Immune-Related lncRNA to Construct Novel Signature and Predict the Immune Landscape of Human Hepatocellular Carcinoma

Effects of NK cell-related lncRNA on the immune microenvironment and molecular subtyping for pancreatic ductal adenocarcinoma

Background

Patients with pancreatic ductal adenocarcinoma (PDAC) face a highly unfavorable outcome and have a poor response to standard treatments. Immunotherapy, especially therapy based on natural killer (NK) cells, presents a promising avenue for the treatment of PDAC.

Aims

This research endeavor seeks to formulate a predictive tool specifically designed for PDAC based on NK cell-related long non-coding RNA (lncRNA), revealing new molecular subtypes of PDAC to promote personalized and precision treatment.

Methods

Utilizing the Tumor Immune Single-cell Hub 2 platform, we discovered genes associated with NK cells in PDAC. We employed the TCGA-PAAD dataset to ascertain the expression profiles of these NK cell-related genes and to screen for lncRNAs correlated with NK cells. Subsequently, we utilized Cox regression analysis for hazard ratios and LASSO regression analysis to identify three NK cell-related lncRNAs that were used to develop a prognostic assessment model. The forecasting accuracy of this model was appraised using the ROC curve and validated using a test set and the complete dataset.

Results

Successful construction of a prognostic model comprising three lncRNAs was achieved, demonstrating good predictive efficiency in the training set, validation dataset, and the entire dataset. NK cells display robust interactions with malignant cells, CD8 T cells, and fibroblasts in the PDAC tumor microenvironment and participate in the transport of various signaling molecules and following immune responses in PDAC. According to the expression patterns of NK cell-related lncRNA, we labeled PDAC patients as four molecular subtypes, exhibiting significant differences in immune cell infiltration, drug sensitivity, and other aspects.

Conclusion

This study Uncovered the activity of NK cells within PDAC, proposed an NK cell-related lncRNA model, and delineated new molecular subtypes, thereby providing targets for personalized therapy.

Cell Cycle-Related LncRNA-Based Prognostic Model for Hepatocellular Carcinoma: Integrating Immune Microenvironment and Treatment Response

OBJECTIVE

Hepatocellular carcinoma (HCC) presents substantial genetic and phenotypic diversity, making it challenging to predict patient outcomes. There is a clear need for novel biomarkers to better identify high-risk individuals. Long non-coding RNAs (lncRNAs) are known to play key roles in cell cycle regulation and genomic stability, and their dysregulation has been closely linked to HCC progression. Developing a prognostic model based on cell cycle-related lncRNAs could open up new possibilities for immunotherapy in HCC patients.

METHODS

Transcriptomic data and clinical samples were obtained from the TCGA-HCC dataset. Cell cycle-related gene sets were sourced from existing studies, and coexpression analysis identified relevant lncRNAs (correlation coefficient >0.4, P<0.001). Univariate analysis identified prognostic lncRNAs, which were then used in a LASSO regression model to create a risk score. This model was validated via cross-validation. HCC samples were classified on the basis of their risk scores. Correlations between the risk score and tumor mutational burden (TMB), tumor immune infiltration, immune checkpoint gene expression, and immunotherapy response were evaluated via R packages and various methods (TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCP-COUNTER, XCELL, and EPIC).

RESULTS

Four cell cycle-related lncRNAs (AC009549.1, AC090018.2, PKD1P6-NPIPP1, and TMCC1-AS1) were significantly upregulated in HCC. These lncRNAs were used to create a risk score (risk score=0.492×AC009549.1+1.390×AC090018.2+1.622×PKD1P6-NPIPP1+0.858×TMCC1-AS1). This risk score had superior predictive value compared to traditional clinical factors (AUC=0.738). A nomogram was developed to illustrate the 1-year, 3-year, and 5-year overall survival (OS) rates for individual HCC patients. Significant differences in TMB, immune response, immune cell infiltration, immune checkpoint gene expression, and drug responsiveness were observed between the high-risk and low-risk groups.

CONCLUSION

The risk score model we developed enhances the prognostication of HCC patients by identifying those at high risk for poor outcomes. This model could lead to new immunotherapy strategies for HCC patients.

Identification of m6A-related lncRNAs prognostic signature for predicting immunotherapy response in cervical cancer

BACKGROUND

N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) play a crucial role in the cancer progression and immunotherapeutic efficacy. The potential function of m6A-related lncRNAs signature in cervical cancer has not been systematically clarified.

METHODS

RNA-seq and the clinical data of cervical cancer were extracted from The Cancer Genome Atlas. All of the patients were randomly classified into training and testing cohorts. The m6A-related lncRNAs prognostic model was constructed by LASSO regression using data in the training cohort.The predictive value of the signature was validated in the whole cohort and testing cohort. Cervical cancer patients were divided into low- and high-risk subgroups by the median value of risk scores. Kaplan-Meier analysis, principal-component analysis (PCA), functional enrichment annotation, and nomogram were used for further evaluation. We also examined the immune response and potential drug sensitivity targeting this model.

RESULTS

Seventy-nine prognostic m6A-related lncRNAs were screened. The risk model comprising four m6A-related lncRNAs (AL139035.1, AC015922.2, AC073529.1, AC008124.1) was identified and verified as an independent prognostic predictor of cervical cancer. A nomogram based on age, tumor grade, clinical stage, TNM stage, and four m6A-related lncRNAs risk signatures was generated. It displayed good accuracy and reliability in predicting the overall survival of patients with CC. Based on our risk model, cervical cancer patients with potential immunotherapy benefits from the candidate drugs could be effectively screened.

CONCLUSION

The four m6A-related lncRNAs signature may provide new targets and allow the prediction of immunotherapy response, which can assist developing individualized treatment for cervical cancer.

Novel signature of ferroptosis-related long non-coding RNA to predict lower-grade glioma overall survival

BACKGROUND

Ferroptosis is a novel type of programmed cell death in various tumors; however, underlying mechanisms remain unclear. We aimed to develop ferroptosis-related long non-coding RNA (FRlncRNA) risk scores to predict lower-grade glioma (LGG) prognosis and to conduct functional analyses to explore potential mechanisms.

METHODS

LGG-related RNA sequencing data were extracted from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Pearson correlation analysis was used to identify the FRlncRNAs, univariate Cox regression analysis was for identify the prognostic FRlncRNAs, and then intersection FRlncRNAs were screened between TCGA and CGGA. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to develop a risk score to predict LGG prognosis.

RESULTS

A total of nine FRlncRNAs were screened to construct the novel prognostic risk score of LGG, and high-risk score patients had a worse overall survival than low-risk score patients both in TCGA and CGGA datasets. The risk score was quite correlated with clinicopathological characteristics (age, WHO grade, status of MGMT Methtlation, IDH mutation, 1p/19q codeletion, and TMB), and could promote current molecular subtyping systems. Comprehensive analyses revealed that signaling pathways of B-cell receptor and T-cell receptor, immune cells of macrophage cell and CD4+ T cell, tumor microenvironment of stroma score and immune score, and immune checkpoints of PD-1, PD-L1, and CTLA4 were all enriched in the high-risk score group.

CONCLUSION

The nine FRlncRNAs risk scores was a promising biomarker to predict the LGG's prognosis and distinguish the characteristics of molecular and immune.

Construction and validation of immune-associated lncRNA model for predicting immune status and therapeutic reactions of triple-negative breast cancer

OBJECTIVE

The immune status of the tumor microenvironment significantly impacts the clinical prognosis of triple-negative breast cancer (TNBC). The involvement of long noncoding RNAs (lncRNAs) in tumor immune infiltration is widely acknowledged. Therefore, it is crucial to explore the role of significant immune-related lncRNAs in TNBC.

METHODS

We acquired RNA, single-cell sequencing, and clinical information on TNBC from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. To identify immune-related lncRNAs, immune infiltration subgroups were determined and verified using single-sample gene-set enrichment analysis, non-negative matrix factorization, and weighted gene co-expression network analysis. CIBERSORTx, deconvolution, drug sensitivity, and Scissor analyses revealed that differences in cell type and drug efficacy were associated with immune grouping.

RESULTS

TNBC samples were classified into immune-desert (cold) and immune-inflamed (hot) subgroups based on a lncRNA model (including LINC01550, LY86-AS1, LINC00494, LINC00877, CHRM3-AS2, HCP5, MIR155HG, and PIK3CD-AS1). Furthermore, using in vitro experiments, we found that LINC01550 promoted malignant phenotypes, including proliferation, survival, and migration of TNBC. The immune-inflamed subgroup exhibited significantly lower half-maximal inhibitory concentration values for common anti-tumor drugs, including palbociclib, ribociclib, mitoxantrone, and sorafenib (T-test, P < 0.001). This may be related to the fact that the immune-inflamed subgroup has more plasmacytoid dendritic cells (pDCs) and B cells than those in immune-desert subgroups (P < 0.001).

CONCLUSIONS

Differences in specific cell infiltration can lead to increased sensitivity of the immune-inflamed subgroup to anti-tumor drugs. The proposed lncRNA model holds great promise to assess the immune landscapes and therapeutic reactions of TNBC patients.

Molecular Mechanisms and Clinical Implications of Noncoding RNAs in Cancer

Noncoding RNAs (ncRNAs), which include small nuclear RNAs (snRNAs), small nucleolar RNAs (snoRNAs), microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs), are RNA molecules that arise from genomic regions without protein-coding potential and display a variety of mechanisms and functions by regulating gene expression at the transcriptional, RNA processing, and translational levels and participating in virtually all cellular processes [...].

Cuproptosis-Related lncRNA Predict Prognosis and Immune Response of LUAD

Background

Lung cancer is the leading cause of cancer deaths worldwide, primarily due to lung adenocarcinoma (LUAD). However, the heterogeneity of programmed cell death results in varied prognostic and predictive outcomes. This study aimed to develop an LUAD evaluation marker based on cuproptosis-related lncRNAs.

Methods

First, transcriptome data and clinical data related to LUAD were downloaded from the Cancer Genome Atlas (TCGA), and cuproptosis-related genes were analyzed to identify cuproptosis-related lncRNAs. Univariate, LASSO, and multivariate Cox regression analyses were conducted to construct cuproptosis-associated lncRNA models. LUAD patients were categorized into high-risk and low-risk groups using prognostic risk values. Kaplan-Meier analysis, PCA, GSEA, and nomograms were employed to evaluate and validate the results.

Results

7 cuproptosis-related lncRNAs were identified, and a risk model was created. High-risk tumors exhibited cuproptosis-related gene alterations in 95.54% of cases, while low-risk tumors showed alterations in 85.65% of cases, mainly involving TP53. The risk value outperformed other clinical variables and tumor mutation burden as a predictor of 1-, 3-, and 5-year overall survival. The cuproptosis-related lncRNA-based risk model demonstrated high validity for LUAD evaluation, potentially influencing individualized treatment approaches. Expression analysis of four candidate cuproptosis-related lncRNAs (AL606834.1, AL161431.1, AC007613.1, and LINC02835) in LUAD tissues and adjacent normal tissues revealed significantly higher expression levels of AL606834.1 and AL161431.1 in LUAD tissues, positively correlating with tumor stage, lymph node metastasis, and histopathological grade. Conversely, AC007613.1 and LINC02835 exhibited lower expression levels, negatively correlating with these factors. High expression of AL606834.1 and AL161431.1 indicated poor prognosis, while low expression of AC007613.1 and LINC02835 was associated with unfavorable outcomes. Univariate and multivariate analyses confirmed these lncRNAs as independent risk factors for LUAD prognosis.

