Inflammation-Related Long Non-Coding RNA Signature Predicts the Prognosis of Gastric Carcinoma

Dysregulation of LINC01094 is involved in the pathogenesis of pulpitis by regulating the miR-340-5p expression

Pulpitis seriously affects people's living standards and dental health, so identifying effective therapeutic targets is crucial for pulpitis. The research aimed to explore the underlying regulatory mechanism of LINC01094 and miR-340-5p in pulpitis. The study involved a total of 173 subjects (97 pulpitis and 76 healthy individuals). The expression of LINC01094 and miR-340-5p were evaluated through the polymerase chain reaction (PCR). The association linking LINC01094 and miR-340-5p expression was assessed by Pearson correlation analysis. The Human dental pulp cells (HDPCs) injury model was conducted by lipopolysaccharide (LPS). Cell proliferation was examined through the Cell Counting Kit-8 assay and flow cytometry. Cell apoptosis was also evaluated by flow cytometry. The caspase-3 levels and inflammatory cytokines were quantified using an enzyme-linked immunosorbent assay (ELISA). Upregulated LINC01094 and downregulated miR-340-5p expression were observed in pulpitis and LPS-induced HDPC injury models. A negative correlation was observed between miR-340-5p and LINC01094 expression in pulpitis. LPS could suppress proliferation and promote apoptosis of HDPCs. The TNF-α, IL-6, and IL-1β levels in LPS-induced HDPCs were also elevated. The HDPC injury induced by LPS could be aggravated by the LINC01094 overexpression. MiR-340-5p showed a relieved effect on HDPC injury and could alleviate the HDPC injury aggravated by LINC01094 overexpression. In summary, upregulated LINC01094 and downregulated miR-340-5p expression was observed in pulpitis. LINC01094 could accelerate the pulpitis progression via targeting miR-340-5p.

A New Cuproptosis-Related lncRNAs Model for Predicting the Prognosis of Hepatitis B Virus-Associated Hepatocellular Carcinoma and Experimental Validation of LINC01269

Background

Hepatocellular carcinoma (HCC) triggered by Hepatitis B virus (HBV) remains a significant clinical challenge, necessitating novel therapeutic interventions. Copper ionophores, recognized for introducing an innovative type of programmed cell death termed cuproptosis, present promising potentials for cancer therapy. Nevertheless, The role of cuproptosis-related lncRNAs (CRLRs) in HBV-HCC has not been clearly elucidated.

Methods

This study utilised univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariable Cox regression analyses to establish a signature for CRLRs in HBV-HCC. This prognostic model was validated with an independent internal validation cohort, combined with clinical parameters, and used to construct a nomogram for patient survival predictions. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were employed to explore associated biological pathways. Additionally, a protein-protein interaction (PPI) network was developed, and implications for tumour mutational burden (TMB) and drug response were examined. A comprehensive bioinformatics analysis of these hub CRLRs was performed, followed by experimental validation through quantitative real-time PCR (qRT-PCR) and functional cellular assays.

Results

The nomogram showed high predictive accuracy for HBV-HCC patient survival. GO and GSEA analyses indicated that these lncRNAs are involved in pathways related to cancer and oestrogen metabolism. A PPI network consisting of 201 nodes and 568 edges was developed, and the TMB and drug response differed significantly between high- and low-risk groups. Analyses identified three hub CRLRs, SOS1-IT1, AC104695.3, and LINC01269, which were significantly differentially expressed in HCC tissues. In vitro, LINC01269 was found to enhance HCC cell proliferation, invasion, and migration.

Conclusion

The first systematic exploration of the roles of CRLRs in HBV-HCC demonstrates their critical involvement in the disease's pathogenesis and possible therapeutic implication. The distinct expression patterns and significant biological pathways suggest that these lncRNAs may facilitate novel therapeutic targets.

The LINC01094/miR-545-3p/SLC7A11 Signaling Axis Promotes the Development of Gastric Cancer by Regulating Cell Growth and Ferroptosis

This study aimed to investigate the role and mechanism of action of LINC01094 in the development of gastric cancer (GC). The expression levels of LINC01094 in GC patients and healthy individuals were analyzed online using the Cancer Genome Atlas database. Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot analyses were performed to determine the expression of LINC01094/miR-545-3p/SLC7A11 in GC tissues and cells. Functional experiments (MTT assay, colony formation assay, and flow cytometry) were conducted to assess the effect of LINC01094 and miR-545-3p on cell proliferation, viability, apoptosis, cell cycle, and reactive oxygen species. Correlations between LINC01094 and miR-545-3p, as well as SLC7A11, were analyzed and validated using the dual-luciferase reporter assay and RNA immunoprecipitation. The levels of Fe2+, malondialdehyde, and glutathione in the cells were measured biochemically, and the protein expression levels of Bcl-2, cleaved caspase3, Cyclin D1, and p21 were detected by Western blotting. LINC01094 was significantly upregulated in the GC tissues and cells with a targeting relationship with miR-545-3p; the expression levels of LINC01094 and miR-545-3p were negatively correlated. Knockdown of LINC01094 notably inhibited the proliferation and viability of GC cells and promoted cell ferroptosis, which, however, was abrogated by the silencing of miR-545-3p. These findings indicate that miR-545-3p could target and positively correlate with SLC7A11 expression. Additionally, LINC01094 could promote GC cell progression and affect cellular ferroptosis by regulating the miR-545-3p/SLC7A11 signaling axis.

