Development and Validation of a Prognostic Signature Associated With Tumor Microenvironment Based on Autophagy-Related lncRNA Analysis in Hepatocellular Carcinoma

Basement membrane-related lncRNA signature for the prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the main type of primary liver cancer. This study aimed to develop a basement membrane (BM) related lncRNAs risk signature to evaluate the prognosis of HCC patients. We screened differentially expressed BM-related lncRNAs (DE-BMRlncRNAs) for risk evaluation, and identified six DE-BMRlncRNAs (AC072054.1, NUP50-DT, AC026412.3, AC109322.2, POLH-AS1 and LINC00595) for prognostic risk signature. HCC patients were divided to high or low risk according to median risk score. Our prognostic model predicted that patients with higher risk score had worse prognosis. We also created a nomogram to assist clinical decision-making according to risk score and clinicopathological features. Meanwhile, we confirmed the expression of six lncRNAs in HCC tissue and cells. POLH-AS1 knockdown inhibited the migration and invasion of HCC cells. In conclusion, we established a predictive model based on BMRlncRNAs to predict the prognosis of HCC. Our findings offer a rationale to further explore BM-related biomarkers for HCC.

Integrating ferroptosis-related genes (FRGs) and prognostic models to enhance UCEC outcome prediction and therapeutic insights

Ferroptosis is closely associated with uterine corpus endometrial carcinoma (UCEC) development. This project aimed to identify new potential biomarkers to predict the prognosis of UCEC. In this work, UCEC transcriptome data along with clinical information was retrieved from the TCGA database including a total of 382 FRGs. We performed univariate Cox regression analysis to evaluate ferroptosis-related genes (FRGs) for prognostic significance. The genes with prognostic significance were then analyzed using LASSO-Cox to construct a prognosis model. The model genes were further characterized through various proteomic analyses and expression detection in clinical samples. A multivariate Cox regression model was constructed containing four FRGs (CDKN1A, CDKN2A, CEBPG, NOS2). Among four FRGs, higher expressions of CDKN2A, CEBPG, and NOS2 were associated with poorer overall survival probability, while higher expression of CDKN1A was associated with better overall survival probability. The area under the receiver operating characteristic curve of the risk model was 0.617, 0.688, and 0.693 for 1 year, 3 years, and 5 years, respectively. Moreover, proteomic analysis showed that the protein expression of CDKN1A, CDKN2A, and CEBPG was higher in tumor tissues than that in normal tissues. Higher protein expression of CDKN1A and CDKN2A predicted poorer survival probability. Besides, CDKN1A protein had an interaction relationship with CDKN2A protein or NOS2 protein. In clinical samples, all four FRGs were upregulated in UCEC tissues, regardless of gene expression or protein expression. Our four FRGs risk model provides new insights for predicting the prognosis of UCEC patients.

Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma

BACKGROUND

Near 70% of hepatocellular carcinoma (HCC) recurrence is early recurrence within 2-year post surgery. Long non-coding RNAs (lncRNAs) are intensively involved in HCC progression and serve as biomarkers for HCC prognosis. The aim of this study is to construct a lncRNA-based signature for predicting HCC early recurrence.

METHODS

Data of RNA expression and associated clinical information were accessed from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Recurrence associated differentially expressed lncRNAs (DELncs) were determined by three DEG methods and two survival analyses methods. DELncs involved in the signature were selected by three machine learning methods and multivariate Cox analysis. Additionally, the signature was validated in a cohort of HCC patients from an external source. In order to gain insight into the biological functions of this signature, gene sets enrichment analyses, immune infiltration analyses, as well as immune and drug therapy prediction analyses were conducted.

RESULTS

A 4-lncRNA signature consisting of AC108463.1, AF131217.1, CMB9-22P13.1, TMCC1-AS1 was constructed. Patients in the high-risk group showed significantly higher early recurrence rate compared to those in the low-risk group. Combination of the signature, AFP and TNM further improved the early HCC recurrence predictive performance. Several molecular pathways and gene sets associated with HCC pathogenesis are enriched in the high-risk group. Antitumor immune cells, such as activated B cell, type 1 T helper cell, natural killer cell and effective memory CD8 T cell are enriched in patients with low-risk HCCs. HCC patients in the low- and high-risk group had differential sensitivities to various antitumor drugs. Finally, predictive performance of this signature was validated in an external cohort of patients with HCC.

CONCLUSION

Combined with TNM and AFP, the 4-lncRNA signature presents excellent predictability of HCC early recurrence.

LncRNA TEX41 regulates autophagy by increasing Runx2 expression in lung adenocarcinoma bone metastasis

OBJECTIVE

To investigate the mechanism underlying the role of TEX41 in lung adenocarcinoma (LUAD) bone metastasis (BM).

METHODS

We analyzed the biological functions and molecular mechanisms of TEX41 using bioinformatics. TEX41 and Runx2 expressions were measured in clinical tissue samples and cell lines by quantitative PCR. The effects of TEX41 on LUAD cell proliferation, migration, invasion and metastasis as well as its mechanism of action were investigated. Fluorescence in-situ hybridization (FISH) was performed to determine TEX41 and Runx2 colocalization. Subcutaneous tumor growth and BM were evaluated in nude mice by X-ray and hematoxylin and eosin (HE) staining.

