Stromal Cell Signature Associated with Response to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer

Gene features of tumor-specific T cells relevant to immunotherapy, targeted therapy and chemotherapy in lung cancer

1 Background

In lung cancer, the use of small-molecule inhibitors, chemotherapy and immunotherapy has led to unprecedented survival benefits in selected patients. Considering most patients will experience a relapse within a short period of time due to single drug resistance, combination therapy is also particularly important to improve patient prognosis. Therefore, more robust biomarkers to predict responses to immunotherapy, targeted therapy, chemotherapy and rationally drug combination therapies may be helpful in clinical treatment choices.

2 Methods

We defined tumor-specific T cells (TSTs) and their features (TSTGs) by single-cell RNA sequencing. We applied LASSO regression to filter out the most survival-relevant TSTGs to form the Tumor-specific T cell score (TSTS). Immunological characteristics, enriched pathways, and mutation were evaluated in high- and low TSTS groups.

3 Results

We identified six clusters of T cells as TSTs in lung cancer, and four most robust genes from 9 feature genes expressed only on tumor-specific T cells were screened to construct a tumor-specific T cells score (TSTS). TSTS was positively correlated with immune infiltration and angiogenesis and negatively correlated with malignant cell proliferation. Moreover, potential vascular-immune crosstalk in lung cancer provides the theoretical basis for combined anti-angiogenic and immunotherapy. Noticeable, patients in high TSTS had better response to ICB and targeted therapy and patients in the low TSTS group often benefit from chemotherapy.

4 Conclusion

The proposed TSTS is a promising indicator to predict immunotherapy, targeted therapy and chemotherapy responses in lung cancer patients for helping clinical treatment choices.

Microfluidics-enabled fluorinated assembly of EGCG-ligands-siTOX nanoparticles for synergetic tumor cells and exhausted t cells regulation in cancer immunotherapy

Immune checkpoint inhibitor (ICI)-derived evolution offers a versatile means of developing novel immunotherapies that targets programmed death-ligand 1 (PD-L1)/programmed death-1 (PD-1) axis. However, one major challenge is T cell exhaustion, which contributes to low response rates in "cold" tumors. Herein, we introduce a fluorinated assembly system of LFNPs/siTOX complexes consisting of fluorinated EGCG (FEGCG), fluorinated aminolauric acid (LA), and fluorinated polyethylene glycol (PEG) to efficiently deliver small interfering RNA anti-TOX (thymus high mobility group box protein, TOX) for synergistic tumor cells and exhausted T cells regulation. Using a microfluidic approach, a library of LFNPs/siTOX complexes were prepared by altering the placement of the hydrophobe (LA), the surface PEGylation density, and the siTOX ratio. Among the different formulations tested, the lead formulation, LFNPs3-3/siTOX complexes, demonstrated enhanced siRNA complexation, sensitive drug release, improved stability and delivery efficacy, and acceptable biosafety. Upon administration by the intravenous injection, this formulation was able to evoke a robust immune response by inhibiting PD-L1 expression and mitigating T cell exhaustion. Overall, this study provides valuable insights into the fluorinated assembly and concomitant optimization of the EGCG-based delivery system. Furthermore, it offers a promising strategy for cancer immunotherapy, highlighting its potential in improving response rates in ''cold'' tumors.

The cellular composition of the tumor microenvironment is an important marker for predicting therapeutic efficacy in breast cancer

At present, the incidence rate of breast cancer ranks first among new-onset malignant tumors in women. The tumor microenvironment is a hot topic in tumor research. There are abundant cells in the tumor microenvironment that play a protumor or antitumor role in breast cancer. During the treatment of breast cancer, different cells have different influences on the therapeutic response. And after treatment, the cellular composition in the tumor microenvironment will change too. In this review, we summarize the interactions between different cell compositions (such as immune cells, fibroblasts, endothelial cells, and adipocytes) in the tumor microenvironment and the treatment mechanism of breast cancer. We believe that detecting the cellular composition of the tumor microenvironment is able to predict the therapeutic efficacy of treatments for breast cancer and benefit to combination administration of breast cancer.