Conclusion

The 4 cuproptosis-related (lncRNAsAL606834.1, AL161431.1, AC007613.1, and LINC02835) can accurately predict the prognosis of patients with LUAD and may provide new insights into clinical applications and immunotherapy.

Identification of necroptosis-related gene signatures for predicting the prognosis of ovarian cancer

Ovarian cancer (OC) is one of the most prevalent and fatal malignant tumors of the female reproductive system. Our research aimed to develop a prognostic model to assist inclinical treatment decision-making.Utilizing data from The Cancer Genome Atlas (TCGA) and copy number variation (CNV) data from the University of California Santa Cruz (UCSC) database, we conducted analyses of differentially expressed genes (DEGs), gene function, and tumor microenvironment (TME) scores in various clusters of OC samples.Next, we classified participants into low-risk and high-risk groups based on the median risk score, thereby dividing both the training group and the entire group accordingly. Overall survival (OS) was significantly reduced in the high-risk group, and two independent prognostic factors were identified: age and risk score. Additionally, three genes-C-X-C Motif Chemokine Ligand 10 (CXCL10), RELB, and Caspase-3 (CASP3)-emerged as potential candidates for an independent prognostic signature with acceptable prognostic value. In Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, pathways related to immune responses and inflammatory cell chemotaxis were identified. Cellular experiments further validated the reliability and precision of our findings. In conclusion, necroptosis-related genes play critical roles in tumor immunity, and our model introduces a novel strategy for predicting the prognosis of OC patients.

Enhancing m6A modification of lncRNA through METTL3 and RBM15 to promote malignant progression in bladder cancer

Objective

Bladder cancer is one of the most prominent malignancies affecting the urinary tract, characterized by a poor prognosis. Our previous research has underscored the pivotal role of m6A methylation in the progression of bladder cancer. Nevertheless, the precise relationship between N6-methyladenosine (m6A) regulation of long non-coding RNA (lncRNA) and bladder cancer remains elusive.

Methods

This study harnessed sequencing data and clinical records from 408 bladder cancer patients in the TCGA database. Employing R software, we conducted bioinformatics analysis to establish an m6A-lncRNA co-expression network. Analyzing the differences between high and low-risk groups, particularly at the immunological level, and subsequently investigating the primary regulatory factors of these lncRNA, validating the findings through experiments, and exploring their specific cellular functions.

Results

We identified 50 m6A-related lncRNA with prognostic significance through univariate Cox regression analysis. In parallel, we employed a LASSO-Cox regression model to pinpoint 11 lncRNA and calculate risk scores for bladder cancer patients. Based on the median risk score, patients were categorized into low-risk and high-risk groups. The high-risk cohort exhibited notably lower survival rates than their low-risk counterparts. Further analysis pointed to RBM15 and METTL3 as potential master regulators of these m6A-lncRNA. Experimental findings also shed light on the upregulated expression of METTlL3 and RBM15 in bladder cancer, where they contributed to the malignant progression of tumors. The experimental findings demonstrated a significant upregulation of METTL3 and RBM15 in bladder cancer specimens, implicating their contributory role in the oncogenic progression. Knockdown of METTL3 and RBM15 resulted in a marked attenuation of tumor cell proliferation, invasion, and migration, which was concomitant with a downregulation in the cellular m6A methylation status. Moreover, these results revealed that RBM15 and METTL3 function in a synergistic capacity, positing their involvement in cancer promotion via the upregulation of m6A modifications in long non-coding RNAs. Additionally, this study successfully developed an N-methyl-N-nitrosourea (MNU)-induced rat model of in situ bladder carcinoma, confirming the elevated expression of RBM15 and METTL3, which paralleled the overexpression of m6A-related- lncRNAs observed in bladder cancer cell lines. This congruence underscores the potential utility of these molecular markers in in vivo models that mirror human malignancies.

Conclusion

This study not only offers novel molecular targets,but also enriches the research on m6A modification in bladder cancer, thereby facilitating its clinical translation.

A pyroptosis-related lncRNA-based prognostic index for hepatocellular carcinoma by relative expression orderings

Background

Hepatocellular carcinoma (HCC) is an invasive malignant tumor, and pyroptosis makes an important contribution to the pathology and progression of liver cancer. Many prognostic models have been proposed for HCC based on the quantitative expression level of candidate genes, which are unsuitable for clinical application due to their vulnerability against experimental batch effects. The aim of this study was to develop a novel pyroptosis-related long non-coding RNA (lncRNA)-based prognostic index (PLPI) for HCC based on relative expression orderings (REOs).

Methods

Firstly, the pyroptosis-related lncRNAs were identified through the Wilcoxon rank-sum test and gene co-expression analyses. Then, the novel prognostic model PLPI was constructed by pyroptosis-related lncRNA pairs, which were identified by multiple machine learning algorithms. Gene set enrichment, somatic mutation, and drug sensitivity analyses were conducted to measure the differences between high- and low-risk patients. Multiple immune analyses were used to explore the association between PLPI and the immunological microenvironment.

Results

In this study, a novel prognostic model PLPI based on 10 pyroptosis-related lncRNA pairs was constructed, which was proven to be an independent prognostic risk factor. The receiver operating characteristic (ROC) curves showed that the model had a good prognostic ability in the training, testing, and external set, respectively [5-year area under the curve (AUC) =0.73, 5-year AUC =0.81, 4-year AUC =0.79]. The results of survival, somatic mutation, and immune analyses showed that the patients in the low-risk group had a better prognosis, lower rates of somatic mutation, and better immune cell infiltration. Personalized chemotherapeutic drugs were also identified for the patients with HCC.

Conclusions

The novel PLPI not only greatly predicted the prognosis of patients with HCC but could also offer novel ideas and approaches for the therapeutic management of HCC.

A novel immune-related long noncoding RNA (lncRNA) pair model to predict the prognosis of triple-negative breast cancer

Background

Breast cancer (BC) is the most prevalent cancer type and is the principal cause of cancer-related death in women. Anti-programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) immunotherapy has shown promising effects in metastatic triple-negative breast cancer (TNBC), but the potential factors affecting its efficacy have not been elucidated. Immune-related long noncoding RNAs (irlncRNAs) have been reported to be involved in immune escape to influence the carcinogenic process through the PD-1/PD-L1 signaling pathway. Therefore, exploring the potential regulatory mechanism of irlncRNAs in PD-1/PD-L1 immunotherapy in TNBC is of great importance.

Methods

We retrieved transcriptome profiling data from The Cancer Genome Atlas (TCGA) and identified differentially expressed irlncRNA (DEirlncRNA) pairs. Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to construct a risk assessment model.

Results

Receiver operating characteristic (ROC) curve analysis indicated that the risk model may serve as a potential prediction tool in TNBC patients. Clinical stage and risk score were proved to be independent prognostic predictors by univariate and multivariate Cox regression analyses. Subsequently, we investigated the correlation between the risk model and tumor-infiltrating immune cells and immune checkpoints. Finally, we identified USP30-AS1 through the StarBase and Multi Experiment Matrix (MEM) databases, predicted the potential target genes of USP30-AS1, and then discovered that these target genes were closely associated with immune responses.

Conclusions

Our study constructed a risk assessment model by irlncRNA pairs regardless of expression levels, which contributed to predicting the efficacy of immunotherapy in TNBC. Furthermore, the lncRNA USP30-AS1 in the model was positively correlated with the expression of PD-L1 and provided a potential therapeutic target for TNBC.

A novel immune-related lncRNA signature predicts the prognosis and immune landscape in ccRCC

BACKGROUND

As one of the most common tumors, the pathogenesis and progression of clear cell renal cell carcinoma (ccRCC) in the immune microenvironment are still unknown.

METHODS

The differentially expressed immune-related lncRNA (DEirlncRNA) was screened through co-expression analysis and the limma package of R, which based on the ccRCC project of the TCGA database. Then, we designed the risk model by irlncRNA pairs. In RCC patients, we have compared the area under the curve, calculated the Akaike Information Criterion (AIC) value of the 5-year receiver operating characteristic curve, determined the cut-off point, and established the optimal model for distinguishing the high-risk group from the low-risk group. We used the model for immune system assessment, immune point detection and drug sensitivity analysis after verifying the feasibility of the above model through clinical features.

RESULTS

In our study, 1541 irlncRNAs were included. 739 irlncRNAs were identified as DEirlncRNAs to construct irlncRNA pairs. Then, 38 candidate DEirlncRNA pairs were included in the best risk assessment model through improved LASSO regression analysis. As a result, we found that in addition to age and gender, T stage, M stage, N stage, grade and clinical stage are significantly related to risk. Moreover, univariate and multivariate Cox regression analysis results reveals that in addition to gender, age, grade, clinical stage and risk score are independent prognostic factors. The results show that patients in the high-risk group are positively correlated with tumor infiltrating immune cells when the above model is applied to the immune system. But they are negatively correlated with endothelial cells, macrophages M2, mast cell activation, and neutrophils. In addition, the risk model was positively correlated with overexpressed genes (CTLA, LAG3 and SETD2, P<0.05). Finally, risk models can also play as an important role in predicting the sensitivity of targeted drugs.

CONCLUSIONS

The new risk model may be a new method to predict the prognosis and immune status of ccRCC.

The role of SLC39A4 in the prognosis, immune microenvironment, and contribution to malignant behavior in vivo and in vitro of cervical cancer

BACKGROUND

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are becoming more common in younger women. Solute carrier family 39 member 4 (SLC39A4) produces a zinc ion transporter involved in metastasis and invasion of tumors.

METHODS

The Cancer Genome Atlas RNA-seq data was used to investigate the expression of SLC39A4 and its prognostic potential. The assessment of the effect of SLC39A4 on cell growth and migration in CESC was conducted using MTT, colony formation, and Transwell assays. SLC39A4 was studied in vivo using a xenograft mouse model, and its functional involvement in oncogenesis was investigated by identifying the associated differentially expressed genes (DEGs). We evaluated the relationships among SLC39A4 levels, chemosensitivity, radiosensitivity and immune infiltration.

RESULTS

SLC39A4 was upregulated in CESC samples, and individuals with greater SLC39A4 mRNA expression had shorter overall survival. SLC39A4 has been identified to be a regulator of tumor cell metastasis and proliferation in vivo and in vitro, with an area under the curve of 0.874 for diagnosing CESC. In total, 948 DEGs were discovered to be enriched in key CESC progression-related signaling pathways. Additionally, intratumoral immune checkpoint and infiltration activity were associated with SLC39A4 expression. High SLC39A4 expression exhibited poor chemosensitivity and radiosensitivity profiles.

