Integrated Bioinformatics and Machine Learning Analysis Identify ACADL as a Potent Biomarker of Reactive Mesothelial Cells.

Mesothelial cells with reactive hyperplasia are difficult to distinguish from malignant mesothelioma cells based on cell morphology. This study aimed to identify and validate potential biomarkers that distinguish mesothelial cells from mesothelioma cells through machine learning combined with immunohistochemistry. It integrated the gene expression matrix from three Gene Expression Omnibus data sets (GSE2549, GSE12345, and GSE51024) to analyze the differently expressed genes between normal and mesothelioma tissues. Then, three machine learning algorithms, least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were used to screen and obtain four shared candidate markers, including ACADL, EMP2, GPD1L, and HMMR. The receiver operating characteristic curve analysis showed that the area under the curve for distinguishing normal mesothelial cells from mesothelioma was 0.976, 0.943, 0.962, and 0.956, respectively. The expression and diagnostic performance of these candidate genes were validated in two additional independent data sets (GSE42977 and GSE112154), indicating that the performances of ACADL, GPD1L, and HMMR were consistent between the training and validation data sets. Finally, the optimal candidate marker ACADL was verified by immunohistochemistry assay. Acyl-CoA dehydrogenase long chain (ACADL) was stained strongly in mesothelial cells, especially for reactive hyperplasic mesothelial cells, but was negative in malignant mesothelioma cells. Therefore, ACADL has the potential to be used as a specific marker of reactive hyperplasic mesothelial cells in the differential diagnosis of mesothelioma.

Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data.

BACKGROUND: Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner. RESULTS: This paper proposes a global GRNs inference framework based on dictionary learning, named dlGRN. The method intends to learn atomic regulators (ARs) from gene expression data using a modified dictionary learning (DL) algorithm, which reflects the whole gene regulatory system, and predicts the regulation between a known regulator and a target gene in a global regression way. The modified DL algorithm fits the scale-free property of biological network, rendering dlGRN intrinsically discern direct and indirect regulations. CONCLUSIONS: Extensive experimental results on simulation and real-world data demonstrate the effectiveness and efficiency of dlGRN in reverse engineering GRNs. A novel predicted transcription regulation between a TF TFAP2C and an oncogene EGFR was experimentally verified in lung cancer cells. Furthermore, the real application reveals the prevalence of DNA methylation regulation in gene regulatory system. dlGRN can be a standalone tool for GRN inference for its globalization and robustness.

[Human papillomavirus infection in men with condyloma acuminatum: Genotype analysis of 70 cases].

OBJECTIVE: To investigate the distribution of the gene subtypes of human papillomavirus (HPV) in male patients with condyloma acuminatum (CA) and analyze the characteristics of the gene subtypes. METHODS: We extracted genomic DNA of the HPV virus from the genital tissue of 70 male CA patients, detected the DNA subtypes of HPV using the PCR-reverse dot hybridization technique, and analyzed the rates of different subtypes identified and their characteristics of distribution in different age groups. RESULTS: The male HPV-positive patients were mainly infected at the age of 20－39 years, primarily with high- and low-risk mixed infection of various subtypes, which accounted for 61.54% in the 20- to 29-year-olds and 42.86% in the 30- to 39-year-olds. Among the 70 CA patients, 22 HPV subtypes were identified, the top five subtypes including HPV 11 (21.08%), HPV 6 (19.46%), HPV 42 (6.49%), HPV 59 (6.49%) and HPV 53 (5.95%); 20 infected with a single subtype (28.57%), 19 with two subtypes (27.14%) and 31 with three or more (44.29%); and 30 infected with a low-risk single subtype (42.86%) and 40 with both high- and low-risk multiple subtypes (57.14%). CONCLUSIONS: Male patients with CA are mainly infected with HPV 11 and HPV 6, with a significantly higher rate of multi-subtype than single-subtype infection, and the multi-subtype patients chiefly with high- and low-risk mixed infection. Men aged 20－39 years old are most commonly affected by CA.

High-resolution terahertz coded-aperture imaging for near-field three-dimensional target.

In this paper, we propose a high-resolution terahertz coded-aperture imaging method with fast beam scanning for near-field three-dimensional targets. This method utilizes a coded aperture to modulate incident terahertz waves randomly and drive the terahertz beam to scan the entire imaging space step by step. Theoretical analyses based on physical optics are performed, and simulation experiments are implemented to demonstrate the feasibility of the proposed method.