Residual ANTXR1+ myofibroblasts after chemotherapy inhibit anti-tumor immunity via YAP1 signaling pathway

Although cancer-associated fibroblast (CAF) heterogeneity is well-established, the impact of chemotherapy on CAF populations remains poorly understood. Here we address this question in high-grade serous ovarian cancer (HGSOC), in which we previously identified 4 CAF populations. While the global content in stroma increases in HGSOC after chemotherapy, the proportion of FAP+ CAF (also called CAF-S1) decreases. Still, maintenance of high residual CAF-S1 content after chemotherapy is associated with reduced CD8+ T lymphocyte density and poor patient prognosis, emphasizing the importance of CAF-S1 reduction upon treatment. Single cell analysis, spatial transcriptomics and immunohistochemistry reveal that the content in the ECM-producing ANTXR1+ CAF-S1 cluster (ECM-myCAF) is the most affected by chemotherapy. Moreover, functional assays demonstrate that ECM-myCAF isolated from HGSOC reduce CD8+ T-cell cytotoxicity through a Yes Associated Protein 1 (YAP1)-dependent mechanism. Thus, efficient inhibition after treatment of YAP1-signaling pathway in the ECM-myCAF cluster could enhance CD8+ T-cell cytotoxicity. Altogether, these data pave the way for therapy targeting YAP1 in ECM-myCAF in HGSOC.

Patient-derived scaffolds representing breast cancer microenvironments influence chemotherapy responses in adapted cancer cells consistent with clinical features

BACKGROUND

The tumor microenvironment clearly influences cancer progressing properties but less is known about how individual cancer microenvironments potentially moderate cancer treatment effects. By cultivating and treating cancer cell lines in patient-derived scaffolds (PDS), the impact of specific characteristics of individual cancer microenvironments can be incorporated in human-like growth modelling and cancer drug treatment testing.

METHODS

PDSs from 78 biobanked primary breast cancer samples with known patient outcomes, were prepared and repopulated with donor breast cancer cell lines, followed by treatment with 5-fluorouracil or doxorubicin after cellular adaption to the various microenvironments. Cancer cell responses to the treatments were monitored by RNA-analyses, highlighting changes in gene sets representative for crucial tumor biological processes such as proliferation, cancer stem cell features, differentiation and epithelial-to-mesenchymal transition.

RESULTS

The chemotherapy treatments induced distinct gene expression patterns in adapted cancer cells with clusters of similar treatment responses depending on the patient-derived cancer microenvironment used as growth substrate. The doxorubicin treatment displayed a favorable gene signature among surviving cancer cells with low proliferation (MKI67) and pluripotency features (NANOG, POU5F1), in comparison to 5-fluorouracil showing low proliferation but increased pluripotency. Specific gene changes monitored post-treatment were also significantly correlated with clinical data, including histological grade (NANOG), lymph node metastasis (SLUG) and disease-free patient survival (CD44).

CONCLUSIONS

This laboratory-based treatment study using patient-derived scaffolds repopulated with cancer cell lines, clearly illustrates that the human cancer microenvironment influences chemotherapy responses. The differences in treatment responses defined by scaffold-cultures have potential prognostic and treatment predictive values.

ioSearch: An approach for identifying interacting multiomics biomarkers using a novel algorithm with application on breast cancer data sets

Identification of biomarkers by integrating multiple omics together is important because complex diseases occur due to an intricate interplay of various genetic materials. Traditional single-omics association tests neither explore this crucial interomics dependence nor identify moderately weak signals due to the multiple-testing burden. Conversely, multiomics data integration imparts complementary information but suffers from an increased multiple-testing burden, data diversity inherent with different omics features, high-dimensionality, and so forth. Most of the available methods address subtype classification using dimension-reduction techniques to circumvent the sample size issue but interacting multiomics biomarker identification methods are unavailable. We propose a two-step model that first investigates phenotype-omics association using logistic regression. Then, selects disease-associated omics using sparse principal components which explores the interrelationship of multiple variables from two omics in a multivariate multiple regression framework. On the basis of this model, we developed a multiomics biomarker identification algorithm, interacting omics search (ioSearch), that jointly tests the effect of multiple omics with disease and between-omics associations by using pathway information that subsequently reduces the multiple-testing burden. Further, inference in terms of p values potentially makes it an easily interpretable biomarker identification tool. Extensive simulation demonstrates ioSearch as statistically powerful with a controlled Type-I error rate. Its application to publicly available breast cancer data sets identified relevant omics features in important pathways.