CONCLUSION

In conclusion, SLC39A4 is a key regulator of CESC development, prognosis, and the composition of the tumor immune microenvironment. SLC39A4 could be used as a prognostic or diagnostic screening tool and as a potential target for CESC treatment.

Identification of a novel m6A-related lncRNAs signature and immunotherapeutic drug sensitivity in pancreatic adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PDAC) ranks as the fourth leading cause for cancer-related deaths worldwide. N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) are closely related with poor prognosis and immunotherapeutic effect in PDAC. The aim of this study is to construct and validate a m6A-related lncRNAs signature and assess immunotherapeutic drug sensitivity in PDAC.

METHODS

RNA-seq data for 178 cases of PDAC patients and 167 cases of normal pancreatic tissue were obtained from TCGA and GTEx databases, respectively. A set of 21 m6A-related genes were downloaded based on the previous report. Co-expression network was conducted to identify m6A-related lncRNAs in PDAC. Cox analyses and least absolute shrinkage and selection operator (Lasso) regression model were used to construct a risk prognosis model. The relationship between signature genes and immune function was explored by single-sample GSEA (ssGSEA). The tumor immune dysfunction and exclusion (TIDE) score and tumor mutation burden (TMB) were utilized to evaluate the response to immunotherapy. Furthermore, the expression levels of 4 m6A-related lncRNAs on PDAC cell lines were measured by the quantitative real-time PCR (qPCR). The drug sensitivity between the high- and low-risk groups was validated using PDAC cell lines by Cell-Counting Kit 8 (CCK8).

RESULTS

The risk prognosis model was successfully constructed based on 4 m6A-related lncRNAs, and PDAC patients were divided into the high- and low-risk groups. The overall survival (OS) of the high-risk groups was more unfavorable compared with the low-risk groups. Receiver operating characteristic (ROC) curves demonstrated that the risk prognosis model reasonably predicted the 2-, 3- and 5-year OS of PDAC patients. qPCR analysis confirmed the decreased expression levels of 4 m6A-related lncRNAs in PDAC cells compared to the normal pancreatic cells. Furthermore, CCK8 assay revealed that Phenformin exhibited higher sensitivity in the high-risk groups, while Pyrimethamine exhibited higher sensitivity in the low-risk groups.

CONCLUSION

The prognosis of patients with PDAC were well predicted in the risk prognosis model based on m6A-related lncRNAs, and selected immunotherapy drugs have potential values for the treatment of pancreatic cancer.

MIR222HG/LIN28B/ATG5 Axis Drives M2 Macrophage Polarization and Proliferation of Hepatocellular Carcinoma Cells

Long non-coding RNAs (lncRNAs) are involved in the pathogenesis of hepatocellular carcinoma (HCC). This study aimed to investigate the potential of MIR222HG in HCC. HCC cells were co-cultured with U937 cells. Gene expression was determined using reverse transcription-quantitative (RT-q) PCR and western blot. Functional analysis was performed using Cell Counting Kit 8 (CCK-8), colony formation, and flow cytometry assays. We found that MIR222HG was overexpressed in HCC patients as well as HepG2 and Huh7 cells. MIR222HG-mediated upregulation of autophagy related 5 (ATG5) promoted tumor cell autophagy and the activation of M2-like tumor-associated macrophages (TAM2). Moreover, MIR222HG-mediated the activation of TAM2 drove the proliferation of HCC cells. Additionally, MIR222HG increased the mRNA expression as well as promoted the mRNA stability of ATG5 via binding to lin-28 homolog B (LIN28B). In conclusion, MIR222HG-mediated autophagy and the activation of TAM2 promote the aggressiveness of HCC cells via regulating LIN28B/ATG5 signaling.

m6A-related lncRNAs as potential biomarkers and the lncRNA ELFN1-AS1/miR-182-5p/BCL-2 regulatory axis in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoid subtype. However, unsatisfactory survival outcomes remain a major challenge, and the underlying mechanisms are poorly understood. N6-methyladenosine (m6A), the most common internal modification of eukaryotic mRNA, participates in cancer pathogenesis. In this study, m6A-associated long non-coding RNAs (lncRNA) were retrieved from publicly available databases. Univariate, LASSO, and multivariate Cox regression analyses were performed to establish an m6A-associated lncRNA model specific to DLBCL. Kaplan-Meier curves, principal component analysis, functional enrichment analyses and nomographs were used to study the risk model. The underlying clinicopathological characteristics and drug sensitivity predictions against the model were identified. Risk modelling based on the three m6A-associated lncRNAs was an independent prognostic factor. By regrouping patients using our model-based method, we could differentiate patients more accurately for their response to immunotherapy. In addition, prospective compounds that can target DLBCL subtypes have been identified. The m6A-associated lncRNA risk-scoring model developed herein holds implications for DLBCL prognosis and clinical response prediction to immunotherapy. In addition, we used bioinformatic tools to identify and verify the ceRNA of the m6A-associated lncRNA ELFN1-AS1/miR-182-5p/BCL-2 regulatory axis. ELFN1-AS1 was highly expressed in DLBCL and DLBCL cell lines. ELFN1-AS1 inhibition significantly reduced the proliferation of DLBCL cells and promoted apoptosis. ABT-263 inhibits proliferation and promotes apoptosis in DLBCL cells. In vitro and in vivo studies have shown that ABT-263 combined with si-ELFN1-AS1 can inhibit DLBCL progression.

Novel Immune-Related LncRNA Pairs are Associated with Immunol Infiltration and Survival Status in Glioblastoma

Background

Immune-related lncRNA is involved in tumor initiation and progression, while its effect in glioblastoma (GBM) is still unknown.

Objective

We sought to investigate the association between immune-related lncRNA (ir-lncRNA) and GBM.

Methods

Transcriptomic and clinical data were obtained from the TCGA dataset, and we found 2008 ir-lncRNA differentially expressed between GBM and adjacent brain tissues.

Results

Appling the univariate Cox and Lasso regression model, we found 30 prognosis-related ir-lncRNA pairs to construct a Cox regression risk model to associate the outcome of GBM patients. Furthermore, with this risk model, we can identify the tumor immune infiltration status, the expression of immunosuppressive biomarkers, and chemical sensitivity in GBM patients.

Conclusions

We constructed an immunologic risk model with lncRNA to associate the survival outcome of GBM patients, which can provide useful biomarkers.

Construction of a novel signature based on immune-related lncRNA to identify high and low risk pancreatic adenocarcinoma patients

BACKGROUND

Pancreatic adenocarcinoma is one of the most lethal tumors in the world with a poor prognosis. Thus, an accurate prediction model, which identify patients within high risk of pancreatic adenocarcinoma is needed to adjust the treatment and elevate the prognosis of these patients.

METHODS

We obtained RNAseq data of The Cancer Genome Atlas (TCGA) pancreatic adenocarcinoma (PAAD) from UCSC Xena database, identified immune-related lncRNAs (irlncRNAs) by correlation analysis, and identified differential expressed irlncRNAs (DEirlncRNAs) between pancreatic adenocarcinoma tissues from TCGA and normal pancreatic tissues from TCGA and Genotype-Tissue Expression (GTEx). Further univariate and lasso regression analysis were performed to construct prognostic signature model. Then, we calculated the areas under curve and identified the best cut-off value to identify high- and low-risk patients with pancreatic adenocarcinoma. The clinical characteristics, immune cell infiltration, immunosuppressive microenvironment, and chemoresistance were compared between high- and low-risk patients with pancreatic adenocarcinoma.

RESULTS

We identified 20 DEirlncRNA pairs and grouped the patients by the best cut-off value. We proved that our prognostic signature model possesses a remarkable efficiency to predict prognosis of PAAD patients. The AUC for ROC curve was 0.905 for 1-year prediction, 0.942 for 2-year prediction, and 0.966 for 3-year prediction. Patients in high-risk group have poor survival rate and worse clinical characteristics. We also proved that patients in high-risk groups were in immunosuppressive status and may be resistant to immunotherapy. Anti-cancer drug evaluation was performed based on in-silico predated tool, such as paclitaxel, sorafenib, and erlotinib, may be suitable for PAAD patients in high-risk group.

CONCLUSIONS

Overall, our study constructed a novel prognostic risk model based on pairing irlncRNAs, exhibited a promising prediction value in patients with pancreatic adenocarcinoma. Our prognostic risk model may help distinguish PAAD patients suitable for medical treatments.

Establishment of a novel signature to predict prognosis and immune characteristics of pancreatic cancer based on necroptosis-related long non-coding RNA

BACKGROUND

Necroptosis plays an important role in tumorigenesis and tumour progression. Long noncoding RNAs (lncRNAs) have been proven to be regulatory factors of necroptosis in various tumours. However, the real role of necroptosis-related lncRNAs (NRLs) and their potential to predict the prognosis of pancreatic cancer (PC) remain largely unclear. The goal of this study was to identify NRLs and create a predictive risk signature in PC, explore its prognostic predictive performance, and further assess immunotherapy and chemotherapy responses.

METHODS

RNA sequencing data, tumour mutation burden (TMB) data, and clinical profiles of 178 PC patients were downloaded from The Cancer Genome Atlas (TCGA) database. NRLs were identified using Pearson correlation analysis. Then, patients were divided into the training set and the validation set at a 1:1 ratio. Subsequently, Cox and LASSO regression analyses were conducted to establish a prognostic NRL signature in the training set and validation set. The predictive efficacy of the 5-NRL signature was assessed by survival analysis, nomogram, Cox regression, clinicopathological feature correlation analysis, and receiver operating characteristic (ROC) curve analysis. Furthermore, correlations between the risk score (RS) and immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and anticancer drug sensitivity were analysed. Finally, we used quantitative reverse transcription polymerase chain reaction (qRT-PCR) to validate the 5-NRLs.

RESULTS

A 5-NRL signature was established to predict the prognosis of PC, including LINC00857, AL672291.1, PTPRN2-AS1, AC141930.2, and MEG9. The 5-NRL signature demonstrated a high degree of predictive power according to ROC and Kaplan‒Meier curves and was revealed to be an independent prognostic risk factor via stratified survival analysis. Nomogram and calibration curves indicated the clinical adaptability of the signature. Immune-related pathways were linked to the 5-NRL signature according to enrichment analysis. Additionally, immune cell infiltration, immune checkpoint molecules, somatic gene mutations and the half-maximal inhibitory concentration (IC50) of chemotherapeutic agents were significantly different between the two risk subgroups. These results suggested that our model can be used to evaluate the effectiveness of immunotherapy and chemotherapy, providing a potential new strategy for treating PC.

CONCLUSIONS

The novel 5-NRL signature is helpful for assessing the prognosis of PC patients and improving therapy options, so it can be further applied clinically.