Genetic association and causal effects between inflammatory bowel disease and conjunctivitis

Background

Inflammatory bowel disease (IBD) is often clinically associated with conjunctivitis, which may result from genetic associations and causal effects.

Methods

Genetic correlations were investigated through the genome-wide association study (GWAS) data on IBD and conjunctivitis using the linkage disequilibrium score regression (LDSC) and heritability estimated in summary statistics (HESS). The causal effect analysis was performed using four methods of Mendelian randomization (MR) and the genetic risk loci common to both diseases were identified by the statistical method of conditional/conjoint false discovery rate (cond/conjFDR), followed by genetic overlap analysis. Finally, a multi-trait GWAS analysis (MTAG) was performed to validate the identified shared loci.

Results

IBD (including CD and UC) and conjunctivitis showed a significant overall correlation at the genomic level; however, the local correlation of IBD and CD with conjunctivitis was significant and limited to chromosome 11. MR analysis suggested a significant positive and non-significant negative correlation between IBD (including CD and UC) and conjunctivitis. The conjFDR analysis confirmed the genetic overlap between the two diseases. Additionally, MTAG was employed to identify and validate multiple genetic risk loci.

Conclusion

The present study provides evidence of genetic structure and causal effects for the co-morbidity between IBD (both CD and UC) and conjunctivitis, expanding the epidemiologic understanding of the two diseases.

Development of an immunogenic cell death-related lncRNAs signature for prognostic risk assessment in gastric cancer

Background

Immunogenic cell death (ICD) is a functionally specialized form of apoptosis induced by endoplasmic reticulum (ER) stress and is associated with a variety of cancers, including gastric cancer (GC). In recent years, long non-coding RNAs (lncRNAs) have been shown to be important mediators in the regulation of ICD. However, the specific role and prognostic value of ICD-related lncRNAs in GC remain unclear. This study aims to develop an ICD-related lncRNAs signature for prognostic risk assessment in GC.

Methods

The ICD-related lncRNAs signature (ICDlncSig) of GC was constructed by univariate Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression model and multivariate Cox regression analysis, and the signature was correlated with immune infiltration. The potential response of GC patients to immunotherapy was predicted by the tumor immune dysfunction and rejection (TIDE) algorithm. In vitro functional experiments were conducted to assess the impact of lncRNAs on the proliferation, migration, and invasion capabilities of GC cells.

Results

We constructed a novel ICDlncSig and found that this signature could be used as a prognostic risk model to predict survival of GC patients by validating it in the training cohort, testing cohort and entire cohort. The robust predictive power of the signature was demonstrated by building a Nomogram based on ICDlncSig scores and clinical characteristics. Furthermore, immune cell subpopulations, expression of immune checkpoint genes, and response to chemotherapy and immunotherapy differed significantly between the high- and low-risk groups. The in vitro functional experiments revealed that AP002954.1 and AP000695.1 can promote the proliferation, migration, and invasion of GC cells.

Conclusions

In conclusion, our ICDlncSig model has significant predictive value for the prognosis of GC patients and may provide clinical guidance for individualized immunotherapy.

Comprehensive analysis of immune-related lncRNAs in AML patients uncovers potential therapeutic targets and prognostic biomarkers

Purpose

The objective of this study was to provide theoretically feasible strategies by understanding the relationship between the immune microenvironment and the diagnosis and prognosis of AML patients. To this end, we built a ceRNA network with lncRNAs as the core and analyzed the related lncRNAs in the immune microenvironment by bioinformatics analysis.

Methods

AML transcriptome expression data and immune-related gene sets were obtained from TCGA and ImmPort. Utilizing Pearson correlation analysis, differentially expressed immune-related lncRNAs were identified. Then, the LASSO-Cox regression analysis was performed to generate a risk signature consisting immune-related lncRNAs. Accuracy of signature in predicting patient survival was evaluated using univariate and multivariate analysis. Next, GO and KEGG gene enrichment and ssGSEA were carried out for pathway enrichment analysis of 183 differentially expressed genes, followed by drug sensitivity and immune infiltration analysis with pRRophetic and CIBERSORT, respectively. Cytoscape was used to construct the ceRNA network for these lncRNAs.