Adaptively capturing the heterogeneity of expression for cancer biomarker identification.

BACKGROUND: Identifying cancer biomarkers from transcriptomics data is of importance to cancer research. However, transcriptomics data are often complex and heterogeneous, which complicates the identification of cancer biomarkers in practice. Currently, the heterogeneity still remains a challenge for detecting subtle but consistent changes of gene expression in cancer cells. RESULTS: In this paper, we propose to adaptively capture the heterogeneity of expression across samples in a gene regulation space instead of in a gene expression space. Specifically, we transform gene expression profiles into gene regulation profiles and mathematically formulate gene regulation probabilities (GRPs)-based statistics for characterizing differential expression of genes between tumor and normal tissues. Finally, an unbiased estimator (aGRP) of GRPs is devised that can interrogate and adaptively capture the heterogeneity of gene expression. We also derived an asymptotical significance analysis procedure for the new statistic. Since no parameter needs to be preset, aGRP is easy and friendly to use for researchers without computer programming background. We evaluated the proposed method on both simulated data and real-world data and compared with previous methods. Experimental results demonstrated the superior performance of the proposed method in exploring the heterogeneity of expression for capturing subtle but consistent alterations of gene expression in cancer. CONCLUSIONS: Expression heterogeneity largely influences the performance of cancer biomarker identification from transcriptomics data. Models are needed that efficiently deal with the expression heterogeneity. The proposed method can be a standalone tool due to its capacity of adaptively capturing the sample heterogeneity and the simplicity in use. SOFTWARE AVAILABILITY: The source code of aGRP can be downloaded from https://github.com/hqwang126/aGRP .

A regulation probability model-based meta-analysis of multiple transcriptomics data sets for cancer biomarker identification.

BACKGROUND: Large-scale accumulation of omics data poses a pressing challenge of integrative analysis of multiple data sets in bioinformatics. An open question of such integrative analysis is how to pinpoint consistent but subtle gene activity patterns across studies. Study heterogeneity needs to be addressed carefully for this goal. RESULTS: This paper proposes a regulation probability model-based meta-analysis, jGRP, for identifying differentially expressed genes (DEGs). The method integrates multiple transcriptomics data sets in a gene regulatory space instead of in a gene expression space, which makes it easy to capture and manage data heterogeneity across studies from different laboratories or platforms. Specifically, we transform gene expression profiles into a united gene regulation profile across studies by mathematically defining two gene regulation events between two conditions and estimating their occurring probabilities in a sample. Finally, a novel differential expression statistic is established based on the gene regulation profiles, realizing accurate and flexible identification of DEGs in gene regulation space. We evaluated the proposed method on simulation data and real-world cancer datasets and showed the effectiveness and efficiency of jGRP in identifying DEGs identification in the context of meta-analysis. CONCLUSIONS: Data heterogeneity largely influences the performance of meta-analysis of DEGs identification. Existing different meta-analysis methods were revealed to exhibit very different degrees of sensitivity to study heterogeneity. The proposed method, jGRP, can be a standalone tool due to its united framework and controllable way to deal with study heterogeneity.

ctPath: Demixing pathway crosstalk effect from transcriptomics data for differential pathway identification.

Identifying differentially expressed pathways (DEPs) plays important roles in understanding tumor etiology and promoting clinical treatment of cancer or other diseases. By assuming gene expression to be a sparse non-negative linear combination of hidden pathway signals, we propose a pathway crosstalk-based transcriptomics data analysis method (ctPath) for identifying differentially expressed pathways. Biologically, pathways of different functions work in concert at the systematic level. The proposed method interrogates the crosstalks between pathways and discovers hidden pathway signals by mapping high-dimensional transcriptomics data into a low-dimensional pathway space. The resulted pathway signals reflect the activity level of pathways after removing pathway crosstalk effect and allow a robust identification of DEPs from inherently complex and noisy transcriptomics data. CtPath can also correct incomplete and inaccurate pathway annotations which frequently occur in public repositories. Experimental results on both simulation data and real-world cancer data demonstrate the superior performance of ctPath over other popular approaches. R code for ctPath is available for non-commercial use at the URL http://micblab.iim.ac.cn/Download/.

jNMFMA: a joint non-negative matrix factorization meta-analysis of transcriptomics data.