Identification and verification of novel immune-related ferroptosis signature with excellent prognostic predictive and clinical guidance value in hepatocellular carcinoma

Background: Immunity and ferroptosis often play a synergistic role in the progression and treatment of hepatocellular carcinoma (HCC). However, few studies have focused on identifying immune-related ferroptosis gene biomarkers. Methods: We performed weighted gene co-expression network analysis (WGCNA) and random forest to identify prognostic differentially expressed immune-related genes (PR-DE-IRGs) highly related to HCC and characteristic prognostic differentially expressed ferroptosis-related genes (PR-DE-FRGs) respectively to run co-expression analysis for prognostic differentially expressed immune-related ferroptosis characteristic genes (PR-DE-IRFeCGs). Lasso regression finally identified 3 PR-DE-IRFeCGs for us to construct a prognostic predictive model. Differential expression and prognostic analysis based on shared data from multiple sources and experimental means were performed to further verify the 3 modeled genes' biological value in HCC. We ran various performance testing methods to test the model's performance and compare it with other similar signatures. Finally, we integrated composite factors to construct a comprehensive quantitative nomogram for accurate prognostic prediction and evaluated its performance. Results: 17 PR-DE-IRFeCGs were identified based on co-expression analysis between the screened 17 PR-DE-FRGs and 34 PR-DE-IRGs. Multi-source sequencing data, QRT-PCR, immunohistochemical staining and testing methods fully confirmed the upregulation and significant prognostic influence of the three PR-DE-IRFeCGs in HCC. The model performed well in the performance tests of multiple methods based on the 5 cohorts. Furthermore, our model outperformed other related models in various performance tests. The immunotherapy and chemotherapy guiding value of our signature and the comprehensive nomogram's excellent performance have also stood the test. Conclusion: We identified a novel PR-DE-IRFeCGs signature with excellent prognostic prediction and clinical guidance value in HCC.

Advanced therapeutics avenues in hepatocellular carcinoma: a novel paradigm

Hepatocellular carcinoma (HCC) is the most frequent type of primary liver cancer, and it poses a significant risk to patients health and longevity due to its high morbidity and fatality rates. Surgical ablation, radiotherapy, chemotherapy, and, most recently, immunotherapy have all been investigated for HCC, but none have yielded the desired outcomes. Several unique nanocarrier drug delivery techniques have been studied for their potential therapeutic implications in the treatment of HCC. Nanoparticle-based imaging could be effective for more accurate HCC diagnosis. Since its inception, nanomedicine has significantly transformed the approach to both the treatment and diagnostics of liver cancer. Nanoparticles (NPs) are being studied as a potential treatment for liver cancer because of their ability to carry small substances, such as treatment with chemotherapy, microRNA, and therapeutic genes. The primary focus of this study is on the most current discoveries and practical uses of nanomedicine-based diagnostic and therapeutic techniques for liver cancer. In this section, we had gone over what we know about metabolic dysfunction in HCC and the treatment options that attempt to fix it by targeting metabolic pathways. Furthermore, we propose a multi-target metabolic strategy as a viable HCC treatment option. Based on the findings given here, the scientists believe that smart nanomaterials have great promise for improving cancer theranostics and opening up new avenues for tumor diagnosis and treatment.

Building an Immune-Related Genes Model to Predict Treatment, Extracellular Matrix, and Prognosis of Head and Neck Squamous Cell Carcinoma

Due to the considerable heterogeneity of head and neck squamous cell carcinoma (HNSCC), individuals with comparable TNM stages who receive the same treatment strategy have varying prognostic outcomes. In HNSCC, immunotherapy is developing quickly and has shown effective. We want to develop an immune-related gene (IRG) prognostic model to forecast the prognosis and response to immunotherapy of patients. In order to analyze differential expression in normal and malignant tissues, we first identified IRGs that were differently expressed. Weighted gene coexpression network analysis (WGCNA) was used to identify modules that were highly related, and univariate and multivariate Cox regression analyses were also used to create a predictive model for IRGs that included nine IRGs. WGCNA identified the four most noteworthy related modules. Patients in the model's low-risk category had a better chance of survival. The IRGs prognostic model was also proved to be an independent prognostic predictor, and the model was also substantially linked with a number of clinical characteristics. The low-risk group was associated with immune-related pathways, a low incidence of gene mutation, a high level of M1 macrophage infiltration, regulatory T cells, CD8 T cells, and B cells, active immunity, and larger benefits from immune checkpoint inhibitors (ICIs) therapy. The high-risk group, on the other hand, had suppressive immunity, high levels of NK and CD4 T-cell infiltration, high gene mutation rates, and decreased benefits from ICI therapy. As a result of our research, a predictive model for IRGs that can reliably predict a patient's prognosis and their response to both conventional and immunotherapy has been created.

Bioinformatics analysis of necroptosis‑related lncRNAs and immune infiltration, and prediction of the prognosis of patients with esophageal carcinoma

Esophageal carcinoma (ESCA) is one of the most common malignancies in the world, and has high morbidity and mortality rates. Necrosis and long noncoding RNAs (lncRNAs) are involved in the progression of ESCA; however, the specific mechanism has not been clarified. The aim of the present study was to investigate the role of necrosis-related lncRNAs (nrlncRNAs) in patients with ESCA by bioinformatics analysis, and to establish a nrlncRNA model to predict ESCA immune infiltration and prognosis. To form synthetic matrices, ESCA transcriptome data and related information were obtained from The Cancer Genome Atlas. A nrlncRNA model was established by coexpression, univariate Cox (Uni-Cox), and least absolute shrinkage and selection operator analyses. The predictive ability of this model was evaluated by Kaplan-Meier, receiver operating characteristic (ROC) curve, Uni-Cox, multivariate Cox regression, nomogram and calibration curve analyses. A model containing eight nrlncRNAs was generated. The areas under the ROC curves for 1-, 3- and 5-year overall survival were 0.746, 0.671 and 0.812, respectively. A high-risk score according to this model could be used as an indicator for systemic therapy use, since the half-maximum inhibitory concentration values varied significantly between the high-risk and low-risk groups. Based on the expression of eight prognosis-related nrlncRNAs, the patients with ESCA were regrouped using the 'ConsensusClusterPlus' package to explore potential molecular subgroups responding to immunotherapy. The patients with ESCA were divided into three clusters based on the eight nrlncRNAs that constituted the risk model: The most low-risk group patients were classified into cluster 1, and the high-risk group patients were mainly concentrated in clusters 2 and 3. Survival analysis showed that Cluster 1 had a better survival than the other groups (P=0.016). This classification system could contribute to precision treatment. Furthermore, two nrlncRNAs (LINC02811 and LINC00299) were assessed in the esophageal epithelial cell line HET-1A, and in the human esophageal cancer cell lines KYSE150 and TE1. There were significant differences in the expression levels of these lncRNAs between tumor and normal cells. In conclusion, the present study suggested that nrlncRNA models may predict the prognosis of patients with ESCA, and provide guidance for immunotherapy and chemotherapy decision making. Furthermore, the present study provided strategies to promote the development of individualized and precise treatment for patients with ESCA.

Necroptosis-related signatures identify two distinct hepatocellular carcinoma subtypes: Implications for predicting drug sensitivity and prognosis

Background

Necroptosis is associated with oncogenesis, tumor immunity and progression. This research aims to investigate the association of necroptosis-related genes with drug sensitivity and prognosis in hepatocellular carcinoma (HCC).

Methods

Based on necroptosis-related signatures, HCC patients retrieved from the TCGA database were categorized. Survival outcomes, mutation profile, immune microenvironment, and drug sensitivity between HCC subtypes were further compared. Then, LASSO analysis was performed to construct a necroptosis-related prognostic signature, which was further evaluated using another independent cohort.

Results

A total of 371 patients with HCC could be categorized into two necroptosis-related subtypes. About 36% of patients were allocated to subtype A, with worse survival, more mutant TP53, and a lower likelihood of immunotherapy response. In contrast, patients in subtype B had a favorable prognosis, with lower expression of immunosuppressive signatures but a lower abundance of B and CD8⁺ T-cell infiltration. The prognostic risk score calculated using the expression levels of nine genes involved in the necroptosis pathway (MLKL, FADD, XIAP, USP22, UHRF1, CASP8, RIPK3, ZBP1, and FAS) showed a significant association with tumor stage, histologic grade, and Child‒Pugh score. Additionally, the risk score model was proven to be accurate in both the training and independent external validation cohorts and performed better than the TNM staging system and three well-recognized risk score models.

Conclusions

Based on necroptosis-related signatures, we identified two HCC subtypes with distinctive immune microenvironments, mutation profiles, drug sensitivities, and survival outcomes. A novel well-performing prognostic model was further constructed.

A novel prognostic signatures based on metastasis- and immune-related gene pairs for colorectal cancer

Background

Metastasis remains the leading cause of mortality in patients diagnosed with colorectal cancer (CRC). The pivotal contribution of the immune microenvironment in the initiation and progression of CRC metastasis has gained significant attention.

Methods

A total of 453 CRC patients from The Cancer Genome Atlas (TCGA) were included as the training set, and GSE39582, GSE17536, GSE29621, GSE71187 were included as the validation set. The single-sample gene set enrichment analysis (ssGSEA) was performed to assess the immune infiltration of patients. Least absolute shrinkage and selection operator (LASSO) regression analysis, Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier analysis were used to construct and validate risk models based on R package. CTSW and FABP4-knockout CRC cells were constructed via CRISPR-Cas9 system. Western-blot and Transwell assay were utilized to explore the role of fatty acid binding protein 4 (FABP4) / cathepsin W (CTSW) in CRC metastasis and immunity.

Results

Based on the normal/tumor, high-/low-immune cell infiltration, and metastatic/non-metastatic group, we identified 161 differentially expressed genes. After random assignment and LASSO regression analysis, a prognostic model containing 3 metastasis- and immune-related gene pairs was constructed and represented good prognostic prediction efficiency in the training set and 4 independent CRC cohorts. According to this model, we clustered patients and found that the high-risk group was associated with stage, T and M stage. In addition, the high-risk group also shown higher immune infiltration and high sensitivity to PARP inhibitors. Further, FABP4 and CTSW derived from the constitutive model were identified to be involved in metastasis and immunity of CRC.

Conclusion

In conclusion, a validated prognosis predictive model for CRC was constructed. CTSW and FABP4 are potential targets for CRC treatment.

An immune-related signature for optimizing prognosis prediction and treatment decision of hepatocellular carcinoma

BACKGROUND

An immune-related gene signature (IGS) was established for discriminating prognosis, predicting benefit of immunotherapy, and exploring therapeutic options in hepatocellular carcinoma (HCC).

METHODS

Based on Immune-related hub genes and The Cancer Genome Atlas (TCGA) LIHC dataset (n = 363), an immune-related gene signature (IGS) was established by least absolute shrinkage and selection operator (LASSO) analysis. The prognostic significance and clinical implications of IGS were verified in International Cancer Genome Consortium (ICGC) and Chinese HCC (CHCC) cohorts. The molecular and immune characteristics and the benefit of immune checkpoint inhibitor (ICI) therapy in IGS-defined subgroups were analyzed. In addition, by leveraging the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing datasets, we determined the potential therapeutic agents for high IGS-risk patients.

