搜索结果：Cancer diagnosis & prognosis

The aim of this review is to summarize present evidence of an association between circulating levels of C-reactive protein (CRP) and cancer risk, and to evaluate whether elevated circulating CRP levels cause cancer. Additionally, the review provides background information on the acute-phase response, chronic inflammation, the molecular biology, function and measurement of CRP, circulating levels of CRP in health and disease, the principle of Mendelian randomization, the association between circulating levels of CRP and cancer prognosis, and cancer biomarkers. In the Copenhagen General Population Study of approximately 63,500 individuals, the distribution of circulating levels of CRP was markedly skewed to the right with 97% of the participants having CRP levels<10 mg/L. The median plasma CRP concentration was 1.53 mg/L (IQR, 1.14-2.51) and 34% of the participants had circulating CRP levels of ≥2 mg/L. Epidemiologic studies suggest that in patients with several types of solid cancers, elevated circulating levels of CRP are associated with poor prognosis, whereas in apparently healthy individuals from the general population, elevated levels of CRP are associated with increased future risk of cancer of any type, lung cancer, and possibly colorectal cancer, but not breast or prostate cancer. The robust association between circulating levels of CRP and cancer risk may be due to (1) causality: elevated CRP levels cause cancer, (2) reverse causality: occult cancer increases CRP levels, (3) or confounding: a third factor, e.g. inflammation, increases both CRP levels and the risk of cancer. Genetic epidemiologic studies (Mendelian randomization studies), which have examined the association between genetic polymorphisms influencing circulating levels of CRP and cancer risk suggest that circulating levels of CRP do not cause cancer. A lack of causality between elevated CRP levels and increased cancer risk does, however, not invalidate the potential clinical use of slightly increased CRP levels to predict risk of certain cancer types, and to improve staging and treatment allocation in patients diagnosed with cancer. Indeed, in a study of the general population, individuals with CRP levels in the highest versus the lowest quintile had a 1.3-fold increased risk of cancer of any type and a 2-fold increased risk of lung cancer. Among individuals diagnosed with cancer during the study period, individuals with a high baseline CRP (>3 mg/L) had an 80% greater risk of early death compared with those with low CRP levels (<1 mg/L). Accordingly, patients with invasive breast cancer and CRP levels>3 mg/L at diagnosis had a 1.7-fold increased risk of death from breast cancer compared to patients with CRP levels<1 mg/L at diagnosis.

Exosomes participate in cell-cell communication by transferring molecular components between cells. Previous studies have shown that exosomal molecules derived from cancer cells and liquid biopsies can serve as biomarkers for cancer diagnosis and prognosis. The exploration of the molecules transferred by lung cancer-derived exosomes can advance the understanding of exosome-mediated signaling pathways and mechanisms. However, the molecular characterization and functional indications of exosomal proteins and lipids have not been comprehensively organized. This review thoroughly collected data concerning exosomal proteins and lipids from various lung cancer samples, including cancer cell lines and cancer patients. As potential diagnostic and prognostic biomarkers, exosomal proteins and lipids are available for clinical use in lung cancer. Potential therapeutic targets are mentioned for the future development of lung cancer therapy. Molecular functions implying their possible roles in exosome-mediated signaling are also discussed. Finally, we emphasized the importance and value of lung cancer stem cell-derived exosomes in lung cancer therapy. In summary, this review presents a comprehensive description of the protein and lipid composition and function of lung cancer-derived exosomes for lung cancer diagnosis, prognosis, and treatment.

