An angiogenesis-related three-long non-coding ribonucleic acid signature predicts the immune landscape and prognosis in hepatocellular carcinoma

GMIP: A Novel Prognostic Biomarker Influencing Immune Infiltration and Tumour Dynamics Across Cancer Types

GMIP, a member of the RhoGAP family, plays a critical role in cytoskeletal remodelling, cell migration and immune modulation. Its aberrant expression in cancers suggests a pivotal role in tumour progression. GMIP expression was assessed using transcriptomic datasets from GDC and UCSC XENA, and protein distribution across tissues via HPA and GeneMANIA. The TISCH database identified primary GMIP-expressing cell types in the tumour microenvironment. Univariate Cox regression assessed GMIP's prognostic potential, while cBioPortal and GSCA explored genomic alterations. TIMER 2.0 was used to investigate immune cell infiltration and GMIP's role in immune regulation. GSEA and GSVA unveiled GMIP-related biological pathways, and molecular docking with CellMiner identified potential drug interactions. In vitro assays confirmed GMIP's functional relevance in breast cancer. GMIP exhibits differential expression across multiple cancer types, demonstrating significant prognostic implications. Its expression is inversely correlated with CNV and methylation in several cancers. GMIP is closely linked to immunotherapy biomarkers and immune suppression, influencing therapeutic responses. Functional studies suggest that GMIP inhibition reduces cancer cell proliferation and migration. GMIP is identified as a promising oncological biomarker, particularly in breast cancer, with potential therapeutic implications. GMIP's therapeutic potential is especially pronounced in BRCA-mutated tumours, underscoring its relevance for novel anticancer interventions.

Exploration of SUSD3 in pan-cancer: studying its role, predictive analysis, and biological significance in various malignant tumors in humans

Background

The SUSD3 protein, marked by the Sushi domain, plays a key role in cancer progression, with its expression linked to tumor advancement and patient prognosis. Altered SUSD3 levels could serve as a predictive biomarker for cancer progression. Recognized as a novel susceptibility marker, SUSD3 presents a promising target for antibody-based therapies, offering a potential approach for the prevention, diagnosis, and treatment of breast cancer.

Methods

Using the HPA and GeneMANIA platforms, the distribution of SUSD3 protein across tissues was analyzed, while expression levels in tumor and healthy tissues were compared using The Cancer Genome Atlas data. The TISCH and STOmics DB databases facilitated the mapping of SUSD3 expression in different cell types and its spatial relationship with cancer markers. Univariate Cox regression assessed the prognostic significance of SUSD3 expression in various cancers. Genomic alterations of SUSD3 were explored through the cBioPortal database. The potential of SUSD3 as a predictor of immunotherapy response was investigated using TIMER2.0, and GSEA/GSVA identified related biological pathways. Drugs targeting SUSD3 were identified through CellMiner, CTRP, and GDSC databases, complemented by molecular docking studies. In vitro experiments demonstrated that SUSD3 knockdown in breast cancer cell lines significantly reduced proliferation and migration.

Results

SUSD3 expression variations in pan-cancer cohorts are closely linked to the prognosis of various malignancies. In the tumor microenvironment (TME), SUSD3 is predominantly expressed in monocytes/macrophages and CD4+ T cells. Research indicates a strong correlation between SUSD3 expression and key cancer immunotherapy biomarkers, immune cell infiltration, and immunomodulatory factors. To explore its immune regulatory role, StromalScore, ImmuneScore, ESTIMATE, and Immune Infiltration metrics were employed. Molecular docking studies revealed that selumetinib inhibits tumor cell proliferation. Finally, SUSD3 knockdown reduced cancer cell proliferation and migration.

Conclusion

These findings provide valuable insights and establish a foundation for further exploration of SUSD3's role in pan-carcinomas. Additionally, they offer novel perspectives and potential targets for the development of innovative therapeutic strategies in cancer treatment.

Systematic Analysis of Disulfidptosis-Related lncRNAs in Hepatocellular Carcinoma with Vascular Invasion Revealed That AC131009.1 Can Promote HCC Invasion and Metastasis through Epithelial-Mesenchymal Transition

