Construction and Validation of a Novel Pyroptosis-Related Four-lncRNA Prognostic Signature Related to Gastric Cancer and Immune Infiltration

Research progress on the effect of pyroptosis on the occurrence, development, invasion and metastasis of colorectal cancer

Pyroptosis is a type of programmed cell death mediated by gasdermines (GSDMs). The N-terminal domain of GSDMs forms pores in the plasma membrane, causing cell membrane rupture and the release of cell contents, leading to an inflammatory response and mediating pyrodeath. Pyroptosis plays an important role in inflammatory diseases and malignant tumors. With the further study of pyroptosis, an increasing number of studies have shown that the pyroptosis pathway can regulate the tumor microenvironment and antitumor immunity of colorectal cancer and is closely related to the occurrence, development, treatment and prognosis of colorectal cancer. This review aimed to explore the molecular mechanism of pyroptosis and the role of pyroptosis in the occurrence, development, treatment and prognosis of colorectal cancer (CRC) and to provide ideas for the clinical diagnosis and treatment of CRC.

Genomics and proteomics to determine novel molecular subtypes and predict the response to immunotherapy and the effect of bevacizumab in glioblastoma

Glioblastoma (GBM) is a highly aggressive, infiltrative malignancy that cannot be completely cured by current treatment modalities, and therefore requires more precise molecular subtype signatures to predict treatment response for personalized precision therapy. Expression subtypes of GBM samples from the Cancer Genome Atlas (TCGA) were identified using BayesNM and compared with existing molecular subtypes of GBM. Biological features of the subtypes were determined by single-sample gene set enrichment analysis. Genomic and proteomic data from GBM samples were combined and Genomic Identification of Significant Targets in Cancer analysis was used to screen genes with recurrent somatic copy-number alterations phenomenon. The immune environment among subtypes was compared by assessing the expression of immune molecules and the infiltration of immune cells. Molecular subtypes adapted to immunotherapy were identified based on Tumor Immune Dysfunction and Exclusion (TIDE) score. Finally, least absolute shrinkage and selection operator (LASSO) logistic regression was performed on the expression profiles of S2, S3 and S4 in TCGA-GBM and RPPA to determine the respective corresponding best predictive model. Four novel molecular subtypes were classified. Specifically, S1 exhibited a low proliferative profile; S2 exhibited the profile of high proliferation, IDH1 mutation, TP53 mutation and deletion; S3 was characterized by high immune scores, innate immunity and adaptive immune infiltration scores, with the lowest TIDE score and was most likely to benefit from immunotherapy; S4 was characterized by high proliferation, EGFR amplification, and high protein abundance, and was the most suitable subtype for bevacizumab. LASSO analysis constructed the best prediction model composed of 13 genes in S2 with an accuracy of 96.7%, and the prediction model consisting of 17 genes in S3 with an accuracy of 86.7%, and screened 14 genes as components of the best prediction model in S4 with an accuracy of 93%. To conclude, our study classified reproducible and robust molecular subtypes of GBM, and these findings might contribute to the identification of patients responding to immunotherapy, thereby improving GBM prognosis.

Construction of prognostic markers for gastric cancer and comprehensive analysis of pyroptosis-related long non-coding RNAs

BACKGROUND

China's most frequent malignancy is gastric cancer (GC), which has a very poor survival rate, and the survival rate for patients with advanced GC is dismal. Pyroptosis has been connected to the genesis and development of cancer. The function of pyroptosis-related long non-coding RNAs (PRLs) in GC, on the other hand, remains uncertain.

AIM

To explore the construction and comprehensive analysis of the prognostic characteristics of long non-coding RNA (lncRNA) related to pyroptosis in GC patients.

METHODS

The TCGA database provided us with 352 stomach adenocarcinoma samples, and we obtained 28 pyroptotic genes from the Reactome database. We examined the correlation between lncRNAs and pyroptosis using the Pearson correlation coefficient. Prognosis-related PRLs were identified through univariate Cox analysis. A predictive signature was constructed using stepwise Cox regression analysis, and its reliability and independence were assessed. To facilitate clinical application, a nomogram was created based on this signature. we analyzed differences in immune cell infiltration, immune function, and checkpoints between the high-risk group (HRG) and low-risk group (LRG).

RESULTS

Five hundred and twenty-three PRLs were screened from all lncRNAs (absolute correlation coefficient > 0.4, P < 0.05). Nine PRLs were included in the risk prediction signature that was created through stepwise Cox regression analysis. We determined the risk score for GC patients and employed the median value as the dividing line between HRG and LRG. The ability of the risk signature to predict the overall survival (OS) of GC is demonstrated by the Kaplan-Meier analysis, risk curve, receiver operating characteristic curve, and decision curve analysis curve. The risk signature was shown to be an independent prognostic factor for OS in both univariate and multivariate Cox regression analyses. HRG showed a more efficient local immune response or modulation compared to LRG, as indicated by the predicted signal pathway analysis and examination of immune cell infiltration, function, and checkpoints (P < 0.05).

CONCLUSION

In general, we have created a brand-new prognostic signature using PRLs, which may provide ideas for immunotherapy in patients with GC.

