Silencing LINC00665 inhibits cutaneous melanoma in vitro progression and induces apoptosis via the miR-339-3p/TUBB

Multi-omics integration reveals potential stage-specific druggable targets in T-cell acute lymphoblastic leukemia

T-cell acute lymphoblastic leukemia (T-ALL), a heterogeneous hematological malignancy, is caused by the developmental arrest of normal T-cell progenitors. The development of targeted therapeutic regimens is impeded by poor knowledge of the stage-specific aberrances in this disease. In this study, we performed multi-omics integration analysis, which included mRNA expression, chromatin accessibility, and gene-dependency database analyses, to identify potential stage-specific druggable targets and repositioned drugs for this disease. This multi-omics integration helped identify 29 potential pathological genes for T-ALL. These genes exhibited tissue-specific expression profiles and were enriched in the cell cycle, hematopoietic stem cell differentiation, and the AMPK signaling pathway. Of these, four known druggable targets (CDK6, TUBA1A, TUBB, and TYMS) showed dysregulated and stage-specific expression in malignant T cells and may serve as stage-specific targets in T-ALL. The TUBA1A expression level was higher in the early T cell precursor (ETP)-ALL cells, while TUBB and TYMS were mainly highly expressed in malignant T cells arrested at the CD4 and CD8 double-positive or single-positive stage. CDK6 exhibited a U-shaped expression pattern in malignant T cells along the naïve to maturation stages. Furthermore, mebendazole and gemcitabine, which target TUBA1A and TYMS, respectively, exerted stage-specific inhibitory effects on T-ALL cell lines, indicating their potential stage-specific antileukemic role in T-ALL. Collectively, our findings might aid in identifying potential stage-specific druggable targets and are promising for achieving more precise therapeutic strategies for T-ALL.

Identification and Validation of Prognostic Markers for Endometriosis-Associated Ovarian Cancer

Background: Growing evidence suggests that endometriosis (EMs) is a risk factor for endometriosis-associated ovarian cancer (EAOC). The aim was to identify and validate gene signatures associated with EMs that may serve as potential biomarkers for evaluating the prognosis of patients with EAOC. Methods: The data of EMs and control samples was obtained from GEO database. The weighted gene co-expression network analysis (WGCNA) identified modular genes significantly associated with EMs. The KEGG pathway and GO functional enrichment analyses were also performed. Univariate Cox regression analysis was conducted to screen marker genes associated with the prognosis of EAOC patients. Finally, RT-qPCR and immunohistochemical verified the expression of ADAMTS19 and TUBB in normal ovarian and EAOC tissues, and the biological functions of ADAMTS19 and TUBB were preliminarily explored by CCK8 and Transwell assays. Results: The WGCNA identified 2 co-expression modules, which in total included 615 genes, and 7642 differentially expressed genes (DEGs) were detected thorough analysis of the EAOC dataset. After taking the intersection of 615 modular genes and 7642 DEGs, 214 shared genes were obtained, and univariate COX regression analysis pointed 10 genes associated with the prognosis of EAOC. Moreover, it was demonstrated by RT-qPCR and immunohistochemical staining experiments that ADAMTS19 expression was elevated, while TUBB expression was reduced in EAOC compared with normal ovarian cells and tissues. Finally, cell experiments revealed that ADAMTS19 promoted the proliferation and invasion in EAOC cells, while overexpression of TUBB inhibited these processes. Conclusions: The present study identified and validated new EMs-associated gene markers, which could serve as potential biomarkers for assessing the prognostic risk of EAOC patients. In addition, some of these genes may have significance as novel therapeutic targets and could be used to guide clinical applications.

TUBB, a robust biomarker with satisfying abilities in diagnosis, prognosis, and immune regulation via a comprehensive pan-cancer analysis

Purpose

TUBB can encode a beta-tubulin protein. At present, the role of TUBB has not been ascertained in cancers. Hence, the importance of further systematic pan-cancer analyses is stressed to explore its value in the diagnosis, prognosis, and immune function of cancers.

Methods

By collecting and handling integrative data from the TCGA, Firehose, UCSC Xena, cBioPortal, GEO, CPTAC, TIMER2.0, TISCH, CellMiner, GDSC, and CTRP databases, we explored the potential diagnostic and prognostic roles of TUBB in pan-cancers from multiple angles. Moreover, the GSEA analysis was conducted to excavate the biological functions of TUBB in pan-cancers. In addition, survival profiles were described, and the differential expressions of TUBB in different molecular subtypes were discussed. Also, we utilized the cMAP function to search drugs or micro-molecules that have an impact on TUBB expressions.

