SRARP and HSPB7 are epigenetically regulated gene pairs that function as tumor suppressors and predict clinical outcome in malignancies

Exploring the molecular mechanisms of asthma across multiple datasets

BACKGROUND

Asthma, a prevalent chronic respiratory disorder, remains enigmatic, notwithstanding considerable advancements in our comprehension. Continuous efforts are crucial for discovering novel molecular targets and gaining a comprehensive understanding of its pathogenesis.

MATERIALS AND METHODS

In this study, we analyzed gene expression data from 212 individuals, including asthma patients and healthy controls, to identify 267 differentially expressed genes, among which C1orf64 and C7orf26 emerged as potential key genes in asthma pathogenesis. Various bioinformatics tools, including differential gene expression analysis, pathway enrichment, drug target prediction, and single-cell analysis, were employed to explore the potential roles of the genes.

RESULTS

Quantitative PCR demonstrated differential expression of C1orf64 and C7orf26 in the asthmatic airway epithelial tissue, implying their potential involvement in asthma pathogenesis. GSEA enrichment analysis revealed significant enrichment of these genes in signaling pathways associated with asthma progression, such as ABC transporters, cell cycle, CAMs, DNA replication, and the Notch signaling pathway. Drug target prediction, based on upregulated and downregulated differential expression, highlighted potential asthma treatments, including Tyrphostin-AG-126, Cephalin, Verrucarin-a, and Emetine. The selection of these drugs was based on their significance in the analysis and their established anti-inflammatory and antiviral invasion properties. Utilizing Seurat and Celldex packages for single-cell sequencing analysis unveiled disease-specific gene expression patterns and cell types. Expression of C1orf64 and C7orf26 in T cells, NK cells, and B cells, instrumental in promoting hallmark features of asthma, was observed, suggesting their potential influence on asthma development and progression.

CONCLUSION

This study uncovers novel genetic aspects of asthma, highlighting potential therapeutic pathways. It exemplifies the power of integrative bioinformatics in decoding complex disease patterns. However, these findings require further validation, and the precise roles of C1orf64 and C7orf26 in asthma warrant additional investigation to validate their therapeutic potential.

Heat shock protein B7 (HSPB7) inhibits lung adenocarcinoma progression by inhibiting glycolysis

In the present study, it was aimed to investigate the effects and potential mechanisms of heat shock protein B7 (HSPB7) on lung adenocarcinoma (LUAD). Bioinformatic analysis was performed to explore the association between HSPB7 expression and patients with LUAD. MTT, colony formation, wound healing and Transwell assays were performed to examine the proliferative, migratory and invasive abilities of H1975 and A549 cells. Western blot analysis was conducted to determine the corresponding protein expression. Co‑Immunoprecipitation and Chromatin immunoprecipitation assays were carried out to reveal the interaction between HSPB7 and myelodysplastic syndrome 1 and ecotropic viral integration site 1 complex locus (MECOM). In addition, an animal model was conducted by the subcutaneous injection of A549 cells into BALB/c nude mice, and tumor weight and size were measured. HSPB7 was downregulated in LUAD tissues and cells, and its expression level correlated with patient prognosis. Cell functional data revealed that silencing of HSPB7 promoted lung cancer cell proliferation, migration, invasion and epithelial mesenchymal transition (EMT); whereas overexpression of HSPB7 led to the opposite results. Furthermore, bioinformatics analysis showed that HSPB7 inhibited glycolysis. HSPB7 decreased glucose consumption, lactic acid production, and lactate dehydrogenase A, hexokinase 2 and pyruvate kinase muscle isoform 2 protein levels. The results demonstrated that MECOM was a transcription factor of HSPB7. Collectively, these results suggested that HSPB7 is regulated by MECOM, and that HSPB7 attenuates LUAD cell proliferation, migration, invasion and EMT by inhibiting glycolysis.

Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment

Breast cancer is the second leading cause of cancer death in women among all cancer types. It is highly heterogeneous in nature, which means that the tumors have different morphologies and there is heterogeneity even among people who have the same type of tumor. Several staging and classifying systems have been developed due to the variability of different types of breast cancer. Due to high heterogeneity, personalized treatment has become a new strategy. Out of all breast cancer subtypes, triple-negative breast cancer (TNBC) comprises ∼10%-15%. TNBC refers to the subtype of breast cancer where cells do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptors (ERs, PRs, and HERs). Tumors in TNBC have a diverse set of genetic markers and prognostic indicators. We scanned the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases for potential drugs using human breast cancer cell lines and drug sensitivity data. Three different machine-learning approaches were used to evaluate the prediction of six effective drugs against the TNBC cell lines. The top biomarkers were then shortlisted on the basis of their involvement in breast cancer and further subjected to testing for radion resistance using data from the Cleveland database. It was observed that Panobinostat, PLX4720, Lapatinib, Nilotinib, Selumetinib, and Tanespimycin were six effective drugs against the TNBC cell lines. We could identify potential derivates that may be used against approved drugs. Only one biomarker (SETD7) was sensitive to all six drugs on the shortlist, while two others (SRARP and YIPF5) were sensitive to both radiation and drugs. Furthermore, we did not find any radioresistance markers for the TNBC. The proposed biomarkers and drug sensitivity analysis will provide potential candidates for future clinical investigation.

Heat Shock Protein B7 Inhibits the Progression of Endometrial Carcinoma by Inhibiting PI3K/AKT/mTOR Pathway

PURPOSE

To investigate the role and mechanism of action of Heat shock protein B7 (HSPB7) in endometrial carcinoma (EC).

METHODS

GEPIA (Gene Expression Profiling Interactive Analysis) was used to analyze the expression and prognostic value of HSPB7 in TCGA data. HSPB7 mRNA and protein expression levels were detected by qRT-PCR and Western blot, respectively. EC cell proliferation, apoptosis, migration, and invasion were determined by colony formation, EdU, flow cytometry, and transwell assays. Mitochondrial membrane potential was determined using JC-1 probe. In addition, apoptosis-related and metastasis-related proteins were quantitatively evaluated. A gene set enrichment analysis of the signaling pathways by which HSPB7 influences EC was performed and the levels of enriched pathway-related proteins were evaluated.

RESULTS

We first proved that HSPB7 was downregulated in EC tissues and HSPB7 levels were positively related to survival rates. In functional assays, HSPB7 overexpression suppressed the proliferation, migration, and invasion of EC cells and conversely promoted apoptosis. Moreover, HSPB7 overexpression decreased the mitochondrial membrane potential of EC cells significantly. Bioinformatics analyses revealed that the PI3K/AKT/mTOR pathway was significantly enriched in EC. HSPB7 inhibited the phosphorylation of the PI3K/AKT/mTOR pathway to reduce proliferation, migration and invasion, and increased apoptosis in EC cells.

CONCLUSION

HSPB7 was downregulated in EC and influenced EC cell proliferation, invasion, migration, and apoptosis via the PI3K/AKT/mTOR signaling pathway. These findings provide a novel perspective for the development of EC treatment strategies.

Machine-Learning-Based m5C Score for the Prognosis Diagnosis of Osteosarcoma

Background

Osteosarcoma is a common and highly metastatic malignant tumor, and m5C RNA methylation regulates various biological processes. The purpose of this study was to explore the prognostic role of m5C in osteosarcoma using machine learning.

Methods

Osteosarcoma gene data and the corresponding clinical information were downloaded from the GEO database. Machine learning methods were used to screen m5C-related genes and construct m5C scores. In addition, the clusterProfiler package was used to predict the m5C-related functional pathways. xCell and CIBERSORT were used to calculate the immune microenvironment cells. GSVA was applied to analyze different categories of m5C genes, and the correlation between the GSVA and m5C scores was evaluated.

Results

Twenty m5C genes were identified, and 54 related genes were screened. The m5C score was constructed based on the PCA score. With an increase in the m5C score, the expression of m5C genes and their related genes changed. Functional analysis indicated that the focal adhesion, cell-substrate adherens junction, cell adhesion molecule binding, and E2F targets might change with the m5C score. The naive B cells and CD4⁺ memory T cell also changed with the m5C score. The results of the correlation analysis showed that the m5C score was significantly correlated with the reader and eraser genes.

Conclusion

The m5C score might be a prognostic index for osteosarcoma.