MOTIVATION: Tremendous amount of omics data being accumulated poses a pressing challenge of meta-analyzing the heterogeneous data for mining new biological knowledge. Most existing methods deal with each gene independently, thus often resulting in high false positive rates in detecting differentially expressed genes (DEG). To our knowledge, no or little effort has been devoted to methods that consider dependence structures underlying transcriptomics data for DEG identification in meta-analysis context. RESULTS: This article proposes a new meta-analysis method for identification of DEGs based on joint non-negative matrix factorization (jNMFMA). We mathematically extend non-negative matrix factorization (NMF) to a joint version (jNMF), which is used to simultaneously decompose multiple transcriptomics data matrices into one common submatrix plus multiple individual submatrices. By the jNMF, the dependence structures underlying transcriptomics data can be interrogated and utilized, while the high-dimensional transcriptomics data are mapped into a low-dimensional space spanned by metagenes that represent hidden biological signals. jNMFMA finally identifies DEGs as genes that are associated with differentially expressed metagenes. The ability of extracting dependence structures makes jNMFMA more efficient and robust to identify DEGs in meta-analysis context. Furthermore, jNMFMA is also flexible to identify DEGs that are consistent among various types of omics data, e.g. gene expression and DNA methylation. Experimental results on both simulation data and real-world cancer data demonstrate the effectiveness of jNMFMA and its superior performance over other popular approaches. AVAILABILITY AND IMPLEMENTATION: R code for jNMFMA is available for non-commercial use via http://micblab.iim.ac.cn/Download/. CONTACT: hqwang@ustc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Second-Order Nonlinear Optical Properties of Carboranylated Square-Planar Pt(II) Zwitterionic Complexes: One-/Two-Dimensional Difference and Substituent Effect.

Zwitterionic complexes have been the subject of great interest in the past several decades due to their multifunctional application in supramolecular chemistry. Herein, a series of internally stable charge-compensated carboranylated square-planar Pt(II) zwitterionic complexes have been explored by density functional theory aim to assessing their structures, the first hyperpolarizabilities, first hyperpolarizability densities, and electronic absorption spectra. It is found that the first hyperpolarizabilities of two-dimensional (2D) structure complexes are much larger with respect to the one-dimensional complex. It is ascribed to the lower transition energy and more obvious charge transfer, which can be further illustrated by their large amplitude and separate distribution of first hyperpolarizability density. In addition, the first hyperpolarizabilities of 2D complexes can be further significantly modified by introducing electron-donating/withdrawing groups on the carborane cage. As a consequence, we believe that these 2D zwitterionic complexes can behave as novel second-order nonlinear optical chromophore with a promising future.

[Bisphenol A alters glucose metabolism in rat Sertoli cells in vitro].

OBJECTIVE: To observe the influence of different concentrations of bisphenol A (BPA) on glucose metabolism and lactate dehydrogenase (LDH) expression in rat Sertoli cells in vitro and investigate the mechanisms of BPA inducing male infertility. METHODS: Using two-step enzyme digestion, we isolated Sertoli cells from male Wistar rats and constructed a primary Sertoli cell system, followed by immunohistochemical FasL staining. We randomly divided the Sertoli cells into a control group to be cultured in the serum-free minimal essential medium (MEM) plus dimethyl sulfoxide (DMSO) and three experimental groups to be treated with 100 nmol/L, 10 µmol/L, and 1 mmol/L BPA, respectively, in the MEM plus DMSO. After 48 hours of treatment, we measured the proliferation of the cells by CCK-8 assay, determined the concentrations of metabolites by NMR spectroscopy, and detected the expression of LDH in the Sertoli cells by RT-PCR and Western blot. RESULTS: The purity of the isolated Sertoli cells was (96.05 ± 1.28)% (n = 10). Compared with the control group, the 100 nmol/L, 10 µmol/L, and 1 mmol/L BPA groups showed no remarkable changes in the proliferation of Sertoli cells ([98 ± 8]%, [96 ± 3]%, and [95 ± 3]%, P >0.05), but the 10 µmol/L and 1 mmol/L of BPA groups exhibited significantly decreased concentrations of intracellular glucose ([3.89 ± 0.07] vs [3.36 ± 0.24] and [3.04 ± 0.21] pmol/cell, P <0.05) and lactate ([0.43 ± 0.06] vs [0.29 ± 0.05] and [0.20 ± 0.03] pmol/cell, P <0.05). The expression of LDH mRNA was decreased with the increased concentration of BPA, while that of LDH protein reduced only in the 1 mmol/L BPA group (P <0.05). CONCLUSION: High-concentration BPA decreases the expression of LDH and alters glucose metabolism in Sertoli cells, and therefore may reduce the provision of lactate for germ cells and impair spermatogenesis.

