Hasil untuk "Neoplasms. Tumors. Oncology. Including cancer and carcinogens"

A. Alluri, J. C. Truong

4 Background: Genitourinary (GU) malignancies, including prostate, bladder, renal, and testicular cancers, comprise a major proportion of global oncologic morbidity and mortality. Despite their significance, GU oncology receives limited attention in medical student curricula. Insufficient exposure may impair clinical competency, delay diagnosis, and reduce interest in urologic subspecialties. Methods: A cross-sectional content analysis was performed on publicly available curricula from 12 accredited medical schools (8 Caribbean, 4 South American) between May and August 2025. Each school’s preclinical and clinical syllabi, curriculum maps, and lecture schedules were extracted and standardized. Oncology-related content was identified using keyword-based text mining (“cancer,” “carcinoma,” “neoplasm,” “tumor,” “oncology,” “genitourinary,” “prostate,” “bladder,” “renal,” “testicular”). Content was categorized by organ system (genitourinary, gastrointestinal, hematologic, breast, pulmonary, endocrine, neurologic, dermatologic) and stratified by level (preclinical, clerkship, elective). Quantitative variables included total lecture hours, dedicated GU sessions, and number of assessment items. Data were independently verified by two reviewers. Comparative analyses assessed GU oncology representation relative to other malignancy categories. Continuous variables were summarized as means ± SD, and group comparisons used paired t-tests and ANOVA with p < 0.05. Results: Across the 12 programs, 486 oncology-related instructional hours were identified. GU malignancies accounted for 8.4% (40.8 ± 9.6 hours) of total oncology instruction, significantly lower than gastrointestinal (23.5%) and breast (17.9%) content (p < 0.01). Only 3 programs included a dedicated prostate cancer lecture, and none covered bladder cancer screening or testicular cancer survivorship. GU oncology appeared mainly within general pathology modules without clinical integration in 10 schools. Conclusions: GU malignancies are markedly underrepresented in medical student education across Caribbean and South American schools. This gap may hinder early detection competency and reduce interest in urologic oncology. Incorporating structured GU modules and clinical exposure could align curricula with cancer epidemiology and strengthen future physician readiness.

Lourdes Hontecillas-Prieto, Daniel J. García-Domínguez, Carlos Jiménez-Cortegana et al.

Abstract Background Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide and is characterized by its heterogeneity. Although first-line therapy improves survival outcomes for DLBCL patients, approximately one third will relapse, often with a poor prognosis. Among the factors influencing prognosis and response to treatment in cancer patients, including those with lymphoma, overweight and obesity have emerged as significant considerations. However, the role of excess weight in DLBCL remains controversial, with studies reporting both negative and positive effects on cancer outcomes. In this translational substudy of the R2-GDP-GOTEL trial, we have evaluated the impact of excess weight as a predictor of treatment response and survival in patients with relapsed/refractory (R/R) DLBCL, and examining its relationship with immune cell dynamics. Methods Of the 79 patients who received the R2-GDP scheme in the phase II trial, weight and height parameters were obtained in 75 patients before starting treatment. Blood samples were analyzed by flow cytometry. Statistical analyses were performed to determine the prognostic value of overweight and obesity at baseline in R/R DLBCL patients. Results Our results indicate that overweight (including obese) patients exhibit longer survival compared to patients of ideal weight. This group also demonstrated a reduction of regulatory T cells with supposedly protumor activity and an increase of Natural Killer (NK)-like T cells with supposedly antitumor activity. Additionally, we have found that excess weight correlates with better treatment response, associated with elevated levels of vitamin D and CD8 + NK cells. Conclusions Our findings suggest that excess weight does not exacerbate the progression of DLBCL. Instead, it appears to confer a survival advantage and improve treatment response, with the immune system playing a possible pivotal role in mediating these effects. Trial registration EudraCT, ID:2014–001620-29.

Tiancheng Liu, Tiancheng Liu, Sishi Huang et al.