TMT-Based Quantitative Proteomic Analysis Reveals the Effect of Bone Marrow Derived Mesenchymal Stem Cell on Hair Follicle Regeneration

Hair loss (HL) is a common chronic problem of poorly defined etiology. Herein, we explored the functionality of bone marrow-derived mesenchymal stem cell (BMSC) and conditioned medium (MSC-CM) as regulators of hair follicle proliferation and regeneration, and the mechanistic basis for such activity. BMSC were cultured and identified in vitro through the induction of multilineage differentiation and the use of a CCK-8 kit. The dorsal skin of mice was then injected with BMSC and MSC-CM, and the impact of these injections on hair cycle transition and hair follicle stem cell (HFSC) proliferation was then evaluated via hematoxylin and eosin (H&E) staining and immunofluorescent (IF) staining. We then conducted a tandem mass tags (TMT)-based quantitative proteomic analysis of control mice and mice treated with BMSC or MSC-CM to identify differentially expressed proteins (DEPs) associated with these treatments. Parallel reaction monitoring (PRM) was utilized as a means of verifying our proteomic analysis results. Herein, we found that BMSC and MSC-CM injection resulted in the transition of telogen hair follicles to anagen hair follicles, and we observed the enhanced proliferation of HFSCs positive for Krt15 and Sox9. Our TMT analyses identified 1,060 and 770 DEPs (fold change＞1.2 or＜0.83 and p ＜ 0.05) when comparing the BMSC vs. control and MSC-CM vs. control groups, respectively. Subsequent PRM validation of 14 selected DEPs confirmed these findings, and led to the identification of Stmn1, Ncapd2, Krt25, and Ctps1 as hub DEPs in a protein-protein interaction network. Together, these data suggest that BMSC and MSC-CM treatment can promote the proliferation of HFSCs, thereby facilitating hair follicle regeneration. Our proteomics analyses further indicate that Krt25, Cpm, Stmn1, and Mb may play central roles in hair follicle transition in this context and may represent viable clinical targets for the treatment of HL.

Cardio-Vascular Heat Shock Protein (cvHsp, HspB7), an Unusual Representative of Small Heat Shock Protein Family

HspB7 is one of ten human small heat shock proteins. This protein is expressed only in insulin-dependent tissues (heart, skeletal muscle, and fat tissue), and expression of HspB7 is regulated by many different factors. Single nucleotide polymorphism is characteristic for the HspB7 gene and this polymorphism correlates with cardio-vascular diseases and obesity. HspB7 has an unusual N-terminal sequence, a conservative α-crystallin domain, and very short C-terminal domain lacking conservative IPV tripeptide involved in a small heat shock proteins oligomer formation. Nevertheless, in the isolated state HspB7 forms both small oligomers (probably dimers) and very large oligomers (aggregates). HspB7 is ineffective in suppression of amorphous aggregation of model proteins induced by heating or reduction of disulfide bonds, however it is very effective in prevention of aggregation of huntingtin fragments enriched with Gln residues. HspB7 can be an effective sensor of electrophilic agents. This protein interacts with the contractile and cytoskeleton proteins (filamin C, titin, and actin) and participates in protection of the contractile apparatus and cytoskeleton from different adverse conditions. HspB7 possesses tumor suppressive activity. Further investigations are required to understand molecular mechanisms of HspB7 participation in numerous biological processes.

Alteration in Gene Pair Correlations in Tryptophan Metabolism as a Hallmark in Cancer Diagnosis

Tryptophan metabolism plays essential roles in both immunomodulation and cancer development. Indoleamine 2,3-dioxygenase, a rate-limiting enzyme in the metabolic pathway, is overexpressed in different types of cancer. To get a better understanding of the involvement of tryptophan metabolism in cancer development, we evaluated the expression and pairwise correlation of 62 genes in the metabolic pathway across 12 types of cancer. Only gene AOX1, encoding aldehyde oxidase 1, was ubiquitously downregulated, Furthermore, we observed that the 62 genes were widely and strongly correlated in normal controls, however, the gene pair correlations were significantly lost in tumor patients for all 12 types of cancer. This implicated that gene pair correlation coefficients of the tryptophan metabolic pathway could be applied as a prognostic and/or diagnostic biomarker for cancer.

HSPB7 regulates osteogenic differentiation of human adipose derived stem cells via ERK signaling pathway

Background

Heat shock protein B7 (HSPB7), which belongs to small heat shock protein family, has been reported to be involved in diverse biological processes and diseases. However, whether HSPB7 regulates osteogenic differentiation of human adipose derived stem cells (hASCs) remains unexplored.