Stress-responsive hydroxycinnamate glycosyltransferase modulates phenylpropanoid metabolism in Populus.

The diversity of phenylpropanoids offers a rich inventory of bioactive chemicals that can be exploited for plant improvement and human health. Recent evidence suggests that glycosylation may play a role in the partitioning of phenylpropanoid precursors for a variety of downstream uses. This work reports the functional characterization of a stress-responsive glycosyltransferase, GT1-316 in Populus. GT1-316 belongs to the UGT84A subfamily of plant glycosyltransferase family 1 and is designated UGT84A17. Recombinant protein analysis showed that UGT84A17 is a hydroxycinnamate glycosyltransferase and able to accept a range of unsubstituted and substituted cinnamic and benzoic acids as substrates in vitro. Overexpression of GT1-316 in transgenic Populus led to plant-wide increases of hydroxycinnamoyl-glucose esters, which were further elevated under N-limiting conditions. Levels of the two most abundant flavonoid glycosides, rutin and kaempferol-3-O-rutinoside, decreased, while levels of other less abundant flavonoid and phenylpropanoid conjugates increased in leaves of the GT1-316-overexpressing plants. Transcript levels of representative phenylpropanoid pathway genes were unchanged in transgenic plants, supporting a glycosylation-mediated redirection of phenylpropanoid carbon flow as opposed to enhanced phenylpropanoid pathway flux. The metabolic response of N-replete transgenic plants overlapped with that of N-stressed wild types, as the majority of phenylpropanoid derivatives significantly affected by GT1-316 overexpression were also significantly changed by N stress in the wild types. These results suggest that UGT84A17 plays an important role in phenylpropanoid metabolism by modulating biosynthesis of hydroxycinnamoyl-glucose esters and their derivatives in response to developmental and environmental cues.

[Expression of GDNF in rat Sertoli cells cultured in vitro at different temperatures].

OBJECTIVE: To explore the mechanism of hyperthermia inducing infertility by observing the expression of glial cell line-derived neurotrophic factor (GDNF) in rat Sertoli cells cultured in vitro at different temperatures. METHODS: Using combination enzyme digestion and selective adhesion, we isolated Sertoli cells from male Wistar rats and cultured them in vitro at different temperatures, followed by observation of the changes in their adhesion and morphology and identification by FasL immunohistochemical staining. We divided the Sertoli cells into a control group (35 degrees C) and four experimental groups (36 degrees C, 37 degrees C, 38 degrees C, and 39 degrees C), measured their proliferation by CCK-8, observed their morphology and structure by HE staining, and determined the expression of GDNF by RT-PCR, immunofluorescence and Western blot. RESULTS: Sertoli cells were successfully isolated and in vitro-cultured, with a purity of (95.30 +/- 2.15)% (n = 10). The CCK-8 assay showed that the proliferation of the Sertoli cells was the highest at 36 degrees C, gradually decreasing with the temperature above 36 degrees C, and significantly inhibited at 39 degrees C (P < 0.01). Immunofluorescence revealed the expression of GDNF in the cytoplasm, with the highest fluorescence intensity at 36 degrees C. RT-PCR and Western blot exhibited a decreasing trend of the GDNF expression with the increasing temperature above 36 degrees C. There were statistically significant differences in the expression of GDNF between the control group and the four experimental groups (P < 0.01). CONCLUSION: The proliferation and GDNF expression of in vitro-cultured Sertoli cells differ significantly at different temperatures. At > 36 degrees C, the higher the temperature is, the lower the Sertoli cell proliferation and GDNF expression are. Our findings suggest that high temperature above 36 degrees C suppresses the function of Sertoli cells and may also damage spermatogenesis.