RESULTS

The IGS was constructed based on 8 immune-related hub genes with individual coefficients. The IGS risk model could robustly predict the survival of HCC patients in TCGA, ICGC, and CHCC cohorts. Compared with 4 previous established immune genes-based signatures, IGS exhibited superior performance in survival prediction. Additionally, for immunological characteristics and enriched pathways, a low-IGS score was correlated with IL-6/JAK/STAT3 signaling, inflammatory response and interferon α/γ response pathways, low TP53 mutation rate, high infiltration level, and more benefit from ICI therapy. In contrast, high IGS score manifested an immunosuppressive microenvironment and activated aggressive pathways. Finally, by in silico screening potential compounds, vindesine, ispinesib and dasatinib were identified as potential therapeutic agents for high-IGS risk patients.

CONCLUSIONS

This study developed a robust IGS model for survival prediction of HCC patients, providing new insights into integrating tailored risk stratification with precise immunotherapy and screening potentially targeted agents.

Integrated analysis identifies oxidative stress-related lncRNAs associated with progression and prognosis in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the most common cancers in the world. Oxidative stress reactions have been reportedly associated with oncogenesis and tumor progression. By analyzing mRNA expression data and clinical information from The Cancer Genome Atlas (TCGA), we aimed to construct an oxidative stress-related long noncoding RNA (lncRNA) risk model and identify oxidative stress-related biomarkers to improve the prognosis and treatment of CRC.

RESULTS

Differentially expressed oxidative stress-related genes (DEOSGs) and oxidative stress-related lncRNAs were identified by using bioinformatics tools. An oxidative stress-related lncRNA risk model was constructed based on 9 lncRNAs (AC034213.1, AC008124.1, LINC01836, USP30-AS1, AP003555.1, AC083906.3, AC008494.3, AC009549.1, and AP006621.3) by least absolute shrinkage and selection operator (LASSO) analysis. The patients were then divided into high- and low-risk groups based on the median risk score. The high-risk group had a significantly worse overall survival (OS) (p < 0.001). Receiver operating characteristic (ROC) and calibration curves displayed the favorable predictive performance of the risk model. The nomogram successfully quantified the contribution of each metric to survival, and the concordance index and calibration plots demonstrated its excellent predictive capacity. Notably, different risk subgroups showed significant differences in terms of their metabolic activity, mutation landscape, immune microenvironment and drug sensitivity. Specifically, differences in the immune microenvironment implied that CRC patients in certain subgroups might be more responsive to immune checkpoint inhibitors.

CONCLUSIONS

Oxidative stress-related lncRNAs can predict the prognosis of CRC patients, which provides new insight for future immunotherapies based on potential oxidative stress targets.

A risk stratification and prognostic prediction model for lung adenocarcinoma based on aging-related lncRNA

To create a risk model of aging-related long non-coding RNAs (arlncRNAs) and determine whether they might be useful as markers for risk stratification, prognosis prediction, and targeted therapy guidance for patients with lung adenocarcinoma (LUAD). Data on aging genes and lncRNAs from LUAD patients were obtained from Human Aging Genomic Resources 3 and The Cancer Genome Atlas, and differential co-expression analysis of established differentially expressed arlncRNAs (DEarlncRNAs) was performed. They were then paired with a matrix of 0 or 1 by cyclic single pairing. The risk coefficient for each sample of LUAD individuals was obtained, and a risk model was constructed by performing univariate regression, least absolute shrinkage and selection operator regression analysis, and univariate and multivariate Cox regression analysis. Areas under the curve were calculated for the 1-, 3-, and 5-year receiver operating characteristic curves to determine Akaike information criterion-based cutoffs to identify high- and low-risk groups. The survival rate, correlation of clinical characteristics, malignant-infiltrating immune-cell expression, ICI-related gene expression, and chemotherapeutic drug sensitivity were contrasted with the high- and low-risk groups. We found that 99 DEarlncRNAs were upregulated and 12 were downregulated. Twenty pairs of DEarlncRNA pairs were used to create a prognostic model. The 1-, 3-, and 5-year survival curve areas of LUAD individuals were 0.805, 0.793, and 0.855, respectively. The cutoff value to classify patients into two groups was 0.992. The mortality rate was higher in the high-risk group. We affirmed that the LUAD outcome-related independent predictor was the risk score (p < 0.001). Validation of tumor-infiltrating immune cells and ICI-related gene expression differed substantially between the groups. The high-risk group was highly sensitive to docetaxel, erlotinib, gefitinib, and paclitaxel. Risk models constructed from arlncRNAs can be used for risk stratification in patients with LUAD and serve as prognostic markers to identify patients who might benefit from targeted and chemotherapeutic agents.

Prognosis and Novel Drug Targets for Key lncRNAs of Epigenetic Modification in Colorectal Cancer

Background

Colorectal cancer (CRC) has been the 3rd most commonly malignant tumor of the gastrointestinal tract in the world. 5-Methylcytosine (m⁵C) and long noncoding RNAs (lncRNAs) have an essential role in predicting the prognosis and immune response for CRC patients. Therefore, we built a m⁵C-related lncRNA (m⁵CRlncRNA) model to investigate the prognosis and treatment methods for CRC patients.

Methods

Firstly, we secured the transcriptome and clinical data for CRC from The Cancer Genome Atlas (TCGA). Then, m⁵CRlncRNAs were recognized by coexpression analysis. Then, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were utilized to build m⁵C-related prognostic characteristics. Besides, Kaplan-Meier analysis, ROC, PCA, C-index, enrichment analysis, and nomogram were performed to investigate the model. Additionally, immunotherapy responses and antitumor medicines were explored for CRC patients.

Results

A total of 8 m⁵C-related lncRNAs (AC093157.1, LINC00513, AC025171.4, AC090948.2, ZEB1-AS1, AC109449.1, AC009041.3, and LINC02516) were adopted to construct a risk model to investigate survival and prognosis for CRC patients. CRC samples were separated into low- and high-risk groups, with the latter having a worse prognosis. The m⁵C-related lncRNA model helps us to better distinguish immunotherapy responses and IC50 of antitumor medicines in different groups of CRC patients.

Conclusion

The research may give new perspectives on tailored therapy approaches as well as novel theories for forecasting the prognosis of CRC patients.

Cuproptosis-related LncRNAs are potential prognostic and immune response markers for patients with HNSCC via the integration of bioinformatics analysis and experimental validation

Introduction

Head and neck squamous cell carcinoma (HNSCC) is a malignant neoplasm typically induced by alcohol and tobacco consumption, ranked the sixth most prevalent cancer globally. This study aimed to establish a cuproptosis-related lncRNA predictive model to assess the clinical significance in HNSCC patients.

Methods

The Cancer Genome Atlas (TCGA) database was utilized to download cuproptosis-related genes, lncRNAs profiles, and selected clinical information of 482 HNSCC samples. Cuproptosis-related lncRNAs were analyzed by Pearson correlation method, with the least absolute shrinkage and selection operator (LASSO) and univariate/multivariate Cox analyses performed to establish the cuproptosis-related lncRNA predictive model. Subsequently, the time-dependent receiver operating characteristics (ROC) and Kaplan-Meier analysis were applied to assess its prediction ability, and the model was verified by a nomogram, univariate/multivariate Cox analysis, and calibration curves. Furthermore, the principal component analysis (PCA), immune analysis, and gene set enrichment analyses (GSEA) were performed, and the 50% inhibitory concentration (IC50) prediction in the risk groups was calculated. Furthermore, the expression of six cuproptosis-related lncRNAs in HNSCC and paracancerous tissues was detected by quantitative real-time PCR (qRT-PCR).

Results

A total of 467 lncRNAs were screened as cuproptosis-associated lncRNAs in HNSCC tissues to establish an eight cuproptosis-related lncRNA prognostic signature consisting of AC024075.3, AC090587.2, AC116914.2, AL450384.2, CDKN2A-DT, FAM27E3, JPX, and LNC01089. For the high-risk group, the results demonstrated a satisfactory predicting performance with considerably worse overall survival (OS). Multivariate Cox regression confirmed that the risk score was a reliable predictive factor (95% CI: 1.089-1.208, hazard ratio =1.147), with the area of 1-, 3-, and 5-year OS under the ROC curve of 0.690, 0.78524, and 0.665, respectively. The differential analysis revealed that JPX was significantly upregulated in HNSCC tissues, while AC024075.3, AC090587.2, AC116914.2, AL450384.2, CDKN2A-DT were downregulated in HNSCC tissues by qRT-PCR assays. In addition, this gene signature was also associated with some immune-related pathways and immune cell infiltration and affected the anti-cancer immune response. Furthermore, Bexarotene, Bleomycin, Gemcitabine, etc., were identified as potential therapeutic compounds for HNSCC.

Discussions

This novel cuproptosis-related lncRNAs prognostic signature could predict prognosis and help propose novel individual therapeutic targets for HNSCC.

Identification of a cuproptosis-related lncRNA signature to predict the prognosis and immune landscape of head and neck squamous cell carcinoma

Background

Cuproptosis is considered a novel copper-induced cell death model regulated by targeting lipoylated TCA cycle proteins. In this study, we established a novel signature based on cuproptosis-related lncRNAs (crlncRNAs) to predict the prognosis and immune landscape of head and neck squamous cell carcinoma.

Methods

RNA-seq matrix, somatic mutation files, and clinical data were obtained from The Cancer Genome Atlas database. After dividing patients into two sets, a crlncRNA signature was established based on survival related crlncRNAs, which were selected by the univariate Cox analysis and least absolute shrinkage and selection operator Cox regression. To evaluate the model, Kaplan-Meier survival analysis and time-dependent receiver operating characteristic (ROC) were utilized, and a nomogram was established for survival prediction. Immune landscape analysis, drug sensitivity, cluster analysis, tumor mutation burden (TMB) and ceRNA network analysis were conducted subsequently.

Results

A crlncRNA related prognosis signature was finally constructed with 12 crlncRNAs. The areas under the ROC curves (AUCs) were 0.719, 0.705 and 0.693 respectively for 1, 3, and 5-year's overall survival (OS). Patients in the low-risk group behaved a better prognosis, lower TMB, higher immune function activity and scores. In addition, patients from cluster 2 were more sensitive to chemotherapy and immunotherapy.

Conclusion

In this study, we constructed a novel crlncRNA risk model to predict the survival of HNSCC patients. This reliable and acceptable prognostic signature may guide and promote the progress of novel treatment strategies for HNSCC patients.

Identification and validation of endocrine resistance-related and immune-related long non-coding RNA (lncRNA) signatures for predicting endocrinotherapy response and prognosis in breast cancer

Background

Endocrine resistance remains a major challenge in breast cancer (BRCA). Increasing evidence has revealed that long non-coding RNA (lncRNA) are closely implicated in tumorigenesis, drug resistance, and the immune-related pathways of cancer. However, the immune-related lncRNA remains to be thoroughly investigated in predicting the endocrine therapeutic response and prognosis of BRCA.