Results

816 common lncRNAs were selected to acquire the components related to prognosis. The final risk signature established by multivariate Cox and stepwise regression analysis contained 12 lncRNAs engaged in tumor apoptotic and metastatic processes: LINC02595, HCP5, AC020934.2, AC008770.3, LINC01770, AC092718.4, AL589863.1, AC131097.4, AC012368.1, C1RL-AS1, STARD4-AS1, and AC243960.1. Based on this predictive model, high-risk patients exhibited lower overall survival rates than low-risk patients. Signature lncRNAs showed significant correlation with tumor-infiltrating immune cells. In addition, significant differences in PD-1/PD-L1 expression and bleomycin/paclitaxel sensitivity were observed between risk groups.

Conclusion

LncRNAs related to immune microenvironment were prospective prognostic and therapeutic options for AML.

Integrated single-cell sequencing, spatial transcriptome sequencing and bulk RNA sequencing highlights the molecular characteristics of parthanatos in gastric cancer

BACKGROUND

Parthanatos is a novel programmatic form of cell death based on DNA damage and PARP-1 dependency. Nevertheless, its specific role in the context of gastric cancer (GC) remains uncertain.

METHODS

In this study, we integrated multi-omics algorithms to investigate the molecular characteristics of parthanatos in GC. A series of bioinformatics algorithms were utilized to explore clinical heterogeneity of GC and further predict the clinical outcomes.

RESULTS

Firstly, we conducted a comprehensive analysis of the omics features of parthanatos in various human tumors, including genomic mutations, transcriptome expression, and prognostic relevance. We successfully identified 7 cell types within the GC microenvironment: myeloid cell, epithelial cell, T cell, stromal cell, proliferative cell, B cell, and NK cell. When compared to adjacent non-tumor tissues, single-cell sequencing results from GC tissues revealed elevated scores for the parthanatos pathway across multiple cell types. Spatial transcriptomics, for the first time, unveiled the spatial distribution characteristics of parthanatos signaling. GC patients with different parthanatos signals often exhibited distinct immune microenvironment and metabolic reprogramming features, leading to different clinical outcomes. The integration of parthanatos signaling and clinical indicators enabled the creation of novel survival curves that accurately assess patients' survival times and statuses.

CONCLUSIONS

In this study, the molecular characteristics of parthanatos' unicellular and spatial transcriptomics in GC were revealed for the first time. Our model based on parthanatos signals can be used to distinguish individual heterogeneity and predict clinical outcomes in patients with GC.

Identification of an inflammation-related risk signature for prognosis and immunotherapeutic response prediction in bladder cancer

Tumor inflammation is one of the hallmarks of tumors and is closely related to tumor occurrence and development, providing individualized prognostic prediction. However, few studies have evaluated the relationship between inflammation and the prognosis of bladder urothelial carcinoma (BLCA) patients. Therefore, we constructed a novel inflammation-related prognostic model that included six inflammation-related genes (IRGs) that can precisely predict the survival outcomes of BLCA patients. RNA-seq expression and corresponding clinical data from BLCA patients were downloaded from The Cancer Genome Atlas database. Enrichment analysis was subsequently performed to determine the enrichment of GO terms and KEGG pathways. K‒M analysis was used to compare overall survival (OS). Cox regression and LASSO regression were used to identify prognostic factors and construct the model. Finally, this prognostic model was used to evaluate cell infiltration in the BLCA tumor microenvironment and analyze the effect of immunotherapy in high- and low-risk patients. We established an IRG signature-based prognostic model with 6 IRGs (TNFRSF12A, NR1H3, ITIH4, IL1R1, ELN and CYP26B1), among which TNFRSF12A, IL1R1, ELN and CYP26B1 were unfavorable prognostic factors and NR1H3 and ITIH4 were protective indicators. High-risk score patients in the prognostic model had significantly poorer OS. Additionally, high-risk score patients were associated with an inhibitory immune tumor microenvironment and poor immunotherapy response. We also found a correlation between IRS-related genes and bladder cancer chemotherapy drugs in the drug sensitivity data. The IRG signature-based prognostic model we constructed can predict the prognosis of BLCA patients, providing additional information for individualized prognostic judgment and treatment selection.

Single cell and bulk transcriptome analysis identified oxidative stress response-related features of Hepatocellular Carcinoma