The chemokine CCL20 can assist AFP in serological diagnosis of hepatocellular carcinoma.

The chemokine 20 (CCL20) is a member of the CC chemokine family and plays a role in tumor immunity and autoimmune disease. This work investigated the value of CCL20 as a serum diagnostic marker for primary hepatocellular carcinoma (HCC). Based on the data of hepatocellular carcinoma patients in the TCGA database, the up-regulated genes encoding secretory proteins were analyzed in each pathological stage, and the candidate marker CCL20 gene was selected. Serum concentrations of CCL20 in patients with primary HCC, benign liver disease, and healthy subjects were analyzed by enzyme-linked immunosorbent assay (ELISA). The ROC curve evaluated the efficacy of CCL20 alone or in combination with AFP in the diagnosis of HCC. It was found the expression of CCL20 in HCC patients was significantly higher than that in the benign liver disease group and healthy controls (P < 0.05); The AUC of ROC curve to distinguish HCC patients from healthy controls was 0.859, the sensitivity was 73.42%, and the specificity was 86.84%. After combination with AFP, the AUC increased to 0.968, the sensitivity was 88.16%, and the specificity was 97.37%. Although CCL20 was increased in the serum of patients with benign liver diseases, combined with AFP, the AUC to distinguish HCC patients from non-HCC cohorts (benign liver disease group and healthy control group) was 0.902, with a sensitivity of 91.67% and a specificity of 75.26%. Collectively, serum CCL20 is closely related to the occurrence of HCC, and detection of serum CCL20 can assist AFP in improving the diagnostic sensitivity of HCC.

Personalized anti-tumor drug efficacy prediction based on clinical data.

Anti-tumor drug efficacy prediction poses an unprecedented challenge to realizing personalized medicine. This paper proposes to predict personalized anti-tumor drug efficacy based on clinical data. Specifically, we encode the clinical text as numeric vectors featured with hidden topics for patients using Latent Dirichlet Allocation model. Then, to classify patients into two classes, responsive or non-responsive to a drug, drug efficacy predictors are established by machine learning based on the Latent Dirichlet Allocation topic representation. To evaluate the proposed method, we collected and collated clinical records of lung and bowel cancer patients treated with platinum. Experimental results on the data sets show the efficacy and effectiveness of the proposed method, suggesting the potential value of clinical data in cancer precision medicine. We hope that it will promote the research of drug efficacy prediction based on clinical data.

[Hypoxia reduces the proliferation and occludin expression of primary sertoli cells].

OBJECTIVE: To investigate the effect of hypoxia on the proliferation and occludin expression of primary rat Sertoli METHODS: We constructed a primary Sertoli cell system by two-step enzymatic digestion in 18 -22 days old Wistar rats and identified it by oil red O and immunofluorescence methods. We randomly divided the Sertoli cells into five groups to be cultured in oxygen at the concentrations of 20%, 15%, 10%, 5% and 1%, respectively, for 6, 12, 24, 48 and 72 hours. We detected the proliferation of the Sertoli cells by CCK-8 assay, determined the expression of occludin by Western blot, and analyzed the differences among the five groups. RESULTS: Oil red O staining revealed red lipid droplets in the cytoplasm of the Sertoli cells, and immunofluorescence showed the positive expression of the FasL protein, with the purity of Sertoli cells over 95% in vitro. Compared with the 20% normoxic group, the proliferation of the Sertoli cells was gradually reduced in the 15% and 10% hypoxia groups, and significantly declined in the 5% and 1% groups (P < 0.01). At 12 hours, the expression of occludin began to decrease with the prolonging of time and reduction of oxygen concentration (P < 0.01). CONCLUSION: Hypoxia suppresses the proliferation of Sertoli cells and reduces the expression of occludin. It could be inferred that hypoxia could damage the integrity of blood-testis barrier and spermatogenesis of the testis.

A prediction model for prognosis of gastric adenocarcinoma based on six metabolism-related genes.

