Low-rank regression models for multiple binary responses and their applications to cancer cell-line encyclopedia data.

In this paper, we study high-dimensional multivariate logistic regression models in which a common set of covariates is used to predict multiple binary outcomes simultaneously. Our work is primarily motivated from many biomedical studies with correlated multiple responses such as the cancer cell-line encyclopedia project. We assume that the underlying regression coefficient matrix is simultaneously low-rank and row-wise sparse. We propose an intuitively appealing selection and estimation framework based on marginal model likelihood, and we develop an efficient computational algorithm for inference. We establish a novel high-dimensional theory for this nonlinear multivariate regression. Our theory is general, allowing for potential correlations between the binary responses. We propose a new type of nuclear norm penalty using the smooth clipped absolute deviation, filling the gap in the related non-convex penalization literature. We theoretically demonstrate that the proposed approach improves estimation accuracy by considering multiple responses jointly through the proposed estimator when the underlying coefficient matrix is low-rank and row-wise sparse. In particular, we establish the non-asymptotic error bounds, and both rank and row support consistency of the proposed method. Moreover, we develop a consistent rule to simultaneously select the rank and row dimension of the coefficient matrix. Furthermore, we extend the proposed methods and theory to a joint Ising model, which accounts for the dependence relationships. In our analysis of both simulated data and the cancer cell line encyclopedia data, the proposed methods outperform the existing methods in better predicting responses.

Debiased inference for heterogeneous subpopulations in a high-dimensional logistic regression model.

Due to the prevalence of complex data, data heterogeneity is often observed in contemporary scientific studies and various applications. Motivated by studies on cancer cell lines, we consider the analysis of heterogeneous subpopulations with binary responses and high-dimensional covariates. In many practical scenarios, it is common to use a single regression model for the entire data set. To do this effectively, it is critical to quantify the heterogeneity of the effect of covariates across subpopulations through appropriate statistical inference. However, the high dimensionality and discrete nature of the data can lead to challenges in inference. Therefore, we propose a novel statistical inference method for a high-dimensional logistic regression model that accounts for heterogeneous subpopulations. Our primary goal is to investigate heterogeneity across subpopulations by testing the equivalence of the effect of a covariate and the significance of the overall effects of a covariate. To achieve overall sparsity of the coefficients and their fusions across subpopulations, we employ a fused group Lasso penalization method. In addition, we develop a statistical inference method that incorporates bias correction of the proposed penalized method. To address computational issues due to the nonlinear log-likelihood and the fused Lasso penalty, we propose a computationally efficient and fast algorithm by adapting the ideas of the proximal gradient method and the alternating direction method of multipliers (ADMM) to our settings. Furthermore, we develop non-asymptotic analyses for the proposed fused group Lasso and prove that the debiased test statistics admit chi-squared approximations even in the presence of high-dimensional variables. In simulations, the proposed test outperforms existing methods. The practical effectiveness of the proposed method is demonstrated by analyzing data from the Cancer Cell Line Encyclopedia (CCLE).

The role of myokines in cancer: crosstalk between skeletal muscle and tumor.

Loss of skeletal muscle mass is a primary feature of sarcopenia and cancer cachexia. In cancer patients, tumor-derived inflammatory factors promote muscle atrophy via tumor-to-muscle effects, which is closely associated with poor prognosis. During the past decade, skeletal muscle has been considered to function as an autocrine, paracrine, and endocrine organ by releasing numerous myokines. The circulating myokines can modulate pathophysiology in the other organs, as well as in the tumor microenvironment, suggesting myokines function as muscleto-tumor signaling molecules. Here, we highlight the roles of myokines in tumorigenesis, particularly in terms of crosstalk between skeletal muscle and tumor. Better understanding of tumor-to-muscle and muscle-to-tumor effects will shed light on novel strategies for the diagnosis and treatment of cancer. [BMB Reports 2023; 56(7): 365-373].

Direct Contact with Platelets Induces Podoplanin Expression and Invasion in Human Oral Squamous Cell Carcinoma Cells.

