Integrative medicine cancer

Multiple prospective clinical studies have demonstrated that chemotherapy combined with immune checkpoint inhibitors (ICIs) can prolong the survival of patients with far-advanced gastric cancer. For patients with locally advanced gastric cancer (LAGC), preoperative neoadjuvant chemotherapy combined with immunotherapy has also achieved some encouraging results which benefit some patients, achieve clinical downstage, attenuate hidden metastases, increase R0 resection rate and reduce postoperative recurrence rate, etc...

Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework...

BACKGROUND: Paclitaxel is commonly used as a second-line therapy for advanced gastric cancer (AGC). The decision to proceed with second-line chemotherapy and select an appropriate regimen is critical for vulnerable patients with AGC progressing after first-line chemotherapy. However, no predictive biomarkers exist to identify patients with AGC who would benefit from paclitaxel-based chemotherapy. METHODS: This study included 288 patients with AGC receiving second-line paclitaxel-based chemotherapy between 2017 and 2022 as part of the K-MASTER project, a nationwide government-funded precision medicine initiative...

Advancements in genomic technologies have shown remarkable promise for improving health trajectories. The Human Genome Project has catalyzed the integration of genomic tools into clinical practice, such as disease risk assessment, prenatal testing and reproductive genomics, cancer diagnostics and prognostication, and therapeutic decision making. Despite the promise of genomic technologies, their full potential remains untapped without including individuals of diverse ancestries and integrating social determinants of health (SDOHs)...

Breast cancer, a highly formidable and diverse malignancy predominantly affecting women globally, poses a significant threat due to its intricate genetic variability, rendering it challenging to diagnose accurately. Various therapies such as immunotherapy, radiotherapy, and diverse chemotherapy approaches like drug repurposing and combination therapy are widely used depending on cancer subtype and metastasis severity. Our study revolves around an innovative drug discovery strategy targeting potential drug candidates specific to RTK signalling, a prominently targeted receptor class in cancer...

PURPOSES: This study aimed to develop and validate algorithms for automating intensity modulated radiation therapy (IMRT) planning in breast cancer patients, with a focus on patient anatomical characteristics. MATERIAL AND METHODS: We retrospectively selected 400 breast cancer patients without lymph node involvement for automated treatment planning. Automation was achieved using the Eclipse Scripting Application Programming Interface (ESAPI) integrated into the Eclipse Treatment Planning System...

PURPOSE: Chemotherapy-induced nausea and vomiting (CINV) is a common adverse events in cancer patients and can negatively affect their quality of life (QoL). This study aimed to evaluate the clinical efficacy of an electric massage chair (EMC) for the treatment of CINV. METHODS: A randomized phase II cross-over trial was conducted on solid cancer patients who received moderate (MEC) to high emetogenic chemotherapy (HEC). The participants were randomly assigned to receive their first chemotherapy either on a standard bed (Group A) or in an EMC (Group B) during the infusion...

Despite increasing numbers of regulatory approvals, deep learning-based computational pathology systems often overlook the impact of demographic factors on performance, potentially leading to biases. This concern is all the more important as computational pathology has leveraged large public datasets that underrepresent certain demographic groups. Using publicly available data from The Cancer Genome Atlas and the EBRAINS brain tumor atlas, as well as internal patient data, we show that whole-slide image classification models display marked performance disparities across different demographic groups when used to subtype breast and lung carcinomas and to predict IDH1 mutations in gliomas...

Ovarian cancer (OC) is the leading cause of gynecological cancer death. Cancer-associated fibroblasts (CAF) is involved in wound healing and inflammatory processes, tumor occurrence and progression, and chemotherapy resistance in OC. GSE184880 dataset was used to identify CAF-related genes in OC. CAF-related signature (CRS) was constructed using integrative 10 machine learning methods with the datasets from the Cancer Genome Atlas, GSE14764, GSE26193, GSE26712, GSE63885, and GSE140082. The performance of CRS in predicting immunotherapy benefits was verified using 3 immunotherapy datasets (GSE91061, GSE78220, and IMvigor210) and several immune calculating scores...

OBJECTIVE: Brain tumor segmentation on magnetic resonance imaging (MRI) plays an important role in assisting the diagnosis and treatment of cancer patients. Recently, cascaded U-Net models have achieved excellent performance via conducting coarse-to-fine segmentation of MRI brain tumors. However, they still suffer from obvious global and local differences among various brain tumors, which are difficult to solve with conventional convolutions. APPROACH: To address the issue, this work proposes a novel Adaptive Cascaded Transformer U-Net (ACTransU-Net) for MRI brain tumor segmentation, which simultaneously integrates Transformer and dynamic convolution into a single cascaded U-Net architecture to adaptively capture global information and local details of brain tumors...