The short peptide encoded by long non-coding RNA RNF217-AS1 inhibits stomach cancer tumorigenesis, macrophage recruitment, and pro-inflammatory responses

Certain long non-coding RNAs (lncRNAs) have potential peptide-coding abilities. Here, the role and molecular basis of the RNF217-AS1-encoded peptide in stomach cancer (SC) tumorigenesis were explored. Here, lncRNAs associated with SC pathogenesis and macrophage infiltration and lncRNAs with peptide-coding potential were searched by bioinformatics analysis. The gene mRNA and protein levels were examined by RT-qPCR and western blot assays, respectively. Cell viability, migratory, and invasive abilities were measured by CCK-8, Transwell migration, and Transwell invasion assays, respectively. The potential biological processes related to lncRNA RNF217-AS1 were identified by single-gene GSEA analysis. The effect of RNF217-AS1-encoded peptide on SC tumorigenesis was examined by mouse xenograft experiments. The results showed that lncRNA NR2F1-AS1 and RNF217-AS1 were differentially expressed and associated with macrophage infiltration in SC, and they had the ability to translate into short peptides. The RNF217-AS1 ORF-encoded peptide could reduce SC cell viability, inhibit cell migration and invasion, as well as hinder the development of SC xenograft tumors. The RNF217-AS1 ORF-encoded peptide in human SC AGS cells suppressed THP-1 cell migration, triggered the differential expression of CXCL1/CXCL2/CXCL8/CXCL12, and inactivated the TLR4/NF-κB/STAT1 signaling pathways. As a conclusion, the RNF217-AS1 ORF-encoded peptide hindered SC progression in vitro and in vivo and suppressed macrophage recruitment and pro-inflammatory responses in SC.

Long Non-Coding RNAs in Drug Resistance of Gastric Cancer: Complex Mechanisms and Potential Clinical Applications

Gastric cancer (GC) ranks as the third most prevalent malignancy and a leading cause of cancer-related mortality worldwide. However, the majority of patients with GC are diagnosed at an advanced stage, highlighting the urgent need for effective perioperative and postoperative chemotherapy to prevent relapse and metastasis. The current treatment strategies have limited overall efficacy because of intrinsic or acquired drug resistance. Recent evidence suggests that dysregulated long non-coding RNAs (lncRNAs) play a significant role in mediating drug resistance in GC. Therefore, there is an imperative to explore novel molecular mechanisms underlying drug resistance in order to overcome this challenging issue. With advancements in deep transcriptome sequencing technology, lncRNAs-once considered transcriptional noise-have garnered widespread attention as potential regulators of carcinogenesis, including tumor cell proliferation, metastasis, and sensitivity to chemo- or radiotherapy through multiple regulatory mechanisms. In light of these findings, we aim to review the mechanisms by which lncRNAs contribute to drug therapy resistance in GC with the goal of providing new insights and breakthroughs toward overcoming this formidable obstacle.

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.

Application of pyroptosis score in the treatment and prognosis evaluation of gastric cancer

Pyroptosis is a kind of proinflammatory programmed cell death mediated by inflammasome. It affects the occurrence and development of gastric cancer through different ways, showing dual effects. On the one hand, inflammasome-mediated inflammatory response is highly likely to participate in the formation and development of early tumors; on the other hand, drugs can inhibit the deterioration process of tumor proliferation, invasion and metastasis through activating the pathways of inflammasome and pyroptosis. Recently, many agents based on pyroptosis have been found to inhibit gastric cancer by promoting the secondary pyroptosis pathway, regulating NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome and inhibiting caspase-1. The establishment of cell pyrodeath models can predict the prognosis of gastric cancer patients. Most of the models show that gastric cancer patients with high pyroptosis level have better prognosis and longer overall survival. Pyroptosis scores can also be used to predict the response of gastric cancer patients to immunotherapy and to screen potential anti-gastric cancer drugs. Therefore, in-depth understanding of the potential mechanism of pyroptosis affecting the progression of gastric cancer and the role of pyroptosis score in the treatment and prognosis assessment of gastric cancer will be helpful to find a new and effective method for the treatment of gastric cancer.

Effect of tumor exosome-derived Lnc RNA HOTAIR on the growth and metastasis of gastric cancer

PURPOSE

HOX transcribed antisense RNA (HOTAIR) is a long noncoding RNA (LncRNA) that promotes tumor progression. Exosomes are critically involved in cancer progression. The presence of HOTAIR in the circulating exosomes and the roles of exosomal HOTAIR in gastric cancer (GC) remains unknown. This study aimed to investigate the role of HOTAIR in exosomes in promoting the growth and metastasis of GC.

METHODS

Serum exosomes from GC patients were captured by CD63 immunoliposome magnetic spheres (CD63-IMS), and the biological characteristics of the exosomes were identified. The expression levels of HOTAIR in GC cells, tissues, serum and serum exosomes were detected by fluorescence quantitative PCR (qRT-PCR), and the clinicopathological correlation was statistically analyzed. The growth and metastasis abilities of GC cells with HOTAIR knockdown in vitro were evaluated by cell experiment. The effects of HOTAIR highly-expressed NCI-N87 cell-derived exosomes were used to treat HOTAIR lowly-expressed MKN45 cells on GC growth and metastasis were also evaluated.

RESULTS

The exosomes isolated by CD63-IMS had a particle size of 89.78 ± 4.8 nm and were oval membranous particles. The expression of HOTAIR in tumor tissues and serum of GC patients was increased (P < 0.05), and the expression of HOTAIR in serum exosomes was significantly increased (P < 0.01). The in NCI-N87 and MKN45 cell experiment demonstrated that HOTAIR knockdown by RNA interference suppressed cell growth and metastasis in NCI-N87 cells. Coculture of exosomes secreted by NCI-N87 cells with MKN45 cells significantly increased the expression of HOTAIR, and enhanced cell growth and metastasis.

CONCLUSION

LncRNA HOTAIR can be used as a potential biomarker which provides a new way for the diagnosis and treatment of GC.

