Cancer Investigation最新文献

Lung cancer (LC) is one of the major causes of death worldwide. Early diagnosis helps to improve the patient survival outcome. The surgeon makes use of Computed Tomography (CT) for detecting LC using the aid of a Computer-Aided Diagnosis (CAD) system to identify LC effectively, but it has issues related to processing time and diagnostic precision that continue to pose significant challenges. To address this, a Deep Residual Xception Network (DRX-Net) approach has been introduced for identifying the LC. Initially, the CT image is obtained and then denoising is performed using a Wiener filter. Subsequently, the segmentation of lung nodule is conducted using Pyramidal Attention-based Y Net (PAY-Net), which uses a hybrid loss function combining Binary Cross Entropy, Tanimoto Similarity, and Dice Loss. The segmented image undergoes data augmentation followed by feature extraction. For LC detection, the selected features are processed using DRX-Net, which merges the Xception with a Deep Residual Network (DRN). Furthermore, the results show that the proposed DRX-Net achieved an accuracy of 93.988%, a True Positive Rate (TPR) of 95.567%, and a True Negative Rate (TNR) of 91.432% when evaluated using a K Group of 8.

Breast cancer primarily affects women, caused due to the excess growth of malignant breast tissues. The segmentation and early detection process suffered due to the complex and varied nature of breast tissue. To address this challenge, this research proposes a Convolutional Neural Network model with Contextual Relationship Embedding to accurately segment pathological mass regions in mammogram images. In this research work, the mammogram images are collected from datasets and are preprocessed to enhance image quality, noise reduction and contrast enhancement. By using a Deep Convolutional Neural Network, the edges in the highly contrasted regions, complex structure and spatial relationships of the images are gathered by using different operators. The extracted features are concatenated through the Fully Connected-Convolutional Block Attention Module. The contextual relationship embedded features are integrated with the original features, guided by the cross-entropy loss function with contextual relationship constraints. This enables the model to generate more precise decisions for segmentation and boundary identification. The proposed method's efficiency is validated and the proposed model achieves superior performance with an accuracy of 99.59% and an error rate of 0.405%. Overall, this research article concludes that the proposed model is more efficient for breast cancer detection than other existing models.

Colorectal cancer is a leading cause of morbidity and mortality worldwide. This study investigates the association between GLP-1 receptor agonists (GLP-1 RAs) and five-year mortality in patients with primary colon cancer, considering BMI. Using data from the University of California Health Data Warehouse, 6,871 patients were analyzed. Five-year mortality was 15.5% for GLP-1 RA users compared to 37.1% for non-users. Analyses showed significantly lower odds of five-year mortality with GLP-1 RA use (OR = 0.38, 95% CI: 0.21-0.64). This benefit persisted after adjusting for confounders, including disease severity, but was found to only extend to high obese patients (BMI > 35) in stratified modeling.

Deep learning (DL) has transformed medical imaging, particularly in the realm of Oral Cancer (OC) diagnosis using histopathological images. Timely detection of OC is essential for enhancing precision medicine and saving lives. However, incorrect diagnosis may impede effective treatment. In this study, we have proposed a DL model for OC classification, enhanced diagnosis decision-making, and interpretability. We achieve this by starting with color normalization of histopathology images using the Vahadane Three-Stain Parameter Normalization and watershed segmentation method, followed by tiling and augmentation. Key features are selected using the Weighted Fisher Score (WFS) to address class imbalance. The U-Net classifier has been improved by using feature-based inputs instead of full images, reducing computational complexity and training time. The integration of Vahadane normalization for consistent preprocessing across samples, WFS, and Explainable Artificial Intelligence (XAI) addresses critical challenges in histopathological image analysis. The proposed model surpasses existing approaches with a classification accuracy of 99.54% and outperforms DenseNet201 and VGG10 in precision and reliability. The efficiency in handling imbalanced datasets and explainability features make it suitable for early precise OC detection, which can reduce diagnostic errors and enhance treatment outcomes.​.