RESULTS

TEX41 was dramatically increased in LUAD BM tissue, indicating a poorer prognosis in patients with LUAD and BM. TEX41 knockdown suppressed the migration and metastasis of LUAD cells, whereas TEX41 overexpression promoted these processes. Data from X-ray and HE staining showed that TEX41 supported the BM in LUAD. TEX41 overexpression induced autophagy in LUAD cells, as demonstrated by changes in autophagy markers. Results of FISH showed that TEX41 and Runx2 colocalized in the nucleus, and Runx2 expression was regulated by TEX41. The effects of TEX41 on LUAD cell migration, invasion, metastasis and autophagy were counteracted by Runx2 inhibition. Moreover, the role of TEX41 in the metastasis was partially dependent on autophagy, and phosphoinositide 3-kinase (PI3K)-AKT might be the major signaling pathway involved in TEX41-regulated autophagy.

CONCLUSION

TEX41 promotes autophagy in LUAD cells by upregulating Runx2 to mediate LUAD migration, invasion and BM.

A Novel Risk Model for lncRNAs Associated with Oxidative Stress Predicts Prognosis of Bladder Cancer

Background

Oxidative stress (OS) reactions are closely related to the development and progression of bladder cancer (BCa). This project aimed to identify new potential biomarkers to predict the prognosis of BCa and improve immunotherapy.

Methods

We downloaded transcriptomic information and clinical data on BCa from The Cancer Genome Atlas (TCGA). Screening for OS genes was statistically different between tumor and adjacent normal tissue. A coexpression analysis between lncRNAs and differentially expressed OS genes was performed to identify OS-related lncRNAs. Then, differentially expressed oxidative stress lncRNAs (DEOSlncRNAs) between tumors and normal tissues were identified. Univariate/multivariate Cox regression analysis was performed to select the lncRNAs for risk assessment. LASSO analysis was conducted to establish a prognostic model. The prognostic risk model could accurately predict BCa patient prognosis and reveal a close correlation with clinicopathological features. We analyzed the principal component analysis (PCA), immune microenvironment, and half-maximal inhibitory concentration (IC50) in the risk groups.

Results

We constructed a model containing eight DEOSlncRNAs (AC021321.1, AC068196.1, AC008750.1, SETBP1-DT, AL590617.2, THUMPD3-AS1, AC112721.1, and NR4A1AS). The prognostic risk model showed better results in predicting the prognosis of BCa patients and was strongly correlated with clinicopathological characteristics. We found great agreement between the calibration plots and prognostic predictions in this model. The areas under the receiver operating characteristic (ROC) curve (AUCs) at 1, 3, and 5 years were 0.792, 0.804, and 0.843, respectively. This model also showed good predictive ability regarding the tumor microenvironment and tumor mutation burden. In addition, the high-risk group was more sensitive to eight therapeutic agents, and the low-risk group was more responsive to five therapeutic agents. Sixteen immune checkpoints were significantly different between the two risk groups.

Conclusion

Our eight DEOSlncRNA risk models provide new insights into predicting prognosis and clinical progression in BCa patients.

Prognostic significance and identification of basement membrane-associated lncRNA in bladder cancer

Based on the importance of basement membrane (BM) in cancer invasion and metastasis, we constructed a BM-associated lncRNA risk model to group bladder cancer (BCa) patients. Transcriptional and clinical data of BCa patients were downloaded from The Cancer Genome Atlas (TCGA), and the expressed genes of BM-related proteins were obtained from the BM-BASE database. We download the GSE133624 chip data from the GEO database as an external validation dataset. We screened for statistically different BM genes between tumors and adjacent normal tissues. Co-expression analysis of lncRNAs and differentially expressed BM genes was performed to identify BM-related lncRNAs. Then, differentially expressed BM-related lncRNAs (DEBMlncRNAs) between tumor and normal tissues were identified. Univariate/multivariate Cox regression analysis was performed to select lncRNAs for risk assessment. LASSO analysis was performed to build a prognostic model. We constructed a model containing 8 DEBMlncRNAs (AC004034.1, AL662797.1, NR2F1-AS1, SETBP1-DT, AC011503.2, AC093010.2, LINC00649 and LINC02321). The prognostic risk model accurately predicted the prognosis of BCa patients and revealed that tumor aggressiveness and distant metastasis were associated with higher risk scores. In this model, we constructed a nomogram to assist clinical decision-making based on clinicopathological characteristics such as age, T, and N. The model also showed good predictive power for the tumor microenvironment and mutational burden. We validated the expression of eight lncRNAs using the dataset GSE133624 and two human bladder cancer cell lines (5637, BIU-87) and examined the expression and cellular localization of LINC00649 and AC011503.2 using a human bladder cancer tissue chip. We found that knockdown of LINC00649 expression in 5637 cells promoted the proliferation of 5637 cells.Our eight DEBMlncRNA risk models provide new insights into predicting prognosis, tumor invasion, and metastasis in BCa patients.