Long Non-Coding RNA LOC339059 Attenuates IL-6/STAT3-Signaling-Mediated PDL1 Expression and Macrophage M2 Polarization by Interacting with c-Myc in Gastric Cancer

Background: Long non-coding RNA (lncRNA) was identified as a novel diagnostic biomarker in gastric cancer (GC). However, the functions of lncRNAs in immuno-microenvironments have not been comprehensively explored. In this study, we explored a critical lncRNA, LOC339059, that can predict the clinical prognosis in GC related to the modulation of PD-L1 and determined its influence upon macrophage polarization via the IL-6/STAT3 pathway. Methods: To date, accumulating evidence has demonstrated that the dysregulation of LOC339059 plays an important role in the pathological processes of GC. It acts as a tumor suppressor, regulating GC cell proliferation, migration, invasion, tumorigenesis, and metastasis. A flow cytometry assay showed that the loss of LOC339059 enhanced PDL1 expression and M2 macrophage polarization. RNA sequencing, RNA pull-down, RNA immunoprecipitation, Chip-PCR, and a luciferase reporter assay revealed the pivotal role of signaling alternation between LOC339059 and c-Myc. Results: A lower level of LOC339059 RNA was found in primary GC tissues compared to adjacent tissues, and such a lower level is associated with a poorer survival period (2.5 years) after surgery in patient cohorts. Moreover, we determined important immunological molecular biomarkers. We found that LOC339059 expression was correlated with PD-L1, CTLA4, CD206, and CD204, but not with TIM3, FOXP3, CD3, C33, CD64, or CD80, in a total of 146 GC RNA samples. The gain of LOC339059 in SGC7901 and AGS inhibited biological characteristics of malignancy, such as proliferation, migration, invasion, tumorigenesis, and metastasis. Furthermore, our data gathered following the co-culture of THP-1 and U937 with genomic GC cells indicate that LOC339059 led to a reduction in the macrophage cell ratio, in terms of CD68+/CD206+, to 1/6, whereas the selective knockdown of LOC339059 promoted the abovementioned malignant cell phenotypes, suggesting that it has a tumor-suppressing role in GC. RNA-Seq analyses showed that the gain of LOC339059 repressed the expression of the interleukin family, especially IL-6/STAT3 signaling. The rescue of IL-6 in LOC339059-overexpressing cells reverted the inhibitory effects of the gain of LOC339059 on malignant cell phenotypes. Our experiments verified that the interaction between LOC339059 and c-Myc resulted in less c-Myc binding to the IL-6 promoter, leading to the inactivation of IL-6 transcription. Conclusions: Our results establish that LOC339059 acts as a tumor suppressor in GC by competitively inhibiting c-Myc, resulting in diminished IL-6/STAT3-signaling-mediated PDL1 expression and macrophage M2 polarization.

A novel pyroptosis-related signature predicts prognosis and indicates immunotherapy in oral squamous cell carcinoma

BACKGROUND

Oral squamous cell carcinoma (OSCC) has been recognized as a frequently occurring oral malignant tumor. Pyroptosis plays an extremely important role in the occurrence and development of cancer, but the role of pyroptosis in OSCC remains unclear.

METHODS

OSCC-related data were obtained from the TCGA and GEO databases. A PSscore risk model was constructed through LASSO regression analysis. The GEO database was utilized as the validation set of the model. The "ESTIMATE" and "CIBERSORT" algorithms were utilized to additionally evaluate the relationship between the immune cell score and PSscore. TIDE and IPS algorithms were used to assess patient response to immunotherapy. In addition, Western blot analysis and MTT assay was used to further validate key genes.

RESULTS

Comprehensive bioinformatics analysis showed that a low-PSscore had a significant survival advantage, richer immune cell infiltration, more active immune-related pathways, higher TME scores, and lower tumor purity. The results of TIDE and IPS analysis indicated that the high-PSscore group had higher immune escape potential and was less sensitive to immunotherapy. In contrast, the low-PSscore group patients might be more sensitive to PD1 and CTLA4 + PD1 immunotherapy. Univariate and multivariate COX results indicated that PSscore was an independent prognostic factor in OSCC patients. Another important finding is that BAK1 is a potential target of OSCC and is related to the Nod-like receptor signaling pathway. Knockdown of BAK1 can significantly reduce the proliferation of OSCC cells.

CONCLUSION

The PSscore model could be utilized as a powerful prognostic indicator and can help in the development of new immunotherapies.

Pyroptosis and inflammasomes in cancer and inflammation

Nonprogrammed cell death (NPCD) and programmed cell death (PCD) are two types of cell death. Cell death is significantly linked to tumor development, medication resistance, cancer recurrence, and metastatic dissemination. Therefore, a comprehensive understanding of cell death is essential for the treatment of cancer. Pyroptosis is a kind of PCD distinct from autophagy and apoptosis in terms of the structure and function of cells. The defining features of pyroptosis include the release of an inflammatory cascade reaction and the expulsion of lysosomes, inflammatory mediators, and other cellular substances from within the cell. Additionally, it displays variations in osmotic pressure both within and outside the cell. Pyroptosis, as evidenced by a growing body of research, is critical for controlling the development of inflammatory diseases and cancer. In this paper, we reviewed the current level of knowledge on the mechanism of pyroptosis and inflammasomes and their connection to cancer and inflammatory diseases. This article presents a theoretical framework for investigating the potential of therapeutic targets in cancer and inflammatory diseases, overcoming medication resistance, establishing nanomedicines associated with pyroptosis, and developing risk prediction models in refractory cancer. Given the link between pyroptosis and the emergence of cancer and inflammatory diseases, pyroptosis-targeted treatments may be a cutting-edge treatment strategy.