BackgroundAnatomic basal segmentectomy is often regarded as a complex procedure, particularly in the presence of complex anatomical variations.Case reportHerein, we present a case of a 55-year-old female patient diagnosed with invasive lung adenocarcinoma. Three-dimensional computed tomography bronchography and angiography (3D-CTBA) displayed a unique combined variation in the basal segment of right lower lobe (RLL): The medial basal subsegmental bronchi (BX7a+BX7t) originated from both the anterior and posterior segmental bronchi; The medial basal subsegmental arteries emanated from the anterior and posterior basal segmental arteries; The medial anterior segmental vein shared a trunk with both the anterior basal and lateral basal subsegmental veins. With precise preoperative planning, thoracoscopic anterior basal segmentectomy was successfully executed.ConclusionThis case again highlights the importance of 3D-reconstruction in pulmonary segmentectomy. Detailed understanding of anatomic features of segmental bronchi and vessels is imperative for those with complex anatomical variations.

Gordon Sands, Chung Tin Lo, Jemma Nunn et al.

IntroductionUpright radiotherapy has gained increasing attention in recent years due to its potential advantages, including lower room costs, improved patient comfort, and possible anatomical/physiological benefits. In this pre-clinical study, we assess the feasibility of implementing upright radiotherapy for breast cancer by evaluating beam access, inframammary skin fold size, field length, and set-up comfort across a range of upright positions.Materials and methodsTwenty-one healthy participants were enrolled in the study. Each participant was set-up on an upright patient positioning system (Eve from Leo Cancer Care Ltd) with three different arm positions (arms up, arms down, and arms behind). Setups were conducted both without a bra (topless) and with the Chabner XRT Bra for Radiotherapy, an indexable bra designed for immobilisation during treatment. Optical surface scans were acquired, and the external contour of the breast was used to approximate a clinical target volume. Beam access was evaluated in multiple regions of the breast while field size and the ISF were measured for the different positions. Participants rated their comfort using a survey. ArUco markers were employed to evaluate ease of setup by measuring the unaided reproducibility of each upright position.ResultsThe ISF was smallest in the arms-up position when participants wore the Chabner XRT Bra. Beam access for photon treatment planning was assessed for 15 participants. Arm position significantly affected photon beam angle flexibility; with arms down, participants had fewer available angles for beam angle entry. The Chabner XRT Bra consistently reduced the required field length across all positions. Participants could typically reposition themselves with sub-centimetre accuracy, without any assistance from the study team.ConclusionOverall, this study demonstrates the feasibility of delivering breast radiotherapy in the upright position for both photon and proton treatments. The arms-up position was preferable in terms of photon beam access. While photon beam access was limited for the arms down position, it remained achievable in the majority of cases. The use of the Chabner XRT Bra significantly reduced the size of the ISF, which may help lower skin toxicity, as well as the field length required for treatment, potentially decreasing unwanted lung dose.

André Rocha, Claudia Manini, José I López et al.

We study the effect of intratumor heterogeneity in the likelihood of cancer cells moving from a primary tumor to other sites in the human body, generating a metastatic process. We model different scenarios of competition between tumor cells using a static evolutionary game in which cells compete for nutrients and oxygen and might choose to stay and proliferate in the primary tumor or opt to a motility strategy in order to find resources in a metastatic site. The theoretical results found in the evolutionarily equilibrium in the mathematical model are in line with the empirical results observed in oncology, namely, the coexistence of both primary and metastatic tumors and the conditions that favor a metastatic process. Particularly, the model finds mathematical support for what is empirically observed in punctuated and branching cancers for the specific case of clear cell renal cell carcinomas: motility of cells is larger in punctuated cancers if the proportion of BAP1 mutations remain below a given cell proportion threshold.