Methods

Based on the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, and calculating the correlation of lncRNAs with immune-related genes obtained from ImmPort and InnateDB databases, we finally obtained endocrine resistance-related and immune-related long non-coding RNAs (ERIR-lncRNAs). Univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox regression were performed to screen prognosis-associated ERIR-lncRNAs and establish signatures, using 2 separate datasets from GEO for external validation. Principal component analysis (PCA), Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and multivariate Cox regression were performed to demonstrate the robustness and predictability of the signature. We investigated tumor immune infiltration and tumor mutation burden (TMB) between high- and low-risk groups, and the role of key lncRNAs in endocrine resistant breast cancer was confirmed by quantitative real-time polymerase chain reaction (qRT-PCR), Cell Counting Kit 8 (CCK 8) and transwell assays.

Results

A total of 781 endocrine resistance related lncRNAs were identified, of which 12 lncRNAs were associated with immunity. Then, three ERIR-lncRNAs with prognostic relevance were screened to successfully construct the risk signature. Compared to sensitive patients, the endocrine resistant patients had higher risk scores in both the training and validation sets (P<0.05). The high-risk group had significantly shorter survival times (P<0.001) with area under the curve (AUC) values of 0.710, 0.649, and 0.672 at 1, 3, and 5 years. Univariate and multivariate Cox regression indicated that our signature was an independent prognostic factor (P<0.001). Through immune infiltration analysis, it was revealed that the high-risk scores were associated with T follicular helper (Tfh) differentiation and exhibited a pro-tumor phenomenon with the Th1/Th2 balance shifting toward Th2. The key lncRNAs promote cell proliferation and migration as confirmed by qRT-PCR, CCK-8 and transwell assays.

Conclusions

The ERIR-lncRNA signature is valuable in predicting endocrine therapeutic response and prognosis of BRCA, revealing a potential relationship between endocrine resistance and TME.

Comprehensive analysis of anoikis-related long non-coding RNA immune infiltration in patients with bladder cancer and immunotherapy

Background

Anoikis is a form of programmed cell death or programmed cell death(PCD) for short. Studies suggest that anoikis involves in the decisive steps of tumor progression and cancer cell metastasis and spread, but what part it plays in bladder cancer remains unclear. We sought to screen for anoikis-correlated long non-coding RNA (lncRNA) so that we can build a risk model to understand its ability to predict bladder cancer prognosis and the immune landscape.

Methods

We screened seven anoikis-related lncRNAs (arlncRNAs) from The Cancer Genome Atlas (TCGA) and designed a risk model. It was validated through ROC curves and clinicopathological correlation analysis, and demonstrated to be an independent factor of prognosis prediction by uni- and multi-COX regression. In the meantime, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, immune infiltration, and half-maximal inhibitory concentration prediction (IC50) were implemented with the model. Moreover, we divided bladder cancer patients into three subtypes by consensus clustering analysis to further study the differences in prognosis, immune infiltration level, immune checkpoints, and drug susceptibility.

Result

We designed a risk model of seven arlncRNAs, and proved its accuracy using ROC curves. COX regression indicated that the model might be an independent prediction factor of bladder cancer prognosis. KEGG enrichment analysis showed it was enriched in tumors and immune-related pathways among the people at high risk. Immune correlation analysis and drug susceptibility results indicated that it had higher immune infiltration and might have a better immunotherapy efficacy for high-risk groups. Of the three subtypes classified by consensus clustering analysis, cluster 3 revealed a positive prognosis, and cluster 2 showed the highest level of immune infiltration and was sensitive to most chemistries. This is helpful for us to discover more precise immunotherapy for bladder cancer patients.

Conclusion

In a nutshell, we found seven arlncRNAs and built a risk model that can identify different bladder cancer subtypes and predict the prognosis of bladder cancer patients. Immune-related and drug sensitivity researches demonstrate it can provide individual therapeutic schedule with greater precision for bladder cancer patients.

Molecular subtypes and a prognostic model for hepatocellular carcinoma based on immune- and immunogenic cell death-related lncRNAs

Background

Accumulating evidence shows that immunogenic cell death (ICD) enhances immunotherapy effectiveness. In this study, we aimed to develop a prognostic model combining ICD, immunity, and long non-coding RNA biomarkers for predicting hepatocellular carcinoma (HCC) outcomes.

Methods

Immune- and immunogenic cell death-related lncRNAs (IICDLs) were identified from The Cancer Genome Atlas and Ensembl databases. IICDLs were extracted based on the results of differential expression and univariate Cox analyses and used to generate molecular subtypes using ConsensusClusterPlus. We created a prognostic signature based on IICDLs and a nomogram based on risk scores. Clinical characteristics, immune landscapes, immune checkpoint blocking (ICB) responses, stemness, and chemotherapy responses were also analyzed for different molecular subtypes and risk groups.

Result

A total of 81 IICDLs were identified, 20 of which were significantly associated with overall survival (OS) in patients with HCC. Cluster analysis divided patients with HCC into two distinct molecular subtypes (C1 and C2), with patients in C1 having a shorter survival time than those in C2. Four IICDLs (TMEM220-AS1, LINC02362, LINC01554, and LINC02499) were selected to develop a prognostic model that was an independent prognostic factor of HCC outcomes. C1 and the high-risk group had worse OS (hazard ratio > 1.5, p < 0.01), higher T stage (p < 0.05), higher clinical stage (p < 0.05), higher pathological grade (p < 0.05), low immune cell infiltration (CD4⁺ T cells, B cells, macrophages, neutrophils, and myeloid dendritic cells), low immune checkpoint gene expression, poor response to ICB therapy, and high stemness. Different molecular subtypes and risk groups showed significantly different responses to several chemotherapy drugs, such as doxorubicin (p < 0.001), 5-fluorouracil (p < 0.001), gemcitabine (p < 0.001), and sorafenib (p < 0.01).

Conclusion

Our study identified molecular subtypes and a prognostic signature based on IICDLs that could help predict the clinical prognosis and treatment response in patients with HCC.

Identification of prognostic immune-related lncRNAs in pancreatic cancer

Long noncoding RNAs (lncRNAs) play a critical role in the immune regulation and tumor microenvironment of pancreatic cancer (PaCa). To construct a novel immune-related prognostic risk model for PaCa and evaluate the prognostic prediction of lncRNAs, essential immune-related lncRNAs (IRlncRNAs) were identified by Pearson correlation analysis of differentially expressed immune-related genes (IRGs) and IRlncRNAs in PaCa from The Cancer Genome Atlas (TCGA) and GTEx databases. Least absolute shrinkage and selection operator (LASSO) regression was also applied to construct a prognostic risk model of IRlncRNAs, and gene set enrichment analysis (GSEA) was further applied for functional annotation for these IRlncRNAs. A total of 148 IRlncRNAs were identified in PaCa to construct a prognostic risk model. Among them, lncRNA LINC02325, FNDC1-AS1, and ZEB2-AS1 were significantly upregulated in 69 pairs of PaCa tissues by qRT-PCR. ROC analyses showed that LINC02325 (AUC = 0.80), FNDC1-AS1 (AUC = 0.76), and ZEB2-AS1 (AUC = 0.75) had a good predictive effect on 5-year survival prognosis. We demonstrated that high expression levels of ZEB2-AS1 and LINC02325 were not only positively associated with tumor size and CA199, but elevated levels of ZEB2-AS1 and FNDC1-AS1 were also positively correlated with tumor stage. GSEA further revealed that immune-related pathways were mainly enriched in the high-risk groups. Several immune-related algorithms demonstrated that four IRlncRNAs were related to immune infiltration, immune checkpoints, and immune-related functions. Thus, the prognostic risk model based on IRlncRNAs in Paca indicates that the four IRlncRNA signatures may serve as predictors of survival and potential predictive biomarkers of the pancreatic tumor immune response.

Bioinformatics Analysis and Validation of the Role of Lnc-RAB11B-AS1 in the Development and Prognosis of Hepatocellular Carcinoma

Lnc-RAB11B-AS1 is reported to be dysregulated in several types of cancers and can function as both an oncogene and tumor suppressor gene. To evaluate the potential role of lnc-RAB11B-AS1 in hepatocellular carcinoma (HCC), we investigated and evaluated its expression in HCC based on the data mining of a series of public databases, including TCGA, GEO, ICGC, HPA, DAVID, cBioPortal, GeneMIANA, TIMER, and ENCORI. The data showed downregulation of lnc-RAB11B-AS1 in HCC and was accompanied by the synchronous downregulation of the targeted RAB11B mRNA and its protein. Low expression of lnc-RAB11B-AS1 was associated with shorter overall survival (OS) and disease-free survival (DFS) of HCC patients, PD1/PD-L1 was correlated with low expression of RAB11B. Furthermore, Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed a correlation between immune cell change and non-alcoholic fatty liver disease. The above findings revealed that lnc-RAB11B-AS1 was down-regulated in HCC and closely associated with the clinical stage of the HCC patients, suggesting that lnc-RAB11B-AS1 could be a possible predictor for HCC and a potential new therapeutic target for the treatment of HCC.

Identification and validation of an immune-related lncRNAs signature to predict the overall survival of ovarian cancer

Ovarian cancer (OC) is the most lethal gynecological cancer in women. Studies had reported that immune-related lncRNAs signatures were valuable in predicting the survival and prognosis of patients with various cancers. In our study, the prognostic value of immune-related lncRNAs was investigated in OC patients from TCGA-RNA-seq cohort (n=378) and HG-U133_Plus_2 cohort (n=590), respectively. Pearson correlation analysis was implemented to screen the immune-related lncRNA and then univariate Cox regression analysis was performed to explore their prognostic value in OC patients. Five prognostic immune-related lncRNAs were identified as prognostic lncRNAs. Besides, they were inputted into a LASSO Cox regression to establish and validate an immune-related lncRNA prognostic signature in TCGA-RNA-Seq cohort and HG-U133_Plus_2 cohort, respectively. Based on the best cut-off value of risk score, patients were divided into high- and low-risk groups. Survival analysis suggested that patients in the high-risk group had a worse overall survival (OS) than those in the low-risk group in both cohorts. The association between clinicopathological feathers and risk score was then evaluated by using stratification analysis. Moreover, we constructed a nomogram based on risk score, age and stage, which had a strong ability to forecast the OS of the OC patients. The influence of risk score on immune infiltration and immunotherapy response were assessed and the results suggested that patients with high-risk score might recruit multiple immune cells and stromal cells, leading to facilitating immune surveillance evasive. Ultimately, we demonstrated that the risk model was associated with chemotherapy response of multiple antitumor drugs, especially for paclitaxel, metformin and veliparib, which are commonly used in treating OC patients. In conclusion, we constructed a novel immune-related lncRNA signature, which had a potential prognostic value for OC patients and might facilitate personalized counselling for immunotherapy and chemotherapy.

A Novel Oxidative Stress-Related lncRNA Signature That Predicts the Prognosis and Tumor Immune Microenvironment of Breast Cancer

Background

The association between oxidative stress and lncRNAs within the cancer-related researching field has been a controversial subject. At present, the exact function of oxidative stress as well as lncRNAs exert in breast cancer (BC) are still unclear. Therefore, the present study examined the lncRNAs oxidative stress-related in BC.