Unlocking the role of non-coding RNAs in prostate cancer progression: exploring the interplay with the Wnt signaling pathway

Prostate cancer (PCa) is one of the most common cancers in males, exhibiting a wide spectrum of clinical manifestations that pose challenges in its diagnosis and treatment. The Wnt signaling pathway, a conserved and complex pathway, is crucial for embryonic development, tissue homeostasis, and various physiological processes. Apart from the classical Wnt/β-catenin signaling pathway, there exist multiple non-classical Wnt signaling pathways, including the Wnt/PCP and Wnt/Ca2+ pathways. Non-coding RNAs (ncRNAs) are involved in the occurrence and development of PCa and the response to PCa treatment. ncRNAs are known to execute diverse regulatory roles in cellular processes, despite their inability to encode proteins. Among them, microRNAs, long non-coding RNAs, and circular RNAs play key roles in the regulation of the Wnt signaling pathway in PCa. Aberrant expression of these ncRNAs and dysregulation of the Wnt signaling pathway are one of the causes of cell proliferation, apoptosis, invasion, migration, and angiogenesis in PCa. Moreover, these ncRNAs affect the characteristics of PCa cells and hold promise as diagnostic and prognostic biomarkers. Herein, we summarize the role of ncRNAs in the regulation of the Wnt signaling pathway during the development of PCa. Additionally, we present an overview of the current progress in research on the correlation between these molecules and clinical features of the disease to provide novel insights and strategies for the treatment of PCa.

LncRNA THUMPD3-AS1 promotes invasion and EMT in gastric cancer by regulating the miR-1297/BCAT1 pathway

Long noncoding RNA (lncRNA) plays crucial roles in the development of gastric cancer (GC); however, studies of their mechanisms of action are needed to determine their clinical value. The aim of this study is to explore the effects and mechanisms of THUMPD3-AS1 in GC. Elevated levels of THUMPD3-AS1 were observed in GC and demonstrated a significant positive correlation with poor prognosis. Functionally, THUMPD3-AS1 promoted GC cell proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) and induced tumor growth in vivo. THUMPD3-AS1 exerts its regulatory function on BCAT1 through competitive binding with miR-1297. Further investigations confirmed that both THUMPD3-AS1 and miR-1297 interact with BCAT1. These findings suggest that THUMPD3-AS1 promotes GC invasion and EMT by regulating the miR-1297/BCAT1 pathway, indicating that THUMPD3-AS1 may serve as a biomarker and therapeutic target for GC.

Application of multiple machine learning approaches to determine key pyroptosis molecules in type 2 diabetes mellitus

Objective

Pyroptosis, a lytic and inflammatory programmed cell death, has been implicated in type 2 diabetes mellitus (T2DM) and its complications. Nonetheless, it remains elusive exactly which pyroptosis molecule exerts an essential role in T2DM, and this study aims to solve such issue.

Methods

Transcriptional profiling datasets of T2DM, i.e., GSE20966, GSE95849, and GSE26168, were acquired. Four machine learning models, namely, random forest, support vector machine, extreme gradient boosting, and generalized linear modeling, were built based on pyroptosis genes. A nomogram of key pyroptosis genes was also generated, and the clinical value was appraised via calibration curves and decision curve analysis. Immune infiltration was inferred utilizing CIBERSORT. Drug-druggable target relationships were acquired from the Drug Gene Interaction Database. Through WGCNA, key pyroptosis-relevant genes were selected.

Results

Most pyroptosis genes exhibited upregulation in T2DM relative to controls, indicating the activity of pyroptosis in T2DM. The SVM model composed of BAK1, CHMP2B, NLRP6, PLCG1, and TIRAP exhibited the best performance in T2DM diagnosis, with AUC = 1. The nomogram can predict the risk of T2DM for clinical practice. NK cells resting exhibited a lower abundance in T2DM versus normal specimens, with a higher abundance of neutrophils. NLRP6 was positively linked with neutrophils. Drugs (keracyanin, 9,10-phenanthrenequinone, diclofenac, phosphomethylphosphonic acid adenosyl ester, acetaminophen, cefixime, aspirin, ustekinumab) potentially targeted the key pyroptosis genes. Additionally, CHMP2B-relevant genes were determined.

Conclusion

Altogether, this work proposes the key pyroptosis genes in T2DM, which might become possible molecules for the management and treatment of T2DM and its complications.

The DNA damage repair-related lncRNAs signature predicts the prognosis and immunotherapy response in gastric cancer

Background

Gastric cancer (GC) is one of the most prevalent cancers, and it has unsatisfactory overall treatment outcomes. DNA damage repair (DDR) is a complicated process for signal transduction that causes cancer. lncRNAs can influence the formation and incidence of cancers by influencing DDR-related mRNAs/miRNAs. A DDR-related lncRNA prognostic model is urgently needed to improve treatment strategies.

Methods

The data of GC samples were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. A total of 588 mRNAs involved in DDR were selected from MSigDB, 62 differentially expressed mRNAs from TCGA-STAD were obtained, and 137 lncRNAs were correlated with these mRNAs. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to develop a DDR-related lncRNA prognostic model. Based on the risk model, the differentially expressed gene signature A/B in the low-risk and high-risk groups of TCGA-STAD was identified for further validation.

