resistance in lung cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr PR004.

Analysis of positive predictors in diagnosing lung cancer on transthoracic and transbronchial procedure

Background: Lung cancer remains the most cause of death due to cancer. Due to high mortality rate of lung cancer, early diagnosis of lung cancer plays a very important role in therapeutic management. Clinical symptoms and tumor characteristics such as size and location are found to vary in lung cancer patients. Various modalities can be used to obtain materials or samples include transthoracic and transbronchial methods. Research was conducted to analyze positive predictors of cancer between transthoracic and transbronchial procedures in lung cancer patients. Methods: This research is an analytical observational study with a cross-sectional design in using secondary data through medical records from January 2022 to December 2023. Univariate analysis presents data in the form of frequency, mean and standard deviation. Bivariate analysis was carried out using chi-square and multivariate analysis using multiple logistic regression. The measure used as a predictor is the adjusted odds ratio. Inference or conclusion is based on the 95% confident interval and p value at the alpha value limit of 0.05. The entire data process above uses SPSS 26.0 statistical software Results: The total research subjects who met the research requirements were 111 patients. The characteristics of the research subjects were symptoms of cough (n=97; 87.4%), hemoptyisis (n=51; 45.9%), breathlessness (n=104; 93.7%) and chest pain (n=87; 78.4%), mostly peripheral tumor (n=70; 63.1%) and size >3 cm (n=103; 92.8%). Logistic regression analysis showed cough symptoms and peripheral tumor location each had an adjOR value of 5.247 (95% CI 1.432-19.552; p=0.013) and adjOR 0.088 (95% CI 0.034-0.229; p=0.000) for lung cancer positivity on transbronchial procedure. On transthoracic procedure, cough symptoms and peripheral tumor location respectively had an adjOR value of 0.190 (95% CI 0.051-0.703; p=0.013) and adjOR 11.407 (95% CI 4.374-29.747; p=0.000) for lung cancer positivity. Conclusions: Cough is a positive predictor of lung cancer in transbronchial procedures. Meanwhile, peripheral tumor location is a positive predictor of lung cancer in transthoracic procedures.

A new perspective on diagnostic strategies concerning the potential of saliva-based miRNA signatures in oral cancer.

Oral cancer, the most prevalent cancer worldwide, is far more likely to occur after the age of forty-five, according to the World Health Organization. Although many biomarkers have been discovered over the years using non-invasive saliva samples, biopsies, and human blood, these biomarkers have not been incorporated into standard clinical practice. Investigating the function of microRNAs (miRNAs) in the diagnosis, aetiology, prognosis, and treatment of oral cancer has drawn more attention in recent years. Though salivary microRNA can act as a window into the molecular environment of the tumour, there are challenges due to the heterogeneity of oral squamous cell carcinoma (OSCC), diversity in sample collection, processing techniques, and storage conditions. The up and downregulation of miRNAs has been found to have a profound role in OSCC as it regulates tumour stages by targeting many genes. As a result, the regulatory functions of miRNAs in OSCC underscore their significance in the field of cancer biology. Salivary miRNAs are useful diagnostic and prognostic indicators because their abnormal expression profiles shed light on tumour behaviour and patient prognosis. In addition to their diagnostic and prognostic value, miRNAs hold promise as therapeutic targets for oral cancer intervention. The current review sheds light on the challenges and potentials of microRNA studies that could lead to a better understanding of oral cancer prognosis, diagnosis, and therapeutic intervention. Furthermore, the clinical translation of OSCC biomarkers requires cooperation between investigators, physicians, regulatory bodies, and business partners. There is much potential for improving early identification, tracking therapy response, and forecasting outcomes in OSCC patients by including saliva-based miRNAs as biomarkers.

Radioiodinated Nanobody immunoPET probe for in vivo detection of CD147 in pan-cancer.