Background: Hepatocellular Carcinoma (HCC) is a common lethal digestive system tumor. The oxidative stress mechanism is crucial in the HCC genesis and progression. Methods: Our study analyzed single-cell and bulk sequencing data to compare the microenvironment of non-tumor liver tissues and HCC tissues. Through these analyses, we aimed to investigate the effect of oxidative stress on cells in the HCC microenvironment and identify critical oxidative stress response-related genes that impact the survival of HCC patients. Results: Our results showed increased oxidative stress in HCC tissue compared to non-tumor tissue. Immune cells in the HCC microenvironment exhibited higher oxidative detoxification capacity, and oxidative stress-induced cell death of dendritic cells was attenuated. HCC cells demonstrated enhanced communication with immune cells through the MIF pathway in a highly oxidative hepatoma microenvironment. Meanwhile, using machine learning and Cox regression screening, we identified PRDX1 as a predictor of early occurrence and prognosis in patients with HCC. The expression level of PRDX1 in HCC was related to dysregulated ribosome biogenesis and positively correlated with the expression of immunological checkpoints (PDCD1LG2, CTLA4, TIGIT, LAIR1). High PRDX1 expression in HCC patients correlated with better sensitivity to immunotherapy agents such as sorafenib, IGF-1R inhibitor, and JAK inhibitor. Conclusion: In conclusion, our study unveiled variations in oxidative stress levels between non-tumor liver and HCC tissues. And we identified oxidative stress gene markers associated with hepatocarcinogenesis development, offering novel insights into the oxidative stress response mechanism in HCC.

Development and validation of cuproptosis-related lncRNAs associated with pancreatic cancer immune microenvironment based on single-cell

Background

Cuproptosis, a novel mode of cell death associated with the tricarboxylic acid (TCA) cycle, is relevant to the development of cancer. However, the impact of single-cell-based Cuproptosis-associated lncRNAs on the Tumor immune microenvironment (TIME) of Pancreatic adenocarcinoma (PAAD) and its potential value for individualized immunotherapy has not been clarified.

Methods

14 immune-related CRGs were screened by exploring the interaction between differentially expressed Immune-Related Genes (IRGs) and Cuproptosis-Related Genes (CRGs) in PAAD. Next, the expression amount and expression distribution of CRGs in single-cell samples were analyzed by focusing on 7-CRGs with significant expressions. On the one hand, MAP2K2, SOD1, and VEGFA, which were significantly differentially expressed between PAAD sites and normal tissues adjacent to them, were subjected to immunohistochemical validation and immune landscape analysis. On the other hand, from these 7-CRGs, prognostic signatures of lncRNAs were established by co-expression and LASSO-COX regression analysis, and their prognostic value and immune relevance were assessed. In addition, this study not only validated the hub CRGs and the lncRNAs constituting the signature in a PAAD animal model treated with immunotherapy-based combination therapy using immunohistochemistry and qRT-PCR but also explored the potential value of the combination of targeted, chemotherapy and immunotherapy.

Results

Based on the screening of 7-CRGs significantly expressed in a PAAD single-cell cohort and their co-expressed Cuproptosis-Related lncRNAs (CRIs), this study constructed a prognostic signature of 4-CRIs named CIR-score. A Nomogram integrating the CIR-score and clinical risk factors was constructed on this basis to predict the individualized survival of patients. Moreover, high and low-risk groups classified according to the median of signatures exhibited significant differences in clinical prognosis, immune landscape, bioenrichment, tumor burden, and drug sensitivity. And the immunohistochemical and qRT-PCR results of different mouse PAAD treatment strategies were consistent with the trend of inter-group variability in drug sensitivity of hub CRGs and CIR-score. The combination of immunotherapy, targeted therapy, and chemotherapy exhibited a better tumor suppression effect.

Conclusion

CIR-score, as a Cuproptosis-related TIME-specific prognostic signature based on PAAD single cells, not only predicts the prognosis and immune landscape of PAAD patients but also provides a new strategy for individualized immunotherapy-based combination therapy.

The potential use and experimental validation of genomic instability-related lncRNA in pancreatic carcinoma

This study explored the potential role of long noncoding RNA (lncRNAs) associated with genomic instability in the diagnosis and treatment of pancreatic adenocarcinoma (PAAD). Transcriptome and single-nucleotide variation data of PAAD samples were downloaded from the cancer genome atlas database to explore genomic instability-associated lncRNAs. We constructed a genomic instability-associated lncRNA prognostic signature. Then gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses were used to explore the physiological role of lncRNAs involved in genomic instability. Tumor microenvironments, immunotherapy response, immune cell infiltration, immune checkpoint, and drug sensitivity were compared between high-risk and low-risk groups. In vitro experiments were performed for external validation. Six lncRNAs associated with genomic instability were identified, capable of predicting the prognosis of PAAD. Patients were assigned to low-risk or high-risk groups using these biomarkers, with better or worse prognosis, respectively. The tumor immune score, immune cell infiltration, and efficacy of immunotherapy were worse in the high-risk group. A drug sensitivity analysis revealed the high- and low-risk groups had different half-maximal inhibitory concentrations. The expression of cancer susceptibility candidate 8 was significantly higher in tumor tissues than in normal tissues, while the expression of LYPLAL1-AS1 exhibited an opposite pattern. They may be potential diagnostic or prognostic biomarkers for patients with pancreatic cancer. Genomic instability-associated lncRNAs were explored in this study and predicted the prognosis of PAAD and stratified patients risk in PAAD. These lncRNAs also predicted the efficacy of immunotherapy and potential therapeutic targets in PAAD.