Oral squamous cell carcinoma (OSCC) is mostly diagnosed at an advanced stage, with local and/or distal metastasis. Thus, locoregional and/or local control of the primary tumor is crucial for a better prognosis in patients with OSCC. Platelets have long been considered major players in cancer metastasis. Traditional antiplatelet agents, such as aspirin, are thought to be potential chemotherapeutics, but they need to be used with caution because of the increased bleeding risk. Podoplanin (PDPN)-expressing cancer cells can activate platelets and promote OSCC metastasis. However, the reciprocal effect of platelets on PDPN expression in OSCC has not been investigated. In this study, we found that direct contact with platelets upregulated PDPN and integrin ß1 at the protein level and promoted invasiveness of human OSCC Ca9.22 cells that express low levels of PDPN. In another human OSCC HSC3 cell line that express PDPN at an abundant level, silencing of the PDPN gene reduced cell invasiveness. Analysis of the public database further supported the co-expression of PDPN and integrin ß1 and their increased expression in metastatic tissues compared to normal and tumor tissues of the oral cavity. Taken together, these data suggest that PDPN is a potential target to regulate platelet-tumor interaction and metastasis for OSCC treatment, which can overcome the limitations of traditional antiplatelet drugs.

Oral-Gut Microbiome Axis in Gastrointestinal Disease and Cancer.

It is well-known that microbiota dysbiosis is closely associated with numerous diseases in the human body. The oral cavity and gut are the two largest microbial habitats, playing a major role in microbiome-associated diseases. Even though the oral cavity and gut are continuous regions connected through the gastrointestinal tract, the oral and gut microbiome profiles are well-segregated due to the oral-gut barrier. However, the oral microbiota can translocate to the intestinal mucosa in conditions of the oral-gut barrier dysfunction. Inversely, the gut-to-oral microbial transmission occurs as well in inter- and intrapersonal manners. Recently, it has been reported that oral and gut microbiomes interdependently regulate physiological functions and pathological processes. Oral-to-gut and gut-to-oral microbial transmissions can shape and/or reshape the microbial ecosystem in both habitats, eventually modulating pathogenesis of disease. However, the oral-gut microbial interaction in pathogenesis has been underappreciated to date. Here, we will highlight the oral-gut microbiome crosstalk and its implications in the pathogenesis of the gastrointestinal disease and cancer. Better understanding the role of the oral-gut microbiome axis in pathogenesis will be advantageous for precise diagnosis/prognosis and effective treatment.

Platelet CLEC2-Podoplanin Axis as a Promising Target for Oral Cancer Treatment.

Cancer tissues are not just simple masses of malignant cells, but rather complex and heterogeneous collections of cellular and even non-cellular components, such as endothelial cells, stromal cells, immune cells, and collagens, referred to as tumor microenvironment (TME). These multiple players in the TME develop dynamic interactions with each other, which determines the characteristics of the tumor. Platelets are the smallest cells in the bloodstream and primarily regulate blood coagulation and hemostasis. Notably, cancer patients often show thrombocytosis, a status of an increased platelet number in the bloodstream, as well as the platelet infiltration into the tumor stroma, which contributes to cancer promotion and progression. Thus, platelets function as one of the important stromal components in the TME, emerging as a promising chemotherapeutic target. However, the use of traditional antiplatelet agents, such as aspirin, has limitations mainly due to increased bleeding complications. This requires to implement new strategies to target platelets for anti-cancer effects. In oral squamous cell carcinoma (OSCC) patients, both high platelet counts and low tumor-stromal ratio (high stroma) are strongly correlated with increased metastasis and poor prognosis. OSCC tends to invade adjacent tissues and bones and spread to the lymph nodes for distant metastasis, which is a huge hurdle for OSCC treatment in spite of relatively easy access for visual examination of precancerous lesions in the oral cavity. Therefore, locoregional control of the primary tumor is crucial for OSCC treatment. Similar to thrombocytosis, higher expression of podoplanin (PDPN) has been suggested as a predictive marker for higher frequency of lymph node metastasis of OSCC. Cumulative evidence supports that platelets can directly interact with PDPN-expressing cancer cells via C-type lectin-like receptor 2 (CLEC2), contributing to cancer cell invasion and metastasis. Thus, the platelet CLEC2-PDPN axis could be a pinpoint target to inhibit interaction between platelets and OSCC, avoiding undesirable side effects. Here, we will review the role of platelets in cancer, particularly focusing on CLEC2-PDPN interaction, and will assess their potentials as therapeutic targets for OSCC treatment.

Integrating multidimensional data for clustering analysis with applications to cancer patient data.

