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Current Oncology (Toronto, Ont.) Mar 2022Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise... (Review)
Review
Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients' survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed.
Topics: Artificial Intelligence; Colorectal Neoplasms; Early Detection of Cancer; Humans; Prognosis; Research
PubMed: 35323346
DOI: 10.3390/curroncol29030146 -
Frontiers in Immunology 2022Osteosarcoma is a malignant bone tumor with poor outcomes affecting the adolescents and elderly. In this study, we comprehensively assessed the metabolic characteristics...
OBJECTIVES
Osteosarcoma is a malignant bone tumor with poor outcomes affecting the adolescents and elderly. In this study, we comprehensively assessed the metabolic characteristics of osteosarcoma patients and constructed a hexosamine biosynthesis pathway (HBP)-based risk score model to predict the prognosis and tumor immune infiltration in patients with osteosarcoma.
METHODS
Gene expression matrices of osteosarcoma were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. GSVA and univariate Cox regression analysis were performed to screen the metabolic features associated with prognoses. LASSO regression analysis was conducted to construct the metabolism-related risk model. Differentially expressed genes (DEGs) were identified and enrichment analysis was performed based on the risk model. CIBERSORT and ESTIMATE algorithms were executed to evaluate the characteristics of tumor immune infiltration. Comparative analyses for immune checkpoints were performed and the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to predict immunotherapeutic response. Finally, hub genes with good prognostic value were comprehensive analyzed including drug sensitivity screening and immunohistochemistry (IHC) experiments.
RESULTS
Through GSVA and survival analysis, the HBP pathway was identified as the significant prognostic related metabolism feature. Five genes in the HBP pathway including GPI, PGM3, UAP1, OGT and MGEA5 were used to construct the HBP-related risk model. Subsequent DEGs and enrichment analyses showed a strong correlation with immunity. Further, CIBERSORT and ESTIMATE algorithms showed differential immune infiltration characteristics correlated with the HBP-related risk model. TIDE algorithms and immune checkpoint analyses suggested poor immunotherapeutic responses with low expression of immune checkpoints in the high-risk group. Further analysis revealed that the UAP1 gene can predict metastasis. IHC experiments suggested that UAP1 expression correlated significantly with the prognosis and metastasis of osteosarcoma patients. When screening for drug sensitivity, high UAP1 expression was suggestive of great sensitivity to antineoplastic drugs including cobimetinib and selumetinib.
CONCLUSION
We constructed an HBP-related gene signature containing five key genes (GPI, PGM3, UAP1, OGT, MGEA5) which showed a remarkable prognostic value for predicting prognosis and can guide immunotherapy and targeted therapy for osteosarcoma.
Topics: Adolescent; Humans; Aged; Hexosamines; Osteosarcoma; Prognosis; Bone Neoplasms; Survival Analysis
PubMed: 36275679
DOI: 10.3389/fimmu.2022.1028263 -
Current Oncology (Toronto, Ont.) Mar 2023Reliable tools for prognosis prediction are crucially needed by oncologists so they can tailor individual treatments. However, the wide spectrum of histologies and... (Review)
Review
Reliable tools for prognosis prediction are crucially needed by oncologists so they can tailor individual treatments. However, the wide spectrum of histologies and prognostic behaviors of sarcomas challenges their development. In this field, nomograms could definitely better account for their granularity compared to the more widely used AJCC/UICC TNM staging system. Nomograms are predictive tools that incorporate multiple risk factors and return a numerical probability of a clinical event. Since the development of the first nomogram in 2002, several other nomograms have been built, either general, site-specific, histology-specific, or both. Recently, some new "dynamic" nomograms and prognostic tools have been developed, allowing doctors to "recalculate" a patient's prognosis by taking into account the time since primary surgery, the event history, and the potential time-dependent effect of covariates. Due to these new tools, prognosis prediction is no longer limited to the time of the first computation but can be adapted and recalculated based on the occurrence (or not) of any event as time passes from the first computation. In this review, we aimed to give an overview of the available nomograms for STS and to help clinicians in the process of selecting the best tool for each patient.