Results

The prognosis model of 5 genes (AC145285.6, MAGI2-AS3, AL590705.3, AC007405.3, and LINC00106) was constructed and classified into two risk groups. We found that GC patients with a low-risk score had a better OS than those with a high-risk score. We found that the high-risk group tended to have higher TME scores. We also found that patients in the high-risk group had a higher proportion of resting CD4 T cells, monocytes, M2 macrophages, resting dendritic cells, and resting mast cells, whereas the low-risk subgroup had a greater abundance of activated CD4 T cells, follicular helper T cells, M0 macrophages, and M1 macrophages. We observed significant differences in the T-cell exclusion score, T-cell dysfunction, MSI, and TMB between the two risk groups. In addition, we found that patients treated with immunotherapy in the low-RS score group had a longer survival and a better prognosis than those in the high-RS score group.

Conclusion

The prognostic model has a significant role in the TME, clinicopathological characteristics, prognosis, MSI, and drug sensitivity. We also discovered that patients treated with immunotherapy in the low-RS score group had a better prognosis. This work provides a foundation for improving the prognosis and response to immunotherapy among patients with GC.

Integrative profiling analysis reveals prognostic significance, molecular characteristics, and tumor immunity of angiogenesis-related genes in soft tissue sarcoma

Background

Soft tissue sarcoma (STS) is a class of malignant tumors originating from mesenchymal stroma with a poor prognosis. Accumulating evidence has proved that angiogenesis is an essential hallmark of tumors. Nevertheless, there is a paucity of comprehensive research exploring the association of angiogenesis-related genes (ARGs) with STS.

Methods

The ARGs were extracted from previous literature, and the differentially expressed ARGs were screened for subsequent analysis. Next, the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were conducted to establish the angiogenesis-related signature (ARSig). The predictive performance of the novel ARSig was confirmed using internal and external validation, subgroup survival, and independent analysis. Additionally, the association of the ARSig with the tumor immune microenvironment, tumor mutational burden (TMB), and therapeutic response in STS were further investigated. Notably, we finally conducted in vitro experiments to verify the findings from the bioinformatics analysis.

Results

A novel ARSig is successfully constructed and validated. The STS with a lower ARSig risk score in the training cohort has an improved prognosis. Also, consistent results were observed in the internal and external cohorts. The receiver operating characteristic (ROC) curve, subgroup survival, and independent analysis further indicate that the novel ARSig is a promising independent prognostic predictor for STS. Furthermore, it is proved that the novel ARSig is relevant to the immune landscape, TMB, immunotherapy, and chemotherapy sensitivity in STS. Encouragingly, we also validate that the signature ARGs are significantly dysregulated in STS, and ARDB2 and SRPK1 are closely connected with the malignant progress of STS cells.

Conclusion

In sum, we construct a novel ARSig for STS, which could act as a promising prognostic factor for STS and give a strategy for future clinical decisions, immune landscape, and personalized treatment of STS.

A standardised nomenclature for long non-coding RNAs

The HUGO Gene Nomenclature Committee (HGNC) is the sole group with the authority to approve symbols for human genes, including long non-coding RNA (lncRNA) genes. Use of approved symbols ensures that publications and biomedical databases are easily searchable and reduces the risks of confusion that can be caused by using the same symbol to refer to different genes or using many different symbols for the same gene. Here, we describe how the HGNC names lncRNA genes and review the nomenclature of the seven lncRNA genes most mentioned in the scientific literature.

Preliminary study based on methylation and transcriptome gene sequencing of lncRNAs and immune infiltration in hypopharyngeal carcinoma

Background

Hypopharyngeal squamous cell cancer (HSCC) is one of the most malignant tumors of the head and neck. It is not easy to detect in the early stage due to its hidden location; thus, lymph node metastasis is highly likely at diagnosis, leading to a poor prognosis. It is believed that epigenetic modification is related to cancer invasion and metastasis. However, the role of m6A-related lncRNA in the tumor microenvironment (TME) of HSCC remains unclear.

Methods

The whole transcriptome and methylation sequencing of 5 pairs of HSCC tissues and adjacent tissues were performed to identify the methylation and transcriptome profiles of lncRNAs. The biological significance of lncRNAs differentially expressing the m6A peak was analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. By constructing an m6A lncRNA-microRNA network, the mechanism of m6A lncRNAs in HSCC was analyzed. The relative expression levels of selected lncRNAs were examined by quantitative polymerase chain reaction. The CIBERSORT algorithm was used to evaluate the relative proportion of immune cell infiltration in HSCC and paracancerous tissues.

Results

Based on an in-depth analysis of the sequencing results, 14413 differentially expressed lncRNAs were revealed, including 7329 up-regulated and 7084 down-regulated lncRNAs. Additionally, 4542 up-methylated and 2253 down-methylated lncRNAs were detected. We demonstrated methylation patterns and gene expression profiles of lncRNAs of HSCC transcriptome. In the intersection analysis of lncRNAs and methylated lncRNAs, 51 lncRNAs with up-regulated transcriptome and methylation and 40 lncRNAs with down-regulated transcriptome and methylation were screened, and significantly differentiated lncRNAs were further studied. In the immune cell infiltration analysis, B cell memory was significantly elevated in cancer tissue, while γδT cell amount was significantly decreased.

Conclusion

m6A modification of lncRNAs might be involved in HSCC pathogenesis. Infiltration of immune cells in HSCC might provide a new direction for its treatment. This study provides new insights for exploring the possible HSCC pathogenesis and searching for new potential therapeutic targets.