Nitidine chloride regulates cell function of bladder cancer in vitro through downregulating Lymphocyte antigen 75

Nitidine chloride (NC) is effective on cancer in many tumors, but its effect on bladder cancer (BC) is unknown. We conducted cell function experiments to verify the antineoplastic effect of NC on BC cell lines (5637, T24, and UM-UC-3) in vitro. Then, mRNAs of NC-treated and NC-untreated BC cells were extracted for mRNA sequencing. Differentially expressed genes (DEGs), expression analysis, and drug molecular docking were conducted to discover the target gene of NC. Finally, functional enrichment was analyzed to explore the underlying mechanisms. NC dramatically inhibited proliferation, migration, and invasion, and it induced apoptosis and arrested the S and G2/M phases of BC cell lines. Lymphocyte antigen 75 (LY75) appeared to be the target of NC. LY75 was highly expressed and had the ability to distinguish BC tissue from non-cancerous tissue. Then, drug molecular docking confirmed the targeting relationship between NC and LY75. Gene enrichment analysis showed that the downregulated genes, after being treated with NC, were mainly enriched in pathways relevant to cell pathophysiological processes. NC inhibits BC cell proliferation, migration, and invasion, induces apoptosis, and arrests cell cycles by downregulating the expression of LY75. This study provides molecular and theoretical bases for NC treatment of BC.

A generalized non-linear model predicting efficacy of neoadjuvant therapy in HER2+ breast cancer

Neoadjuvant therapy (NAT) is currently recommended to patients with human epidermal growth factor receptor 2-positive breast cancer (HER2+ BC) that typically exhibit a poor prognosis. The tumor immune microenvironment profoundly affects the efficacy of NAT. However, the correlation between tumor-infiltrating lymphocytes or their specific subpopulations and the response to NAT in HER2+ BC remains largely unknown. In our study, the immune infiltration status of 295 patients was classified as "immune-rich" or "immune-poor" phenotypes. The "immune-rich" phenotype was significantly positively related to pathological complete response (pCR). Ten genes were correlated with both pCR and the immune phenotype based on the results of spline and logistic regression. We constructed a generalized non-linear model combining linear and non-linear gene effects and successfully validated its predictive power using an internal and external validation set (AUC = 0.819, 0.797; respectively) and a clinical set (accuracy = 0.75).

Fibroblasts as Turned Agents in Cancer Progression

Differentiated epithelial cells reside in the homeostatic microenvironment of the native organ stroma. The stroma supports their normal function, their G0 differentiated state, and their expansion/contraction through the various stages of the life cycle and physiologic functions of the host. When malignant transformation begins, the microenvironment tries to suppress and eliminate the transformed cells, while cancer cells, in turn, try to resist these suppressive efforts. The tumor microenvironment encompasses a large variety of cell types recruited by the tumor to perform different functions, among which fibroblasts are the most abundant. The dynamics of the mutual relationship change as the sides undertake an epic battle for control of the other. In the process, the cancer “wounds” the microenvironment through a variety of mechanisms and attracts distant mesenchymal stem cells to change their function from one attempting to suppress the cancer, to one that supports its growth, survival, and metastasis. Analogous reciprocal interactions occur as well between disseminated cancer cells and the metastatic microenvironment, where the microenvironment attempts to eliminate cancer cells or suppress their proliferation. However, the altered microenvironmental cells acquire novel characteristics that support malignant progression. Investigations have attempted to use these traits as targets of novel therapeutic approaches.

Tumor immune microenvironment components and the other markers can predict the efficacy of neoadjuvant chemotherapy for breast cancer

Breast cancer is an epithelial malignant tumor that occurs in the terminal ducts of the breast. Neoadjuvant chemotherapy (NACT) is an important part of breast cancer treatment. Its purpose is to use systemic treatment for some locally advanced breast cancer patients, to decrease the tumor size and clinical stage so that non-operable breast cancer patients can have a chance to access surgical treatment, or patients who are not suitable for breast-conserving surgery can get the opportunity of breast-conserving. However, some patients who do not respond to NACT will lead deterioration in their condition. Therefore, prediction of NACT efficacy in breast cancer is vital for precision therapy. The tumor microenvironment (TME) has a crucial role in the carcinogenesis and therapeutic response of breast cancer. In this review, we summarized the immune cells, immune checkpoints, and other biomarkers in the TME that can evaluate the efficacy of NACT in treating breast cancer. We believe that the detection and evaluation of the TME components in breast cancer are helpful to predict the efficacy of NACT, and the prediction methods are in the prospect. In addition, we also summarized other predictive factors of NACT, such as imaging examination, biochemical markers, and multigene/multiprotein profiling.