Identification of cuproptosis-related lncRNAs to predict prognosis and immune infiltration characteristics in alimentary tract malignancies

BACKGROUND

Alimentary tract malignancies (ATM) caused nearly one-third of all tumor-related death. Cuproptosis is a newly identified cell death pattern. The role of cuproptosis-associated lncRNAs in ATM is unknown.

METHOD

Data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to identify prognostic lncRNAs by Cox regression and LASSO. Then a predictive nomogram was constructed based on seven prognostic lncRNAs. In addition, the prognostic potential of the seven-lncRNA signature was verified via survival analysis, the receiver operating characteristic (ROC) curve, calibration curve, and clinicopathologic characteristics correlation analysis. Furthermore, we explored the associations between the signature risk score and immune landscape, and somatic gene mutation.

RESULTS

We identified 1211 cuproptosis-related lncRNAs and seven survival-related lncRNAs. Patients were categorized into high-risk and low-risk groups with significantly different prognoses. ROC and calibration curve confirmed the good prediction capability of the risk model and nomogram. Somatic mutations between the two groups were compared. We also found that patients in the two groups responded differently to immune checkpoint inhibitors and immunotherapy.

CONCLUSION

The proposed novel seven lncRNAs nomogram could predict prognosis and guide treatment of ATM. Further research was required to validate the nomogram.

Cellular senescence-related gene signature as a valuable predictor of prognosis in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a lethal tumor. Its prognosis prediction remains a challenge. Meanwhile, cellular senescence, one of the hallmarks of cancer, and its related prognostic genes signature can provide critical information for clinical decision-making.

METHOD

Using bulk RNA sequencing and microarray data of HCC samples, we established a senescence score model via multi-machine learning algorithms to predict the prognosis of HCC. Single-cell and pseudo-time trajectory analyses were used to explore the hub genes of the senescence score model in HCC sample differentiation.

RESULT

A machine learning model based on cellular senescence gene expression profiles was identified in predicting HCC prognosis. The feasibility and accuracy of the senescence score model were confirmed in external validation and comparison with other models. Moreover, we analyzed the immune response, immune checkpoints, and sensitivity to immunotherapy drugs of HCC patients in different prognostic risk groups. Pseudo-time analyses identified four hub genes in HCC progression, including CDCA8, CENPA, SPC25, and TTK, and indicated related cellular senescence.

CONCLUSIONS

This study identified a prognostic model of HCC by cellular senescence-related gene expression and insight into novel potential targeted therapies.

Metabolism-related long non-coding RNA in the stomach cancer associated with 11 AMMLs predictive nomograms for OS in STAD

Background: The metabolic processes involving amino acids are intimately linked to the onset and progression of cancer. Long non-coding RNAs (LncRNAs) perform an indispensable function in the modulation of metabolic processes as well as the advancement of tumors. Non-etheless, research into the role that amino acid metabolism-related LncRNAs (AMMLs) might play in predicting the prognosis of stomach adenocarcinoma (STAD) has not been done. Therefore, This study sought to design a model for AMMLs to predict STAD-related prognosis and elucidate their immune properties and molecular mechanisms.

Methods: The STAD RNA-seq data in the TCGA-STAD dataset were randomized into the training and validation groups in a 1:1 ratio, and models were constructed and validated respectively. In the molecular signature database, This study screened for genes involved in amino acid metabolism. AMMLs were obtained by Pearson’s correlation analysis, and predictive risk characteristics were established using least absolute shrinkage and selection operator (LASSO) regression, univariate Cox analysis, and multivariate Cox analysis. Subsequently, the immune and molecular profiles of high- and low-risk patients and the benefit of the drug were examined.

Results: Eleven AMMLs (LINC01697, LINC00460, LINC00592, MIR548XHG, LINC02728, RBAKDN, LINCOG, LINC00449, LINC01819, and UBE2R2-AS1) were used to develop a prognostic model. Moreover, high-risk individuals had worse overall survival (OS) than low-risk patients in the validation and comprehensive groups. A high-risk score was associated with cancer metastasis as well as angiogenic pathways and high infiltration of tumor-associated fibroblasts, Treg cells, and M2 macrophages; suppressed immune responses; and a more aggressive phenotype.

Conclusion: This study identified a risk signal associated with 11 AMMLs and established predictive nomograms for OS in STAD. These findings will help us personalize treatment for gastric cancer patients.

A cuproptosis-related signature for predicting the prognosis of gastric cancer

Background

Gastric cancer (GC) is one of the most common malignancies. Cuproptosis is a newly discovered type of cell death caused by protein toxicity stress, with copper having considerable importance in GC development.