Aneesh Rangnekar, Harini Veeraraghavan

Accurate segmentation of cancerous lesions from 3D computed tomography (CT) scans is essential for automated treatment planning and response assessment. However, even state-of-the-art models combining self-supervised learning (SSL) pretrained transformers with convolutional decoders are susceptible to out-of-distribution (OOD) inputs, generating confidently incorrect tumor segmentations, posing risks to safe clinical deployment. Existing logit-based methods suffer from task-specific model biases, while architectural enhancements to explicitly detect OOD increase parameters and computational costs. Hence, we introduce a lightweight, plug-and-play post-hoc random forests-based OOD detection framework called RF-Deep that leverages deep features with limited outlier exposure. RF-Deep enhances generalization to imaging variations by repurposing the hierarchical features from the pretrained-then-finetuned backbone, providing task-relevant OOD detection by extracting the features from multiple regions of interest anchored to the predicted tumor segmentations. We compared RF-Deep against existing OOD detection methods using 2,056 CT scans across near-OOD (pulmonary embolism, negative COVID-19) and far-OOD (kidney cancer, healthy pancreas) datasets. RF-Deep achieved AUROC > 93.50 for the challenging near-OOD datasets and near-perfect detection (AUROC > 99.00) for the far-OOD datasets, substantially outperforming logit-based and radiomics approaches. RF-Deep maintained consistent performance across networks of different depths and pretraining strategies, demonstrating its effectiveness as a lightweight, architecture-agnostic approach to enhance the reliability of tumor segmentation from CT volumes.

Binesh Sadanandan, Vahid Behzadan

Cancer remains a leading cause of death worldwide, necessitating personalized treatment approaches to improve outcomes. Theranostics, combining molecular-level imaging with targeted therapy, offers potential for precision oncology but requires optimized, patient-specific care plans. This paper investigates state-of-the-art data-driven decision support applications with a reinforcement learning focus in precision oncology. We review current applications, training environments, state-space representation, performance evaluation criteria, and measurement of risk and reward, highlighting key challenges. We propose a framework integrating data-driven modeling with reinforcement learning-based decision support to optimize radiopharmaceutical therapy dosing, addressing identified challenges and setting directions for future research. The framework leverages Neural Ordinary Differential Equations and Physics-Informed Neural Networks to enhance Physiologically Based Pharmacokinetic models while applying reinforcement learning algorithms to iteratively refine treatment policies based on patient-specific data.

Guillermo Lorenzo, David A. Hormuth, Chengyue Wu et al.

Despite advances in methods to interrogate tumor biology, the observational and population-based approach of classical cancer research and clinical oncology does not enable anticipation of tumor outcomes to hasten the discovery of cancer mechanisms and personalize disease management. To address these limitations, individualized cancer forecasts have been shown to predict tumor growth and therapeutic response, inform treatment optimization, and guide experimental efforts. These predictions are obtained via computer simulations of mathematical models that are constrained with data from a patient's cancer and experiments. This book chapter addresses the validation of these mathematical models to forecast tumor growth and treatment response. We start with an overview of mathematical modeling frameworks, model selection techniques, and fundamental metrics. We then describe the usual strategies employed to validate cancer forecasts in preclinical and clinical scenarios. Finally, we discuss existing barriers in validating these predictions along with potential strategies to address them.

M. Evans, F. Abdulla, J. Xiu et al.

e15071 Background: Studies indicate that Merkel cell polyomavirus (MCPyV) infection is a causative factor for the development of virus-positive Merkel cell carcinoma (VP MCC). This neuroendocrine neoplasm (NEN) is sometimes undiagnosed or misdiagnosed when MCPyV infection is not properly identified; accurate diagnosis is essential, as research suggests the utility of immunotherapy in the treatment of MCC compared to other NENs. Consequently, we seek to establish whole exome next-generation sequencing (WES) as a reliable method for finding MCPyV in tumors. Specifically, we propose a novel assay incorporating MCPyV detection into standard-of-care molecular cancer profiling, therefore ensuring proper diagnosis of VP MCC. Methods: WES was validated on DNA extracted from previously diagnosed VP MCC tumor samples submitted to Caris Life Sciences utilizing NovaSeq 6000 technology (Illumina, Inc.). A hybrid pull-down panel of sequencing baits was used to provide enhanced 1500x depth of coverage for 720 cancer-related genes and 500x coverage for the remaining exome. Given bait availability, the panel was also designed to cover the maximal possible amount of the MCPyV genome—82.4%. The presence of virus was suggested if ≥1,000 sequencing reads specific to MCPyV were detected. Orthogonal IHC testing using CM2B4 antibody (Santa Cruz Biotechnology, Inc.) was used to confirm the MCPyV infection status detected by WES. Clinical utility of the sequencing assay was demonstrated by testing primary and metastatic tumor specimens previously classified as NENs not otherwise specified, so as to identify potentially undiagnosed or misdiagnosed MCC. Results: We performed WES on 835 NEN specimens that were not previously diagnosed as MCC. MCPyV was detected in 10/835 (1.2%), suggesting that these cases might have been undiagnosed or misdiagnosed. To verify the validity of the sequencing results, we performed WES on 70 samples with a known diagnosis of MCC, and MCPyV was detected in 49 tumors. Conversely, orthogonal IHC testing identified virus in 45 of the 70 specimens. The presence of viral sequences was confirmed by the Basic Local Alignment Search Tool (BLAST, National Institutes of Health) in 49 of the cases, including the four tumors discordant by IHC, indicating that WES is more sensitive in detecting the virus (100% sensitivity [95% CI: 91.4-100%] and 84.0% specificity [95% CI: 63.9-95.5%]) compared to IHC. Conclusions: This study presents a robust method for identifying MCPyV in VP MCC utilizing standard-of-care cancer genomic profiling for precision oncology. By confirming the presence of viral sequences within neoplastic tissue specimens, we have demonstrated the superior sensitivity of WES in detecting MCPyV compared to IHC. Moreover, our sequencing assay can assist in properly classifying tumors, therefore ensuring that patients receive appropriate treatment based on the diagnosis of MCC.