Identification of novel biomarkers involved in doxorubicin-induced acute and chronic cardiotoxicity, respectively, by integrated bioinformatics

Background

The mechanisms of doxorubicin (DOX) cardiotoxicity were complex and controversial, with various contradictions between experimental and clinical data. Understanding the differences in the molecular mechanism between DOX-induced acute and chronic cardiotoxicity may be an ideal entry point to solve this dilemma.

Methods

Mice were injected intraperitoneally with DOX [(20 mg/kg, once) or (5 mg/kg/week, three times)] to construct acute and chronic cardiotoxicity models, respectively. Survival record and ultrasound monitored the cardiac function. The corresponding left ventricular (LV) myocardium tissues were analyzed by RNA-seq to identify differentially expressed genes (DEGs). Gene Ontology (GO), Kyoto Encyclopedia of Gene and Genome (KEGG), and Gene Set Enrichment Analysis (GSEA) found the key biological processes and signaling pathways. DOX cardiotoxicity datasets from the Gene expression omnibus (GEO) database were combined with RNA-seq to identify the common genes. Cytoscape analyzed the hub genes, which were validated by quantitative real-time PCR. ImmuCo and ImmGen databases analyzed the correlations between hub genes and immunity-relative markers in immune cells. Cibersort analyzed the immune infiltration and correlations between the hub genes and the immune cells. Logistic regression, receiver operator characteristic curve, and artificial neural network analysis evaluated the diagnosis ability of hub genes for clinical data in the GEO dataset.

Results

The survival curves and ultrasound monitoring demonstrated that cardiotoxicity models were constructed successfully. In the acute model, 788 DEGs were enriched in the activated metabolism and the suppressed immunity-associated signaling pathways. Three hub genes (Alas1, Atp5g1, and Ptgds) were upregulated and were negatively correlated with a colony of immune-activating cells. However, in the chronic model, 281 DEGs showed that G protein-coupled receptor (GPCR)-related signaling pathways were the critical events. Three hub genes (Hsph1, Abcb1a, and Vegfa) were increased in the chronic model. Furthermore, Hsph1 combined with Vegfa was positively correlated with dilated cardiomyopathy (DCM)-induced heart failure (HF) and had high accuracy in the diagnosis of DCM-induced HF (AUC = 0.898, P = 0.000).

Conclusion

Alas1, Atp5g1, and Ptgds were ideal biomarkers in DOX acute cardiotoxicity. However, Hsph1 and Vegfa were potential biomarkers in the myocardium in the chronic model. Our research, first, provided bioinformatics and clinical evidence for the discovery of the differences in mechanism and potential biomarkers of DOX-induced acute and chronic cardiotoxicity to find a therapeutic strategy precisely.

Investigation of Transcriptome Patterns in Endometrial Cancers from Obese and Lean Women

Endometrial cancer is the most common gynaecological malignancy in developed countries. One of the largest risk factors for endometrial cancer is obesity. The aim of this study was to determine whether there are differences in the transcriptome of endometrial cancers from obese vs. lean women. Here we investigate the transcriptome of endometrial cancer between obese and lean postmenopausal women using rRNA-depleted RNA-Seq data from endometrial cancer tissues and matched adjacent non-cancerous endometrial tissues. Differential expression analysis identified 12,484 genes (6370 up-regulated and 6114 down-regulated) in endometrial cancer tissues from obese women, and 6219 genes (3196 up-regulated and 3023 down-regulated) in endometrial cancer tissues from lean women (adjusted p-value < 0.1). A gene ontology enrichment analysis revealed that the top 1000 up-regulated genes (by adjusted p-value) were enriched for growth and proliferation pathways while the top 1000 down-regulated genes were enriched for cytoskeleton restructure networks in both obese and lean endometrial cancer tissues. In this study, we also show perturbations in the expression of protein coding genes (HIST1H2BL, HIST1H3F, HIST1H2BH, HIST1H1B, TTK, PTCHD1, ASPN, PRELP, and CDH13) and the lncRNA MBNL1-AS1 in endometrial cancer tissues. Overall, this study has identified gene expression changes that are similar and also unique to endometrial cancers from obese vs. lean women. Furthermore, some of these genes may serve as prognostic biomarkers or, possibly, therapeutic targets for endometrial cancer.