OBJECTIVE: To systematically review the evidence that smoking cessation after diagnosis of a primary lung tumour affects prognosis. DESIGN: Systematic review with meta-analysis. DATA SOURCES: CINAHL (from 1981), Embase (from 1980), Medline (from 1966), Web of Science (from 1966), CENTRAL (from 1977) to December 2008, and reference lists of included studies. STUDY SELECTION: Randomised controlled trials or observational longitudinal studies that measured the effect of quitting smoking after diagnosis of lung cancer on prognostic outcomes, regardless of stage at presentation or tumour histology, were included. DATA EXTRACTION: Two researchers independently identified studies for inclusion and extracted data. Estimates were combined by using a random effects model, and the I(2) statistic was used to examine heterogeneity. Life tables were used to model five year survival for early stage non-small cell lung cancer and limited stage small cell lung cancer, using death rates for continuing smokers and quitters obtained from this review. RESULTS: In 9/10 included studies, most patients studied were diagnosed as having an early stage lung tumour. Continued smoking was associated with a significantly increased risk of all cause mortality (hazard ratio 2.94, 95% confidence interval 1.15 to 7.54) and recurrence (1.86, 1.01 to 3.41) in early stage non-small cell lung cancer and of all cause mortality (1.86, 1.33 to 2.59), development of a second primary tumour (4.31, 1.09 to 16.98), and recurrence (1.26, 1.06 to 1.50) in limited stage small cell lung cancer. No study contained data on the effect of quitting smoking on cancer specific mortality or on development of a second primary tumour in non-small cell lung cancer. Life table modelling on the basis of these data estimated 33% five year survival in 65 year old patients with early stage non-small cell lung cancer who continued to smoke compared with 70% in those who quit smoking. In limited stage small cell lung cancer, an estimated 29% of continuing smokers would survive for five years compared with 63% of quitters on the basis of the data from this review. CONCLUSIONS: This review provides preliminary evidence that smoking cessation after diagnosis of early stage lung cancer improves prognostic outcomes. From life table modelling, the estimated number of deaths prevented is larger than would be expected from reduction of cardiorespiratory deaths after smoking cessation, so most of the mortality gain is likely to be due to reduced cancer progression. These findings indicate that offering smoking cessation treatment to patients presenting with early stage lung cancer may be beneficial.

The ability to identify a specific cancer using minimally invasive biopsy holds great promise for improving the diagnosis, treatment selection, and prediction of prognosis in cancer. Using whole-genome methylation data from The Cancer Genome Atlas (TCGA) and machine learning methods, we evaluated the utility of DNA methylation for differentiating tumor tissue and normal tissue for four common cancers (breast, colon, liver, and lung). We identified cancer markers in a training cohort of 1,619 tumor samples and 173 matched adjacent normal tissue samples. We replicated our findings in a separate TCGA cohort of 791 tumor samples and 93 matched adjacent normal tissue samples, as well as an independent Chinese cohort of 394 tumor samples and 324 matched adjacent normal tissue samples. The DNA methylation analysis could predict cancer versus normal tissue with more than 95% accuracy in these three cohorts, demonstrating accuracy comparable to typical diagnostic methods. This analysis also correctly identified 29 of 30 colorectal cancer metastases to the liver and 32 of 34 colorectal cancer metastases to the lung. We also found that methylation patterns can predict prognosis and survival. We correlated differential methylation of CpG sites predictive of cancer with expression of associated genes known to be important in cancer biology, showing decreased expression with increased methylation, as expected. We verified gene expression profiles in a mouse model of hepatocellular carcinoma. Taken together, these findings demonstrate the utility of methylation biomarkers for the molecular characterization of cancer, with implications for diagnosis and prognosis.

Heat shock proteins (HSPs) are a group of proteins, also known as molecular chaperones, which participate in protein folding and maturation in response to stresses or high temperature. According to their molecular weights, mammalian HSPs are classified into HSP27, HSP40, HSP60, HSP70, HSP90, and large HSPs. Previous studies have revealed that HSPs play important roles in oncogenesis and malignant progression because they can modulate all six hallmark traits of cancer. Because of this, HSPs have been propelled into the spotlight as biomarkers for cancer diagnosis and prognosis, as well as an exciting anticancer drug target. However, the relationship between the expression level of HSPs and their activity and cancer diagnosis, prognosis, metabolism and treatment is not clear and has not been completely established. Herein, this review summarizes and discusses recent advances and perspectives in major HSPs as biomarkers for cancer diagnosis, as regulators for cancer metabolism or as therapeutic targets for cancer therapy, which may provide new directions to improve the accuracy of cancer diagnosis and develop more effective and safer anticancer therapeutics.