To develop the extracellular matrix metalloproteinase inducer (CD147)-targeting therapeutic strategies, accurate detection of CD147 expression in tumors is crucial. Owing to their relatively low molecular weights and high affinities, nanobodies (Nbs) may be powerful candidates for cancer diagnosis and therapy. In this study, we developed a novel CD147-targeted nanobody radiotracer, [124I]I-NB147, which provides guidance for the noninvasive detection of CD147-overexpressing cancers. CD147 expression in human cancers was detected via immunohistochemistry (IHC) on tissue microarrays (TMAs). Western blot (WB) and flow cytometry were used to screen CD147-positive malignant melanoma (MM), triple-negative breast cancer (TNBC), and pancreatic cancer (PCA) cell lines. The CD147 nanobody (NB147) was labeled with [124I]INa using Iodogen as the oxidizing agent and was purified by the PD-10 column. The physicochemical properties, affinity, metabolic characteristics, biodistribution, and immunoPET imaging of [124I]I-NB147 were evaluated Moreover, [18F]F-FDG was used as a control. Finally, CD147 expression analysis was performed via multiplex immunofluorescence (MxIF) and autoradiography on human cancer specimens and tumor-bearing mice tissues. TMAs results revealed that CD147 is highly expressed in MM, TNBC, and PCA. A CD147-specific nanobody, NB147, was successfully generated with excellent in vitro binding characteristics. [124I]I-NB147 was obtained with high radiochemical yield and purity, and was stable for at least 4h in vitro. WB and FCM revealed that CD147 was positive in A375, MDA-MB-435 and ASPC1 cells, whereas SK-MEL-28, 4T1 and BXPC3 cells presented low expression levels. The radio-ELISA results indicated that [124I]I-NB147 had a high binding affinity to CD147. The uptake of [124I]I-NB147 was significantly different between CD147 high-expression cells and CD147 low-expression cells (P < 0.001). The biological half-life of the distribution and clearance phases were 0.05h and 1.58h, respectively. In CD147-positive tumor models, the [124I]I-NB147 accumulated in A375, MDA-MB-435, and ASPC1 tumors, and the uptake value was significantly higher than that of [18F]F-FDG. Uptake in SK-MEL-28, BXPC3, and 4T1 tumors was not clearly observed. Finally, through autoradiography and histological studies, the correlation analysis between tumor uptake and CD147 expression level was determined. The high expression of CD147 in MM, TNBC, and PCA tissuesand in tumor cells was verified. The CD147 nanobody, NB147 was produced and radiolabeled to obtain the immunoPET probe, [124I]I-NB147, which showed high affinity to CD147 and precise visualization for accurate diagnosis of CD147-expressing lesions in different cancers. These results provide insight into the imaging and binding properties of nanobody NB147 over extended periods of time, reinforcing its potential in developing radionuclide therapies for CD147-positive cancer patients.

Metamaterial-based Artificial magnetic conductor for efficient breast cancer diagnosis using a low-cost antenna array.

Breast cancer is the most common malignancy in women globally, stemming from gene mutations that prompt irregular cellular growth and subsequent tumor development. Early-stage detection of cancer cells results in a remarkable 99% survival rate. This research presents a microwave imaging technique for the non-invasive identification of tumors in the initial stages within the women's breast. A low-cost antenna array with an Artificial Magnetic Conductor (AMC) is proposed, featuring a compact structure size of 37.2 37.2 mm . The AMC, a metamaterial, acts as a reflective surface to enhance frequency selectivity, specifically at 8.48 GHz. The maximum gains reached 9.35 dBi in simulated results and 10.51 dBi in measured results. The fabricated antenna validates the simulated findings, and its operational efficiency has undergone experimental validation. Moreover, fidelity factors in face-to-face (FtF) and side-by-side (SbS) scenarios are delineated. The antenna, operating as a transceiver, is applied to a modeled breast phantom across five distinct cases for numerical simulations pertaining to cancer cell detection applications. The outcomes of this research bear considerable implications for advancing early-stage breast cancer detection methodologies.

Comparative Performance Analysis of Ensemble Models for Breast Cancer Classification

Breast cancer remains a prevalent global health issue, accounting for approximately 2.3 million new cases and 670,000 deaths worldwide in 2022. Early detection and accurate diagnosis are crucial to improving patient outcomes, as delayed identification can lead to severe complications. Advances in machine learning (ML) have facilitated improvements in cancer diagnosis, with various algorithms enhancing predictive accuracy. This study proposes a novel ensemble model for breast cancer classification, utilizing 31 features from the University of Wisconsin Breast Cancer dataset. We applied six algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression, Random Forest, Gradient Boosting, and XGBoost—and combined them with ensemble techniques, specifically Hard Voting, to develop a high-accuracy model. The model was evaluated on classification performance metrics, achieving improvements in accuracy, precision, recall, and F1 score. Results indicate that the proposed ensemble model outperforms individual classifiers and other ensembles, showing potential as a reliable tool for early breast cancer detection.