LINC00355 regulates p27KIP expression by binding to MENIN to induce proliferation in late-stage relapse breast cancer

Late-stage relapse (LSR) in patients with breast cancer (BC) occurs more than five years and up to 10 years after initial treatment and has less than 30% 5-year relative survival rate. Long non-coding RNAs (lncRNAs) play important roles in BC yet have not been studied in LSR BC. Here, we identify 1127 lncRNAs differentially expressed in LSR BC via transcriptome sequencing and analysis of 72 early-stage and 24 LSR BC patient tumors. Decreasing expression of the most up-regulated lncRNA, LINC00355, in BC and MCF7 long-term estrogen deprived cell lines decreases cellular invasion and proliferation. Subsequent mechanistic studies show that LINC00355 binds to MENIN and changes occupancy at the CDKN1B promoter to decrease p27Kip. In summary, this is a key study discovering lncRNAs in LSR BC and LINC00355 association with epigenetic regulation and proliferation in BC.

Developing a Novel Indium(III) Agent Based on Human Serum Albumin Nanoparticles: Integrating Bioimaging and Therapy

To effectively integrate diagnosis and therapy for tumors, we proposed to develop an indium (In) agent based on the unique property of human serum albumin (HSA) nanoparticles (NPs). A novel In(III) quinoline-2-formaldehyde thiosemicarbazone compound (C5) was optimized with remarkable cytotoxicity and fluorescence to cancer cells in vitro. An HSA-C5 complex NP delivery system was then successfully constructed. Importantly, the HSA-C5 complex NPs have stronger bioimaging and therapeutic efficiency relative to C5 alone in vivo. Besides, the results of gene chip analysis revealed that C5/HSA-C5 complex NPs act on cancer cells through multiple mechanisms: inducing autophagy, apoptosis, and inhibiting the PI3K-Akt signaling pathway.

STAT3-induced NCK1 elevation promotes migration of triple-negative breast cancer cells via regulating ERK1/2 signaling

BACKGROUND

Noncatalytic region of tyrosine kinase 1 (NCK1) plays a key role in extracellular matrix degradation, which is required for the metastasis of triple-negative breast cancer (TNBC). However, the role NCK1 plays in the metastatic progression of TNBC is unknown.

METHODS AND RESULTS

Based on online databases, NCK1 was found to be highly expressed in TNBC as compared to normal breast-like subjects, which was also confirmed using TNBC cells and a tissue microarray. NCK1 expression gradually decreased with increased tumor stage. High NCK1 expression displayed a poor prognosis in lymph node-positive metastatic TNBC patients, but not in lymph node-negative patients. Using transwell assays and immunoblotting, we confirmed that NCK1 overexpression promoted, while NCK1 downregulation inhibited migration capabilities, as well as the expression of matrix metalloproteinases (MMP2/9), uridylyl phosphate adenosine, and plasminogen activator inhibitor-1 in TNBC cells. Mechanistically, NCK1 upregulation mediated the activation of MMP2/9 through ERK1/2 activity. Signal transducer and activator of transcription 3 (STAT3) was positively correlated with NCK1. STAT3 could directly bind to the promoter region of NCK1 to promote its expression and was accompanied by the elevation of MMP2/9 and ERK1/2 signaling, which were partially abolished by the knockdown of NCK1 in TNBC cells.

CONCLUSIONS

NCK1 may serve as a diagnostic and prognostic marker of metastatic TNBC. STAT3 upregulation promoted the expression of NCK1, which subsequently induced the migration and activity of MMPs in a ERK1/2 signaling-dependent manner in TNBC cells. NCK1 is a promising target for improving TNBC migration.

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.