Novel m7G-related lncRNA signature for predicting overall survival in patients with gastric cancer

Presenting with a poor prognosis, gastric cancer (GC) remains one of the leading causes of disease and death worldwide. Long non-coding RNAs (lncRNAs) regulate tumor formation and have been long used to predict tumor prognosis. N7-methylguanosine (m7G) is the most prevalent RNA modification. m7G-lncRNAs regulate GC onset and progression, but their precise mechanism in GC is unclear. The objective of this research was the development of a new m7G-related lncRNA signature as a biomarker for predicting GC survival rate and guiding treatment. The Cancer Genome Atlas database helped extract gene expression data and clinical information for GC. Pearson correlation analysis helped point out m7G-related lncRNAs. Univariate Cox analysis helped in identifying m7G-related lncRNA with predictive capability. The Lasso-Cox method helped point out seven lncRNAs for the purpose of establishing an m7G-related lncRNA prognostic signature (m7G-LPS), followed by the construction of a nomogram. Kaplan-Meier analysis, univariate and multivariate Cox regression analysis, calibration plot of the nomogram model, receiver operating characteristic curve and principal component analysis were utilized for the verification of the risk model's reliability. Furthermore, q-PCR helped verify the lncRNAs expression of m7G-LPS in-vitro. The study subjects were classified into high and low-risk groups based on the median value of the risk score. Gene enrichment analysis confirmed the constructed m7G-LPS' correlation with RNA transcription and translation and multiple immune-related pathways. Analysis of the clinicopathological features revealed more progressive features in the high-risk group. CIBERSORT analysis showed the involvement of m7G-LPS in immune cell infiltration. The risk score was correlated with immune checkpoint gene expression, immune cell and immune function score, immune cell infiltration, and chemotherapy drug sensitivity. Therefore, our study shows that m7G-LPS constructed using seven m7G-related lncRNAs can predict the survival time of GC patients and guide chemotherapy and immunotherapy regimens as biomarker.

Prognosis Risk Model Based on Pyroptosis-Related lncRNAs for Gastric Cancer

Gastric cancer (GC) is a malignant tumor with a low survival rate, high recurrence rate, and poor prognosis. With respect to this, pyroptosis is a type of programmed cell death that can affect the occurrence and development of tumors. Indeed, long non-coding RNAs (lncRNAs) were broadly applied for the purposes of early diagnosis, treatment, and prognostic analysis in regard to cancer. Based on the association of these three purposes, we developed a novel prognosis risk model based on pyroptosis-related lncRNAs (PRlncRNAs) for GC. The PRlncRNAs were obtained via univariate and multivariate Cox regression in order to build the predictive signatures. The Kaplan–Meier and gene set enrichment analysis (GSEA) methods were used to evaluate the overall survival (OS) and functional differences between the high- and low-risk groups. Moreover, the correlation of the signatures with immune cell infiltration was determined through single-sample gene set enrichment analysis (ssGSEA). Finally, we analyzed this correlation with the treatment responses in the GC patients; then, we performed quantitative reverse transcription polymerase chain reactions (qRT-PCRs) in order to verify the risk model. The high-risk group received a worse performance in terms of prognosis and OS when compared to the low-risk group. With respect to this, the area under the receiver operating characteristic curve (ROC) was found to be 0.808. Through conducting the GSEA, it was found that the high-risk groups possessed a significant enrichment in terms of tumor–immunity pathways. Furthermore, the ssGSEA revealed that the predictive features possessed strong associations with immune cell infiltration in regard to GC. In addition, we highlighted that anti-immune checkpoint therapy, combined with conventional chemotherapy drugs, may be more suitable for high-risk patients. The expression levels of LINC01315, AP003392.1, AP000695.2, and HAGLR were significantly different between the GC cell lines and the normal cell lines. As such, the six PRlncRNAs could be regarded as important prognostic biomarkers for the purposes of subsequent diagnoses, treatments, prognostic predictions, and the mechanism research of GC.

Classification of Signature-Based Phenotypes of Aging-Related Genes to Identify Prognostic and Immune Characteristics in HCC

Hepatocellular carcinoma (HCC), which has become one of the most significant malignancies causing cancer-related mortality, presents genetic and phenotypic heterogeneity that makes predicting prognosis challenging. Aging-related genes have been increasingly reported as significant risk factors for many kinds of malignancies, including HCC. In this study, we comprehensively dissected the features of transcriptional aging-relevant genes in HCC from multiple perspectives. We applied public databases and self-consistent clustering analysis to classify patients into C1, C2, and C3 clusters. The C1 cluster had the shortest overall survival time and advanced pathological features. Least absolute shrinkage and selection operator (LASSO) regression analysis was adopted to build the prognostic prediction model based on six aging-related genes (HMMR, S100A9, SPP1, CYP2C9, CFHR3, and RAMP3). These genes were differently expressed in HepG2 cell lines compared with LO2 cell lines measured by the mRNA expression level. The high-risk score group had significantly more immune checkpoint genes, higher tumor immune dysfunction and exclusion score, and stronger chemotherapy response. The results indicated that the age-related genes have a close correlation with HCC prognosis and immune characteristics. Overall, the model based on six aging-associated genes demonstrated great prognostic prediction ability.

LncRNA and its role in gastric cancer immunotherapy

Gastric cancer (GC) is a potential dominant disease in tumor immunotherapy checkpoint inhibitors, and adoptive cell therapy have brought great hope to GC patients. However, only some patients with GC can benefit from immunotherapy, and some patients develop drug resistance. More and more studies have shown that long non-coding RNAs (lncRNAs) may be important in GC immunotherapy's prognosis and drug resistance. Here, we summarize the differential expression of lncRNAs in GC and their impact on the curative effect of GC immunotherapy, discuss potential mechanisms of activity in GC immunotherapy resistance regulated by lncRNAs. This paper reviews the differential expression of lncRNA in GC and its effect on immunotherapy efficacy in GC. In terms of genomic stability, inhibitory immune checkpoint molecular expression, the cross-talk between lncRNA and immune-related characteristics of GC was summarized, including tumor mutation burden (TMB), microsatellite instability (MSI), and Programmed death 1 (PD-1). At the same time, this paper reviewed the mechanism of tumor-induced antigen presentation and upregulation of immunosuppressive factors, as well as the association between Fas system and lncRNA, immune microenvironment (TIME) and lncRNA, and summarized the functional role of lncRNA in tumor immune evasion and immunotherapy resistance.