CNN-Based Cancer Image Diagnosis: Current Progress and Future Directions

Abstract. With the development of deep learning technology, CNN (Convolutional Neural Network) models have shown great value in medical image analysis, especially in diagnosing early lung cancer, breast cancer, and brain tumors. In this study, we recall and organize the application and progress of CNN models in the field of cancer and tumor diagnosis in the past five years to provide a theoretical basis and reference for related researchers. This article introduces the principles of different CNN cancer diagnostic models and compares and analyzes their results, and ultimately finds that these models have significant advantages in improving the accuracy and efficiency of cancer diagnosis, but at the same time, there are also problems such as too much reliance on large datasets, high model complexity, and poor generalization ability. In the future, we can consider optimizing the performance of CNN network models, enhancing the generalization ability of the models, and developing data enhancement techniques on this basis, so that CNN models can be better applied in the field of cancer diagnosis.

Determining sources in the bioluminescence tomography problem

Abstract In this paper, we revisit the bioluminescence tomography\,(BLT) problem, where one seeks to reconstruct bioluminescence signals (an internal light source) from external measurements of the Cauchy data. As one kind of optical imaging, the BLT has many merits such as high signal-to-noise ratio, non-destructivity and cost-effectiveness etc., and has potential applications such as cancer diagnosis, drug discovery and development as well as gene therapies and so on. In the literature, BLT is extensively studied based on diffusion approximation (DA) equation, where the distribution of peak sources is to be reconstructed and no solution uniqueness is guaranteed without adequate a priori information. Motivated by the solution uniqueness issue, several theoretical results are explored. The major contributions in this work that are new to the literature are two-fold: first, we show the theoretical uniqueness of the BLT problem where the light sources are in the shape of $C^2$ domains or polyhedral- or corona-shaped; second, we support our results with plenty of problem-orientated numerical experiments.

Opportunities and challenges of using circulating tumor DNA to predict lung cancer immunotherapy efficacy.

Immune checkpoint inhibitors (ICIs), particularly anti-programmed death 1 (PD-1)/ programmed death ligand 1 (PD-L1) antibodies, have led to significant progress in lung cancer treatment. However, only a minority of patients have responses to these therapies. Detecting peripheral blood of circulating tumor DNA (ctDNA) allows minimally invasive diagnosis, characterization, and monitoring of lung cancer. ctDNA has potential to be a prognostic biomarker and a predictor of the response to ICI therapy since it can indicate the genomic status and tumor burden. Recent studies on lung cancer have shown that pretreatment ctDNA analysis can detect residual proliferative disease in the adjuvant immunotherapy setting and evaluate tumor burden in patients with metastatic disease. Early ctDNA dynamics can not only predict the clinical outcome of ICI therapy but also help distinguish between pseudoprogression and real progression. Furthermore, in addition to quantitative assessment, ctDNA can also detect genetic predictors of response to ICI therapy. However, barriers still exist in the application of ctDNA analysis in clinical lung cancer treatment. The predictive value of ctDNA in lung cancer immunotherapy requires further identification and resolution of these challenges. This review aims to summarize the existing data of ctDNA analysis in patients receiving immunotherapy for lung cancer, understand the limitations of clinical treatment, and discuss future research directions.

TMEM158, as plasma cfRNA marker, promotes proliferation and doxorubicin resistance in ovarian cancer.

The current study aimed to identify the potential biomarker for the diagnosis of ovarian cancer within plasma cell-free RNA (cfRNA) species and to characterize their oncogenic properties. cfRNAs were isolated from the peripheral blood of ovarian cancer patients and sequenced using an NGS platform. Principal component analysis (PCA) was performed using Salmon software. Gene ontology (GO) analysis was conducted with clusterProfiler. The relative abundance of TMEM158 transcripts was determined by real-time PCR. Cell viability and proliferation was monitored using the MTT and cell counting assays, respectively. The protein levels of TMEM158 and ABCG2 were quantified by immunoblotting. We observed a clear separation of cfRNAs between ovarian cancer patients and healthy individuals. Additionally, we identified TMEM158 as the most significantly differential gene in both peripheral blood and tumor tissues. Overexpression of TMEM158 stimulated cell viability and promoted cell proliferation in ovarian cancer cells. Notably, the aberrant upregulation of TMEM158 was closely associated with doxorubicin resistance in ovarian cancer. Mechanistically, we demonstrated that TMEM158 positively regulates ABCG2 expression, which consequently contributes to drug resistance. In summary, we identified cfRNA TMEM158 as a potential diagnostic biomarker for ovarian cancer and elucidated the critical involvement of TMEM158-ABCG2 signaling axis in the development of doxorubicin resistance.