Breast cancer is a leading global health concern, while the endocrine resistance in breast cancer poses a critical challenge, directly undermining the long-term effectiveness of hormone therapies and significantly impacting patient survival and treatment outcomes. Hence, the present study aims to elucidate the non-genomic mechanism of ERK1/2 signalling pathway, in conjunction with ER and GPR30 receptors involved in regulation of breast cancer progression in MCF-7 and T47D cells. We assessed cell proliferation using MTT and Trypan blue assays, expression studies by reverse transcription quantitative PCR and western blot analysis, the migratory abilities of cells by scratch-wound healing assay. Our results revealed significant down (90%) regulation of E2-induced ERK phosphorylation, inturn suppression of proliferation rate by 30% and migration by 35% using small molecular inhibitors of ERK in MCF-7 and T47D cells confirming ERK as the central direct target for breast cancer proliferation and development. Collectively, our results suggest that E2-induced 1.5-fold upregulation of phospho ERK1/2 expression promotes breast cancer cell proliferation and migration via a Src/EGFR/ERK pathway. These findings provide a novel strategy of combining endocrine therapy with targeted agents (ERK inhibitors), a cornerstone in managing endocrine-resistant condition, delaying progression and improving outcomes in the treatment of breast cancer.

Background: A low absolute lymphocyte/monocyte ratio (LMR) in the peripheral blood is associated with poor prognosis in various cancers; however, its role as a predictive biomarker has not been well defined in the era of treatment with immune checkpoint inhibitors (ICIs).

Methods: We queried a database of advanced cancer patients treated with at least 1 dose of ICI from 2011 to 2019 to study the association of LMR with overall survival (OS). We calculated LMR at baseline and a median of 21 days after the first cycle of ICI (on-treatment LMR) and considered it low if < 2 and high if ≥ 2. OS was calculated from the initiation of ICI to the date of death or censored at the last follow-up.

Results: We identified 1077 patients treated with ICI, including 880 patients with both baseline and on-treatment assessment of LMR. Patients with low baseline LMR had shorter median OS of 8.8 months (95% CI 7.8-10.3) compared to patients with high baseline LMR (19.4 months [95% CI 16.1-21.7], P < 0.0001). Patients with low baseline LMR whose on-treatment LMR increased to high had longer median OS compared to those whose on-treatment LMR remained low (16.8 vs 7.8 months, P < 0.002). Patients with high baseline LMR whose on-treatment LMR remained high had longer median OS compared to patients with low on-treatment LMR (23.9 vs 9.2 months, P < 0.001). In multivariable analysis, high on-treatment LMR was most highly associated with fewer deaths compared to low on-treatment LMR, regardless of baseline LMR.

Conclusions: We observed that baseline LMR, as well as change in LMR from baseline after the first cycle of ICI were associated with OS in cancer patients treated with ICI.

Purpose: Paraneoplastic fever (PF) is an exclusion diagnosis that affects around 10% of patients in oncology, combining fever of unknown origin and the presence of cancer. There is no consensus or guidelines in the literature about the minimum criteria required for the diagnosis of (PF). The objective of this survey was to select clinical and paraclinical criteria to establish the diagnosis of PF.

Methods: After a review of the literature, 23 categories and 48 items were set up in an online survey. A two-round Delphi questionnaire survey was carried out from May to August 2021 with the participation of experts in several specialties in France and abroad.

Results: Thirty-seven and 33 experts responded in the first and second rounds respectively. Nine items obtained consensus. Among them, the need to rule out suspected infection by a directed bacteriological statement, an up-to-date imaging and doppler ultrasound of the lower limbs was highly consensual. No biological criteria were retained. Thirty-six propositions did not reach consensus and five were considered useless in this setting.

Conclusion: The 9 selected criteria confirm the importance to eliminating differential fever aetiologies whereas no specific clinical or biological markers were retained. This survey constitute the first consensus of experts in this field.

Background: The burden of multiple myeloma (MM) in middle-aged and older adults is rising with global aging. This study aimed to assess the global burden and trends of MM from 1990 to 2021, focusing on morbidity, mortality, and disability-adjusted life years (DALYs).

