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Liver oncogenesis is accompanied by discernible protein changes in the bloodstream. By employing plasma proteomic profiling, we can delve into the molecular mechanisms of liver cancer and pinpoint potential biomarkers. In this nested case-control study, we applied liquid chromatography-tandem mass spectrometry for proteome profiling in baseline plasma samples. Differential protein expression was determined and was subjected to functional enrichment, network, and Mendelian randomization (MR) analyses. We identified 193 proteins with notable differential levels between the groups. Of these proteins, MR analysis offered a compelling negative association between apolipoprotein B (APOB) and liver cancer. This association was further corroborated in the UK Biobank cohort: genetically predicted APOB levels were associated with a 31% (95% CI 19–42%) decreased risk of liver cancer; and phenotypic analysis indicated an 11% (95% CI 8–14%) decreased liver cancer risk for every 0.1 g/L increase of circulating APOB levels. Multivariable MR analysis suggested that the hepatic fat content might fully mediate the APOB-liver cancer connection. In summary, we identified some plasma proteins, particularly APOB, as potential biomarkers of liver cancer. Our findings underscore the intricate link between lipid metabolism and liver cancer, offering hints for targeted prophylactic strategies and early detection.

Individual’s aging rates vary across organs. However, there are few methods for assessing aging at organ levels and whether they contribute differently to mortalities remains unknown. We analyzed data from 45,821 adults in the UK Biobank, using plasma proteomics and machine learning to estimate biological ages for 12 major organs. The differences between biological age and chronological age, referred to as “age gaps,” were calculated for each organ. Partial correlation analyses were used to assess the association between age gaps and modifiable factors. Adjusted multivariable Cox regression models were applied to examine the association of age gaps with all-cause mortality, cause-specific mortalities, and cancer-specific mortalities. We reveal a complex network of varied associations between multi-organ aging and modifiable factors. All age gaps increase the risk of all-cause mortality by 6–60%. The risk of death varied from 5.54 to 29.18 times depending on the number of aging organs. Cause-specific mortalities are associated with certain organs’ aging. For mental diseases mortality, and nervous system mortality, only brain aging exhibited a significant increased risk of HR 2.38 (per SD, 95% CI: 2.06–2.74) and 1.99 (per SD, 95% CI: 1.84–2.16), respectively. Age gaps of stomach were also a specific indicator for gastric cancer. Eventually, we find that an organ’s biological age selectively influences the aging of other organ systems. Our study demonstrates that accelerated aging in specific organs increases the risk of mortality from various causes. This provides a potential tool for early identification of at-risk populations, offering a relatively objective method for precision medicine.

ObjectiveChina concentrates a large part of the global burden of HBV infection, playing a pivotal role in achieving the WHO 2030 global hepatitis elimination target.MethodsWe searched for studies reporting HBV surface antigen (HBsAg) seroprevalence in five databases until January 2023. Eligible data were pooled using a generalised linear mixed model with random effects to obtain summary HBsAg seroprevalence. Linear regression was used to estimate annual percentage change (APC) and HBsAg prevalence in 2021.Results3740 studies, including 231 million subjects, were meta-analysed. HBsAg seroprevalence for the general population decreased from 9.6% (95% CI 8.4 to 10.9%) in 1973–1984 to 3.0% (95% CI 2.1 to 3.9%) in 2021 (APC=−3.77; p<0.0001). Decreases were more pronounced in children <5 years (APC=−7.72; p<0.0001) and 5–18 years (−7.58; p<0.0001), than in people aged 19–59 years (−2.44; p<0.0001), whereas HBsAg seroprevalence increased in persons ≥60 years (2.84; p=0.0007). Significant decreases were observed in all six major Chinese regions, in both men (APC=−3.90; p<0.0001) and women (−1.82; p<0.0001) and in high-risk populations. An estimated 43.3 million (95% uncertainty interval 30.7–55.9) persons remained infected with HBV in China in 2021 (3.0%), with notable heterogeneity by region (<1.5% in North China to>6% in Taiwan and Hong Kong) and age (0.3%, 1.0%, 4.7% and 5.6% for <5 years, 5–18 years, 19–59 years and ≥60 years, respectively).ConclusionsChina has experienced remarkable decreases in HBV infection over the last four decades, but variations in HBsAg prevalence persist in subpopulations. Ongoing prevention of HBV transmission is needed to meet HBV elimination targets by 2030.Trial registration numberPROSPERO (CRD42021284217)