S. V. Timofeeva, E. Zlatnik, L. N. Vaschenko et al.

Malignant neoplasms remain one of the leading causes of mortality worldwide, underscoring the need for the development of novel and effective therapeutic strategies for their treatment. In recent years, the scientific community has actively pursued the search for pharmaceutical preparations with minimal adverse effects and high efficacy, capable of achieving complete remission in patients. In this context, berberine, a natural phytochemical compound derived from various species of Berberis, has garnered increasing attention due to its diverse pharmacological properties. Berberine, recognized as a nutraceutical, exhibits a broad spectrum of biological activities, including anti-inflammatory, antioxidant, and antitumor effects. In vitro and in vivo studies have demonstrated that berberine exerts inhibitory effects on several types of cancer, including breast, lung, liver, and colorectal cancers. Its antitumor properties are associated with several key molecular mechanisms through which it targets tumor cells. In this review, we have comprehensively examined the various molecular pathways through which the antitumor effects of berberine are mediated. Specifically, berberine activates the caspase cascade, leading to the induction of apoptosis in tumor cells, and inhibits cell proliferation by blocking key signaling pathways such as PI3K/Akt/mTOR. Additionally, berberine modulates the expression of genes associated with cell migration and invasion, including matrix metalloproteinases and E-cadherin, making it a promising candidate for the development of novel therapeutic approaches. It is also noteworthy that berberine exhibits anti-inflammatory properties, which may contribute to the prevention of carcinogenesis by protecting cells from oxidative stress and inflammatory processes linked to cancer development. These properties position berberine as a promising agent for further research and clinical application, both as a monotherapy and in combination with other anticancer drugs. Thus, berberine represents an intriguing subject for further investigation of its molecular mechanisms of action and potential use in oncology, which could lead to the development of more effective and safer therapeutic strategies for patients with malignant neoplasms.

Cuixin Peng, Yanning Chen, Mingzhang Jiang

Breast cancer is one of the most prevalent malignancies affecting women worldwide, with its incidence increasingly observed in younger populations. In recent years, drug resistance has emerged as a significant challenge in the treatment of breast cancer, making it a central focus of contemporary research aimed at identifying strategies to overcome this issue. Growing evidence indicates that inducing ferroptosis through various mechanisms, particularly by inhibiting System Xc-, depleting glutathione (GSH), and inactivating glutathione peroxidase 4 (GPX4), holds great potential in overcoming drug resistance in breast cancer. It is anticipated that therapies targeting ferroptosis will emerge as a promising strategy to reverse tumor resistance, offering new hope for breast cancer patients. This review will explore the latest advancements in understanding ferroptosis in the context of breast cancer drug resistance, with a particular emphasis on the roles of ferroptosis inducers and inhibitors, and the impact of ferroptotic pathways on overcoming drug resistance in breast cancer.

Shulin Li, Liqiang Yu, Bo Liu et al.