Results

Based on the TCGA data, we found that TUBB was differentially expressed in a variety of tumors and showed an early-diagnostic value. Mutations, somatic copy number alterations, and DNA methylation would lead to its abnormal expression. TUBB expressions had relations with many clinical features. What's more, TUBB expressions were validated to be related to many metabolism-related, metastasis-related, and immune-related pathways. High TUBB expressions were proved to have a great impact on the prognosis of various types of cancers and would affect the sensitivity of some drugs. We also demonstrated that the expression of TUBB was significantly correlated to immunoregulator molecules and biomarkers of lymphocyte subpopulation infiltration.

Conclusion

TUBB and its regulatory genes were systemically analyzed in this study, showing that TUBB had satisfying performances in disease diagnosing and prognosis predicting of multiple cancers. It could remodel the tumor microenvironment and play an integral role in guiding cancer therapies and forecasting responses to chemotherapy.

Machine learning-based signature of necrosis-associated lncRNAs for prognostic and immunotherapy response prediction in cutaneous melanoma and tumor immune landscape characterization

Background

Cutaneous melanoma (CM) is one of the malignant tumors with a relative high lethality. Necroptosis is a novel programmed cell death that participates in anti-tumor immunity and tumor prognosis. Necroptosis has been found to play an important role in tumors like CM. However, the necroptosis-associated lncRNAs' potential prognostic value in CM has not been identified.

Methods

The RNA sequencing data collected from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression Project (GTEx) was utilized to identify differentially expressed genes in CM. By using the univariate Cox regression analysis and machine learning LASSO algorithm, a prognostic risk model had been built depending on 5 necroptosis-associated lncRNAs and was verified by internal validation. The performance of this prognostic model was assessed by the receiver operating characteristic curves. A nomogram was constructed and verified by calibration. Furthermore, we also performed sub-group K-M analysis to explore the 5 lncRNAs' expression in different clinical stages. Function enrichment had been analyzed by GSEA and ssGSEA. In addition, qRT-PCR was performed to verify the five lncRNAs' expression level in CM cell line (A2058 and A375) and normal keratinocyte cell line (HaCaT).

Results

We constructed a prognostic model based on five necroptosis-associated lncRNAs (AC245041.1, LINC00665, AC018553.1, LINC01871, and AC107464.3) and divided patients into high-risk group and low-risk group depending on risk scores. A predictive nomogram had been built to be a prognostic indicator to clinical factors. Functional enrichment analysis showed that immune functions had more relationship and immune checkpoints were more activated in low-risk group than that in high-risk group. Thus, the low-risk group would have a more sensitive response to immunotherapy.

Conclusion

This risk score signature could be used to divide CM patients into low- and high-risk groups, and facilitate treatment strategy decision making that immunotherapy is more suitable for those in low-risk group, providing a new sight for CM prognostic evaluation.

Bioinformatics Identification of TUBB as Potential Prognostic Biomarker for Worse Prognosis in ERα-Positive and Better Prognosis in ERα-Negative Breast Cancer

Tubulin β class I gene (TUBB) is highly expressed in various cancers and plays several roles in carcinogenesis. However, the prognostic value of TUBB in breast cancer remains to be investigated. GEPIA and Breast Cancer Gene-Expression Miner were used to explore TUBB expression in breast cancer patients. Kaplan–Meier Plotter was used to assess the relationship between TUBB expression and several prognostic indicators including overall, distant metastasis-free, and relapse-free survival in ERα-positive and ERα-negative breast cancer. The genes that correlate with TUBB in ERα-positive and ERα-negative breast cancer were explored and the pathways were investigated using GSCA. The correlation between TUBB and several gene markers of immune cells was explored using GEPIA. ERα-positive breast cancer patients with increased TUBB showed worse prognosis, possibly through the activation of the TSC/mTOR pathway, whereas ERα-negative breast cancer patients with increased TUBB mRNA showed better prognosis. Significant positive correlations were observed between TUBB and gene markers of immune cells in ERα-positive breast cancer patients, whereas significant negative correlations were observed in ERα-negative breast cancer patients. The analysis revealed that TUBB might be considered as a predictive biomarker for worse prognosis in ERα-positive and better prognosis in ERα-negative breast cancer.