Cardiovascular health metrics are now widely recognized as modifiable risk factors for cognitive decline and dementia. Metabolic perturbations might play roles in the linkage of cardiovascular diseases and dementia. Circulating metabolites profiling by metabolomics may improve understanding of the potential mechanism by which cardiovascular risk factors contribute to cognitive decline. In a prospective community-based cohort in China (n= 725), 312 serum metabolic phenotypes were quantified, and cardiovascular health score was calculated including smoking, exercise, sleep, diet, body mass index, blood pressure, and blood glucose. Cognitive function assessments were conducted in baseline and follow-up visits to identify longitudinal cognitive decline. A better cardiovascular health was significantly associated with lower risk of concentration decline and orientation decline (hazard ratio (HR): 0.84–0.90; p< 0.05). Apolipoprotein-A1, high-density lipoprotein (HDL) cholesterol, cholesterol ester, and phospholipid concentrations were significantly associated with a lower risk of longitudinal memory and orientation decline (p< 0.05 and adjusted-p< 0.20). Mediation analysis suggested that the negative association between health status and the risk of orientation decline was partly mediated by cholesterol ester and total lipids in HDL-2 and -3 (proportion of mediation: 7.68–8.21%, both p< 0.05). Cardiovascular risk factors were associated with greater risks of cognitive decline, which were found to be mediated by circulating lipoproteins, particularly the medium-size HDL components. These findings underscore the potential of utilizing lipoproteins as targets for early stage dementia screening and intervention.

Neuroblastoma is a highly metastatic cancer, and thus is one of the leading causes of cancer-related mortalities in pediatric patients. More than 50% of NB cases exhibit 17q21-ter partial chromosomal gain, which is independently associated with poor survival, suggesting the clinical importance of genes at this locus in NB. IGF2BP1 is one such proto-oncogene located at 17q locus, and was found to be upregulated in patients with metastatic NBs. Here, utilizing multiple immunocompetent mouse models, along with our newly developed highly metastatic NB cell line, we demonstrate the role of IGF2BP1 in promoting NB metastasis. Importantly, we show the significance of small extracellular vesicles (EVs) in NB progression, and determine the pro-metastatic function of IGF2BP1 by regulating the NB-EV-protein cargo. Through unbiased proteomic analysis of EVs, we discovered two novel targets (SEMA3A and SHMT2) of IGF2BP1, and reveal the mechanism of IGF2BP1 in NB metastasis. We demonstrate that IGF2BP1 directly binds and governs the expression of SEMA3A/SHMT2 in NB cells, thereby modulating their protein levels in NB-EVs. IGF2BP1-affected levels of SEMA3A and SHMT2 in the EVs, regulate the formation of pro-metastatic microenvironment at potential metastatic organs. Finally, higher levels of SEMA3A/SHMT2 proteins in the EVs derived from NB-PDX models indicate the clinical significance of the two proteins and IGF2BP1-SEMA3A/SHMT2 axis in NB metastasis.