At present, the incidence and fatality rate of lung cancer in China rank first among all malignant tumors. Despite the continuous development and improvement of China's medical level, the overall 5-year survival rate of lung cancer patients is still lower than 20% and is staged. A number of studies have confirmed that early diagnosis and treatment of early stage lung cancer is of great significance to improve the prognosis of patients. In recent years, artificial intelligence technology has gradually begun to be applied in oncology. ai is used in cancer screening, clinical diagnosis, radiation therapy (image acquisition, at-risk organ segmentation, image calibration and delivery) and other aspects of rapid development. However, whether medical ai can be socialized depends on the public's attitude and acceptance to a certain extent. However, at present, there are few studies on the diagnosis of early lung cancer by AI technology combined with SCT scanning. In view of this, this study applied the combined method in early lung cancer screening, aiming to find a safe and efficient screening mode and provide a reference for clinical diagnosis and treatment.

Praneeth Kumar T, Nidhi Srivastava, Rakshith Mahishi et al.

In the current technological era, the medical profession has emerged as one of the researchers' favorite subject areas, and cancer is one of them. Because there is now no effective treatment for this illness, it is a matter of concern. Only if this disease is discovered early may patients be rescued (stage I and stage II). The likelihood of survival is quite low if it is discovered in later stages (stages III and IV). The application of machine learning, deep learning, and data mining techniques in the medical industry has the potential to address current issues and bring benefits. Numerous symptoms of cancer exist, including tumors, unusual bleeding, increased weight loss, etc. It is not necessary for all tumor types to be cancerous. There are two sorts of tumors: benign and malignant. To give patients, the right care, symptoms must be carefully examined, and an automated system is to distinguish between benign and malignant tumors. Most data produced in today's online environment comes from websites related to healthcare or social media. Using data mining techniques, it is possible to extract symptoms from this vast amount of data, which will be helpful for identifying or classifying cancer. This research classifies bacteria cells as benign or cancerous using various deep-learning Algorithms. To get the best and most reliable results for the classification, a variety of methodologies and models are trained and improved.

M. Yakubovskaya, T. I. Fetisov, L. G. Solenova et al.

Recent development of molecular and genetic technologies has demonstrated at the molecular level the co-evolutionary principles of interaction between microbiota, virome and the host organism, as well as the role of microorganisms and viruses both in maintaining physiological homeostasis and in the development of various diseases, including malignant neoplasms. The presented review is devoted to the analysis and generalization of modern data on microorganisms and viruses inhabiting the human body, their role in the processes of initiation, promotion and progression of carcinogenesis. The review provides information on known oncogenic viruses and microorganisms according to the modern classification of carcinogenic agents of the International Agency for Research on Cancer. Mechanistic data on the procarcinogenic effect of microbiota and virome are considered in accordance with the modern concept of key characteristics of a carcinogenic agent. Particular attention is paid to the analysis of data on the influence of microbiota and virome on the immunity of the host organism, including both the first results of immunotherapy with Coley toxin of soft tissue sarcomas and osteosarcomas, and data on the influence of individual types of microorganisms on the formation of the immunocompetent cell profile of the host organism. In addition, the influence of intratumor and intracellular microbiota, respectively, on the microenvironment of tumor cells and cellular signaling, including in solid tumors that have no contact with the external environment are also discussed. The data presented are important in terms of the holobiota concept, showing the interdependent existence of the human body, microorganisms and viruses, to improve the prevention and therapy of malignant neoplasms.

Heidi Ko, D. P. Dash, Eric A Severson et al.