Methods

First, differentially expressed (DE) cuproptosis-related genes (CRGs) were screened in GC. The tumor mutation burden (TMB) of CRGs was analyzed. We then performed enrichment analyses of DE-CRGs. Next, we constructed a GC cuproptosis-related (CR) signature (CRs) using Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. The predictive efficacy was assessed using receiver operating characteristic (ROC) curves. Furthermore, we performed gene set enrichment analysis (GSEA). Different methods were used to assess tumor immunity of the CRs, and the Wilcoxon test was used to examine the expressions of m6A-, m7G-, and ferroptosis-related genes. The "pRRophetic" R package (The R Foundation for Statistical Computing) was used to predict the half maximal inhibitory concentration IC50 of common chemotherapeutic agents. Finally, the expression of CRGs in different clusters was analyzed using single-cell RNA sequencing (scRNA-seq).

Results

We identified 8 DE-CRGs in GC. There were 9 CRGs with TMB values >1%. We constructed gene expression networks and CRs for GC. The DE-CRGs were involved in important mitochondrial metabolic pathways, and the CRs was a valuable independent prognosis factor. The GSEA revealed that angiogenesis and metabolic-related pathways were enriched in the high-risk group, whereas the low-risk group showed enrichment in DNA replication mismatch and repair pathways. The expressions of immunological checkpoints, ferroptosis-, m6A-, and m7G-related genes, type II interferon (INF) response, major histocompatibility complex (MHC class-I), and the IC50 of the copper-based carrier drug elesclomol were significantly different between the 2 groups of the CRs. Furthermore, the scRNA-seq analysis showed that most CRGs were mainly upregulated in endothelial cells.

Conclusions

The novel CRs could predict the prognosis of GC.

T-stage-specific abdominal visceral fat, haematological nutrition indicators and inflammation as prognostic factors in patients with clear renal cell carcinoma

Clear cell renal carcinoma (ccRCC) is the most common histological type of renal cancer and has the highest mortality. Several studies have been conducted on the relationship between adipose tissue and ccRCC prognosis, however, the results have been inconsistent to date. The current study aimed at establishing a link between abdominal fat composition and short-term prognosis in patients with ccRCC after T-stage stratification. We retrospectively analysed 250 patients with pathologically confirmed ccRCC (173 low T-stage and 77 high T-stage) in our hospital. The computed tomography (CT) images were evaluated using ImageJ. Then, subcutaneous and visceral fat areas (SFA and VFA), total fat areas (TFA) and the relative VFA (rVFA) were measured and computed. Meanwhile, biochemical indices of blood serum were analysed. The results showed that rVFA in low T-stage cohort who had a history of short-term postoperative complications were significantly lower than those who did not. No such association was observed in the high T-stage cohort. Further investigation revealed that the correlations between biochemical indexes and fat area-related variables varied across T-stage groups. As a result, rVFA is a reliable independent predictor of short-term prognosis in patients with low T-stage ccRCC but not in patients with high T-stage ccRCC.

A prognostic signature based on cuprotosis-related long non-coding RNAs predicts the prognosis and sensitivity to chemotherapy in patients with colorectal cancer

Cuprotosis, a newly proposed mechanism of cell death, can trigger acute oxidative stress that leads to cell death by mediating protein lipidation in the tricarboxylic acid cycle. However, cuprotosis-related long non-coding RNAs (CRLNCs) and their relationship with prognosis and the immunological landscape of colorectal cancer (CRC) are unclear. We have developed a lncRNA signature to predict survival time, immune infiltration, and sensitivity to chemotherapy. CRLNCs were screened using the Cor function of the R software and the differentially expressed lncRNAs were collected with the limma package. Differentially expressed long non-coding RNAs (lncRNAs) associated with prognosis were selected using univariate regression analysis. A prognostic signature was developed using the least absolute shrinkage and selection operator (LASSO) and multivariate regression analysis. Patients with CRC were divided into two groups based on the risk score. The low-risk group had a more favorable prognosis, higher expression of immune checkpoints, and a higher level of immune cell infiltration compared with the high-risk group. Furthermore, there was a close association between the risk score and the clinical stage, tumor mutational burden, cancer stem cell index, and microsatellite instability. We also assessed chemotherapy response in the two risk groups. Our study analyzed the role of CRLNCs in CRC and provided novel targets and strategies for CRC chemotherapy and immunotherapy.

A novel risk score model based on four angiogenesis long non-coding RNAs for prognosis evaluation of pancreatic adenocarcinoma

BACKGROUND

Long non-coding RNAs (lncRNAs) have been reported to play significant roles in tumour angiogenesis which prominently facilitates pancreatic adenocarcinoma (PAAD) progression.

METHODS

The clinical PAAD data were obtained from TCGA database and clinical specimens of 122 PAAD patients. The Molecular Signatures Database v4.0 was used to identify angiogenesis-related long non-coding RNAs (ARLNRs). Survival-related ARLNRs (sARLNRs) were further validated by univariate and multivariate COX regression analyses. The expressions of CASC8, AC015660.1, Z97832.2 and PAN3-AS1 in PAAD cell lines and tissues were examined by qPCR. The correlations between sARLNRs (CASC8 and AC015660.1) and clinicopathological characteristics of the 122 PAAD patients were analyzed by the chi-square test and Fisher's exact probability method.