Methods

The expression level of HSPB7 during the osteogenesis of hASCs was examined by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western blot analysis. Lentivirus transfection was used to knock down or overexpress HSPB7, which enabled us to investigate the effect of HSPB7 on osteogenic differentiation of hASCs. U0126 and extracellular signal-regulated protein kinase 1/2 (ERK1/2) siRNA were used to identify the mechanism of the HSPB7/ERK1/2 axis in regulating osteogenic differentiation of hASCs. Moreover, ectopic bone formation in nude mice and osteoporosis mice model was used to investigate the effect of HSPB7 on osteogenesis in vivo.

Results

In this study, we found the expression of HSPB7 was significantly downregulated during the osteogenic differentiation of hASCs. HSPB7 knockdown remarkably promoted osteogenic differentiation of hASCs, while HSPB7 overexpression suppressed osteogenic differentiation of hASCs both in vitro and in vivo. Moreover, we discovered that the enhancing effect of HSPB7 knockdown on osteogenic differentiation was related to the activation of extracellular signal-regulated protein kinase (ERK) signaling pathway. Inhibition of ERK signaling pathway with U0126 or silencing ERK1/2 effectively blocked the stimulation of osteogenic differentiation induced by HSPB7 knockdown. Additionally, we found that HSPB7 expression was markedly increased in mouse bone marrow mesenchymal stem cells (mBMSCs) from the osteoporotic mice which suggested that HSPB7 might be utilized as a potential target in the development of effective therapeutic strategies to treat osteoporosis and other bone diseases.

Conclusion

Taken together, these findings uncover a previously unrecognized function of HSPB7 in regulating osteogenic differentiation of hASCs, partly via the ERK signaling pathway.

Genomic and epigenetic aberrations of chromosome 1p36.13 have prognostic implications in malignancies

Deletions of chromosome 1p36 are common in malignancies; however, there is limited information regarding the biological and prognostic implications of 1p36 in cancer. Steroid Receptor-Associated and Regulated Protein (SRARP) is a tumor suppressor on chromosome 1p36.13 that its inactivation predicts poor cancer outcome, indicating that the 1p36.13 segment requires further studies. Therefore, a comprehensive multi-omics analysis of The Cancer Genome Atlas (TCGA), the Pan-Cancer Analysis of Whole Genomes (PCAWD), the International Cancer Genome Consortium (ICGC), and the Genomic Data Commons (GDC) Pan-Cancer datasets was conducted to investigate the prognostic implications of 1p36.13 in malignancies. This study revealed that expression and DNA methylation of multiple genes on 1p36.13 are significantly associated with survival in primary tumors and normal adjacent tissues. In addition, copy-number loss in every gene on 1p36.13 predicts poor cancer outcome. Importantly, copy-number loss and somatic mutations of chromosome 1p36.13 segment are associated with worse survival in primary tumors, and DNA hypermethylation of 1p36.13 predicts poor outcome in normal adjacent tissues. Therefore, genomic and epigenetic aberrations of chromosome 1p36.13 have promising prognostic implications in cancer.

Comparison of methods for the detection of outliers and associated biomarkers in mislabeled omics data

Background

Previous studies have reported that labeling errors are not uncommon in omics data. Potential outliers may severely undermine the correct classification of patients and the identification of reliable biomarkers for a particular disease. Three methods have been proposed to address the problem: sparse label-noise-robust logistic regression (Rlogreg), robust elastic net based on the least trimmed square (enetLTS), and Ensemble. Ensemble is an ensembled classification based on distinct feature selection and modeling strategies. The accuracy of biomarker selection and outlier detection of these methods needs to be evaluated and compared so that the appropriate method can be chosen.

Results

The accuracy of variable selection, outlier identification, and prediction of three methods (Ensemble, enetLTS, Rlogreg) were compared for simulated and an RNA-seq dataset. On simulated datasets, Ensemble had the highest variable selection accuracy, as measured by a comprehensive index, and lowest false discovery rate among the three methods. When the sample size was large and the proportion of outliers was ≤5%, the positive selection rate of Ensemble was similar to that of enetLTS. However, when the proportion of outliers was 10% or 15%, Ensemble missed some variables that affected the response variables.

Overall, enetLTS had the best outlier detection accuracy with false positive rates < 0.05 and high sensitivity, and enetLTS still performed well when the proportion of outliers was relatively large. With 1% or 2% outliers, Ensemble showed high outlier detection accuracy, but with higher proportions of outliers Ensemble missed many mislabeled samples. Rlogreg and Ensemble were less accurate in identifying outliers than enetLTS. The prediction accuracy of enetLTS was better than that of Rlogreg. Running Ensemble on a subset of data after removing the outliers identified by enetLTS improved the variable selection accuracy of Ensemble.