Background: There were an estimated 2.3 million new breast cancer cases worldwide in 2020, with breast cancer now the most diagnosed type of cancer representing 25% of cancer cases and 17% of cancer deaths (1). The GLOBOCAN Cancer Tomorrow prediction tool estimates that incidence will increase by > 45% by 2040 (2). Optimal treatment for breast cancer is increasingly dependent upon knowing a patient’s somatic and germline genomic alteration status. To highlight the importance of these genomic alterations, we provide an overview of the genomic findings in 987 consecutive breast cancer patients tested in the course of routine clinical care. Methods: Comprehensive Genomic and Immune Profiling (CGIP) was performed on 987 qualified breast cancer samples at a CAP/CLIA and NYS CLEP certified reference laboratory with the OmniSeq INSIGHT ® test (3). OmniSeq INSIGHT ® is a next generation sequencing-based laboratory developed test for the detection of genomic variants, signatures, HLA Class I genotypes, and immune gene expression in formalin-fixed paraffin-embedded (FFPE) tumor tissue. DNA is sequenced to detect small variants in the full exonic coding region of 523 genes, copy number alterations in 59 genes (gains and losses), as well as analysis of microsatellite instability (MSI) and tumor mutational burden (TMB). RNA is sequenced to detect fusions and splice variants in 55 genes, in addition to mRNA expression in 64 immune genes. The resultant information, along with PD-L1 protein expression by immunohistochemistry (IHC), is intended for use by qualified health care professionals in accordance with professional guidelines in oncology for management of patients with solid neoplasms. Tier IA variants include variants with strong clinical significance as per AMP–ASCO–CAP recommendations (4). Results: There were 384 Tier IA variants detected in 370 of 987 breast cancer patients (~37.5%) with 251 in PIK3CA (65.3%), 60 in ERBB2 (15.6%), 46 in BRCA2 (11.9%), 19 in BRCA1 (4.9%), 4 in NTRK3 (1.0%), 1 in NTRK1 (0.26%) and 1 in PALB2 (0.26%) including copy number variations (CNV), gene fusion and single nucleotide variants (SNV). At the individual gene level for Tier IA variants, PIK3CA had 251 SNVs detected, ERBB2 had 55 CNVs, 1 gene fusion and 4 SNVs detected, BRCA2 had 39 SNVs and 8 CNVs detected, BRCA1 had 19 SNVs, NTRK3 had 4 gene fusions, NTRK1 had 1 gene fusion and PALB2 had 1 SNV detected. At the variant class level, there were 63 CNVs, 6 gene fusions and 315 SNVs observed. Conclusions: CGIP for breast cancer patients identified one or more clinically significant Tier IA genomic alterations that directs targeted therapy in ~ 37.5% of patients in a cohort of real world patients tested during the standard course of clinical care. This highlights the need for comprehensive genomic testing in breast cancer patients to drive therapeutic decision making. References: 1. Sung H et al.; Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries; CA Cancer J Clin, 2021 May;71(3):209-249. 2. Heer E et al.; Global burden and trends in premenopausal and postmenopausal breast cancer: a population-based study; Lancet Glob Health, 2020 3. Conroy JM et al.; A scalable high-throughput targeted next-generation sequencing assay for comprehensive genomic profiling of solid tumors; PLoS One. 2021 4. Li MM et al.; Standards and guidelines for the interpretation and reporting of sequence variants in cancer: a joint consensus recommendation of the association for molecular pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagnost. 2017;19(1):4–23. Citation Format: Heidi Ko, Durga Prasad Dash, Eric Severson, Kyle Strickland, Zachery Bliss, Paul DePietro, Jeffrey Conroy, Shakti Ramkissoon, Shengle Zhang. Findings of clinically significant variants (Tier IA) with OmniSeq INSIGHT ® in a breast cancer cohort of 987 patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-14-11.

R. Chemaly, Mandy Nakhle

Ying-jie Gao, Shi-rong Li, Yuan Huang

BackgroundDue to the viral infection, chronic inflammation significantly increases the likelihood of hepatocellular carcinoma (HCC) development. Nevertheless, an inflammation-based signature aimed to predict the prognosis and therapeutic effect in virus-related HCC has rarely been established.MethodBased on the integrated analysis, inflammation-associated genes (IRGs) were systematically assessed. We comprehensively investigated the correlation between inflammation and transcriptional profiles, prognosis, and immune cell infiltration. Then, an inflammation-related risk model (IRM) to predict the overall survival (OS) and response to treatment for virus-related HCC patients was constructed and verified. Also, the potential association between IRGs and tumor microenvironment (TME) was investigated. Ultimately, hub genes were validated in plasma samples and cell lines via qRT-PCR. After transfection with shCCL20 combined with overSLC7A2, morphological change of SMMC7721 and huh7 cells was observed. Tumorigenicity model in nude mouse was established.ResultsAn inflammatory response-related gene signature model, containing MEP1A, CCL20, ADORA2B, TNFSF9, ICAM4, and SLC7A2, was constructed by conjoint analysis of least absolute shrinkage and selection operator (LASSO) Cox regression and gaussian finite mixture model (GMM). Besides, survival analysis attested that higher IRG scores were positively relevant to worse survival outcomes in virus-related HCC patients, which was testified by external validation cohorts (the ICGC cohort and GSE84337 dataset). Univariate and multivariate Cox regression analyses commonly proved that the IRG was an independent prognostic factor for virus-related HCC patients. Thus, a nomogram with clinical factors and IRG was also constructed to superiorly predict the prognosis of patients. Featured with microsatellite instability-high, mutation burden, and immune activation, lower IRG score verified a superior OS for sufferers. Additionally, IRG score was remarkedly correlated with the cancer stem cell index and drug susceptibility. The measurement of plasma samples further validated that CCL20 upexpression and SLC7A2 downexpression were positively related with virus-related HCC patients, which was in accord with the results in cell lines. Furthermore, CCL20 knockdown combined with SLC7A2 overexpression availably weakened the tumor growth in vivo.ConclusionsCollectively, IRG score, serving as a potential candidate, accurately and stably predicted the prognosis and response to immunotherapy in virus-related HCC patients, which could guide individualized treatment decision-making for the sufferers.