Disulfidptosis, a recently identified pathway of cellular demise, served as the focal point of this research, aiming to pinpoint relevant lncRNAs that differentiate between hepatocellular carcinoma (HCC) with and without vascular invasion while also forecasting survival rates and responses to immunotherapy in patients with vascular invasion (VI+). First, we identified 300 DRLRs in the TCGA database. Subsequently, utilizing univariate analysis, LASSO-Cox proportional hazards modeling, and multivariate analytical approaches, we selected three DRLRs (AC009779.2, AC131009.1, and LUCAT1) with the highest prognostic value to construct a prognostic risk model for VI+ HCC patients. Multivariate Cox regression analysis revealed that this model is an independent prognostic factor for predicting overall survival that outperforms traditional clinicopathological factors. Pathway analysis demonstrated the enrichment of tumor and immune-related pathways in the high-risk group. Immune landscape analysis revealed that immune cell infiltration degrees and immune functions had significant differences. Additionally, we identified valuable chemical drugs (AZD4547, BMS-536924, BPD-00008900, dasatinib, and YK-4-279) for high-risk VI+ HCC patients. In-depth bioinformatics analysis was subsequently conducted to assess immune characteristics, drug susceptibility, and potential biological pathways involving the three hub DRLRs. Furthermore, the abnormally elevated transcriptional levels of the three DRLRs in HCC cell lines were validated through qRT-PCR. Functional cell assays revealed that silencing the expression of lncRNA AC131009.1 can inhibit the migratory and invasive capabilities of HCC cells, a finding further corroborated by the chorioallantoic membrane (CAM) assay. Immunohistochemical analysis and hematoxylin-eosin staining (HE) staining provided preliminary evidence that AC131009.1 may promote the invasion and metastasis of HCC cells by inducing epithelial-mesenchymal transition (EMT) in both subcutaneous xenograft models and orthotopic HCC models within nude mice. To summarize, we developed a risk assessment model founded on DRLRs and explored the potential mechanisms by which hub DRLRs promote HCC invasion and metastasis.

Comprehensive analysis of POLH-AS1 as a prognostic biomarker in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC), a prevalent primary malignant tumor, is notorious for its high mortality rate. Despite advancements in HCC treatment, patient outcomes remain suboptimal. This study endeavors to assess the potential prognostic significance of POLH-AS1 in HCC.

METHODS

In this research, we gathered RNA-Seq information from individuals with HCC in The Cancer Genome Atlas (TCGA). We analyzed the levels of POLH-AS1 expression in both HCC cells and tissues using statistical tests. Additionally, we examined various prognostic factors in HCC using advanced methodologies. Furthermore, we employed Spearman's rank correlation analysis to examine the association between POLH-AS1 expression and the tumor's immune microenvironment. Finally, the functional roles of POLH-AS1 in HCC were validated in two HCC cell lines (HEP3B and HEPG2).

RESULTS

Our analysis revealed elevated POLH-AS1 expression across various cancers, including HCC, with heightened expression correlating with HCC progression. Notably, POLH-AS1 expression emerged as a potential biomarker for HCC patient survival and prognosis. Mechanistically, we identified the involvement of POLH-AS1 in tumorigenesis pathways such as herpes simplex virus 1 infection, interactions with neuroactive receptors, and the cAMP signaling pathway. Lastly, inhibition of POLH-AS1 was discovered to hinder the proliferation, invasion and migration of HEP3B and HEPG2 HCC cells.

CONCLUSIONS

POLH-AS1 emerges as a promising prognostic biomarker and therapeutic target for HCC, offering potential avenues for enhanced patient management and treatment strategies.

Cervical cancer-specific long non-coding RNA landscape reveals the favorable prognosis predictive performance of an ion-channel-related signature model

BACKGROUND

Ion channels play an important role in tumorigenesis and progression of cervical cancer. Multiple long non-coding RNA genes are widely involved in ion channel-related signaling regulation. However, the association and potential clinical application of lncRNAs in the prognosis of cervical cancer are still poorly explored.

METHODS

Thirteen patients with cervical cancer were enrolled in current study. Whole transcriptome (involving both mRNAs and lncRNAs) sequencing was performed on fresh tumor and adjacent normal tissues that were surgically resected from patients. A comprehensive cervical cancer-specific lncRNA landscape was obtained by our custom pipeline. Then, a prognostic scoring model of ion-channel-related lncRNAs was established by regression algorithms. The performance of the predictive model as well as its association with the clinical characteristics and tumor microenvironment (TME) status were further evaluated.

RESULTS

To comprehensively identify cervical cancer-specific lncRNAs, we sequenced 26 samples of cervical cancer patients and integrated the transcriptomic results. We built a custom analysis pipeline to improve the accuracy of lncRNA identification and functional annotation and obtained 18,482 novel lncRNAs in cervical cancer. Then, 159 ion channel- and tumorigenesis-related (ICTR-) lncRNAs were identified. Based on nine ICTR-lncRNAs, we also established a prognostic scoring model and validated its accuracy and robustness in assessing the prognosis of patients with cervical cancer. Besides, the TME was characterized, and we found that B cells, activated CD8+ T, and tertiary lymphoid structures were significantly associated with ICTR-lncRNAs signature scores.

CONCLUSION

We provided a thorough landscape of cervical cancer-specific lncRNAs. Through integrative analyses, we identified ion-channel-related lncRNAs and established a predictive model for assessing the prognosis of patients with cervical cancer. Meanwhile, we characterized its association with TME status. This study improved our knowledge of the prominent roles of lncRNAs in regulating ion channel in cervical cancer.