RESULTS

590 lncRNAs were identified as ARLNRs, of which four sARLNRs were further used to establish an angiogenesis-related risk score model (ARRS), by which patients in the low-risk group have better survival probabilities than those in the high-risk group. The expression levels of CASC8 and AC015660.1 were significantly higher in PAAD cell lines and tumor tissues especially in patients with advanced grades and T-stages, while Z97832.2 and PAN3-AS1 were inverse. In addition, the higher expression of CASC8 and AC015660.1 prominently associated with the larger tumour size, and the more advanced grade and T-stage. However, the relevance between the sARLNRs (CASC8 and AC015660.1) expression and lymph node metastasis status was not significant.

CONCLUSIONS

In the study, we illuminate the clinical significance, angiogenesis relevance and prognosis-predictive value of four sARLNRs for PAAD. The results build a bridge between sARLNRs and tumour vascularization, and also establish a reliable and accurate risk scoring model for PAAD antiangiogenic strategy.

A novel risk model construction and immune landscape analysis of gastric cancer based on cuproptosis-related long noncoding RNAs

Recent studies have identified cuproptosis, a new mechanism of regulating cell death. Accumulating evidence suggests that copper homeostasis is associated with tumorigenesis and tumor progression, however, the clinical significance of cuproptosis in gastric cancer (GC) is unclear. In this study, we obtained 26 prognostic cuproptosis-related lncRNAs (CRLs) based on 19 cuproptosis-related genes (CRGs) via Pearson correlation analysis, differential expression analysis, and univariate Cox analysis. A risk model based on 10 CRLs was established with the least absolute shrinkage and selection operator (LASSO) Cox regression analysis and multivariate Cox proportional hazards model to predict the prognosis and immune landscape of GC patients from The Cancer Genome Atlas (TCGA). The risk model has excellent accuracy and efficiency in predicting prognosis of GC patients (Area Under Curve (AUC) = 0.742, 0.803, 0.806 at 1,3,5 years, respectively, P < 0.05). In addition, we found that the risk score was negatively correlated with the infiltration of natural killer (NK) cells and helper T cells, while positively correlated with the infiltration of monocytes, macrophages, mast cells, and neutrophils. Moreover, we evaluated the difference in drug sensitivity of patients with different risk patterns. Furthermore, low-risk patients showed higher tumor mutation burden (TMB) and better immunotherapy response than high-risk patients. In the end, we confirmed the oncogenic role of AL121748.1 which exhibited the highest Hazard Ratio (HR) value among 10 CRLs in GC via cellular functional experiments. In conclusion, our risk model shows a significant role in tumor immunity and could be applied to predict the prognosis of GC patients.

Identification and validation of a novel cellular senescence-related lncRNA prognostic signature for predicting immunotherapy response in stomach adenocarcinoma

Background: Cellular senescence is a novel hallmark of cancer associated with patient outcomes and tumor immunotherapy. However, the value of cellular senescence-related long non-coding RNAs (lncRNAs) in predicting prognosis and immunotherapy response for stomach adenocarcinoma (STAD) patients needs further investigation.

Methods: The transcriptome and corresponding clinical information of STAD and cellular senescence-related genes were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and CellAge databases. Differential expression analysis and coexpression analysis were performed to obtain cellular senescence-related lncRNAs. Univariate regression analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were conducted to establish the cellular senescence-related lncRNA prognostic signature (CSLPS). Next, the survival curve, ROC curve, and nomogram were developed to assess the capacity of predictive models. Moreover, principal component analysis (PCA), gene set enrichment analysis (GSEA), tumor microenvironment (TME), tumor mutation burden (TMB), microsatellite instability (MSI), and tumor immune dysfunction and exclusion (TIDE) score analysis were performed between high- and low-risk groups.

Results: A novel CSLPS involving fifteen lncRNAs (REPIN1-AS1, AL355574.1, AC104695.3, AL033527.2, AC083902.1, TYMSOS, LINC00460, AC005165.1, AL136115.1, AC007405.2, AL391152.1, SCAT1, AC129507.1, AL121748.1, and ADAMTS9-AS1) was developed. According to the nomogram, the risk model based on the CSLPS was an independent prognostic factor and could predict 1-, 3-, and 5-year overall survival for STAD patients. GSEA suggested that the high-risk group was mainly associated with Toll-like receptor, JAK/STAT, NOD-like receptor, and chemokine signaling pathways. Further analysis revealed that STAD patients in the low-risk group with better clinical outcomes had a higher TMB, higher proportion of high microsatellite instability (MSI-H), better immune infiltration, and lower TIDE scores.

Conclusion: A fifteen-CSlncRNA prognostic signature could predict survival outcomes, and patients in the low-risk group may be more sensitive to immunotherapy.