Serum liver enzymes (alanine aminotransferase [ALT], aspartate aminotransferase [AST], λ-glutamyl transferase [GGT] and alkaline phosphatase [ALP]) are the leading biomarkers to measure liver injury, and they have been reported to be associated with several intrahepatic and extrahepatic diseases in observational studies. We conducted a phenome-wide association study (PheWAS) to identify disease phenotypes associated with genetically predicted liver enzymes based on the UK Biobank cohort. Univariable and multivariable Mendelian randomization (MR) analyses were performed to obtain the causal estimates of associations that detected in PheWAS. Our PheWAS identified 40 out of 1,376 pairs (16, 17, three and four pairs for ALT, AST, GGT and ALP, respectively) of genotype–phenotype associations reaching statistical significance at the 5% false discovery ratethreshold. A total of 34 links were further validated in Mendelian randomization analyses. Most of the disease phenotypes that associated with genetically determined ALT level were liver-related, including primary liver cancer and alcoholic liver damage. The disease outcomes associated with genetically determined AST involved a wide range of phenotypic categories including endocrine/metabolic diseases, digestive diseases, and neurological disorder. Genetically predicted GGT level was associated with the risk of other chronic non-alcoholic liver disease, abnormal results of function study of liver, and cholelithiasis. Genetically determined ALP level was associated with pulmonary heart disease, phlebitis and thrombophlebitis of lower extremities, and hypercholesterolemia. Our findings reveal novel links between liver enzymes and disease phenotypes providing insights into the full understanding of the biological roles of liver enzymes.

This study aimed to identify the predictors and threshold of failure in neonatal acute respiratory distress syndrome.

FLT3 is a frequently mutated gene that is highly associated with a poor prognosis in acute myeloid leukemia (AML). Despite initially responding to FLT3 inhibitors, most patients eventually relapse with drug resistance. The mechanism by which resistance arises and the initial response to drug treatment that promotes cell survival is unknown. Recent studies show that a transiently maintained subpopulation of drug-sensitive cells, so-called drug-tolerant "persisters" (DTPs), can survive cytotoxic drug exposure despite lacking resistance-conferring mutations. Using RNA sequencing and drug screening, we find that treatment of FLT3 internal tandem duplication AML cells with quizartinib, a selective FLT3 inhibitor, upregulates inflammatory genes in DTPs and thereby confers susceptibility to anti-inflammatory glucocorticoids (GCs). Mechanistically, the combination of FLT3 inhibitors and GCs enhances cell death of FLT3 mutant, but not wild-type, cells through GC-receptor–dependent upregulation of the proapoptotic protein BIM and proteasomal degradation of the antiapoptotic protein MCL-1. Moreover, the enhanced antileukemic activity by quizartinib and dexamethasone combination has been validated using primary AML patient samples and xenograft mouse models. Collectively, our study indicates that the combination of FLT3 inhibitors and GCs has the potential to eliminate DTPs and therefore prevent minimal residual disease, mutational drug resistance, and relapse in FLT3-mutant AML.

FLT3 is a frequently mutated gene that is highly associated with a poor prognosis in acute myeloid leukemia (AML). Despite initially responding to FLT3 inhibitors, most patients eventually relapse with drug resistance. The mechanism by which resistance arises and the initial response to drug treatment that promotes cell survival is unknown. Recent studies show that a transiently maintained subpopulation of drug-sensitive cells, so-called drug-tolerant "persisters" (DTPs), can survive cytotoxic drug exposure despite lacking resistance-conferring mutations. Using RNA sequencing and drug screening, we find that treatment of FLT3 internal tandem duplication AML cells with quizartinib, a selective FLT3 inhibitor, upregulates inflammatory genes in DTPs and thereby confers susceptibility to anti-inflammatory glucocorticoids (GCs). Mechanistically, the combination of FLT3 inhibitors and GCs enhances cell death of FLT3 mutant, but not wild-type, cells through GC-receptor–dependent upregulation of the proapoptotic protein BIM and proteasomal degradation of the antiapoptotic protein MCL-1. Moreover, the enhanced antileukemic activity by quizartinib and dexamethasone combination has been validated using primary AML patient samples and xenograft mouse models. Collectively, our study indicates that the combination of FLT3 inhibitors and GCs has the potential to eliminate DTPs and therefore prevent minimal residual disease, mutational drug resistance, and relapse in FLT3-mutant AML.

Insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) is an oncofetal protein expressed in various cancers including leukemia. In this study, we assessed the role of IGF2BP1in orchestrating leukemia stem cell properties. Tumor-initiating potential, sensitivity to chemotherapeutic agents, and expression of cancer stem cell markers were assessed in a panel of myeloid, B-, and T-cell leukemia cell lines using gain- and loss-of-function systems, cross-linking immunoprecipitation (CLIP), and photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP) techniques. Here, we report that genetic or chemical inhibition of IGF2BP1decreases leukemia cells’ tumorigenicity, promotes myeloid differentiation, increases leukemia cell death, and sensitizes leukemia cells to chemotherapeutic drugs. IGF2BP1affects proliferation and tumorigenic potential of leukemia cells through critical regulators of self-renewal HOXB4and MYBand through regulation of expression of the aldehyde dehydrogenase, ALDH1A1. Our data indicate that IGF2BP1 maintains leukemia stem cell properties by regulating multiple pathways of stemness through transcriptional and metabolic factors.

Insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) is a multifunctional RNA-binding protein with an oncofetal pattern of expression shown to be implicated in the development of a variety of malignancies. In this study, we explored the role and mechanisms of IGF2BP1 in melanoma development and progression. In two different in vivo models, we showed that although genetic deletion or shRNA-mediated suppression of IGF2BP1 did not affect primary tumor formation, it drastically suppressed lung metastasis. Here we demonstrated that extracellular vesicles (EVs) secreted by melanoma cells mediate the effects of IGF2BP1 on metastasis: EVs from the IGF2BP1 knockdown melanoma cells failed to promote metastasis, whereas EVs isolated from IGF2BP1-overexpressed melanoma cells further accelerated EV-induced metastasis. Moreover, the EVs from IGF2BP1 knockdown melanoma cells inhibited fibronectin deposition and accumulation of CD45+cells in the lungs compared with control EVs, thus blocking the pre-metastatic niche formation potential of EVs. IGF2BP1 knockdown did not affect size, number, or protein/RNA concentration of secreted EVs or their uptake by recipient cells in vitro or in vivo. However, RNA-sequencing and proteomics analysis of the EVs revealed differential expression in a number of mRNA, proteins, and miRNAs. This suggested that IGF2BP1 is intimately involved in the regulation of the cargo of EVs, thereby affecting the pro-metastatic function of melanoma-derived EVs. To the best of our knowledge, this is the first study that demonstrates the role of RNA-binding protein IGF2BP1 in EV-mediated promotion of melanoma metastasis and may provide novel avenues for the development of metastatic inhibitors.

Heterogeneous prostatic carcinoma-associated fibroblasts (CAF) contribute to tumor progression and resistance to androgen signaling deprivation therapy (ADT). CAF subjected to extended passaging, compared to low passage CAF, were found to lose tumor expansion potential and heterogeneity. Cell surface endoglin (CD105), known to be expressed on proliferative endothelia and mesenchymal stem cells, was diminished in high passage CAF. RNA-sequencing revealed SFRP1 to be distinctly expressed by tumor-inductive CAF, which was further demonstrated to occur in a CD105-dependent manner. Moreover, ADT resulted in further expansion of the CD105+fibroblastic population and downstream SFRP1 in 3-dimensional cultures and patient-derived xenograft tissues. In patients, CD105+fibroblasts were found to circumscribe epithelia with neuroendocrine differentiation. CAF-derived SFRP1, driven by CD105 signaling, was necessary and sufficient to induce prostate cancer neuroendocrine differentiation in a paracrine manner. A partially humanized CD105 neutralizing antibody, TRC105, inhibited fibroblastic SFRP1 expression and epithelial neuroendocrine differentiation. In a novel synthetic lethality paradigm, we found that simultaneously targeting the epithelia and its microenvironment with ADT and TRC105, respectively, reduced castrate-resistant tumor progression, in a model where either ADT or TRC105 alone had little effect.