Background: The study of tumor metabolism is of great value to elucidate the mechanism of tumorigenesis and predict the prognosis of patients. However, the prognostic role of metabolism-related genes (MRGs) in gastric adenocarcinoma (GAD) remains poorly understood. Methods: We downloaded the gene chip dataset GSE79973 (n = 20) of GAD from the Gene Expression Omnibus (GEO) database to compare differentially expressed genes (DEGs) between normal and tumor tissues. We then extracted MRGs from these DEGs and systematically investigated the prognostic value of these differential MRGs for predicting patients' overall survival by univariable and multivariable Cox regression analysis. Six metabolic genes (ACOX3, APOE, DIO2, HSD17B4, NUAK1, and WHSC1L1) were identified as prognosis-associated hub genes, which were used to build a prognostic model in the training dataset GSE15459 (n = 200), and then validated in the dataset GSE62254 (n = 300). Results: Patients were divided into high-risk and low-risk subgroups based on the model's risk score, and it was found that patients in the high-risk subgroup had shorter overall survival than those in the low-risk subgroup, both in the training and testing datasets. In addition, for the training and testing cohorts, the area under the ROC curve of the prognostic model for one-year survival prediction was 0.723 and 0.667, respectively, indicating that the model has good predictive performance. Furthermore, we established a nomogram based on tumor stage and risk score to effectively predict the overall survival (OS) of GAD patients. The expression of 6 MRGs at the protein level was confirmed by immunohistochemistry (IHC). Kaplan-Meier survival analysis further confirmed that their expression influenced OS in GAD patients. Conclusion: Collectively, the 6 MRGs signature might be a reliable tool for assessing OS in GAD patients, with potential application value in clinical decision-making and individualized therapy.

Pan-cancer analysis reveals the associations between MMP13 high expression and carcinogenesis and its value as a serum diagnostic marker.

BACKGROUND: Matrix metalloproteinase-13 (MMP13) is a member of the endopeptidase matrix metalloproteinase family, which is involved in many normal physiological processes and even tumorigenesis. However, its co-carcinogenic signature in different cancers is not fully understood. METHODS: In this study, we first analyzed the expression of MMP13 in pan-cancer and its association with prognosis, immune infiltration, and cancer-related signaling pathways through integrated bioinformatics. Furthermore, western blotting (WB) was used to verify the expression of MMP13 and epithelial-mesenchymal transition (EMT) factors in cancer tissues. Finally, the value of MMP13 as a serum diagnostic marker was analyzed by enzyme-linked immunosorbent assay (ELISA). RESULTS: MMP13 expression is frequently upregulated in multiple cancers that always indicate an adverse prognosis. MMP13 undergoes comprehensive genetic alterations and promoter methylation reduction in various tumors. Additionally, immune correlation analysis showed that MMP13 expression was significantly associated with TMB, MSI, and tumor immune infiltration. Pathway enrichment analysis showed that MMP13 upregulation was correlated with activation of the EMT signaling pathway, which was verified by WB in lung adenocarcinoma tissues. Most importantly, ELISA results showed that serum MMP13 levels could be used for the diagnosis of multiple tumors, including BRCA, HNSC, LUAD, and LUSC, with the area under the curve (AUC) values of 0.8494, 0.9259, 0.7144, and 0.8575, respectively. CONCLUSIONS: MMP13 is often overexpressed across cancers and predicts poor prognosis, with the potential as a therapeutic target. Furthermore, the up-regulation of its expression can be effectively reflected in the serum protein level, thus serving as a valuable diagnostic marker.

SLIM: a sliding linear model for estimating the proportion of true null hypotheses in datasets with dependence structures.

MOTIVATION: The pre-estimate of the proportion of null hypotheses (π(0)) plays a critical role in controlling false discovery rate (FDR) in multiple hypothesis testing. However, hidden complex dependence structures of many genomics datasets distort the distribution of p-values, rendering existing π(0) estimators less effective. RESULTS: From the basic non-linear model of the q-value method, we developed a simple linear algorithm to probe local dependence blocks. We uncovered a non-static relationship between tests' p-values and their corresponding q-values that is influenced by data structure and π(0). Using an optimization framework, these findings were exploited to devise a Sliding Linear Model (SLIM) to more reliably estimate π(0) under dependence. When tested on a number of simulation datasets with varying data dependence structures and on microarray data, SLIM was found to be robust in estimating π(0) against dependence. The accuracy of its π(0) estimation suggests that SLIM can be used as a stand-alone tool for prediction of significant tests. AVAILABILITY: The R code of the proposed method is available at http://aspendb.uga.edu/downloads for academic use.