Synthesis of motexafin gadolinium: A promising radiosensitizer and imaging agent for cancer therapy

Abstract An improved, greener synthesis of motexafin gadolinium (MGd), a promising radiosensitizer and imaging agent that has the potential to treat or diagnose cancer, has been developed. Notably, this study addresses critical difficulties related to MGd synthesis and analysis of possible approaches, minimizing hazardous reagents/solvents, without column chromatography. The synthesis has been improved to enhance the overall yield and purity of MGd. The process changes outlined in this study are intended to lower production costs and time while maintaining the compound’s characteristics. This synthesis of MGd is an important step toward attaining its full potential in the diagnosis and treatment of cancer.

BCDNet: A Deep Learning Model with Improved Convolutional Neural Network for Efficient Detection of Bone Cancer Using Histology Images

Among the several types of cancer, bone cancer is the most lethal prevailing in the world. Its prevention is better than cure. Besides early detection of bone cancer has potential to have medical intervention to prevent spread of malignant cells and help patients to recover from the disease. Many medical imaging modalities such as histology, histopathology, radiology, X-rays, MRIs, CT scans, phototherapy, PET and ultrasounds are being used in bone cancer detection research. However, hematoxylin and eosin stained histology images are found crucial for early diagnosis of bone cancer. Existing Convolutional Neural Network (CNN) based deep learning techniques are found suitable for medical image analytics. However, the models are prone to mediocre performance unless configured properly with empirical study. Within this article, we suggested a framework centered on deep learning for automatic bone cancer detection. We also proposed a CNN variant known as Bone Cancer Detection Network (BCDNet) which is configured and optimized for detection of a common kind of bone cancer named Osteosarcoma. An algorithm known as Learning based Osteosarcoma Detection (LbOD). It exploits BCDNet model for both binomial and multi-class classification. Osteosarcoma-Tumor-Assessment is the histology dataset used for our empirical study. Our the outcomes of the trial showed that BCDNet outperforms baseline models with 96.29% accuracy in binary classification and 94.69% accuracy in multi-class classification.

Cervical intraepithelial neoplasia and cervical cytology in pregnancy

Cervical cancer screening during pregnancy presents unique challenges for cytologic interpretation. This review focuses on pregnancy-associated cytomorphological changes and their impact on diagnosis of cervical intraepithelial neoplasia (CIN) and cervical cancer. Pregnancy-induced alterations include navicular cells, hyperplastic endocervical cells, immature metaplastic cells, and occasional decidual cells or trophoblasts. These changes can mimic abnormalities such as koilocytosis, adenocarcinoma in situ, and high-grade squamous intraepithelial lesions, potentially leading to misdiagnosis. Careful attention to nuclear features and awareness of pregnancy-related changes are crucial for correct interpretation. The natural history of CIN during pregnancy shows higher regression rates, particularly for CIN 2, with minimal risk of progression. Management of abnormal cytology follows modified risk-based guidelines to avoid invasive procedures, with treatment typically deferred until postpartum. The findings reported in this review emphasize the importance of considering pregnancy status in cytological interpretation, highlight potential problems, and provide guidance on differentiating benign pregnancy-related changes from true abnormalities. Understanding these nuances is essential for accurate diagnosis and proper management of cervical abnormalities in pregnant women.

Systematic Review of AI-Assisted MRI in Prostate Cancer Diagnosis: Enhancing Accuracy Through Second Opinion Tools

Background: Prostate cancer is a leading cause of cancer-related deaths in men worldwide, making accurate diagnosis critical for effective treatment. Recent advancements in artificial intelligence (AI) and machine learning (ML) have shown promise in improving the diagnostic accuracy of prostate cancer. Objectives: This systematic review aims to evaluate the effectiveness of AI-based tools in diagnosing prostate cancer using MRI, with a focus on accuracy, specificity, sensitivity, and clinical utility compared to conventional diagnostic methods. Methods: A comprehensive search was conducted across PubMed, Embase, Ovid MEDLINE, Web of Science, Cochrane Library, and Institute of Electrical and Electronics Engineers (IEEE) Xplore for studies published between 2019 and 2024. Inclusion criteria focused on full-text, English-language studies involving AI for Magnetic Resonance Imaging (MRI) -based prostate cancer diagnosis. Diagnostic performance metrics such as area under curve (AUC), sensitivity, and specificity were analyzed, with risk of bias assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Results: Seven studies met the inclusion criteria, employing various AI techniques, including deep learning and machine learning. These studies reported improved diagnostic accuracy (with AUC scores of up to 97%) and moderate sensitivity, with performance varying based on training data quality and lesion characteristics like Prostate Imaging Reporting and Data System (PI-RADS) scores. Conclusions: AI has significant potential to enhance prostate cancer diagnosis, particularly when used for second opinions in MRI interpretations. While these results are promising, further validation in diverse populations and clinical settings is necessary to fully integrate AI into standard practice.