Topics: Humans; Nomograms; Prognosis; Neoplasm Staging; Sarcoma; Soft Tissue Neoplasms
PubMed: 37185391
DOI: 10.3390/curroncol30040278 -
Annals of Clinical and Translational... Nov 2023Most patients with Marchiafava-Bignami disease (MBD) had unfavorable prognosis, with disability or death. We aimed to determine the risk factors of early unfavorable...
OBJECTIVE
Most patients with Marchiafava-Bignami disease (MBD) had unfavorable prognosis, with disability or death. We aimed to determine the risk factors of early unfavorable prognosis of MBD, and to develop a predictive nomogram for early unfavorable prognosis of MBD.
METHODS
MBD patients admitted to our hospital between 1 January 2013 and 31 December 2021 were included. Unfavorable prognosis was defined as mRS score ≥3, the independent risk factors for unfavorable prognosis of MBD with the odds ratio (OR) and 95% confidential interval (CI) acquired by multiple logistic regression were included in development of the predictive nomogram for early unfavorable prognosis of MBD, and the area under curve (AUC) of the receiver operating characteristic curve was calculated. The published case reports of MBD were used as the external validation group to verify the predictive ability of the nomogram.
RESULTS
Independent risk factors for early unfavorable prognosis of MBD included Glasgow Coma Scale score (OR = 0.636, 95% CI = 0.506-0.800, p = 0.004) and pneumonia (OR = 2.317, 95% CI = 1.003-5.352, p = 0.049). The AUC of the nomogram was 0.852. Ninety-four case reports, a total of 100 cases of MBD were included as the external validation group, its AUC was 0.840. The online dynamic nomogram for early unfavorable prognosis of MBD was constructed.
INTERPRETATION
It is confirmed by external validation that the nomogram has a preferable predictive ability and clinical efficacy, and the dynamic online predictive nomogram is helpful for physicians to quickly assess the prognosis of MBD.
Topics: Humans; Nomograms; Marchiafava-Bignami Disease; Prognosis; Risk Factors; Treatment Outcome
PubMed: 37649317
DOI: 10.1002/acn3.51888 -
BMC Public Health Aug 2022Anxiety and depression are amongst the most prevalent mental health problems. Their pattern of comorbidity may inform about their etiology and effective treatment, but...
BACKGROUND
Anxiety and depression are amongst the most prevalent mental health problems. Their pattern of comorbidity may inform about their etiology and effective treatment, but such research is sparse. Here, we document long-term prognosis of affective caseness (high probability of being a clinical case) of anxiety and depression, their comorbidity, and a no-caseness condition at three time-points across six years, and identify the most common prognoses of these four conditions.
METHODS
Longitudinal population-based data were collected from 1,837 participants in 2010, 2013 and 2016. Based on the Hospital Anxiety and Depression Scale they formed the four groups of anxiety, depression and comorbidity caseness, and no caseness at baseline.
RESULTS
The three-year associations show that it was most common to recover when being an anxiety, depression or comorbidity caseness (36.8 - 59.4%), and when not being a caseness to remain so (89.2%). It was also rather common to remain in the same caseness condition after three years (18.7 - 39.1%). In comorbidity it was more likely to recover from depression (21.1%) than from anxiety (5.4%), and being no caseness it was more likely to develop anxiety (5.9%) than depression (1.7%). The most common six-year prognoses were recovering from the affective caseness conditions at 3-year follow-up (YFU), and remain recovered at 6-YFU, and as no caseness to remain so across the six years. The second most common prognoses in the affective conditions were to remain as caseness at both 3-YFU and 6-YFU, and in no caseness to remain so at 3-YFU, but develop anxiety at 6-YFU.
CONCLUSIONS
The results suggest that only 37 - 60% of individuals in the general population with high probability of being a clinical case with anxiety, depression, and their comorbidity will recover within a three-year period, and that it is rather common to remain with these affective conditions after 6 years. These poor prognoses, for comorbidity in particular, highlight the need for intensified alertness of their prevalence and enabling treatment in the general population.