Advances in high-throughput genomic technologies coupled with large-scale studies including The Cancer Genome Atlas (TCGA) project have generated rich resources of diverse types of omics data to better understand cancer etiology and treatment responses. Clustering patients into subtypes with similar disease etiologies and/or treatment responses using multiple omics data types has the potential to improve the precision of clustering than using a single data type. However, in practice, patient clustering is still mostly based on a single type of omics data or ad hoc integration of clustering results from individual data types, leading to potential loss of information. By treating each omics data type as a different informative representation from patients, we propose a novel multi-view spectral clustering framework to integrate different omics data types measured from the same subject. We learn the weight of each data type as well as a similarity measure between patients via a non-convex optimization framework. We solve the proposed non-convex problem iteratively using the ADMM algorithm and show the convergence of the algorithm. The accuracy and robustness of the proposed clustering method is studied both in theory and through various synthetic data. When our method is applied to the TCGA data, the patient clusters inferred by our method show more significant differences in survival times between clusters than those inferred from existing clustering methods.

STAT3 Stabilizes IKKα Protein through Direct Interaction in Transformed and Cancerous Human Breast Epithelial Cells.

Signal transducer and activator of transcription 3 (STAT3) and nuclear factor-κB (NF-κB) are two representative transcription factors that play a critical role in inflammation-associated tumorigenesis through multi-level cooperation. Unlike other types of tumors, breast carcinomas have shown a significant dependency on the non-classical NF-κB pathway as well as the classical one. The α subunit of the inhibitor of the κB kinase (IKK) complex, IKKα, is involved in both classical and non-classical activation of NF-κB. Although the cross-talk between STAT3 and NF-κB has been suggested in several studies, the interplay between STAT3 and the regulators of NF-κB including IKKα has not been fully clarified yet. In this study, we observed overexpression and co-localization of IKKα and STAT3 in human breast cancer tissues as well as in H-Ras transformed human breast epithelial (H-Ras MCF-10A) and breast cancer (MDA-MB-231) cells. By utilizing small interfering RNA (siRNA) technology, we were able to demonstrate that STAT3 up-regulated IKKα, but not IKKß or IKKγ, in these cells. This was attributable to direct binding to and subsequent stabilization of IKKα protein by blocking the ubiquitin-proteasome system. Notably, we identified the lysine 44 residue of IKKα as a putative binding site for STAT3. Moreover, siRNA knockdown of IKKα attenuated viability, anchorage-independent growth and migratory capabilities of H-Ras MCF-10A cells. Taken together, these findings propose a novel mechanism responsible for NF-κB activation by STAT3 through stabilization of IKKα, which contributes to breast cancer promotion and progression. Thus, breaking the STAT3-IKKα alliance can be an alternative therapeutic strategy for the treatment of breast cancer.

NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution.

MOTIVATION: A number of computational methods have been proposed recently to profile tumor microenvironment (TME) from bulk RNA data, and they have proved useful for understanding microenvironment differences among therapeutic response groups. However, these methods are not able to account for tumor proportion nor variable mRNA levels across cell types. RESULTS: In this article, we propose a Nonnegative Matrix Factorization-based Immune-TUmor MIcroenvironment Deconvolution (NITUMID) framework for TME profiling that addresses these limitations. It is designed to provide robust estimates of tumor and immune cells proportions simultaneously, while accommodating mRNA level differences across cell types. Through comprehensive simulations and real data analyses, we demonstrate that NITUMID not only can accurately estimate tumor fractions and cell types' mRNA levels, which are currently unavailable in other methods; it also outperforms most existing deconvolution methods in regular cell type profiling accuracy. Moreover, we show that NITUMID can more effectively detect clinical and prognostic signals from gene expression profiles in tumor than other methods. AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented in R. The source code can be downloaded at https://github.com/tdw1221/NITUMID. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Production of bio-oil with reduced polycyclic aromatic hydrocarbons via continuous pyrolysis of biobutanol process derived waste lignin.

The fast pyrolysis of waste lignin derived from biobutanol production process was performed to determine the optimal pyrolysis conditions and pyrolysis product properties. Four types of pyrolysis reactors, e.g.: micro-scale pyrolyzer-gas chromatography/mass spectrometry, lab and bench scale fixed bed (FB) reactors, and bench scale rotary kiln (RK) reactor, were employed to compare the pyrolysis reaction conditions and product properties obtained from different reactors. The yields of char, oil, and gas obtained from lab scale and bench scale reactor were almost similar compared to FB reactor. RK reactor produced desirable bio-oil with much reduced yield of poly aromatic hydrocarbons (cancer precursor) due to its higher cracking reaction efficiency. In addition, char agglomeration and foaming of lignin pyrolysis were greatly restricted by using RK reactor compared to the FB reactor.

Effect of statin on hepatocellular carcinoma in patients with type 2 diabetes: A nationwide nested case-control study.