Identification of a Hypoxia-Angiogenesis lncRNA Signature Participating in Immunosuppression in Gastric Cancer

Hypoxia and angiogenesis are the leading causes of tumor progression, and their strong correlation has been discovered in many cancers. However, their collective function's prognostic and biological roles were not reported in gastric cancer. Hence, we aimed to investigate the effects of hypoxia and angiogenesis on gastric cancer via sequencing data. This study used weighted gene coexpression network analysis and random forest regression to build a hypoxia-angiogenesis-related model (HARM) via the TCGA-STAD lncRNA data. It estimated the HARM's correlation with clinical features and its accuracy for survival prediction. Sequential functional analyses were conducted to investigate its biological role, and we next sought the immune landscape status and immunological function variation by ESTIMATE score calculation and GSVA, respectively. Seven different algorithms were conducted to assess the immunocyte infiltration, and TIDE score and immune checkpoint levels were compared between the high- and low-HARM groups. As a result, we found that HARM predicted patient survival with high accuracy and was correlated with higher stages of gastric cancer. Various cancer-associated pathways and macrophage-related regulations were upregulated in the high-HRAM group. The high-HARM group harbored higher immune levels, and M2 macrophages and cancer-associated fibroblasts were particularly highly unfiltered. Furthermore, globally upregulated immune checkpoints and higher TIDE scores were observed in the high-HARM group. Finally, we filtered eight drugs with lower IC50 in the high-HARM group as potential drugs for the HARM-targeted therapy. We believe this study opens up novel perspectives into the interaction between hypoxia-angiogenesis and immunosuppression and will provide novel insights for gastric cancer immunotherapy.

An inflammation-related signature could predict the prognosis of patients with kidney renal clear cell carcinoma

Background: Kidney renal clear cell carcinoma (KIRC) is an inflammation-related carcinoma, and inflammation has been recognized as an important factor in inducing carcinogenesis. To further explore the role of inflammation in KIRC, we developed an inflammation-related signature and verified its correlation with the tumor micro-environment.

Methods: After the differential inflammation-related prognostic genes were screened by Lasso regression, the inflammation-related signature (IRS) was constructed based on the risk score of multivariate Cox regression. Then, the prognostic value of the IRS was evaluated by Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis and multivariate Cox regression. Gene set variation analysis (GSVA) was applied to screen out enriched signaling pathways. Infiltrated immune cells, tumor mutational burden (TMB) and immune checkpoints were explored by CIBERSORTx and maftool.

Results: Four genes (TIMP1, PLAUR, CCL22, and IL15RA) were used to construct the IRS in patients with KIRC. Kaplan-Meier analysis and multivariate Cox regression identified that the IRS could independently predict the prognosis of patients with KIRC in the training and validation groups. The diagnostic value of the nomogram increased from 0.811 to 0.845 after adding the IRS to the multiparameter ROC analysis. The GSVA results indicated that IRS was closely related to primary immunodeficiency and antigen processing and presentation. The immune checkpoint LAG3 was highly expressed in patients with high-risk score (p < 0.05), while CD274 (PD-L1) and HAVCR2 were highly expressed in patients with low-risk score (p < 0.001). There was a significant positive correlation between the high-risk score group and CD8⁺ T, activated CD4⁺ memory T, gamma and delta regulatory T and M0 macrophage cells, while the low-risk score group was negatively associated with B memory, plasma, resting CD4⁺ memory T, activated NK, M1 macrophages and resting mast cells.

Conclusion: We found that the IRS might serve as a biomarker to predict the survival of KIRC. Moreover, patients with high or low-risk score might be sensitive to immune drugs at different immune checkpoints.

Identification and Comprehensive Prognostic Analysis of a Novel Chemokine-Related lncRNA Signature and Immune Landscape in Gastric Cancer

Gastric cancer (GC) is a malignant tumor with poor survival outcomes. Immunotherapy can improve the prognosis of many cancers, including GC. However, in clinical practice, not all cancer patients are sensitive to immunotherapy. Therefore, it is essential to identify effective biomarkers for predicting the prognosis and immunotherapy sensitivity of GC. In recent years, chemokines have been widely reported to regulate the tumor microenvironment, especially the immune landscape. However, whether chemokine-related lncRNAs are associated with the prognosis and immune landscape of GC remains unclear. In this study, we first constructed a novel chemokine-related lncRNA risk model to predict the prognosis and immune landscape of GC patients. By using various algorithms, we identified 10 chemokine-related lncRNAs to construct the risk model. Then, we determined the prognostic efficiency and accuracy of the risk model. The effectiveness and accuracy of the risk model were further validated in the testing set and the entire set. In addition, our risk model exerted a crucial role in predicting the infiltration of immune cells, immune checkpoint genes expression, immunotherapy scores and tumor mutation burden of GC patients. In conclusion, our risk model has preferable prognostic performance and may provide crucial clues to formulate immunotherapy strategies for GC.