Journal of Cancer Research and Clinical Oncology最新文献

Background: Thrombocytopenia is a common side effect of cytotoxic chemotherapies, which is often dose-limiting. Predicting an individual's risk is of high clinical importance, as otherwise, a small subgroup of patients limits dosages for the overall population for safety reasons.

Methods: We aim to predict individual platelet dynamics using non-linear auto-regressive networks with exogenous inputs (NARX). We consider different architectures of the NARX networks, namely feed-forward networks (FNN) and gated recurrent units (GRU). To cope with the relative sparsity of individual patient data, we employ transfer learning (TL) approaches based on a semi-mechanistic model of hematotoxicity. We use a large data set of patients with high-grade non-Hodgkin's lymphoma to learn the respective models on an individual scale and to compare prediction performances with that of the semi-mechanistic model.

Results: Of the examined network models, the NARX with GRU architecture performs best. In comparison to the semi-mechanistic model, the network model can result in a substantial improvement of prediction accuracy for patients with irregular dynamics, given well-spaced measurements. TL improves individual prediction performances.

Conclusion: NARX networks can be utilized to predict an individual's thrombotoxic response to cytotoxic chemotherapy treatment. For reasonable model learning, we recommend at least three well-spaced measurements per cycle: at baseline, during the nadir phase and during the recovery phase. We aim at generalizing our approach to other treatment scenarios and blood lineages in the future.

FBXO protein family plays an essential role in the ubiquitination process acting as E3 ligases, which may contribute to the progression of cancers. However, the molecular functions of FBXOs in hepatocellular carcinoma (HCC) remain incompletely understood. Here, we investigated the overlapping genes between the FBXOs and differentially expressed genes (DEGs) of HCC identified by utilizing The Cancer Genome Atlas (TCGA) dataset, then, a prognostic model with effective predictive capacity was constructed based on the uni-cox and LASSO regression analyses. To elucidate the underlying mechanism of the FBXO model genes, KEGG analysis was carried out. Drug metabolism-cytochrome P450 and retinol metabolism were revealed as the potential pathway, which Increased the credibility of subsequent drug prediction research. Meanwhile, patients divided by the prognostic model showed a different immune infiltrating status and we also found FBXO model genes may ubiquitinate P53, inducing TP53 more prone to mutations, thereby promoting the occurrence and development of tumors. Consistent with these findings, the result of immunohistochemistry (IHC) validated an elevated expression of these model genes in HCC tissues than in the adjacent tissues. The primary aim of this investigation is to formulate a prognostic model while exploring the underlying mechanisms associated with FBXO genes in HCC. These findings offer initial research perspectives on the involvement of FBXO genes in HCC and contribute to the discovery of dependable biomarkers for the management, prognostication, and early detection of HCC in patients.

Background: Chemotherapy for colorectal cancer (CRC) urgently needs low-toxicity and highly effective phytomedicine. Cepharanthine (Cep) shown to have multiple anti-tumor effects, including colorectal cancer, whose pivotal mechanisms are not fully understood. Herein, the present work aims to reveal the impact of Cep on the mitochondrial and anti-injury functions of CRC cells.

Methods: The TOM70/20 expression was screened by bioinformatic databases. SW480 cells were utilized as the colorectal cancer cell model. The expression of TOM70/20 and the downstream molecules were measured by western blots (WB). The ferroptosis was analyzed using Transmission electron microscopy (TEM), C11-BODIPY, PGSK, and DCFH-DA probes, wherein the detection was performed by flow cytometry and laser confocal microscopy. The anti-cancer efficacy was conducted by CCK-8 and Annexin-V/PI assay. The rescue experiments were carried out using Fer-1 and TOM70 plasmid transfection.

Results: Bioinformatic data identified TOM20 and TOM70 were highly expressed in colorectal cancer, which could be down-regulated by Cep. Further findings disclosed that Cep treatment destroyed the mitochondria and inactivated the NRF2 signaling pathway, an essential pathway for resistance to ferroptosis, thereby promoting reactive oxygen species (ROS) generation in CRC cells. As a result, prominent ferroptosis could be observed in CRC cells in response to Cep, which thereby led to the reduced cell viability of cancer cells. On the contrary, recovery of TOM70 dampened the Cep-elicited mitochondria damage, ferroptosis, and anti-cancer efficacy.

Conclusion: In summary, Cep-mediated TOM inhibition inactivates the NRF2 signaling pathway, thereby triggering ferroptosis and achieving an anti-colorectal cancer effect. The current study provides an innovative chemotherapeutic approach for colorectal cancer with phytomedicine.

Background: The SCY1-like (SCYL) family has been reported to be closely related to cancer metastasis, but it has not been reported in gastric cancer (GC), and its specific mechanism is not clear.