Identification of Cuproptosis-Related Subtypes, Establishment of a Prognostic Signature and Characterization of the Tumor Microenvironment in Gastric Cancer

Purpose

Cuproptosis is a newly identified form of programmed cell death. We aimed to comprehensively discuss the correlation of cuproptosis with gastric cancer (GC) using bioinformatic methods.

Patients and Methods

This study selected GC bulk and single-cell RNA sequencing profiles from public databases. Based on the enrichment pattern of cuproptosis-related gene sets (CRGSs), GC patients were classified into different cuproptosis subtypes. A series of systematic analyses was performed to investigate the correlation of cuproptosis subtype with biological function and immune cell infiltration. In addition, we established a CRGS risk score signature to quantify GC patients' risk level, and analyzed the signature's relationship with clinical features, tumor microenvironment (TME) and treatment responses. Genes used for the construction of the risk score model were also detected in GC tumor and normal tissues by real-time quantitative polymerase chain reaction (RT-qPCR).

Results

First, analysis of scRNA-seq data revealed the alterations in CRGS enrichment scores for patients with GC and precancerous diseases. Then, based on large GC patient cohorts, two cuproptosis subtypes with significant differences in survival, biological function and TME were identified. Furthermore, we established a CRGS risk score signature. High-risk patients on the CRGS risk score signature with worse overall survival were characterized by higher immune and stromal contents in the TME, more advanced clinicopathological features, and better sensitivity to a wider range of anti-tumor drugs. Low-risk patients were correlated with higher tumor purity, and demonstrated more favorable clinical outcomes and higher sensitivity to immunotherapy.

Conclusion

The current work elucidated that cuproptosis plays an important role in the regulation of TME landscapes in GC. Two cuproptosis subtypes with distinct TME characteristics were identified. In addition, the establishment of a CRGS risk score signature could provide novel insights into accurate prediction and personalized treatment for GC patients.

LncRNA DIRC1 is a novel prognostic biomarker and correlated with immune infiltrates in stomach adenocarcinoma

The potential application value of Long non-coding RNA disrupted in renal carcinoma 1 (DIRC1) has not yet been explored, the purpose of this study was to explore the relationship between DIRC1 and stomach adenocarcinoma (STAD) based on the cancer genome atlas database. Wilcoxon rank sum test, Chi-square test, Fisher test and logistic regression were used to evaluate relationships between clinical-pathologic features and DIRC1 expression. Receiver operating characteristic (ROC) curves were used to describe binary classifier value of DIRC1 using area under curve (AUC) score. Kaplan-Meier method was used to assess the impact of DIRC1 on prognosis and the impact of DIRC1-related hub genes on prognosis. Gene oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used to predict the function of differentially expressed genes associated with DIRC1. Gene set enrichment analysis (GSEA) was used to predict biological states or processes associated with DIRC1. Immune infiltration analysis was performed to identify the significantly involved functions of DIRC1. Protein-protein interaction (PPI) networks were established and 10 hub genes identified with Cytoscape software. Real time-polymerase chain reaction (RT-PCR) was used to detect the expression of DIRC1 in Gastric Cancer patients and healthy people. Increased DIRC1 expression in STAD was associated with T stage (P = .004), race (P = .045), histologic grade (P = .029) and anatomic neoplasm subdivision (P = .034). ROC curve suggested the significant diagnostic ability of DIRC1 (AUC = 0.779). High DIRC1 expression predicted a poorer Overall survival (P = .004, hazard ratio: 1.63; 95% confidence interval: 1.17-2.27; P = .034). GO and KEGG analysis demonstrated that DIRC1 is related to epidermis, collagen-containing extracellular matrix, receptor-ligand activity, protein digestion and absorption, etc. GSEA demonstrated that E2F target, G2M checkpoint, Myc target, interferon γ reaction were differentially enriched in the high DIRC1 expression phenotype. SsGSEA and Spearman correlation revealed the relationships between DIRC1 and macrophages, dendritic cells, and Th1 cells were the strongest. Coregulatory proteins were included in the PPI network, higher expressions of 4 hub genes were associated with worse prognosis in STAD. RT-PCR showed that the expression of DIRC1 in the serum of Gastric Cancer patients was higher than healthy people (P = .027). DIRC1 expression was significantly correlated with poor survival and immune infiltrations in STAD, and it may be a promising prognostic biomarker in STAD.

Machine learning-based identification of a novel prognosis-related long noncoding RNA signature for gastric cancer

Gastric cancer (GC) is one of the most common malignancies with a poor prognosis. Immunotherapy has attracted much attention as a treatment for a wide range of cancers, including GC. However, not all patients respond to immunotherapy. New models are urgently needed to accurately predict the prognosis and the efficacy of immunotherapy in patients with GC. Long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and progression of cancers. Recent studies have identified a variety of prognosis-related lncRNA signatures in multiple cancers. However, these studies have some limitations. In the present study, we developed an integrative analysis to screen risk prediction models using various feature selection methods, such as univariate and multivariate Cox regression, least absolute shrinkage and selection operator (LASSO), stepwise selection techniques, subset selection, and a combination of the aforementioned methods. We constructed a 9-lncRNA signature for predicting the prognosis of GC patients in The Cancer Genome Atlas (TCGA) cohort using a machine learning algorithm. After obtaining a risk model from the training cohort, we further validated the model for predicting the prognosis in the test cohort, the entire dataset and two external GEO datasets. Then we explored the roles of the risk model in predicting immune cell infiltration, immunotherapeutic responses and genomic mutations. The results revealed that this risk model held promise for predicting the prognostic outcomes and immunotherapeutic responses of GC patients. Our findings provide ideas for integrating multiple screening methods for risk modeling through machine learning algorithms.