Predicting the immunity landscape and prognosis with an NCLs signature in liver hepatocellular carcinoma

BACKGROUND

Activated neutrophils release depolymerized chromatin and protein particles into the extracellular space, forming reticular Neutrophil Extracellular Traps (NETs). This process is accompanied by programmed inflammatory cell death of neutrophils, known as NETosis. Previous reports have demonstrated that NETosis plays a significant role in immune resistance and microenvironmental regulation in cancer. This study sought to characterize the function and molecular mechanism of NETosis-correlated long non-coding RNAs (NCLs) in the prognostic treatment of liver hepatocellular carcinoma (LIHC).

METHODS

We obtained the transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and evaluated the expression of NCLs in LIHC. A prognostic signature of NCLs was constructed using Cox and Last Absolute Shrinkage and Selection Operator (Lasso) regression, while the accuracy of model was validated by the ROC curves and nomogram, etc. In addition, we analyzed the associations between NCLs and oncogenic mutation, immune infiltration and evasion. Finally, LIHC patients were classified into four subgroups based on consensus cluster analysis, and drug sensitivity was predicted.

RESULTS

After screening, we established a risk model combining 5 hub-NCLs and demonstrated its reliability. Independence checks suggest that the model may serve as an independent predictor of LIHC prognosis. Enrichment analysis revealed a concentration of immune-related pathways in the high-risk group. Immune infiltration indicates that immunotherapy could be more effective in the low-risk group. Upon consistent cluster analysis, cluster subgroup 4 presented a better prognosis. Sensitivity tests showed the distinctions in therapeutic effectiveness among various drugs in different subgroups.

CONCLUSION

Overall, we have developed a prognostic signature that can discriminate different LIHC subgroups through the 5 selected NCLs, with the objective of providing LIHC patients a more precise, personalized treatment regimen.

Comprehensive assessment of regulatory T-cells-related scoring system for predicting the prognosis, immune microenvironment and therapeutic response in hepatocellular carcinoma

INTRODUCTION

Regulatory T cells (Tregs) play important roles in tumor immunosuppression and immune escape. The aim of the present study was to construct a novel Tregs-associated biomarker for the prediction of tumour immune microenvironment (TIME), clinical outcomes, and individualised treatment in hepatocellular carcinoma (HCC).

METHODS

Single-cell sequencing data were obtained from the three independent cohorts. Cox and LASSO regression were utilised to develop the Tregs Related Scoring System (TRSSys). GSE140520, ICGC-LIRI and CHCC cohorts were used for the validation of TRSSys. Kaplan-Meier, ROC, and Cox regression were utilised for the evaluation of TRSSys. The ESTIMATE, TIMER 2.0, and ssGSEA algorithm were utilised to determine the value of TRSSys in predicting the TIME. GSVA, GO, KEGG, and TMB analyses were used for mechanistic exploration. Finally, the value of TRSSys in predicting drug sensitivity was evaluated based on the oncoPredict algorithm.

RESULTS

Comprehensive validation showed that TRSSys had good prognostic predictive efficacy and applicability. Additionally, ssGSEA, TIMER and ESTIMATE algorithm suggested that TRSSys could help to distinguish different TIME subtypes and determine the beneficiary population of immunotherapy. Finally, the oncoPredict algorithm suggests that TRSSys provides a basis for individualised treatment.

CONCLUSIONS

TRSSys constructed in the current study is a novel HCC prognostic prediction biomarker with good predictive efficacy and stability. Additionally, risk stratification based on TRSSys can help to identify the TIME landscape subtypes and provide a basis for individualized treatment options.

A novel PANoptosis-related long non-coding RNA index to predict prognosis, immune microenvironment and personalised treatment in hepatocellular carcinoma

BACKGROUND

PANoptosis is involved in the interaction of apoptosis, necroptosis and pyroptosis, playing a role in programmed cell death. Moreover, long non-coding RNAs (lncRNAs) regulate the PCD. This work aims to explore the role of PANoptosis-associated lncRNAs in hepatocellular carcinoma (HCC).

METHODS

Co-expression analysis identified PANoptosis-associated lncRNAs in HCC. Cox and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms were utilised to filter lncRNAs and establish a PANoptosis-related lncRNA index (PANRI). Additionally, Cox, Kaplan-Meier and receiver operating characteristic (ROC) curves were utilised to systematically evaluate the PANRI. Furthermore, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE), single sample gene set enrichment analysis (ssGSEA) and immune checkpoints were performed to analyse the potential of the PANRI in differentiating different tumour immune microenvironment (TIME) populations. The consensus clustering algorithm was used to distinguish individuals with HCC having different TIME subtypes. Finally, HCC cell lines HepG2 were utilised for further validation in in vitro experiments.