Radiation therapy is the primary intervention for nearly half of the patients with localized advanced prostate cancer and standard of care for recurrent disease following surgery. The development of radiation-resistant disease is an obstacle for nearly 30–50% of patients undergoing radiotherapy. A better understanding of mechanisms that lead to radiation resistance could aid in the development of sensitizing agents to improve outcome. Here we identified a radiation-resistance pathway mediated by CD105, downstream of BMP and TGF-β signaling. Antagonizing CD105-dependent BMP signaling with a partially humanized monoclonal antibody, TRC105, resulted in a significant reduction in clonogenicity when combined with irradiation. In trying to better understand the mechanism for the radio-sensitization, we found that radiation-induced CD105/BMP signaling was sufficient and necessary for the upregulation of sirtuin 1 (SIRT1) in contributing to p53 stabilization and PGC-1α activation. Combining TRC105 with irradiation delayed DNA damage repair compared to irradiation alone. However, in the absence of p53 function, combining TRC105 and radiation resulted in no reduction in clonogenicity compared to radiation alone, despite similar reduction of DNA damage repair observed in p53-intact cells. This suggested DNA damage repair was not the sole determinant of CD105 radio-resistance. As cancer cells undergo an energy deficit following irradiation, due to the demands of DNA and organelle repair, we examined SIRT1’s role on p53 and PGC-1α with respect to glycolysis and mitochondrial biogenesis, respectively. Consequently, blocking the CD105-SIRT1 axis was found to deplete the ATP stores of irradiated cells and cause G2 cell cycle arrest. Xenograft models supported these findings that combining TRC105 with irradiation significantly reduces tumor size over irradiation alone (pvalue = 10−9). We identified a novel synthetic lethality strategy of combining radiation and CD105 targeting to address the DNA repair and metabolic addiction induced by irradiation in p53-functional prostate cancers.

Knowledge of the molecular mechanisms of acute pancreatitis is largely based on studies using rodents. To assess similar mechanisms in humans, we performed ex vivopancreatitis studies in human acini isolated from cadaveric pancreata from organ donors. Because data on these human acinar preparations are sparse, we assessed their functional integrity and cellular and organellar morphology using light, fluorescence, and electron microscopy; and their proteome by liquid chromatography–tandem mass spectrometry. Acinar cell responses to the muscarinic agonist carbachol (CCh) and the bile acid taurolithocholic acid 3-sulfate were also analyzed. Proteomic analysis of acini from donors of diverse ethnicity showed similar profiles of digestive enzymes and proteins involved in translation, secretion, and endolysosomal function. Human acini preferentially expressed the muscarinic acetylcholine receptor M3 and maintained physiological responses to CCh for at least 20 hours. As in rodent acini, human acini exposed to toxic concentrations of CCh and taurolithocholic acid 3-sulfate responded with trypsinogen activation, decreased cell viability, organelle damage manifest by mitochondrial depolarization, disordered autophagy, and pathological endoplasmic reticulum stress. Human acini also secreted inflammatory mediators elevated in acute pancreatitis patients, including IL-6, tumor necrosis factor-α, IL-1β, chemokine (C-C motif) ligands 2 and 3, macrophage inhibitory factor, and chemokines mediating neutrophil and monocyte infiltration. In conclusion, human cadaveric pancreatic acini maintain physiological functions and have similar pathological responses and organellar disorders with pancreatitis-causing treatments as observed in rodent acini.