DNA methylation and histone variants in aging and cancer

Aging-related diseases such as cancer can be traced to the accumulation of molecular disorder including increased DNA mutations and epigenetic drift. We provide a comprehensive review of recent results in mice and humans on modifications of DNA methylation and histone variants during aging and in cancer. Accumulated errors in DNA methylation maintenance lead to global decreases in DNA methylation with relaxed repression of repeated DNA and focal hypermethylation blocking the expression of tumor suppressor genes. Epigenetic clocks based on quantifying levels of DNA methylation at specific genomic sites is proving to be a valuable metric for estimating the biological age of individuals. Histone variants have specialized functions in transcriptional regulation and genome stability. Their concentration tends to increase in aged post-mitotic chromatin, but their effects in cancer are mainly determined by their specialized functions. Our increased understanding of epigenetic regulation and their modifications during aging has motivated interventions to delay or reverse epigenetic modifications using the epigenetic clocks as a rapid readout for efficacity. Similarly, the knowledge of epigenetic modifications in cancer is suggesting new approaches to target these modifications for cancer therapy.

Identification of immune-infiltrating cell-related biomarkers in hepatocellular carcinoma based on gene co-expression network analysis

Background

Hepatocellular carcinoma (HCC) is often caused by chronic liver infection or inflammation. Searching for potential immunotherapy targets will aid the early diagnosis and treatment of HCC.

Methods

Firstly, detailed HCC data were downloaded from The Cancer Genome Atlas database. GDCRNATools was used for the comprehensive analysis of RNA sequencing data. Subsequently, the CIBERSORT package was used to estimate infiltration scores of 22 types of immune cells in complex samples. Furthermore, hub genes were identified via weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis. In addition, multiple databases were used to validate the expression of hub gene in the tumor tissue. Finally, prognostic, diagnostic and immunohistochemical analysis of key hub genes was performed.

Results

In the present study, 9 hub genes were identified using WGCNA and PPI network analysis. Furthermore, the expression levels of 9 genes were positively correlated with the infiltration levels of CD8-positive T (CD8⁺ T) cells. In multiple dataset validations, the expression levels of CCL5, CXCR6, CD3E, and LCK were decreased in cancer tissues. In addition, survival analysis revealed that patients with LCK low expression had a poor survival prognosis (P < 0.05). Immunohistochemistry results demonstrated that CCL5, CD3E and LCK were expressed at low levels in HCC cancer tissues.

Conclusion

The identification of CCL5, CXCR6, CD3E and LCK may be helpful in the development of early diagnosis and therapy of HCC. LCK may be a potential prognostic biomarker for immunotherapy for HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13000-021-01118-y.

Identification of novel candidate genes in rosacea by bioinformatic methods

BACKGROUND

Rosacea is a chronic inflammatory skin disease whose psychological consequences severely affect patient's quality of life.

OBJECTIVE

To identify candidate genes of rosacea for potential development of new target therapies.

METHODS

Gene Expression Omnibus datasets were retrieved to obtain differentially expressed genes (DEGs) between rosacea patients and healthy controls. Gene ontology (GO) analyses were used to identify functions of candidate genes. Related signaling pathways of DEGs were analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis. Protein-protein interaction (PPI) networks were applied using search tools for the retrieval of interacting genes/proteins and modulations involving PPI networks were evaluated with use of the MCODE app.

RESULTS

Samples from 19 rosacea patients and 10 healthy controls of dataset GSE65914 were enrolled. A total of 215 DEGs, 115 GO terms and 6 KEGG pathways were identified. A total of 182 nodes and 456 edges were enriched in PPI networks. Maximal clusters showed 15 central nodes and 96 edges. The toll-like receptor (TLR) signaling pathway was the most significant pathway detected and 5 DEGs were identified as candidate genes which included TLR2, C-C motif chemokine (CCL) 5, C-X-C motif chemokine ligand (CXCL) 9, CXCL10 and CXCL11. The results were verified in rosacea patients with use of real-time polymerase chain reaction and immunohistochemistry. Cell-type enrichment analysis revealed 8 lymphocytes that were enriched in rosacea patients.

CONCLUSIONS

The results suggest that both innate and adaptive immune responses were involved in the etiology of rosacea. Five DEGs in the TLR signaling pathway may serve as potential therapeutic target genes.

Kinase shRNA screening reveals that TAOK3 enhances microtubule-targeted drug resistance of breast cancer cells via the NF-κB signaling pathway

BACKGROUND

Chemotherapy is currently one of the most effective treatments for advanced breast cancer. Anti-microtubule agents, including taxanes, eribulin and vinca-alkaloids are one of the primary major anti-breast cancer chemotherapies; however, chemoresistance remains a problem that is difficult to solve. We aimed to discover novel candidate protein targets to combat chemoresistance in breast cancer.