Topics: Adult; Anxiety; Comorbidity; Depression; Humans; Prognosis; Prospective Studies
PubMed: 35971092
DOI: 10.1186/s12889-022-13966-4 -
Current Heart Failure Reports Oct 2022The balance between inflammation and its resolution plays an important and increasingly appreciated role in heart failure (HF) pathogenesis. In humans, different chronic... (Review)
Review
PURPOSE OF REVIEW
The balance between inflammation and its resolution plays an important and increasingly appreciated role in heart failure (HF) pathogenesis. In humans, different chronic inflammatory conditions and immune-inflammatory responses to infection can lead to diverse HF manifestations. Reviewing the phenotypic and mechanistic diversity of these HF presentations offers useful clinical and scientific insights.
RECENT FINDINGS
HF risk is increased in patients with chronic inflammatory and autoimmune disorders and relates to disease severity. Inflammatory condition-specific HF manifestations exist and underlying pathophysiologic causes may differ across conditions. Although inflammatory disease-specific presentations of HF differ, chronic excess in inflammation and auto-inflammation relative to resolution of this inflammation is a common underlying contributor to HF. Further studies are needed to phenotypically refine inflammatory condition-specific HF pathophysiologies and prognoses, as well as potential targets for intervention.
Topics: Chronic Disease; Heart Failure; Humans; Inflammation; Prognosis
PubMed: 35838874
DOI: 10.1007/s11897-022-00560-3 -
International Journal of Molecular... Oct 2022Hepatocellular carcinoma (HCC) is a common malignant tumor with high mortality and poor prognoses around the world. Within-cell polarity is crucial to cell development...
Hepatocellular carcinoma (HCC) is a common malignant tumor with high mortality and poor prognoses around the world. Within-cell polarity is crucial to cell development and function maintenance, and some studies have found that it is closely related to cancer initiation, metastasis, and prognosis. The aim of our research was to find polarity-related biomarkers which improve the treatment and prognosis of HCC. For the knowledge-driven analysis, 189 polarity-related genes (PRGs) were retrieved and curated manually from the molecular signatures database and reviews. Meanwhile, in the data-driven part, genomic datasets and clinical records of HCC was obtained from the cancer genome atlas database. The potential candidates were considered in the respect to differential expression, mutation rate, and prognostic value. Sixty-one PRGs that passed the knowledge and data-driven screening were applied for function analysis and mechanism deduction. Elastic net model combing least absolute shrinkage and selection operator and ridge regression analysis refined the input into a 12-PRG risk model, and its pharmaceutical potency was evaluated. These findings demonstrated that the integration of multi-omics of PRGs can help us in untangling the liver cancer pathogenesis as well as illustrate the underlying mechanisms and therapeutic targets.
Topics: Humans; Carcinoma, Hepatocellular; Liver Neoplasms; Prognosis; Gene Expression Regulation, Neoplastic; Biomarkers, Tumor
PubMed: 36361574
DOI: 10.3390/ijms232112784 -
BMC Pediatrics Nov 2022Research on myelin oligodendrocyte glycoprotein antibody (MOG-Ab)-associated disease (MOGAD) among Chinese children is relatively rare. Therefore, this study aimed to...
BACKGROUND
Research on myelin oligodendrocyte glycoprotein antibody (MOG-Ab)-associated disease (MOGAD) among Chinese children is relatively rare. Therefore, this study aimed to explore and analyze the clinical characteristics and prognoses of Chinese children with acquired demyelinating syndromes (ADSs) who tested positive or negative for MOG-Ab.
METHODS
The clinical data of children with MOGAD who were treated in the Department of Neurology at Shanghai Children's Hospital from January 2017 to October 2021 were retrospectively collected.
RESULTS
Among 90 children with ADSs, 30 were MOG-Ab-positive, and 60 were MOG-Ab-negative. MOG-Ab-positive children experienced more prodromal infections than did MOG-Ab-negative children (P < 0.05). Acute disseminated encephalomyelitis was the most common ADSs in both groups. There were ten cases of a rebound increase in MOG-Ab titers. There were significant differences in the MOG titer-related prognosis and disease time course between the disease relapse group and the non-relapse group (P < 0.01). Among the MOG-Ab-positive patients, the most affected brain areas detected via magnetic resonance imaging (MRI) were the temporal lobe, cerebellar hemispheres, brainstem, and periventricular lesions. The most common shapes of the lesions were commas, triangles, or patches. The average improvement time based on brain MRI was much longer in MOG-Ab-positive than in MOG-Ab-negative children (P < 0.05). The initial treatment time correlated with the disease time course, and the prognosis may be affected by the disease time course and serum MOG-Ab titer (P < 0.05).