Methods

Transcriptome data of BC obtained from TCGA (The Cancer Genome Atlas) database were used to generate synthetic matrices. Patients with breast cancer were randomly assigned to training, testing, or combined groups. The prognostic signature of oxidative stress was created using the selection operator Cox regression method, and the difference in prognosis between groups was examined using Kaplan-Meier curves, the accuracy of which was calculated using a receiver-operating characteristic-area through the curve (ROC-AUC) analysis with internal validation. Also, the Gene Set Enrichment Analyses (GSEA) was applied for the analysis of the risk groups. To conclude, the half-maximal inhibitory concentration (IC50) of these groups were investigated by immunoassay assay.

Results

A model based on 7 lncRNAs related to oxidative stress was proposed, and the calibration plots and projected prognosis matched well. For prognosis at 5, 3, and 1 year, the area under the ROC curve (AUC) values were 0.777, 0.777, and 0.759. The functions of target genes identified by GSEA appear to be mainly expressed in metabolism, signal transduction, tumorigenesis, and also the progression. The remarkable differences in IC50 and gene expression between risk groups in this study provide a deep insight for further systemic treatment. Higher macrophage scores were acquired in the high-risk group, of which patients showed more response to conventional chemotherapy drugs, such as AKT inhibitor VIII and Lapatinib, as well as immunotherapy strategies including anti-CD80, TNF SF4, CD276, and NRP1.

Conclusion

The prognosis of breast cancer can be independently predicted by the markers, which sheds light on further research of the specific role of lncRNAs which are oxidative stress-related and clinical treatment of breast cancer.

Identification of an Oxidative Stress-Related LncRNA Signature for Predicting Prognosis and Chemotherapy in Patients With Hepatocellular Carcinoma

Background: Oxidative stress plays a critical role in oncogenesis and tumor progression. However, the prognostic role of oxidative stress-related lncRNA in hepatocellular carcinomas (HCC) has not been fully explored. Methods: We used the gene expression data and clinical data from The Cancer Genome Atlas (TCGA) database to identify oxidative stress-related differentially expressed lncRNAs (DElncRNAs) by pearson correlation analysis. A four-oxidative stress-related DElncRNA signature was constructed by LASSO regression and Cox regression analyses. The predictive signature was further validated by Kaplan-Meier (K-M) survival analysis, receiver operating characteristic (ROC) curves, nomogram and calibration plots, and principal component analysis (PCA). Single-sample gene set enrichment analysis (ssGSEA) was used to explore the relationship between the signature and immune status. Finally, the correlation between the signature and chemotherapeutic response of HCC patients was analyzed. Results: In our study, the four-DElncRNA signature was not only proved to be a robust independent prognostic factor for overall survival (OS) prediction, but also played a crucial role in the regulation of progression and chemotherapeutic response of HCC. ssGSEA showed that the signature was correlated with the infiltration level of immune cells. HCC patients in high-risk group were more sensitive to the conventional chemotherapeutic drugs including Sorafenib, lapatinib, Nilotinib, Gefitinib, Erlotinib and Dasatinib, which pave the way for targeting DElncRNA-associated treatments for HCC patients. Conclusion: Our study has originated a prognostic signature for HCC based on oxidative stress-related DElncRNAs, deepened the understanding of the biological role of four key DElncRNAs in HCC and laid a theoretical foundation for the choice of chemotherapy.

Development and validation of an ECM-related prognostic signature to predict the immune landscape of human hepatocellular carcinoma

Background

The global burden of hepatocellular carcinoma (HCC) is increasing, negatively impacting social health and economies. The discovery of novel and valuable biomarkers for the early diagnosis and therapeutic guidance of HCC is urgently needed.

Methods

Extracellular matrix (ECM)-related gene sets, transcriptome data and mutation profiles were downloaded from the Matrisome Project and The Cancer Genome Atlas (TCGA)-LIHC datasets. Coexpression analysis was initially performed with the aim of identifying ECM-related lncRNAs (r > 0.4, p < 0.001). The screened lncRNAs were subjected to univariate analysis to obtain a series of prognosis-related lncRNA sets, which were incorporated into least absolute selection and shrinkage operator (LASSO) regression for signature establishment. Following the grouping of LIHC samples according to risk score, the correlations between the signature and clinicopathological, tumour immune infiltration, and mutational characteristics as well as therapeutic response were also analysed. lncRNA expression levels used for modelling were finally examined at the cellular and tissue levels by real-time PCR. All analyses were based on R software.

Results

AL031985.3 and MKLN1-AS were ultimately identified as signature-related lncRNAs, and both were significantly upregulated in HCC tissue samples and cell lines. The prognostic value of the signature reflected by the AUC value was superior to that of age, sex, grade and stage. Correlation analysis results demonstrated that high-risk groups exhibited significant enrichment of immune cells (DCs, macrophages and Tregs) and increased expression levels of all immune checkpoint genes. Prominent differences in clinicopathological profiles, immune functions, tumour mutation burden (TMB) and drug sensitivity were noted between the two risk groups.

Conclusions

Our signature represents a valuable predictive tool in the prognostic management of HCC patients. Further validation of the mechanisms involved is needed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10049-w.

NXPH4 Used as a New Prognostic and Immunotherapeutic Marker for Muscle-Invasive Bladder Cancer

Background

One of the most common malignant tumors of the urinary system is muscle-invasive bladder cancer (MIBC). With the increased use of immunotherapy, its importance in the field of cancer is becoming abundantly evident. This study classifies MIBC according to GSVA score from the perspective of the GSEA immune gene set.

Methods

This study integrated the sequencing and clinical data of MIBC patients in TCGA and GEO databases, then scored the data using the GSVA algorithm, the CNMF algorithm was implemented to divide the subtypes of GEO and TCGA datasets, respectively, and finally screened and determined the key pathways in combination with clinical data. Simultaneously, LASSO Cox regression model was constructed based on key pathway genes to assess the model's predictive ability (ROC) and describe the immune landscape differences between high- and low-risk groups; key genes were further analyzed and verified in patient tissues.

Results

404 TCGA and 297 GEO datasets were divided into C1-3 groups (TCGA-C1:120/C2:152/C3:132; GEO- C1:112/C2:101/C3:84), of which TCGA-C2 (n = 152) subtype and GEO-C1 (n = 112) subtype had the worst prognosis. LASSO Cox regression model with ROC (train set = 0.718, test set = 0.667) could be constructed. When combined with the Cancer Immunome Atlas database, it was found that patients with high-risk scores were more sensitive to PD-1 inhibitor and PD-1 inhibitor combined with CTLA-4. NXPH4, as a key gene, plays a role in MIBC with tissue validation results show that nxph4 is highly expressed in tumor.

Conclusion

The immune gene score of MIBC data in TCGA and GEO databases was successfully evaluated using GSVA in this research. The lasso Cox expression model was successfully constructed by screening immune genes, the high-risk group had a worse prognosis and higher sensitivity to immunotherapy, PD-1 inhibitors or PD-1 combined with CTLA-4 inhibitors can be preferentially used in high-risk patients who are sensitive to immunotherapy, and NXPH4 may be a molecular target to adjust the effect of immunotherapy.

Constructing a prognostic immune-related lncRNA model for colon cancer

Colon cancer is a common digestive tract tumor. Although many gene prognostic indicators have been used to predict the prognosis of colon cancer patients, the accuracy of these prognostic indicators is still uncertain. Thus, it is necessary to construct a model for the prognostic analysis of colon cancer. We downloaded the original transcriptome data of colon cancer and performed a differential coexpression analysis of immune-related genes to obtain different immune-related long noncoding RNAs, which were paired as differentially expressed immune-related lncRNA pairs (DEirlncRNAPs). Then, the 1-year overall survival rate receiver operating characteristic curve was calculated, and the Akaike information criterion value was evaluated to determine the maximum inflection point, which was used as the cutoff point to identify groups of colon cancer patients at high and low risk for death. Subsequently, the optimal prediction model was established. Finally, we used the patients' survival times, clinicopathological features, tumor infiltrating immune cells, chemotherapy responses, and immunosuppressive biomarkers to verify the DEirlncRNAP model. Seventy-one DEirlncRNAPs were obtained to build the risk assessment model. The patients were divided into a high-risk group and a low-risk group according to the cutoff point. Then, the DEirlncRNAP model was verified using patient survival times, clinicopathological features, tumor-infiltrating immune cells, chemotherapy responses, and immunosuppressive biomarkers. A new DEirlncRNAP model for predicting the prognosis of colon cancer patients was established, which could reveal new insights into the relationships of colon cancer with tumor-infiltrating immune cells and antitumor immunotherapy.

A novel marker based on necroptosis-related long non-coding RNA for forecasting prognostic in patients with clear cell renal cell carcinoma

Background: The incidence of clear cell renal cell carcinoma (ccRCC) is high and has increased gradually in recent years. At present, due to the lack of effective prognostic indicators, the prognosis of ccRCC patients is greatly affected.Necroptosis is a type of cell death, and along with cell necrosis is considered a new cancer treatment strategy. The aim of this study was to construct a new marker for predicting the prognosis of ccRCC patients based on long non-coding RNA (nrlncRNAs) associated with necroptosis.

Methods: RNA sequence data and clinical information of ccRCC patients from the Cancer Genome Atlas database (TCGA) were downloaded. NrlncRNA was identified by Pearson correlation study. The differentially expressed nrlncRNA and nrlncRNA pairs were identified by univariate Cox regression and Lasso-Cox regression. Finally, a Kaplan-Meier survival study, Cox regression, clinicopathological features correlation study, and receiver operating characteristic (ROC) spectrum were used to evaluate the prediction ability of 25-nrlncrnas for markers. In addition, correlations between the risk values and sensitivity to tumor-infiltrating immune cells, immune checkpoint inhibitors, and targeted drugs were also investigated.

Results: In the current research, a novel marker of 25-nrlncRNAs pairs was developed to improve prognostic prediction in patients with ccRCC. Compared with clinicopathological features, nrlncRNAs had a higher diagnostic validity for markers, with the 1-year, 3-years, and 5-years operating characteristic regions being 0.902, 0.835, and 0.856, respectively, and compared with the stage of 0.868, an increase of 0.034. Cox regression and stratified survival studies showed that this marker could be an independent predictor of ccRCC patients. In addition, patients with different risk scores had significant differences in tumor-infiltrating immune cells, immune checkpoint, and semi-inhibitory concentration of targeted drugs. The feature could be used to evaluate the clinical efficacy of immunotherapy and targeted drug therapy.

Conclusion: 25-nrlncRNAs pair markers may help to evaluate the prognosis and molecular characteristics of ccRCC patients, which improve treatment methods and can be more used in clinical practice.

Construction and validation of a ferroptosis-related long noncoding RNA signature in clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is characterized by the accumulation of lipid-reactive oxygen species. Ferroptosis, due to the lipid peroxidation, has been reported to be strongly correlated with tumorigenesis and progression. However, the functions of the ferroptosis process in ccRCC remain unclear.