Relationship on new statin use and the risk of hepatocellular carcinoma (HCC) in patients with incident type 2 diabetes mellitus (T2DM), who might be at the risk of developing HCC, is uncertained. A nationwide population-based nested case-control study was conducted within the National Health Insurance Service National Sample Cohort 2002-2013 in Korea. Newly prescribed statin after newly diagnosed T2DM was defined as statin use. Controls were matched to case patients on age, sex, follow-up time, and the date of diabetes diagnosis at a five-to-one ratio. Odds ratios (ORs) for associations of statin use with HCC were calculated using conditional logistic regression. After at least a 5-year HCC-free period, there were 229 incident HCC cases and 1,145 matched controls from 47,738 patients with incident diabetes. Of these 229 incident HCC cases, 27 (11.8%) were statin users, whereas 378 (33.0%) were statin users among 1,145 controls. Statin use was associated with a reduced risk of HCC development (adjusted OR [AOR]= 0.36, 95% confidence interval [CI] 0.22-0.60) after adjustment for chronic viral hepatitis, liver cirrhosis, alcoholic liver disease, previous cancer, aspirin use, insulin use, sulfonylurea use, metformin use, thiazolidinedione use, history of chronic obstructive pulmonary disease, Charlson comorbidity score, household income level, and residential area. Risk reduction was accentuated with an increase of cumulative defined daily doses (cDDD) compared with non-users (AORs 0.53, 0.36, 0.32, and 0.26 in ≤60, 60-180, 181-365, and >365cDDD, respectively; P for trend <0.0001). The risk reduction was apparent in the presence of liver disease (AOR = 0.27, 95% CI 0.14-0.50), including heterogeneous groups of clinical diagnosis of liver disease, but not significant in the absence of liver disease (AOR = 0.64, 95% CI 0.32-1.29). Among patients with new onset T2DM, statin use before HCC diagnosis may have a beneficial inhibitory effect on HCC development in a dose-dependent manner, especially in individuals with liver disease.

Synthesis and cytotoxicity evaluation of 6,11-dihydro-pyridazo- and 6,11-dihydro-pyrido[2,3-b]phenazine-6,11-diones.

The 6,11-dihydro-pyridazo[2,3-b]phenazine-6,11-dione and 6,11-dihydro-pyrido[2,3-b]phenazine-6,11-dione derivatives were synthesized from 6,7-dichloro-5,8-phthalazinedione and 6,7-dichloro-5,8-quinolinedione, respectively, producing a series of new anticancer drugs. The cytotoxic activities of the prepared compounds were evaluated by a SRB (Sulforhodamine B) assay against the following tumor cell lines: A459 (human lung), SK-OV-3 (human ovarian), SK-MEL-2 (human melanoma), XF498 (human CNS), and HCT 15 (human colon). Almost all the derivatives of the 6,11-dihydro-pyridazo[2[,3-b]phenazine-6,11-dione and 6,11-dihydro-pyrido[2,3-b]phenazine-6,11-dione, tetracyclic heteroquinone analogues with four or three nitrogen atoms, exhibited excellent cytotoxicity on almost all the human tumor cell lines tested. Specifically, 6,11-dihydro-pyridazo[2,3-b]phenazine-6,11-dione (4a) exhibited potent activity against all the tumor cell lines, and in particular, its cytotoxic effect against HCT 15 (ED(50)=0.004 microg/mL) was 25 times greater than that of doxorubicin (ED(50)=0.093 microg/mL).

Synthesis and cytotoxicity of 6,11-dihydro-pyrido- and 6,11-dihydro-benzo[2,3-b]phenazine-6,11-dione derivatives.

6,11-Dihydro-pyrido[2,3-b]phenazine-6,11-diones and 6,11-dihydro-benzo[2,3-b]phenazine-6,11-diones were synthesized from 6,7-dichloro-5,8-quinolinedione and 2,3-dichloro-1,4-naphthoquinone. The study on the cytotoxicity on these products revealed that the pyridophenazinediones, tetracyclic heteroquinone analogues with three nitrogen atoms exhibited a high cytotoxicity on several human tumor cell lines. Compound 9c and 9e showed in vitro antitumor activity comparable or superior to doxorubicin against the human ovarian tumor cells (SK-OV-3) and the human CNS cells (XF 498). The IC(50) value for compound 9e was 0.06 microM against the human CNS cells (XF 498), which was 2.6 times higher than that (0.16 microM) of doxorubicin. In addition, the X-ray crystallographic analysis of two phenazinedione derivatives (9b,c) showed clearly the exact position of the nucleophilic substitution of 6,7-dichloro-5,8-quinolinedione.