From the Publisher: The potential value of artificial neural networks (ANN) as a predictor of malignancy has begun to received increased recognition. Research and case studies can be found scattered throughout a multitude of journals. Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management brings together the work of top researchers - primarily clinicians - who present the results of their state-of-the-art work with ANNs as applied to nearly all major areas of cancer for diagnosis, prognosis, and management of the disease.The book introduces the theory of neural networks and the method of their application in oncology. It is not an exercise in ANN research, but the presentation of a new technique for diagnosing and determining the treatment of cancers. The authors have included almost all cancers for which there exists ANN applications. When the data available is ill-defined and the development of an algorithmic solution difficult, neural networks provide a non-linear approach which helps sift through the maze of information and arrive at a reasonable solution.Highly interdisciplinary in nature, this book provides comprehensive coverage of the most important materials relating to the applications of ANNs in the cancer field. With contributions from prominent research centers worldwide, it serves as an introduction to how neural networks can be used for accurate prediction or diagnosis and shows why neural networks are more accurate. Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management gives you an understanding of this new tool, its applications, and when it should be used.

Two medical applications of linear programming are described in this paper. Specifically, linear programming-based machine learning techniques are used to increase the accuracy and objectivity of breast cancer diagnosis and prognosis. The first application to breast cancer diagnosis utilizes characteristics of individual cells, obtained from a minimally invasive fine needle aspirate, to discriminate benign from malignant breast lumps. This allows an accurate diagnosis without the need for a surgical biopsy. The diagnostic system in current operation at University of Wisconsin Hospitals was trained on samples from 569 patients and has had 100% chronological correctness in diagnosing 131 subsequent patients. The second application, recently put into clinical practice, is a method that constructs a surface that predicts when breast cancer is likely to recur in patients that have had their cancers excised. This gives the physician and the patient better information with which to plan treatment, and may eliminate the need for a prognostic surgical procedure. The novel feature of the predictive approach is the ability to handle cases for which cancer has not recurred (censored data) as well as cases for which cancer has recurred at a specific time. The prognostic system has an expected error of 13.9 to 18.3 months, which is better than prognosis correctness by other available techniques.

Biomarkers are vital in healthcare as they provide valuable insights into disease diagnosis, prognosis, treatment response, and personalized medicine. They serve as objective indicators, enabling early detection and intervention, leading to improved patient outcomes and reduced costs. Biomarkers also guide treatment decisions by predicting disease outcomes and facilitating individualized treatment plans. They play a role in monitoring disease progression, adjusting treatments, and detecting early signs of recurrence. Furthermore, biomarkers enhance drug development and clinical trials by identifying suitable patients and accelerating the approval process. In this review paper, we described a variety of biomarkers applicable for cancer detection and diagnosis, such as imaging-based diagnosis (CT, SPECT, MRI, and PET), blood-based biomarkers (proteins, genes, mRNA, and peptides), cell imaging-based diagnosis (needle biopsy and CTC), tissue imaging-based diagnosis (IHC), and genetic-based biomarkers (RNAseq, scRNAseq, and spatial transcriptomics).

Long non-coding RNAs (lncRNAs) are major components of cellular transcripts that are arising as important players in various biological pathways. They have received extensive attention in recent years, regarded to be involved in both developmental processes and various diseases. Due to their specific expression and functional diversity in a variety of cancers, lncRNAs have promising applications in cancer diagnosis, prognosis and therapy. Studies have shown that lncRNAs with high specificity and accuracy have the potential to become biomarkers in cancers. LncRNAs can be noninvasively extracted from body fluids, tissues and cells, and can be used as independent or auxiliary biomarkers to improve the accuracy of diagnosis or prognosis. Currently, the most well-recognized lncRNA is PCA3, which has been approved for use in the diagnosis of prostate cancer. Moreover, the underlying mechanisms of lncRNAs were explored as therapeutic targets, which have been investigated in clinical trials of several cancers. In this review, we presented a compilation of recent publications, clinical trials and patents, addressing the potential of lncRNAs that could be considered as biomarkers or therapeutic targets, with the hopes of providing promised implications for future cancer therapy.

Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown.

Worldwide, colorectal cancer (CRC) is a deadly disease whose death rate ranks second among cancers though its incidence ranks third. Early CRC detection is key and is associated with improved survival outcomes. However, existing tests for CRC diagnosis have several weaknesses thus rendering them inefficient. Moreover, reliable prognostic tests that can predict the overall cancer outcome and recurrence of the disease as well as predictive markers that can assess effectiveness of therapy are still lacking. Thus, shifting to noninvasive liquid biopsy or blood-based biomarkers is vital to improving CRC diagnosis, prognosis, and prediction. Methylated circulating tumor DNA (ctDNA) has gained increased attention as a type of liquid biopsy that is tumor-derived fragmented DNA with epigenetic alterations. Methylated ctDNA are more consistently present in blood of cancer patients as compared to mutated ctDNA. Hence, methylated ctDNA serves as a potential biomarker for CRC that is worth investigating. In this review, we explore what has been reported about methylated ctDNA as a biomarker for CRC diagnosis that can distinguish between CRC patients or those having adenoma and healthy controls as validated specifically through ROC curves. We also examine methylated ctDNA as a biomarker for CRC prognosis and prediction as confirmed through robust statistical analyses. Finally, we discuss the major technical challenges that limits the use of methylated ctDNA for clinical application and suggest possible recommendations to enhance its usage.

Despite advancement in cancer treatment, oral cancer has a poor prognosis and is often detected at late stage. To overcome these challenges, investigators should search for early diagnostic and prognostic biomarkers. More than 700 bacterial species reside in the oral cavity. The oral microbiome population varies by saliva and different habitats of oral cavity. Tobacco, alcohol, and betel nut, which are causative factors of oral cancer, may alter the oral microbiome composition. Both pathogenic and commensal strains of bacteria have significantly contributed to oral cancer. Numerous bacterial species in the oral cavity are involved in chronic inflammation that lead to development of oral carcinogenesis. Bacterial products and its metabolic by-products may induce permanent genetic alterations in epithelial cells of the host that drive proliferation and/or survival of epithelial cells. Porphyromonas gingivalis and Fusobacterium nucleatum induce production of inflammatory cytokines, cell proliferation, and inhibition of apoptosis, cellular invasion, and migration thorough host cell genomic alterations. Recent advancement in metagenomic technologies may be useful in identifying oral cancer–related microbiome, their genomes, virulence properties, and their interaction with host immunity. It is very important to address which bacterial species is responsible for driving oral carcinogenesis. Alteration in the oral commensal microbial communities have potential application as a diagnostic tool to predict oral squamous cell carcinoma. Clinicians should be aware that the protective properties of the resident microflora are beneficial to define treatment strategies. To develop highly precise and effective therapeutic approaches, identification of specific oral microbiomes may be required. In this review, we narrate the role of microbiome in the progression of oral cancer and its role as an early diagnostic and prognostic biomarker for oral cancer.

Circulating tumor cells (CTCs) are tumor cells that shed from the primary tumor and intravasate into the peripheral blood circulation system responsible for metastasis. Sensitive detection of CTCs from clinical samples can serve as an effective tool in cancer diagnosis and prognosis through liquid biopsy. Current CTC detection technologies mainly reply on the biomarker-mediated platforms including magnetic beads, microfluidic chips or size-sensitive microfiltration which can compromise detection sensitivity due to tumor heterogeneity. A more sensitive, biomarker independent CTCs isolation technique has been recently developed with the surface-charged superparamagnetic nanoprobe capable of different EMT subpopulation CTC capture from 1 mL clinical blood. In this review, this new strategy is compared with the conventional techniques on biomarker specificity, impact of protein corona, effect of glycolysis on cell surface charge, and accurate CTC identification. Correlations between CTC enumeration and molecular profiling in clinical blood and cancer prognosis are provided for clinical cancer management.