Methods: We utilized databases like Deepmap, TCGA, and GEO to identify SCYL1's role in GC. Clinical samples were analyzed for SCYL1 expression and its correlation with patient prognosis. In vitro and in vivo experiments were conducted to assess SCYL1's function in GC cell migration, invasion, and autophagy.

Results: SCYL1 showed an increased expression in GC tissues, which correlated with a negative prognosis. In vitro experiments demonstrated that SCYL1 promotes GC cell migration and invasion and inhibits autophagy. GSEA indicated an inverse relationship between SCYL1 and autophagy, while a direct relationship was observed with the mTORC1 signaling pathway. Knockdown of SCYL1 enhanced autophagy, while activation of mTORC1 reversed this effect.

Conclusions: SCYL1 is a significant contributor to GC progression, promoting metastasis by activating the mTORC1 signaling pathway and inhibiting autophagy. These findings suggest SCYL1 as a potential therapeutic target for GC treatment.

Problem: Breast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process.

Aim: This study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data's high dimensionality and complexity.

Methods: We introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model's performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India.

Results: The proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls.

Conclusion: Our findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.

Background: Presently, the options of concurrent chemo-radiotherapy (CCR) in patients with locally advanced non-small cell lung cancer (LA-NSCLC) are controversial and there is no reliable prediction tool to stratify poor- and good-responders. Although radiomic analysis has provided new opportunities for personalized medicine in oncological practice, the repeatability and reproducibility of radiomic features are critical challenges that hinder their widespread clinical adoption. This study aimed to develop a qualitative radiomic signature based on the within-sample rank of radiomics features, and to use this novel method to predict CCR sensitivity in LA-NSCLC, avoiding the variability of quantitative signatures to multicenter effect.

Methods: We retrospectively analyzed 125 patients with stage III NSCLC who received treatment from our hospital. Radiomic features were extracted from pretreatment plain CT scans and constructed as feature pairs based on their within-sample rank. Fisher and univariate Cox analyses were performed to select feature pairs significantly associated with patients' overall survival (OS). NSCLC-Radiomic (R422) cohort including 104 NSCLC patients was used as an independent testing cohort. NSCLC-Radiogenomic (RG211) cohort with matched RNA sequencing profiles, was used for functional enrichment analysis to reveal the underlying biological mechanism reflected by the signature.

Results: A qualitative signature, consisting of 15 radiomic feature pairs (termed as 15-R_iFPS), was developed based on the Genetic Algorithm, which could optimally distinguish responder from non-responder with significantly improved OS if they received CCR treatment (log-rank P = 0.0009, HR = 13.79, 95% CIs 1.83-104.1). The performance of 15-R_iFPS was validated in an independent public cohort (log-rank P = 0.0037, HR = 2.40, 95% CIs 1.30-4.40). Furthermore, the transcriptomic analyses provided biological pathways ('glutathione metabolic process', 'cellular oxidant detoxification') underlying the signature.

Conclusions: We developed a CT-derived 15-R_iFPS, which could potentially help predict individualized therapeutic benefit of CCR in patients with LA-NSCLC. Additionally, we investigated the underlying intra-tumoral biological characteristics behind 15-R_iFPS which would accelerate its clinical application. This approach could be applied to a wider range of treatments and cancer types.

Purpose: Combined immunotherapy (ipilimumab + nivolumab) has improved survival in stage IV melanoma patients, making Health-related Quality of Life (HrQoL) crucial due to potential immune-related adverse events (irAEs). Previous studies treated HrQoL as secondary/explorative endpoint, and no specific HrQoL questionnaire for melanoma patients on immune checkpoint inhibitor (ICI) therapy exists. This study aimed to gather specific HrQoL data during combined ICI therapy, tracking changes during and after treatment, and examining associations with gender, irAEs, and treatment response.

Methods: 35 melanoma patients (22 males, 13 females) undergoing combined ICI were surveyed using the Short-form 36 questionnaire (SF-36), the Inflammatory Bowel Disease Questionnaire - Deutsch (IBDQ-D), and the distress thermometer (DT). HrQoL was evaluated during treatment, after six months, and at the onset of autoimmune colitis.

Results: irAEs occurred in 51.4% of patients, with colitis being the most common (26.1%). 45.7% had progressive disease. SF-36 showed stable HrQoL during treatment and follow-up. Women had worse HrQoL on the physical component scale than men (p = 0.019). Patients with progression showed worse HrQoL over time in physical (p = 0.015) and mental health scales (p = 0.04). IBDQ-D showed constant HrQoL throughout treatment and follow-up. Distress on DT remained constant, with women reporting higher levels of distress.

Conclusion: HrQoL remained stable during and after therapy. Female gender and disease progression negatively impacted HrQoL. The development of irAEs was not associated with HrQoL, though this may not apply to severe irAEs like colitis, which were not assessed.