Accurately predicting tumor T-cell antigen (TTCA) sequences is a crucial task in the development of cancer vaccines and immunotherapies. TTCAs derived from tumor cells, are presented to immune cells (T cells) through major histocompatibility complex (MHC), via the recognition of specific portions of their structure known as epitopes. More specifically, MHC class I introduces TTCAs to T-cell receptors (TCR) which are located on the surface of CD8+ T cells. However, TTCA sequences are varied and lead to struggles in vaccine design...

PURPOSE: High-grade neuroendocrine carcinoma (HGNEC) of the lung is an aggressive cancer with a complex biology. We aimed to explore the prognostic value of genetic aberrations and poly(ADP-ribose) polymerase-1 (PARP1) expression in HGNEC and to establish a novel prognostic model. MATERIALS AND METHODS: We retrospectively enrolled 191 patients with histologically confirmed HGNEC of the lung. Tumor tissues were analyzed using PARP1 immunohistochemistry (IHC; N = 191) and comprehensive cancer panel sequencing (n = 102)...

Incidence of endometrial cancer (EC) is rising in the developed world. The current standard of care, hysterectomy, is often infeasible for younger patients and those with high body mass index. There are limited non-surgical treatment options and a lack of biologically relevant research models to investigate novel alternatives to surgery for EC. The aim of the present study was to develop a long-term, patient-derived explant (PDE) model of early-stage EC and demonstrate its use for investigating predictive biomarkers for a current non-surgical treatment option, the levonorgestrel intra-uterine system (LNG-IUS)...

BACKGROUND: Liver hepatocellular carcinoma (LIHC) is a prevalent form of primary liver cancer. Research has demonstrated the contribution of tumor stem cells in facilitating tumor recurrence, metastasis, and treatment resistance. Despite this, there remains a lack of established cancer stem cells (CSCs)-associated genes signatures for effectively predicting the prognosis and guiding the treatment strategies for patients diagnosed with LIHC. METHODS: The single-cell RNA sequencing (scRNA-seq) and bulk RNA transcriptome data were obtained based on public datasets and computerized firstly using CytoTRACE package and One Class Linear Regression (OCLR) algorithm to evaluate stemness level, respectively...

Over a decade ago, longitudinal multiomics analysis was pioneered for early disease detection and individually tailored precision health interventions. However, high sample processing costs, expansive multiomics measurements along with complex data analysis have made this approach to precision/personalized medicine impractical. Here we describe in a case report, a more practical approach that uses fewer measurements, annual sampling, and faster decision making. We also show how this approach offers promise to detect an exceedingly rare and potentially fatal condition before it fully manifests...

Patients with cancer and their families comprise a significant population served by palliative care. Close collaboration between oncology clinicians and palliative care teams has demonstrated improved patient-centered care, especially when this care is integrated early in the course of the disease. The American Society of Clinical Oncology recently released their updated clinical practice guidelines for palliative care in oncology, which provide new opportunities for collaboration to expand the scope of care...

Precision medicine is transforming colorectal cancer treatment through the integration of advanced technologies and biomarkers, enhancing personalized and effective disease management. Identification of key driver mutations and molecular profiling have deepened our comprehension of the genetic alterations in colorectal cancer, facilitating targeted therapy and immunotherapy selection. Biomarkers such as microsatellite instability (MSI) and DNA mismatch repair deficiency (dMMR) guide treatment decisions, opening avenues for immunotherapy...

OBJECTIVES: The goal of this investigation was to identify the main compounds and the pharmacological mechanism of the traditional Chinese medicine formulation, Gong Ying San (GYS), by infrared spectral absorption characteristics, metabolomics, network pharmacology, and molecular-docking analysis for mastitis. The antibacterial and antioxidant activities were determined in vitro. METHODS: The chemical constituents of GYS were detected by ultra-high-performance liquid chromatography Q-extractive mass spectrometry (UHPLC-QE-MS)...

MDC1 (Mediator of DNA damage Checkpoint protein 1) functions to facilitate the localization of numerous DNA damage response (DDR) components to DNA double-strand break sites. MDC1 is an integral component in preserving genomic stability and appropriate DDR regulation. There haven't been systematic investigations of MDC1 mutations that induce cancer and genomic instability. Variations in nsSNPs have the potential to modify the protein chemistry and their function. Describing functional SNPs in disease-associated genes presents a significant conundrum for investigators, it is possible to assess potential functional SNPs before conducting larger population examinations...

Introduction: Undergoing complex diagnostic investigation of positron emission tomography-computed tomography (PET-CT) scans is associated with high levels of distress, fear, and anxiety in oncological patients. This study evaluated the effects of a single 20-min session of an innovative mindfulness-based swinging technique (MBST) intervention on emotional distress in cancer patients scheduled for PET-CT scans. Material and Methods: Adult cancer patients undergoing PET-CT scans ( n = 57) were assigned to the intervention group ( n = 27) or the control group ( n = 30)...