Over the past decade, invasive techniques for diagnosing and monitoring cancers are slowly being replaced by non-invasive methods such as liquid biopsy. Liquid biopsies have drastically revolutionized the field of clinical oncology, offering ease in tumor sampling, continuous monitoring by repeated sampling, devising personalized therapeutic regimens, and screening for therapeutic resistance. Liquid biopsies consist of isolating tumor-derived entities like circulating tumor cells, circulating tumor DNA, tumor extracellular vesicles, etc., present in the body fluids of patients with cancer, followed by an analysis of genomic and proteomic data contained within them. Methods for isolation and analysis of liquid biopsies have rapidly evolved over the past few years as described in the review, thus providing greater details about tumor characteristics such as tumor progression, tumor staging, heterogeneity, gene mutations, and clonal evolution, etc. Liquid biopsies from cancer patients have opened up newer avenues in detection and continuous monitoring, treatment based on precision medicine, and screening of markers for therapeutic resistance. Though the technology of liquid biopsies is still evolving, its non-invasive nature promises to open new eras in clinical oncology. The purpose of this review is to provide an overview of the current methodologies involved in liquid biopsies and their application in isolating tumor markers for detection, prognosis, and monitoring cancer treatment outcomes.

Aberrant O-glycans expressed at the surface of cancer cells consist of membrane-tethered glycoproteins (T and Tn antigens) and glycolipids (Lewis a, Lewis x and Forssman antigens). All of these O-glycans have been identified as glyco-markers of interest for the diagnosis and the prognosis of cancer diseases. These epitopes are specifically detected using T/Tn-specific lectins isolated from various plants such as jacalin from Artocarpus integrifola, and fungi such as the Agaricus bisporus lectin. These lectins accommodate T/Tn antigens at the monosaccharide-binding site; residues located in the surrounding extended binding-site of the lectins often participate in the binding of more extended epitopes. Depending on the shape and size of the extended carbohydrate-binding site, their fine sugar-binding specificity towards complex O-glycans readily differs from one lectin to another, resulting in a great diversity in their sugar-recognition capacity. T/Tn-specific lectins have been extensively used for the histochemical detection of cancer cells in biopsies and for the follow up of the cancer progression and evolution. T/Tn-specific lectins also induce a caspase-dependent apoptosis in cancer cells, often associated with a more or less severe inhibition of proliferation. Moreover, they provide another potential source of molecules adapted to the building of photosensitizer-conjugates allowing a specific targeting to cancer cells, for the photodynamic treatment of tumors.

BACKGROUND: Detection of pancreatic cancer (PaC), particularly at early stages, remains a great challenge owing to lack of specific biomarkers. We sought to identify a PaC-specific serum microRNA (miRNA) expression profile and test its specificity and sensitivity as a biomarker in the diagnosis and prognosis of PaC. METHODS: We obtained serum samples from 197 PaC cases and 158 age- and sex-matched cancer-free controls. We screened the differentially expressed serum miRNAs with Illumina sequencing by synthesis technology using pooled serum samples followed by RT-qPCR validation of a large number of samples arranged in multiple stages. We used risk score analysis to evaluate the diagnostic value of the serum miRNA profiling system. To assess the serum miRNA-based biomarker accuracy in predicting PaC, we performed additional double-blind testing in 77 PaC cases and 52 controls and diagnostic classification in 55 cases with clinically suspected PaC. RESULTS: After the selection and validation process, 7 miRNAs displayed significantly different expression levels in PaC compared with controls. This 7 miRNA-based biomarker had high sensitivity and specificity for distinguishing various stages of PaC from cancer-free controls and also accurately discriminated PaC patients from chronic pancreatitis (CP) patients. Among the 7 miRNAs, miR-21 levels in serum were significantly associated with overall PaC survival. The diagnostic accuracy rate of the 7-miRNA profile was 83.6% in correctly classifying 55 cases with clinically suspected PaC. CONCLUSIONS: These data demonstrate that the 7 miRNA-based biomarker can serve as a novel noninvasive approach for PaC diagnosis and prognosis.