METHODS

A lentiviral shRNA-based high-throughput screening platform was designed and developed to screen the global kinome to find new therapeutic targets in paclitaxel-resistant breast cancer cells. The phenotypes were confirmed with alternative expression in vitro and in vivo. Molecular mechanisms were investigated using global phosphoprotein arrays and expression microarrays. Global microarray analysis was performed to determine TAOK3 and genes that induced paclitaxel resistance.

RESULTS

A serine/threonine kinase gene, TAOK3, was identified from 724 screened kinase genes. TAOK3 shRNA exhibited the most significant reduction in IC50 values in response to paclitaxel treatment. Ectopic downregulation of TAOK3 resulted in paclitaxel-resistant breast cancer cells sensitize to paclitaxel treatment in vitro and in vivo. The expression of TAOK3 also was correlated to sensitivity to two other anti-microtubule drugs, eribulin and vinorelbine. Our TAOK3-modulated microarray analysis indicated that NF-κB signaling played a major upstream regulation role. TAOK3 inhibitor, CP43, and shRNA of NF-κB both reduced the paclitaxel resistance in TAOK3 overexpressed cells. In clinical microarray databases, high TAOK3 expressed breast cancer patients had poorer prognoses after adjuvant chemotherapy.

CONCLUSIONS

Here we identified TAOK3 overexpression increased anti-microtubule drug resistance through upregulation of NF-κB signaling, which reduced cell death in breast cancer. Therefore, inhibition of the interaction between TAOK3 and NF-κB signaling may have therapeutic implications for breast cancer patients treated with anti-microtubule drugs. Video abstract.

Expression pattern, regulation, and clinical significance of TOX in breast cancer

Thymocyte selection-associated high mobility group box protein (TOX) is a transcription factor implicated in the regulation of T cell exhaustion during chronic infection and cancer. While TOX is being targeted for cancer immunotherapy, limited information is available about its significance in breast cancer and other solid tumors. We performed a comprehensive analysis of TOX gene expression, its epigenetic regulation, protein localization, relation to tumor infiltrating immune cell composition, and prognostic significance in breast cancer using publicly available datasets. Our results suggest an inverse correlation between TOX expression and DNA methylation in tumor cells. However, its expression is elevated in tumor infiltrating immune cells (TIICs), which may compensates for the total TOX levels in the tumor as a whole. Furthermore, higher TOX levels in tumors are associated with T cell exhaustion signatures along with presence of active inflammatory response, including elevated levels of T cell effector cytokines. Survival analysis also confirmed that higher expression of TOX is associated with better prognosis in breast cancer. Therefore, expression of TOX may serve as a novel prognostic marker for this malignancy.

Gene signature-based prediction of triple-negative breast cancer patient response to Neoadjuvant chemotherapy

Neoadjuvant chemotherapy is the current standard of care for large, advanced, and/or inoperable tumors, including triple‐negative breast cancer. Although the clinical benefits of neoadjuvant chemotherapy have been illustrated through numerous clinical trials, more than half of the patients do not experience therapeutic benefit and needlessly suffer from side effects. Currently, no clinically applicable biomarkers are available for predicting neoadjuvant chemotherapy response in triple‐negative breast cancer; the discovery of such a predictive biomarker or marker profile is an unmet need. In this study, we introduce a generic computational framework to calculate a response‐probability score (RPS), based on patient transcriptomic profiles, to predict their response to neoadjuvant chemotherapy. We first validated this framework in ER‐positive breast cancer patients and showed that it predicted neoadjuvant chemotherapy response with equal performance to several clinically used gene signatures, including Oncotype DX and MammaPrint. Then, we applied this framework to triple‐negative breast cancer data and, for each patient, we calculated a response probability score (TNBC‐RPS). Our results indicate that the TNBC‐RPS achieved the highest accuracy for predicting neoadjuvant chemotherapy response compared to previously proposed 143 gene signatures. When combined with additional clinical factors, the TNBC‐RPS achieved a high prediction accuracy for triple‐negative breast cancer patients, which was comparable to the prediction accuracy of Oncotype DX and MammaPrint in ER‐positive patients. In conclusion, the TNBC‐RPS accurately predicts neoadjuvant chemotherapy response in triple‐negative breast cancer patients and has the potential to be clinically used to aid physicians in stratifying patients for more effective neoadjuvant chemotherapy.