CONCLUSION
The clinical characteristics and imaging features of ADSs differed between MOG-Ab-positive and MOG-Ab-negative children. In addition to existing treatment plans, additional diagnoses and treatment plans should be developed to reduce recurrence and improve the prognoses of children with MOGAD.
Topics: Humans; Myelin-Oligodendrocyte Glycoprotein; Retrospective Studies; Autoantibodies; China; Prognosis; Syndrome
PubMed: 36401212
DOI: 10.1186/s12887-022-03679-3 -
Journal of Genetic Counseling Aug 2023Despite the moniker "precision medicine," genetic diagnoses are often imprecise with respect to prognosis. In a period when prognoses are evolving in lockstep with...
Despite the moniker "precision medicine," genetic diagnoses are often imprecise with respect to prognosis. In a period when prognoses are evolving in lockstep with advances in genetic diagnostics and therapeutics, it is critical that clinicians and researchers consider how prognosis is communicated beyond the moment of diagnosis. Research has shown that genetic diagnoses are described differently in pre- and postnatal contexts, but we know relatively little about how patients and families make sense of prognostic information as affected children grow up. Here, I draw on research and personal narratives to describe how prognostic information impacts individuals' conceptions of the future. A deeper understanding of how patients and families view prognosis is important because parents may need support as prognostic conversations arise and because perceptions of prognosis may influence ideas about the future, psychological health, decisions, and planning. By exploring how specific ideas about an individuals' future take hold, clinicians and researchers may begin to identify the benefits, harms, and accuracy of varied sources of prognostic information, opening new areas of bioethical investigation. In closing, I propose prognostic imagination as a useful concept for considering how patients and families experience prognostic information amidst therapeutic innovations and evolving futures.
Topics: Child; Humans; Genetic Counseling; Prognosis; Imagination; Communication; Precision Medicine
PubMed: 36575577
DOI: 10.1002/jgc4.1660 -
Aging May 2023Epigenetic regulations of immune responses are essential for cancer development and growth. As a critical step, comprehensive and rigorous explorations of m6A...
Integrating machine learning and bioinformatics analysis to m6A regulator-mediated methylation modification models for predicting glioblastoma patients' prognosis and immunotherapy response.
BACKGROUND
Epigenetic regulations of immune responses are essential for cancer development and growth. As a critical step, comprehensive and rigorous explorations of m6A methylation are important to determine its prognostic significance, tumor microenvironment (TME) infiltration characteristics and underlying relationship with glioblastoma (GBM).
METHODS
To evaluate m6A modification patterns in GBM, we conducted unsupervised clustering to determine the expression levels of GBM-related m6A regulatory factors and performed differential analysis to obtain m6A-related genes. Consistent clustering was used to generate m6A regulators cluster A and B. Machine learning algorithms were implemented for identifying TME features and predicting the response of GBM patients receiving immunotherapy.
RESULTS
It is found that the m6A regulatory factor significantly regulates the mutation of GBM and TME. Based on Europe, America, and China data, we established m6Ascore through the m6A model. The model accurately predicted the results of 1206 GBM patients from the discovery cohort. Additionally, a high m6A score was associated with poor prognoses. Significant TME features were found among the different m6A score groups, which demonstrated positive correlations with biological functions (i.e., EMT2) and immune checkpoints.
CONCLUSIONS
m6A modification was important to characterize the tumorigenesis and TME infiltration in GBM. The m6Ascore provided GBM patients with valuable and accurate prognosis and prediction of clinical response to various treatment modalities, which could be useful to guide patient treatments.
Topics: Humans; Computational Biology; Glioblastoma; Immunotherapy; Machine Learning; Methylation; Prognosis; Tumor Microenvironment
PubMed: 37244287
DOI: 10.18632/aging.204495