Identification of cell senescence molecular subtypes in prediction of the prognosis and immunotherapy of hepatitis B virus-related hepatocellular carcinoma

Hepatitis B virus (HBV)-infected hepatocellular carcinoma (HCC) has a high incidence and fatality rate worldwide, being among the most prevalent cancers. The growing body of data indicating cellular senescence (CS) to be a critical factor in hepatocarcinogenesis. The predictive value of CS in HBV-related HCC and its role in the immune microenvironment are unknown. To determine the cellular senescence profile of HBV-related HCC and its role in shaping the immune microenvironment, this study employed a rigorous evaluation of multiple datasets encompassing 793 HBV-related HCC samples. Two novel distinct CS subtypes were first identified by nonnegative matrix factorization, and we found that the senescence-activated subgroup had the worst prognosis and correlated with cancer progression. C1 and C2 were identified as the senescence-suppressed and senescence-activated subgroups. The immune microenvironment indicated that C2 exhibited a relatively low immune status, higher tumor purity, and lower immune scores and estimated scores, while the C1 subgroup possessed a better prognosis. The CS score signature based on five genes (CENPA, EZH2, G6PD, HDAC1, and PRPF19) was established using univariate Cox regression and the lasso method. ICGC-LIRI and GSE14520 cohorts were used to validate the reliability of the CS scoring system. In addition, we examined the association between the risk score and hallmark pathways through gene set variation analysis and gene set enrichment analysis. The results revealed a high CS score to be associated with the activation of cell senescence-related pathways. The CS score and other clinical features were combined to generate a CS dynamic nomogram with a better predictive capacity for OS at 1, 2, and 3 years than other clinical parameters. Our study demonstrated that cellular senescence patterns play a non-negligible role in shaping the characteristics of the immune microenvironment and profoundly affecting tumor prognosis. The results of this study will help predict patient prognosis more accurately and may assist in development of personalized immunotherapy for HBV-related HCC patients.

Constructing and Validating a Pyroptosis-Related Genes Prognostic Signature for Stomach Adenocarcinoma and Immune Infiltration: Potential Biomarkers for Predicting the Overall Survival

Background

Stomach adenocarcinoma (STAD) is a kind of cancer that begins in the stomach cells and has a poor overall survival rate. Following resection surgery, chemotherapy has been suggested as a curative method for stomach cancer. However, it is ineffective. Pyroptosis, a kind of inflammatory programmed cell death, has been shown to play a significant role in the development and progression of STAD. However, whether pyroptosis-related genes (PRGs) can be utilized to predict the diagnosis and prognosis of gastric cancer remains unknown.

Method

The research measured at predictive PRGs in STAD samples from TCGA and GEO. Lasso regression was used to build the prediction model. Coexpression analysis revealed that gene expression was linked to pyroptosis. PRGs were found to be overexpressed in high-risk individuals, implying that they could be used in a model to predict STAD prognosis.

Result

Immunological and tumor-related pathways were discovered using GSEA. In STAD patients, the genes GPX3, PDGFRL, RGS2, and SERPINE1 may be connected to the cancer process. The levels of expression also differed between the two risk groups.

Conclusion

The purpose of this study is to identify and verify STAD-associated PRGs that can effectively guide prognosis and the immunological milieu in STAD patients as well as offer evidence for the development of pyroptosis-related molecularly targeted therapeutics. Therefore, PRGs and the link between immunological and PRGs in STAD may be therapeutic targets.

Identification of the three subtypes and the prognostic characteristics of stomach adenocarcinoma: analysis of the hypoxia-related long non-coding RNAs

Stomach adenocarcinoma (STAD) is one of the most commonly diagnosed cancers. This study analyzed the subtypes and characteristics of STAD subtypes by analyzing hypoxia pathway-related lncRNAs. Potential hub lncRNAs were found and a prognostic model was constructed. Expression profiling data and clinical information of STAD were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Metabolic pathway scores were calculated using single-sample gene set enrichment analysis (ssGSEA) method. Tumor immune microenvironment scores of the samples were assessed by ESTIMATE, MCP-counter, and ssGSEA. Functional analysis of lncRNAs, construction of risk models, and drug sensitivity analysis were performed. Pathway analysis revealed that the hypoxia pathway was a prognostic risk factor. Molecular subtypes were developed based on the hypoxia score-related lncRNAs. Three molecular subtypes (C1, C2, and C3) for gastric STAD were determined. The worst prognosis was in the C2, which was also characterized by the maximum hypoxia pathway-related scores and the maximum immune score. A majority of the immune checkpoints and chemokines were high-expressed in the C2 subtype. Mutations in the C2 subtype were significantly lower than the C1 and C3 subtypes. The subtypes differed in terms of functional and metabolic pathways. Eight hub indicator lncRNAs (MSC-AS1, AC037198.1, LINC00968, AL139393.3, LINC02544, BOLA3-AS1, MIR1915HG, and AC107021.2) capable of predicting patient prognosis were identified. Three hypoxia lncRNA-related molecular subtypes characterized by different prognostic and immune conditions were identified. The results maybe can provide a theoretical basis to improve the clinical diagnosis and treatment of STAD.