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Using short‐term exposure of embryoid bodies to transforming growth factor‐β, the authors directed induced pluripotent stem cells (iPSCs) toward mesenchymal stem cell (MSC) differentiation. Two types of iPSC‐derived MSCs were identified: early (aiMSCs) and late (tiMSCs) outgrowing cells. Both types differentiated in vitro in response to osteogenic or adipogenic supplements; aiMSCs demonstrated higher osteogenic potential than tiMSCs. Upon orthotopic injection into radial defects, both types regenerated bone and contributed to defect repair. Mesenchymal stem cells (MSCs) are currently the most established cells for skeletal tissue engineering and regeneration; however, their availability and capability of self‐renewal are limited. Recent discoveries of somatic cell reprogramming may be used to overcome these challenges. We hypothesized that induced pluripotent stem cells (iPSCs) that were differentiated into MSCs could be used for bone regeneration. Short‐term exposure of embryoid bodies to transforming growth factor‐β was used to direct iPSCs toward MSC differentiation. During this process, two types of iPSC‐derived MSCs (iMSCs) were identified: early (aiMSCs) and late (tiMSCs) outgrowing cells. The transition of iPSCs toward MSCs was documented using MSC marker flow cytometry. Both types of iMSCs differentiated in vitro in response to osteogenic or adipogenic supplements. The results of quantitative assays showed that both cell types retained their multidifferentiation potential, although aiMSCs demonstrated higher osteogenic potential than tiMSCs and bone marrow‐derived MSCs (BM‐MSCs). Ectopic injections of BMP6‐overexpressing tiMSCs produced no or limited bone formation, whereas similar injections of BMP6‐overexpressing aiMSCs resulted in substantial bone formation. Upon orthotopic injection into radial defects, all three cell types regenerated bone and contributed to defect repair. In conclusion, MSCs can be derived from iPSCs and exhibit self‐renewal without tumorigenic ability. Compared with BM‐MSCs, aiMSCs acquire more of a stem cell phenotype, whereas tiMSCs acquire more of a differentiated osteoblast phenotype, which aids bone regeneration but does not allow the cells to induce ectopic bone formation (even when triggered by bone morphogenetic proteins), unless in an orthotopic site of bone fracture. Mesenchymal stem cells (MSCs) are currently the most established cells for skeletal tissue engineering and regeneration of various skeletal conditions; however, availability of autologous MSCs is very limited. This study demonstrates a new method to differentiate human fibroblast‐derived induced pluripotent stem cells (iPSCs) to cells with MSC properties, which we comprehensively characterized including differentiation potential and transcriptomic analysis. We showed that these iPS‐derived MSCs are able to regenerate nonunion bone defects in mice more efficiently than bone marrow‐derived human MSCs when overexpressing BMP6using a nonviral transfection method.

Progressive tissue fibrosis, including idiopathic pulmonary fibrosis (IPF), is characterized by excessive recruitment of fibroblasts to sites of tissue injury and unremitting extracellular matrix deposition associated with severe morbidity and mortality. However, the molecular mechanisms that control progressive IPF have yet to be fully determined. Previous studies suggested that invasive fibroblasts drive disease progression in IPF. Here, we report profiling of invasive and noninvasive fibroblasts from IPF patients and healthy donors. Pathway analysis revealed that the activated signatures of the invasive fibroblasts, the top of which was ERBB2 (HER2), showed great similarities to those of metastatic lung adenocarcinoma cancer cells. Activation of HER2 in normal lung fibroblasts led to a more invasive genetic program and worsened fibroblast invasion and lung fibrosis, while antagonizing HER2 signaling blunted fibroblast invasion and ameliorated lung fibrosis. These findings suggest that HER2 signaling may be a key driver of fibroblast invasion and serve as an attractive target for therapeutic intervention in IPF.