Methods

After sample cleaning, data integration, and batch effect removal, we used the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases to screen out the expression and prognostic value of ferroptosis-related lncRNAs and then performed the molecular subtyping using the K-means method. Then, the functional pathway enrichment and immune microenvironment infiltration between the different clusters were carried out. The results showed a significant difference in immune cell infiltration between the two clusters and the associated marker responded to individualized differences in treatment. Then, least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish a prognostic signature based on 5 lncRNAs. This signature could accurately predicted patient prognosis and served as an independent clinical risk factor. We then combined significant clinical parameters in multivariate Cox regression and the prognostic signature to construct a clinical predictive nomogram, which provides appropriate guidance for predicting the overall survival of ccRCC patients.

Results

The prognostic differentially expressed ferroptosis-related LncRNAs (DEFRlncRNAs) were found, and 5 lncRNAs were finally used to establish the prognostic signature in the TCGA cohort, with subsequently validation in the internal and external cohorts. Moreover, we conducted the molecular subtyping and divided the patients in the TCGA cohort into two clusters showing differences in Hallmark pathways, immune infiltration, immune target expression, and drug therapies. Differences between clusters contributed to individualizing treatment. Furthermore, a nomogram was established to better predict the clinical outcomes of the ccRCC patients.

Conclusions

Our study conducted molecular subtyping and established a novel predictive signature based on the ferroptosis-related lncRNAs, which contributed to the prognostic prediction and individualizing treatment of ccRCC patients.

Identification of cuproptosis-associated IncRNAs signature and establishment of a novel nomogram for prognosis of stomach adenocarcinoma

Purpose: Stomach adenocarcinoma (STAD) is one of the common cancers globally. Cuproptosis is a newly identified cell death pattern. The role of cuproptosis-associated lncRNAs in STAD is unknown.

Methods: STAD patient data from TCGA were used to identify prognostic lncRNAs by Cox regression and LASSO. A nomogram was constructed to predict patient survival. The biological profiles were evaluated through GO and KEGG.

Results: We identified 298 cuproptosis-related lncRNAs and 13 survival-related lncRNAs. Patients could be categorized into either high risk group or low risk group with 9-lncRNA risk model with significantly different survival time (p < 0.001). ROC curve and nomogram confirmed the 9-lncRNA risk mode had good prediction capability. Patients in the lower risk score had high gene mutation burden. We also found that patients in the two groups might respond differently to immune checkpoint inhibitors and some anti-tumor compounds.

Conclusion: The nomogram with 9-lncRNA may help guide treatment of STAD. Future clinical studies are necessary to verify the nomogram.

Development and validation of a novel cellular senescence-related prognostic signature for predicting the survival and immune landscape in hepatocellular carcinoma

Background: Cellular senescence is a typical irreversible form of life stagnation, and recent studies have suggested that long non-coding ribonucleic acids (lncRNA) regulate the occurrence and development of various tumors. In the present study, we attempted to construct a novel signature for predicting the survival of patients with hepatocellular carcinoma (HCC) and the associated immune landscape based on senescence-related (sr) lncRNAs.

Method: Expression profiles of srlncRNAs in 424 patients with HCC were retrieved from The Cancer Genome Atlas database. Lasso and Cox regression analyses were performed to identify differentially expressed lncRNAs related to senescence. The prediction efficiency of the signature was checked using a receiver operating characteristic (ROC) curve, Kaplan–Meier analysis, Cox regression analyses, nomogram, and calibration. The risk groups of the gene set enrichment analysis, immune analysis, and prediction of the half-maximal inhibitory concentration (IC50) were also analyzed. Quantitative real-time polymerase chain reaction (qPCR) was used to confirm the levels of AC026412.3, AL451069.3, and AL031985.3 in normal hepatic and HCC cell lines.

Results: We identified 3 srlncRNAs (AC026412.3, AL451069.3, and AL031985.3) and constructed a new risk model. The results of the ROC curve and Kaplan–Meier analysis suggested that it was concordant with the prediction. Furthermore, a nomogram model was constructed to accurately predict patient prognosis. The risk score also correlated with immune cell infiltration status, immune checkpoint expression, and chemosensitivity. The results of qPCR revealed that AC026412.3 and AL451069.3 were significantly upregulated in hepatoma cell lines.

Conclusion: The novel srlncRNA (AC026412.3, AL451069.3, and AL031985.3) signatures may provide insights into new therapies and prognosis predictions for patients with HCC.

A novel immune-related radioresistant lncRNAs signature based model for risk stratification and prognosis prediction in esophageal squamous cell carcinoma

Background and purpose: Radioresistance remains a major reason of radiotherapeutic failure in esophageal squamous cell carcinoma (ESCC). Our study is to screen the immune-related long non-coding RNA (ir-lncRNAs) of radiation-resistant ESCC (rr-ESCC) via Gene Expression Omnibus (GEO) database and to construct a prognostic risk model.

Methods: Microarray data (GSE45670) related to radioresistance of ESCC was downloaded from GEO. Based on pathologic responses after chemoradiotherapy, patients were divided into a non-responder (17 samples) and responder group (11 samples), and the difference in expression profiles of ir-lncRNAs were compared therein. Ir-lncRNA pairs were constructed for the differentially expressed lncRNAs as prognostic variables, and the microarray dataset (GSE53625) was downloaded from GEO to verify the effect of ir-lncRNA pairs on the long-term survival of ESCC. After modelling, patients are divided into high- and low-risk groups according to prognostic risk scores, and the outcomes were compared within groups based on the COX proportional hazards model. The different expression of ir-lncRNAs were validated using ECA 109 and ECA 109R cell lines via RT-qPCR.

Results: 26 ir-lncRNA genes were screened in the GSE45670 dataset with differential expression, and 180 ir-lncRNA pairs were constructed. After matching with ir-lncRNA pairs constructed by GSE53625, six ir-lncRNA pairs had a significant impact on the prognosis of ESCC from univariate analysis model, of which three ir-lncRNA pairs were significantly associated with prognosis in multivariate COX analysis. These three lncRNA pairs were used as prognostic indicators to construct a prognostic risk model, and the predicted risk scores were calculated. With a median value of 2.371, the patients were divided into two groups. The overall survival (OS) in the high-risk group was significantly worse than that in the low-risk group (p < 0.001). The 1-, 2-, and 3-year prediction performance of this risk-model was 0.666, 0.702, and 0.686, respectively. In the validation setting, three ir-lncRNAs were significantly up-regulated, while two ir-lncRNAs were obviouly down-regulated in the responder group.

Conclusion: Ir-lncRNAs may be involved in the biological regulation of radioresistance in patients with ESCC; and the prognostic risk-model, established by three ir-lncRNAs pairs has important clinical value in predicting the prognosis of patients with rr-ESCC.

COVID-19–associated lncRNAs as predictors of survival in uterine corpus endometrial carcinoma: A prognostic model

Background: Patients with uterine corpus endometrial carcinoma (UCEC) may be susceptible to the coronavirus disease-2019 (COVID-19). Long non–coding RNAs take on a critical significance in UCEC occurrence, development, and prognosis. Accordingly, this study aimed to develop a novel model related to COVID-19–related lncRNAs for optimizing the prognosis of endometrial carcinoma.

Methods: The samples of endometrial carcinoma patients and the relevant clinical data were acquired in the Carcinoma Genome Atlas (TCGA) database. COVID-19–related lncRNAs were analyzed and obtained by coexpression. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were performed to establish a COVID-19–related lncRNA risk model. Kaplan–Meier analysis, principal component analysis (PCA), and functional enrichment annotation were used to analyze the risk model. Finally, the potential immunotherapeutic signatures and drug sensitivity prediction targeting this model were also discussed.

Results: The risk model comprising 10 COVID-19–associated lncRNAs was identified as a predictive ability for overall survival (OS) in UCEC patients. PCA analysis confirmed a reliable clustering ability of the risk model. By regrouping the patients with this model, different clinic-pathological characteristics, immunotherapeutic response, and chemotherapeutics sensitivity were also observed in different groups.

Conclusion: This risk model was developed based on COVID-19–associated lncRNAs which would be conducive to the precise treatment of patients with UCEC.

N6-Methyladenosine (m6A)-Related lncRNAs Are Potential Signatures for Predicting Prognosis and Immune Response in Lung Squamous Cell Carcinoma

Background

Despite increasing understanding of m⁶A-related lncRNAs in lung cancer, the role of m⁶A-related lncRNAs in the prognosis and treatment of lung squamous cell carcinoma is poorly understood to date. Thus, the current study aims to elucidate its role and build a model to predict the prognosis of LUSC patients.

Materials and Methods

The data of the current study were accessed from the TCGA database. Pearson correlation analysis was performed to identify lncRNAs correlated to m⁶A. Next, an m⁶A-related lncRNAs risk model was built using a single factor, least absolute association, selection operator, and multivariate Cox regression analysis.

Results

The relevance between 23 m⁶A genes and 14,056 lncRNAs is shown by Pearson correlation analysis by Sankey diagram. Multivariate Cox regression analysis determined that 11 m⁶A-lncRNAs show predictive potential in prognosis, which is confirmed by the consistency index, Kaplan–Meier analysis, principal component analysis, and ROC curve. Additionally, the immune analysis showed that the enrichment of immune cells, major histocompatibility complex molecules, and immune checkpoints in the high and low-risk subgroups were markedly disparate, with the high-risk group showing a stronger immune escape ability and a worse response to immunotherapy.

Conclusion

In conclusion, the risk model based on m⁶A-related lncRNAs showed great promise in predicting the prognosis and the efficacy of immunotherapy.

Identification of Immune-Related lncRNAs for Predicting Prognosis and Immune Landscape Characteristics of Uveal Melanoma

Immune-related genes and long noncoding RNAs (lncRNAs) have a significant impact on the prognostic value and immunotherapeutic response of uveal melanoma (UM). Therefore, we tried to develop a prognostic model on the basis of irlncRNAs for predicting prognosis and response on immunotherapy of UM patients. We identified 1,664 immune-related genes and 2,216 immune-related lncRNAs (irlncRNAs) and structured a prognostic model with 3 prognostic irlncRNAs by co-expression analysis, univariable Cox, LASSO, and multivariate Cox regression analyses. The Kaplan–Meier analysis indicated that patients in the high-risk group had a shorter survival time than patients in the low-risk group. The ROC curves demonstrated the high sensitivity and specificity of the signature for survival prediction, and the one-, three-, and five-year AUC values, respectively, were 0.974, 0.929, and 0.941 in the entire set. Cox regression analysis, C-index, DCA, PCA analysis, and nomogram were also applied to assess the validity and accuracy of the risk model. The GO and KEGG enrichment analyses indicated that this signature is significantly related to immune-related pathways and molecules. Finally, we investigated the immunological characteristics and immunotherapy of the model and identified various novel potential compounds in the model for UM. In summary, we constructed a new model on the basis of irlncRNAs that can accurately predict prognosis and response on immunotherapy of UM patients, which may provide valuable clinical applications in antitumor immunotherapy.