Purpose: We sought to develop an effective combined model for predicting the survival of patients with diffuse large B-cell lymphoma (DLBCL) based on the multimodal PET-CT deep features radiomics signature (DFR-signature).

Methods: 369 DLBCL patients from two medical centers were included in this study. Their PET and CT images were fused to construct the multimodal PET-CT images using a deep learning fusion network. Then the deep features were extracted from those fused PET-CT images, and the DFR-signature was constructed through an Automated machine learning (AutoML) model. Combined with clinical indexes from the Cox regression analysis, we constructed a combined model to predict the progression-free survival (PFS) and the overall survival (OS) of patients. In addition, the combined model was evaluated in the concordance index (C-index) and the time-dependent area under the ROC curve (tdAUC).

Results: A total of 1000 deep features were extracted to build a DFR-signature. Besides the DFR-signature, the combined model integrating metabolic and clinical factors performed best in terms of PFS and OS. For PFS, the C-indices are 0.784 and 0.739 in the training cohort and internal validation cohort, respectively. For OS, the C-indices are 0.831 and 0.782 in the training cohort and internal validation cohort.

Conclusions: DFR-signature constructed from multimodal images improved the classification accuracy of prognosis for DLBCL patients. Moreover, the constructed DFR-signature combined with NCCN-IPI exhibited excellent potential for risk stratification of DLBCL patients.

Purpose: To develop and evaluate a nomogram that integrates clinical parameters with deep learning radiomics (DLR) extracted from Magnetic Resonance Imaging (MRI) data to enhance the predictive accuracy for preoperative lymph node (LN) metastasis in rectal cancer.

Methods: A retrospective analysis was conducted on 356 patients diagnosed with rectal cancer. Of these, 286 patients were allocated to the training set, and 70 patients comprised the external validation cohort. Preprocessed T2-weighted and diffusion-weighted imaging performed preoperatively facilitated the extraction of DLR features. Five machine learning algorithms-k-nearest neighbor, light gradient boosting machine, logistic regression, random forest, and support vector machine-were utilized to develop DLR models. The most effective algorithm was identified and used to establish a clinical DLR (CDLR) nomogram specifically designed to predict LN metastasis in rectal cancer. The performance of the nomogram was evaluated using receiver operating characteristic curve analysis.

Results: The logistic regression classifier demonstrated significant predictive accuracy using the DLR signature, achieving an Area Under the Curve (AUC) of 0.919 in the training cohort and 0.778 in the external validation cohort. The integrated CDLR nomogram exhibited robust predictive performance across both datasets, with AUC values of 0.921 in the training cohort and 0.818 in the external validation cohort. Notably, it outperformed both the clinical model, which had AUC values of 0.770 and 0.723 in the training and external validation cohorts, respectively, and the stand-alone DLR model.

Conclusion: The nomogram derived from multiparametric MRI data, referred to as the CDLR model, demonstrates strong predictive efficacy in forecasting LN metastasis in rectal cancer.

Purpose: Large language models (LLM) show potential for decision support in breast cancer care. Their use in clinical care is currently prohibited by lack of control over sources used for decision-making, explainability of the decision-making process and health data security issues. Recent development of Small Language Models (SLM) is discussed to address these challenges. This preclinical proof-of-concept study tailors an open-source SLM to the German breast cancer guideline (BC-SLM) to evaluate initial clinical accuracy and technical functionality in a preclinical simulation.

Methods: A multidisciplinary tumor board (MTB) is used as the gold-standard to assess the initial clinical accuracy in terms of concordance of the BC-SLM with MTB and comparing it to two publicly available LLM, ChatGPT3.5 and 4. The study includes 20 fictional patient profiles and recommendations for 5 treatment modalities, resulting in 100 binary treatment recommendations (recommended or not recommended). Statistical evaluation includes concordance with MTB in % including Cohen's Kappa statistic (κ). Technical functionality is assessed qualitatively in terms of local hosting, adherence to the guideline and information retrieval.

Results: The overall concordance amounts to 86% for BC-SLM (κ = 0.721, p < 0.001), 90% for ChatGPT4 (κ = 0.820, p < 0.001) and 83% for ChatGPT3.5 (κ = 0.661, p < 0.001). Specific concordance for each treatment modality ranges from 65 to 100% for BC-SLM, 85-100% for ChatGPT4, and 55-95% for ChatGPT3.5. The BC-SLM is locally functional, adheres to the standards of the German breast cancer guideline and provides referenced sections for its decision-making.

Conclusion: The tailored BC-SLM shows initial clinical accuracy and technical functionality, with concordance to the MTB that is comparable to publicly-available LLMs like ChatGPT4 and 3.5. This serves as a proof-of-concept for adapting a SLM to an oncological disease and its guideline to address prevailing issues with LLM by ensuring decision transparency, explainability, source control, and data security, which represents a necessary step towards clinical validation and safe use of language models in clinical oncology.