The mesoderm- and epithelial-mesenchymal transition-associated transcription factor FOXC1 is specifically overexpressed in basal-like breast cancer (BLBC), but its biochemical function is not understood. Here, we demonstrate that FOXC1 controls cancer stem cell (CSC) properties enriched in BLBC cells via activation of Smoothened (SMO)-independent Hedgehog (Hh) signaling. This non-canonical activation of Hh is specifically mediated by Gli2. Furthermore, we show that the N-terminal domain of FOXC1 (aa 1–68) binds directly to an internal region (aa 898–1168) of Gli2, enhancing the DNA-binding and transcription-activating capacity of Gli2. FOXC1 expression correlates with that of Gli2 and its targets in human breast cancers. Moreover, FOXC1 overexpression reduces sensitivity to anti-Hedgehog (Hh) inhibitors in BLBC cells and xenograft tumors. Together, these findings reveal FOXC1-mediated non-canonical Hh signaling that determines the BLBC stem-like phenotype and anti-Hh sensitivity, supporting inhibition of FOXC1 pathways as potential approaches for improving BLBC treatment.

In this paper, Cox's proportional hazards model with Lp penalty method is developed for simultaneous gene selection and survival prediction. Lp penalty shrinks coefficients and produces some coefficients that are exactly zero, and therefore can be used to identify survival related downstream genes. We also define a novel similarity measure to hunt the regulatory genes that their gene expression changes may be low but they are highly correlated with the selected genes. Experimental results with gene expression data demonstrate that the proposed procedures can be used for identifying important gene clusters that are related to time to death due to cancer and for building parsimonious model for predicting the survival of future patients.

BACKGROUND.Sputum is an easily accessible diagnostic material for lung cancer early detection by cytologic and molecular genetic analysis of exfoliated airway epithelial cells. However, the use of sputum is limited by its cellular heterogeneity, which includes >95 macrophages and neutrophils and only about 1 bronchial epithelial cells. We propose to obtain concentrated and purified bronchial epithelial cells to improve early detection of lung cancer in sputum samples.METHODS.Sputum was collected from patients with stage I nonsmallcell lung cancer, cancerfree smokers, and healthy nonsmokers. Magneticassisted cell sorting MACS with antiCD14 and antiCD16 antibody beads were used to enrich bronchial epithelial cells by depleting macrophages and neutrophils from sputum. Fluorescence in situ hybridization FISH analysis for detection of FHITdeletion and cytology were evaluated in the enriched specimens.RESULTS.The bronchial epithelial cells were concentrated to 40 purity from 1.1 of the starting population, yielding an average of 36fold enrichment and at least 2.3 × 105cells per sample. Detecting FHITdeletions for lung cancer diagnosis produced 58 sensitivity in the enriched sputum, whereas there was 42 sensitivity in the unenriched samples P .02. Cytologic examination of the enriched sputum resulted in 53 sensitivity, as compared with 39 sensitivity in unenriched sputum P .03. Furthermore, only 2 cytocentrifuge slides of the unenriched sputum were needed for the analyses, as compared with up to 10 cytocentrifuge slides required from the unprocessed specimens.CONCLUSIONS.The enrichment of bronchial epithelial cells could improve the diagnostic value of sputum and the efficiency of genetic and cytologic analysis of lung cancer. Cancer Cancer Cytopathol 2008. © 2008 American Cancer Society.

Early detection of cancer is crucial for successful treatments. In this paper, we propose a multiclass Logistic Partial Least Squares (LPLS) algorithm for classification of normal vs. cancer using Mass Spectrometry (MS). LPLS combines the multiclass logistic regression with Partial Least Squares (PLS) algorithm. Wavelet decomposition is also proposed for pre-processing of original data. Wavelet decomposition and the proposed LPLS are applied to real life cancer data. Experimental comparisons show that LPLS with wavelet decomposition outperforms other methods in the analysis of MS data.

For microarray based cancer classification, feature selection is a common method for improving classifier generalisation. Most wrapper methods use cross validation methods to evaluate feature sets. For small sample problems like microarray, however, cross validation methods may overfit the data. In this paper, we propose a Structural Risk Minimisation (SRM) based method for gene selection in cancer classification. SRM principle allows for reducing the probable bound on generalisation error and thus avoids overfitting problems. The experimental results show that the proposed method produces significantly better performance than general wrapper methods that use cross validations.