RESULTS

The PANRI differentiates patients according to risk. Notably, ESTIMATE and ssGSEA algorithms revealed a high immune infiltration status in high-risk patients. Additionally, consensus clustering divided the patients into three clusters to identify different subtypes of TIME. Moreover, in vitro results showed that siRNA-mediated silencing of AL049840.4 inhibited the viability and migration of HepG2 cells and promoted apoptosis.

CONCLUSIONS

This is the first PANoptosis-related, lncRNA-based risk index in HCC to assess patient prognosis, TIME and response to immunotherapy. This study offers novel perspectives on the role of PANoptosis-associated lncRNAs in HCC.

Comprehensive analysis of cuproptosis-related long non-coding RNAs in prognosis, immune microenvironment infiltration and chemotherapy response of hepatocellular carcinoma

The objective of this study is to explore the relationship between cuproptosis-related long noncoding RNAs (lncRNAs) in hepatocellular carcinoma (HCC). RNA-seq data, including lncRNAs and related clinical information of HCC patients, were downloaded from The Cancer Genome Atlas database. A signature composed 3 cuproptosis-related lncRNAs was constructed by LASSO analysis, and HCC patients were classified into high- and low-risk groups. Patients in the high-risk group had a poorer prognosis compared with the low-risk group. Univariate Cox and multivariate Cox regression analyses confirmed that the signature model was an independent risk factor compared to other clinical biomarkers. Furthermore, gene set enrichment analysis indicated that metabolism-related pathways were enriched in low-risk group, including drug metabolism, and fatty acid metabolism. Further research demonstrated that there were markedly differences in drug response between the high- and low-risk group. Immune related analysis showed that the most type of immune cells and immunological function in the high-risk group were different with the risk-group. Finally, TP53 mutation rate and the tumor mutational burden in the high-risk group were higher compared with the low-risk group. In conclusion, we constructed a prognostic signature based on the expression of cuproptosis-related lncRNAs to predict HCC patients' prognosis, drug response and immune microenvironment, and further research will be conducted to uncover the mechanisms.

Identification and validation of a novel anoikis-related long non-coding RNA signature for pancreatic adenocarcinoma to predict the prognosis and immune response

OBJECTIVE

To provide more precise treatment options for pancreatic adenocarcinoma (PAAD) patients and improve their prognosis,we established a novel anoikis-related long non-coding RNA signature (ARLSig) to predict the prognosis and immune response for PAAD patients.

METHODS

We downloaded information on PAAD from The Cancer Genome Atlas (TCGA) database, and screened long non-coding RNA (lncRNA) linked with anoikis, and prognostic signatures with these lncRNAs. After that, ARLSig was verified using receiver operating characteristic (ROC) and C-index curves. To further investigate the role of ARLSig, we also performed enrichment analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). Additionally, using immunological correlation analysis and single-sample genetic enrichment analysis, we investigated the effectiveness of PAAD immunotherapy.

RESULTS

We screened 7 lncRNAs to construct a novel ARLSig and utilized it to predict the efficacy of immunotherapy and the prognosis of PAAD patients.

CONCLUSION

ARLSig can identify patients who will benefit from immunotherapy and improve the prediction of PAAD patient prognosis.

Identification and validation of a cancer-associated fibroblasts-related scoring system to predict prognosis and immune landscape in hepatocellular carcinoma through integrated analysis of single-cell and bulk RNA-sequencing

BACKGROUND

Cancer-associated fibroblasts (CAFs) regulate the malignant biological behaviour of hepatocellular carcinoma (HCC) as a significant component of the tumour immune microenvironment (TIME). This study aimed to develop a CAFs-based scoring system to predict the prognosis and TIME of patients with HCC.

METHODS

Data for the TCGA-LIHC and GSE14520 cohorts were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Single-cell RNA-sequencing data for HCC samples were retrieved from the GSE166635 cohort. The Least Absolute Shrinkage and Selection Operator algorithm was employed to develop a CAFs-related scoring system (CAFRss). The predictive value of the CAFRss was determined using Kaplan-Meier, Cox regression and Receiver Operating Characteristic curves. Additionally, the TIMER platform, single sample Gene Set Enrichment Analysis and the Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data algorithms were performed to determine the TIME landscape. Finally, the pRRophic algorithm was utilised for drug sensitivity analysis.

RESULTS

The evaluation of the CAFRss system demonstrated its superior ability to predict the clinical outcome of patients with HCC. Additionally, CAFRss effectively distinguished HCC populations with distinct TIME landscapes. Furthermore, CAFRss-based risk stratification identified individuals with immune 'hot tumours' and predicted the survival of patients treated with ICBs.

CONCLUSIONS

The developed CAFRss can serve as a predictive tool for determining the clinical outcome of HCC and differentiating populations with diverse TIME characteristics.