Narges Ramezani, Erfan Mohammadi

Cancer, as a complex and devastating sickness, poses a great public health challenge on a global scale. Its far-attaining impact necessitates a devoted branch of medicine referred to as oncology, which makes a specialty in the prevention, diagnosis, and treatment of cancer. In this paper, we aim to offer a complete evaluation of the oncology sector, delving into its rich history, numerous forms of most cancers, diagnostic methods, and treatment alternatives. By exploring recent advances in oncology, which include precision remedy, immunotherapy, and the integration of era, we shed mild on the promising traits of the subject. However, it is miles essential to know the continual challenges that we face, such as the high costs related to remedy and the emergence of drug resistance. Despite these challenges, the final goal of oncology remains unwavering - to provide exceptional feasible outcomes for sufferers of most cancers, using both healing and palliative remedy strategies. As our knowledge of this complicated ailment continues to adapt, we must prioritize prevention and early detection and deal with disparities in access to care. By fostering collaboration and operating collectively, we can seriously improve the lives of tens of millions of individuals affected by cancer around the arena.

O. Malikhova, V. E. Ryabova, V. Lozovaya et al.

Gastric cancer (GC) is one of the most common types of malignant neoplasms in Russia and worldwide. In recent decades, GC incidence and mortality have been declining. However, gastric cancer remains one of the leading causes of morbidity and mortality, facilitating further improvements of GC diagnostics and treatment. As known, early gastric cancer is a tumor limited to the gastric mucosa or the submucosal layer of the stomach wall. The detection of early-stage GC is the goal of screening programs. The rapid development of endoscopic technologies and the introduction of the ZOOM endoscopy and narrow-band imaging for precise diagnostics have dramatically improved the quality and effectiveness of patient examinations. These advances have underpinned the increased detection of early gastric cancer. Early gastric cancer can be completely cured. Until recently, the gold standard treatment for GC, including its early stages, was radical resection and gastrectomy. This treatment approach is fully justified and ideal from the perspective of oncology. At the same time, it is associated with postoperative complications and deaths, as well as with a substantial decrease in the quality of life in long term GC survivors. Latest endoscopic technologies enable to perform organ-preserving operations in patients with early GC. As a result, it is possible to reduce dramatically the probability of postoperative complications and to improve the quality of life of patients. As of today, the optimization of endoscopic techniques for the treatment of early GC, as well as the search and implementation of new techniques are considered as the most important issues. The article presents a review of key endoscopic methods used for diagnosis and treatment of GC and a clinical case of a patient with early subcardial stomach cancer. KEYWORDS: oncology, gastric cancer, early gastric cancer, dysplastic changes in the stomach mucosa, endoscopy, endoscopic diagnostics, endoscopic resection, OVESCO. FOR CITATION: Malikhova O.A., Ryabova V.E., Lozovaya V.V. et al. Early gastric cancer: clinical case. Russian Medical Inquiry. 2022;6(6):334– 340 (in Russ.). DOI: 10.32364/2587-6821-2022-6-6-334-340.

Keisuke Okuno, Keisuke Okuno, Rachana Garg et al.