The number of common single nucleotide polymorphisms (SNPs) in the human genome is estimated to be around 3–6 million. It is highly anticipated that the study of SNPs will help provide a means for elucidating the genetic component of complex diseases and variable drug responses. High-throughput technologies such as oligonucleotide arrays have produced enormous amount of SNP data, which creates great challenges in genome-wide disease linkage and association studies. In this paper, we present an adaptation of the cross entropy (CE) method and propose an iterative CE Monte Carlo (CEMC) algorithm for tagging SNP selection. This differs from most of SNP selection algorithms in the literature in that our method is independent of the notion of haplotype block. Thus, the method is applicable to whole genome SNP selection without prior knowledge of block boundaries. We applied this block-free algorithm to three large datasets (two simulated and one real) that are in the order of thousands of SNPs. The successful applications to these large scale datasets demonstrate that CEMC is computationally feasible for whole genome SNP selection. Furthermore, the results show that CEMC is significantly better than random selection, and it also outperformed another block-free selection algorithm for the dataset considered.

One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N . Standard statistical methods do not work well when N

Gene selection is an important issue in analyzing multiclass microarray data. Among many proposed selection methods, the traditional ANOVA F test statistic has been employed to identify informative genes for both class prediction (classification) and discovery problems. However, the F test statistic assumes an equal variance. This assumption may not be realistic for gene expression data. This paper explores other alternative test statistics which can handle heterogeneity of the variances. We study five such test statistics, which include Brown-Forsythe test statistic and Welch test statistic. Their performance is evaluated and compared with that of F statistic over different classification methods applied to publicly available microarray datasets.

One important feature of the gene expression data is that the number of genes m far exceeds the number of samples n. When applied to analyse the gene expression data, standard statistical methods do not work well when n < m. Development of new methodologies or modification of existing methodologies is needed for the analysis of microarray data. Support vector machine (SVM) has been applied in gene expression data classification. In traditional SVM classification, a classifier is usually built by a small subset of samples called support vectors. This may cause a loss of available information since the number of samples in a gene expression dataset is usually very small. In this paper, we introduce a logistic support vector machine (LSVM) algorithm for the classification task. In LSVM, all the samples are used as support vectors and parameters are estimated via the maximum a posteriori (MAP) estimation procedure. The proposed algorithm also has the advantage of providing an estimate of the underlying probability. This algorithm was applied to five different gene expression datasets. Computational results show that compared with popular classification methods such as traditional SVM, our algorithm usually leads to an improvement in classification accuracy.

Hepatic stellate cells activated by hyaluronan synthase 2–mediated hyaluronan and Notch1 promote liver fibrosis, identifying a potential target.

Abstracted from Gynecol Oncol2015;139:394–400It is unknown whether suboptimal cytoreductive surgery and poor survival in advanced EOC is due to the intrinsic biology of unresectable tumors or insufficient surgical effort that results in residual tumor-sustaining clones.

One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N

Gene selection is an important issue in analyzing multiclass microarray data. Among many proposed selection methods, the traditional ANOVA F test statistic has been employed to identify informative genes for both class prediction (classification) and discovery problems. However, the F test statistic assumes an equal variance. This assumption may not be realistic for gene expression data. This paper explores other alternative test statistics which can handle heterogeneity of the variances. We study five such test statistics, which include Brown-Forsythe test statistic and Welch test statistic. Their performance is evaluated and compared with that of F statistic over different classification methods applied to publicly available microarray datasets.

One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N

Gene selection is an important issue in analyzing multiclass microarray data. Among many proposed selection methods, the traditional ANOVA F test statistic has been employed to identify informative genes for both class prediction (classification) and discovery problems. However, the F test statistic assumes an equal variance. This assumption may not be realistic for gene expression data. This paper explores other alternative test statistics which can handle heterogeneity of the variances. We study five such test statistics, which include Brown-Forsythe test statistic and Welch test statistic. Their performance is evaluated and compared with that of F statistic over different classification methods applied to publicly available microarray datasets.