Methods: Data from the Global Burden of Disease (GBD) 2021 were used, stratified by region, age, and sex. The study focused on individuals over 55 years of age. Trends were analyzed using age-standardized annual percentage change (EAPC) and Bayesian age-partitioned cohort (BAPC) models.

Results: From 1990 to 2021, MM cases, deaths, and DALYs doubled in middle-aged and older adults. Age-standardized incidence, mortality, and DALYs showed significant upward trends. Intermediate socio demographic index (SDI) regions had the highest increase in incidence (2.27 per year). The USA accounted for 20% of the global burden. The disease burden peaked in those aged 65-74. Projections suggest a potential decline in global MM burden by 2050.

Conclusions: MM has become a significant global health burden, with notable regional, gender, and age variations. Targeted strategies are crucial for improving prognosis, particularly in elderly populations.

Gastric cancer (GC) prognosis remains suboptimally defined by conventional clinicopathological parameters, necessitating integrative multi-omics approaches to unravel molecular heterogeneity. This study established a robust multi-omics prognostic framework through synergistic analysis of transcriptomic, epigenomic, and clinical data from 108 GC patients. Genome-wide expression profiling and methylation array analysis identified 1,243 survival-associated transcripts and 8,742 prognostic CpG sites, with cross-omics integration via similarity network fusion revealing three molecular subtypes exhibiting distinct clinical trajectories. The aggressive Subtype 3 demonstrated a 2.87-fold increased mortality risk compared to the favorable Subtype 1, independent of age and tumor stage. A LASSO-derived prognostic signature integrating eight gene expression markers, nine methylation loci, and three clinical parameters achieved superior discrimination (C-index: 0.786 [95% CI: 0.748-0.824], compared to 0.687-0.752 in unimodal models) and 19-28% improvement in time-dependent AUC metrics. The multi-optimized nomogram incorporating molecular risk scores with conventional predictors demonstrated strong calibration (slope 0.967) and clinical utility across validation cohorts (C-index 0.742), significantly outperforming existing stratification systems. Functional characterization revealed subtype-specific enrichment in cell cycle dysregulation and immune evasion pathways, obtaining CDK/PI3K inhibitors as potential therapeutic targets. These findings establish multi-omics integration as a novel strategy for prognostic refinement and precision therapy guidance in GC.

Background: Bone morphogenetic protein 2 (BMP2) is essential for bone development and repair in vertebrates. Its role in tumorigenesis and progression remains incompletely characterized.

Method: Using the Cancer Genome Atlas (TCGA) and bioinformatic tools, we analyzed BMP2 expression, prognostic relevance, genetic alterations, immune infiltration, and signaling pathways across 33 tumor types.

Results: BMP2 exhibited elevated expression in tumor tissues of cholangiocarcinoma (CHOL), glioblastoma (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), and liver hepatocellular carcinoma (LIHC) patients, but reduced expression in 10 other cancers. High BMP2 expression correlated with reduced overall survival (OS) in esophageal carcinoma (ESCA), LIHC, lung squamous cell carcinoma (LUSC), pancreatic adenocarcinoma (PAAD), and thyroid carcinoma (THCA) patients, and shorter disease-free survival (DFS) in uveal melanoma (UVM) patients. BMP2 mutations and amplifications were frequent in diffuse large B-cell lymphoma (DLBC), skin cutaneous melanoma (SKCM), and uterine corpus endometrial carcinoma (UCEC). BMP2 expression positively correlated with cancer-associated fibroblast (CAF) infiltration and interacts physically with ACVR2A, BMP4, BMPR1A/B, BMPR2, CALR, and HSPA5. Pathway analysis implicated transforming growth factor-beta (TGF-β) signaling pathway.

Conclusions: BMP2 expressions and alterations have tissue-specific prognostic implications. BMP2 may serve as a biomarker and therapeutic target in specific tumors via TGF-β signaling modulation.