FOLFIRI (folinic acid, fluorouracil, and irinotecan) increases not efficacy but toxicity compared with single-agent irinotecan as a second-line treatment in metastatic colorectal cancer patients: a randomized clinical trial.

BACKGROUND: FOLFIRI [irinotecan, folinic acid (CF), and fluorouracil] is considered a standard second-line chemotherapy regimen for patients with metastatic colorectal cancer (mCRC) who failed first-line XELOX/FOLFOX regimens. However, it remains unknown whether fluorouracil is still necessary in this case. This trial was designed to test the superiority of FOLFIRI over single-agent irinotecan as a second-line treatment for patients with mCRC. METHODS: This randomized clinical trial was conducted in five hospitals in China. From 4 November 2016 to 17 January 2020, patients aged 18 years or older with histologically confirmed unresectable mCRC and who had failed first-line XELOX/FOLFOX regimens were screened and enrolled. Patients were randomized to receive either FOLFIRI or irinotecan. The primary endpoint was progression-free survival (PFS). Secondary endpoints included overall survival (OS), objective response rate (ORR), and toxicity. Data were analyzed on an intention-to-treat basis. RESULTS: A total of 172 patients with mCRC were randomly treated with FOLFIRI (n = 88) or irinotecan (n = 84). The median PFS was 104 and 112 days (3.5 and 3.7 months) in the FOLFIRI and irinotecan groups, respectively [hazard ratio (HR) = 1.084, 95% confidence interval (CI) = 0.7911-1.485; p = 0.6094], and there was also no significant difference in OS and ORR between the two groups. The incidence of the following adverse events (AEs) was significantly higher in the FOLFIRI group than in the irinotecan group: any grade AEs including leucopenia (73.9% versus 55.4%), neutropenia (72.7% versus 56.6%), thrombocytopenia (31.8% versus 18.1%), jaundice (18.2% versus 7.2%), mucositis (40.9% versus 14.5%), vomiting (37.5% versus 21.7%), and fever (19.3% versus 7.2%) and grade 3-4 neutropenia (47.7% versus 21.7%). CONCLUSION: This is the first head-to-head trial showing that single-agent irinotecan yielded PFS, OS, and ORR similar to FOLFIRI, with a more favorable toxicity profile; therefore, it might be a more favorable standard chemotherapy regimen for mCRC patients who failed first-line XELOX/FOLFOX regimens. TRIAL REGISTRATION: This study is registered with ClinicalTrials.gov, number NCT02935764, registered 17 October 2016, https://clinicaltrials.gov/ct2/show/NCT02935764.

An Immune-Associated Genomic Signature Effectively Predicts Pathologic Complete Response to Neoadjuvant Paclitaxel and Anthracycline-Based Chemotherapy in Breast Cancer.

Breast cancer is now the leading cause of cancer morbidity and mortality among women worldwide. Paclitaxel and anthracycline-based neoadjuvant chemotherapy is widely used for the treatment of breast cancer, but its sensitivity remains difficult to predict for clinical use. In our study, a LASSO logistic regression method was applied to develop a genomic classifier for predicting pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer. The predictive accuracy of the signature classifier was further evaluated using four other independent test sets. Also, functional enrichment analysis of genes in the signature was performed, and the correlations between the prediction score of the signature classifier and immune characteristics were explored. We found a 25-gene signature classifier through the modeling, which showed a strong ability to predict pCR to neoadjuvant chemotherapy in breast cancer. For T/FAC-based training and test sets, and a T/AC-based test set, the AUC of the signature classifier is 1.0, 0.9071, 0.9683, 0.9151, and 0.7350, respectively, indicating that it has good predictive ability for both T/FAC and T/AC schemes. The multivariate model showed that 25-gene signature was far superior to other clinical parameters as independent predictor. Functional enrichment analysis indicated that genes in the signature are mainly enriched in immune-related biological processes. The prediction score of the classifier was significantly positively correlated with the immune score. There were also significant differences in immune cell types between pCR and residual disease (RD) samples. Conclusively, we developed a 25-gene signature classifier that can effectively predict pCR to paclitaxel and anthracycline-based neoadjuvant chemotherapy in breast cancer. Our study also suggests that the immune ecosystem is actively involved in modulating clinical response to neoadjuvant chemotherapy and is beneficial to patient outcomes.