Conclusions

When the proportion of outliers is ≤5%, Ensemble can be used for variable selection. When the proportion of outliers is > 5%, Ensemble can be used for variable selection on a subset after removing outliers identified by enetLTS. For outlier identification, enetLTS is the recommended method. In practice, the proportion of outliers can be estimated according to the inaccuracy of the diagnostic methods used.

Development and Validation of a Gene Signature for Prediction of Relapse in Stage I Testicular Germ Cell Tumors

Background: Testicular germ cell tumors (TGCTs) are commonly diagnosed tumors in young men. However, a satisfactory approach to predict relapse of stage I TGCTs is still lacking. Therefore, this study aimed to develop a robust risk score model for stage I TGCTs. Method: RNA-sequence data of stage I TGCTs and normal testis samples were downloaded and analyzed to identify different expression genes. Gene-based prognostic model was constructed in The Cancer Genome Atlas (TCGA) using least absolute shrinkage and selection operator (LASSO) regression analysis and validated in GSE99420 dataset. Potential biological functions of the genes in prognostic model were determined via Gene Set Enrichment Analysis (GSEA) between high-risk and low-risk patients. Results: A total of 9,391 differentially expressed genes and 84 prognosis-related genes were identified. An eight-gene-based risk score model was constructed to divide patients into high or low risk of relapse. The low-risk patients had a significantly better relapse-free survival (RFS) than high-risk patients in both training and validation cohorts (HR = 0.129, 95% CI = 0.059-0.284, P < 0.001; HR = 0.277, 95% CI = 0.116-0.661, P = 0.004, respectively). The area under the receiver operating characteristic curve (AUC) values at 5 years was 0.805 and 0.724 in the training and validation cohorts, respectively. Functional enrichment analyses showed that DNA replication, ribosome, cell cycle, and TGF-beta signaling pathway may contribute to the relapse process. Conclusion: In summary, our analysis provided a novel eight-gene signature that could predict RFS in stage I TGCT patients.

Steroid receptor-associated and regulated protein is a biomarker in predicting the clinical outcome and treatment response in malignancies

BACKGROUND

Steroid receptor-associated and regulated protein (SRARP) has recently been identified as a novel tumor suppressor in malignancies of multiple tissue origins. SRARP is located on chromosome 1p36.13 and is widely inactivated by deletions and epigenetic silencing in malignancies. Therefore, additional studies are required to explore SRARP as a potential cancer biomarker.

AIM

This study explores the application of SRARP as a novel biomarker in malignancies of multiple tissue origins using the analysis of large genomic datasets.

METHODS AND RESULTS

A comprehensive genomic analysis of large cancer datasets was carried out to examine the association of SRARP expression and copy-number with molecular and clinical features in malignancies of multiple tissue origins. This study demonstrated that SRARP under-expression and copy-number loss are strongly associated with the loss of other tumor suppressors such as TP53 and NF1 mutations and oncogenic gains, including N-MYC amplification and ERG rearrangement, suggesting that SRARP inactivation is associated with wider genomic instability in malignancies. Importantly, SRARP under-expression and copy-number loss are strong predictors of poor clinical and/or pathological features in breast, colorectal, lung, prostate, gastric, endometrial, cervical, brain, ovarian, bladder, thyroid, and hepatocellular cancers as well as neuroblastoma, uveal melanoma, and acute myeloid leukemia with highly significant odds ratios. Finally, higher SRARP expression and copy-number predict a better response to several cancer drugs.

CONCLUSION

This study suggests that the SRARP inactivation presents a robust biomarker in predicting molecular and clinicopathological features, and treatment response in malignancies.

Gene Pair Correlation Coefficients in Sphingolipid Metabolic Pathway as a Potential Prognostic Biomarker for Breast Cancer

Complex diseases such as cancer are usually governed by dynamic and simultaneous modifications of multiple genes. Since sphingolipids are potent bioactive molecules and regulate many important pathophysiological processes such as carcinogenesis, we studied the gene pair correlations of 36 genes (31 genes in the sphingolipid metabolic pathway and 5 genes encoding the sphingosine-1-phosphate receptors) between breast cancer patients and healthy controls. It is remarkable to observe that the gene expressions were widely and strongly correlated in healthy controls but in general lost in breast cancer patients. This study suggests that gene pair correlation coefficients could be applied as a systematic and novel method for the diagnosis and prognosis of breast cancer.