A Necroptosis-Related lncRNA-Based Signature to Predict Prognosis and Probe Molecular Characteristics of Stomach Adenocarcinoma

High LGALS3 expression induced by HCP5/hsa-miR-27b-3p correlates with poor prognosis and tumor immune infiltration in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is widely recognized for its unfavorable prognosis. Increasing evidence has revealed that LGALS3 has an essential function in initiating and developing several malignancies in humans. Nevertheless, thorough analysis of the expression profile, clinical prognosis, pathway prediction, and immune infiltration of LGALS3 has not been fully explored in HCC.

METHODS

In this study, an initial pan-cancer analysis was conducted to investigate the expression and prognosis of LGALS3. Following a comprehensive analysis, which included expression analysis and correlation analysis, noncoding RNAs that contribute to the overexpression of LGALS3 were subsequently identified. This identification was further validated using HCC clinical tissue samples. TIMER2 and GEPIA2 were employed to examine the correlation between LGALS3 and HCP5 with immunological checkpoints, cell chemotaxis, and immune infiltration in HCC. The R program was applied to analyze the expression distribution of immune score in in HCC patients with high and low LGALS3 expression. The expression profiles of immune checkpoints were also analyzed. Use R to perform GSVA analysis in order to explore potential signaling pathways.

RESULTS

First, we conducted pan-cancer analysis for LGALS3 expression level through an in-depth analysis of public databases and found that HCC has a high LGALS3 gene and protein expression level, which were then verified in clinical HCC specimens. Meanwhile, high LGALS3 gene expression is related to malignant progression and poor prognosis of HCC. Univariate and multivariate analyses confirmed that LGALS3 could serve as an independent prognostic marker for HCC. Next, by combining comprehensive analysis and validation on HCC clinical tissue samples, we hypothesize that the HCP5/hsa-miR-27b-3p axis could serve as the most promising LGALS3 regulation mechanism in HCC. KEGG and GO analyses highlighted that the LGALS3-related genes were involved in tumor immunity. Furthermore, we detected a significant positive association between LGALS3 and HCP5 with immunological checkpoints, cell chemotaxis, and immune infiltration. In addition, high LGALS3 expression groups had significantly higher immune cell scores and immune checkpoint expression levels. Finally, GSVA analysis was performed to predict potential signaling pathways linked to LGALS3 and HCP5 in immune evasion and metabolic reprogramming of HCC.

CONCLUSIONS

Our findings indicated that the upregulation of LGALS3 via the HCP5/hsa-miR-27b-3p axis is associated with unfavorable prognosis and increased tumor immune infiltration in HCC.

A degradome-related signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma based on machine learning procedure

Stomach adenocarcinoma (STAD) is one of the subtype of gastric cancer with high invasiveness, extreme heterogeneity, high morbidity, and high mortality. The degradome is the most abundant class of cellular enzymes that play an essential role in regulating cellular activity and carcinogenesis. An integrative machine learning procedure including 10 methods was performed to develop a prognostic degradome-based prognostic signature (DPS) in TCGA, GSE15459, GSE26253, and GSE62254 datasets. Investigations of the DPS concerning immune infiltration, immunotherapy benefits, and drug priority were orchestrated. The DPS developed by Enet [alpha = 0.3] method was regarded as the optimal prognostic model. The DPS had a stable and powerful performance in predicting the clinical outcome of STAD and served as an independent risk factor in training and testing cohorts. The C-index of DPS was higher than that of age, sex, and clinical stage. STAD patients with low DPS scores had a higher abundance of B cells, CD8+ T cells, higher cytolytic scores, and T cell co-stimulation scores. Moreover, low DPS score indicated a lower tumor immune dysfunction and exclusion score, lower T cell dysfunction and exclusion score, higher PD1&CTLA4 immunophenoscore, and higher tumor mutation burden score in STAD, demonstrating a better immunotherapy response. STAD patients with a high DPS score had a lower IC50 value of common chemotherapy and targeted therapy regimens (Cisplatin, Docetaxel, Gefitinib, etc). Our study developed an optimal DPS for STAD. The DPS could predict the prognosis, risk stratification and guide treatment for STAD patients.

Analysis of disulfidptosis- and cuproptosis-related LncRNAs in modulating the immune microenvironment and chemosensitivity in colon adenocarcinoma

The main objective was to establish a prognostic model utilising long non-coding RNAs associated with disulfidptosis and cuproptosis. The data for RNA-Sequence and clinicopathological information of Colon adenocarcinoma (COAD) were acquired from The Cancer Genome Atlas. A prognostic model was constructed using Cox regression and the Least Absolute Shrinkage and Selection Operator method. The model's predictive ability was assessed through principal component analysis, Kaplan-Meier analysis, nomogram etc. The ability of identifying the rates of overall survival, infiltration of immune cells, and chemosensitivity was also explored. In vitro experiments were conducted for the validation of differential expression and function of lncRNAs. A disulfidptosis and cuproptosis-related lncRNA prognostic model was constructed. The prognostic model exhibits excellent independent predictive capability for patient outcomes. Based on the authors' model, the high-risk group exhibited higher tumour mutation burdened worse survival. Besides, differences in immune cell infiltration and responsiveness to chemotherapeutic medications exist among patients with different risk scores. Furthermore, aberrant expressions in certain lncRNAs have been validated in HCT116 cells. In particular, FENDRR and SNHG7 could affect the proliferation and migration of colorectal cancer cells. Our study developed a novel prognostic signature, providing valuable insights into prognosis, immune infiltration, and chemosensitivity in COAD patients.

AGAP2-AS1 promotes the assembly of m6A methyltransferases and activation of the IL6/STAT3 pathway by binding with WTAP in the carcinogenesis of gastric cancer

Owing to the lack of biomarkers for early diagnosis, gastric cancer (GC) is often associated with a poor prognosis. Thus, there is an urgent need to identify early molecular targets in GC. Dysregulated long noncoding RNAs (lncRNAs) have been evaluated by integrated bioinformatics analysis; and we investigate their specific role and potential mechanism via N6-methyladenosine (m6A) methylation modification in the carcinogenesis and progression of GC. In this study, we report upregulation of lncRNA AGAP2-AS1, activated by a gain of H3K4Me3, in GC tissues and cells. AGAP2-AS1 was linked to adverse prognosis in patients with GC. Functionally, AGAP2-AS1 knockdown inhibited cell proliferation and migration of GC cells. Mechanistically, AGAP2-AS1 bound WT1-associated protein (WTAP) to promote the formation of the WTAP/methyltransferase-like 3 (METTL3)/METTL14 m6A methyltransferase complex. AGAP2-AS1 stabilized signal transducer and activator of transcription 3 (STAT3) mRNA in an m6A-dependent manner and, thus, activated the interleukin 6 (IL6)/STAT3 pathway. Importantly, activation of the AGAP2-AS1/WTAP/STAT3 pathways promoted cell proliferation and migration in GC. Collectively, the present findings revealed a novel regulatory relationship between lncRNA and m6A modification. Furthermore, targeting the AGAP2-AS1/WTAP/STAT3 axis may be a promising strategy for the inhibition of inflammation-mediated carcinogenesis and progression in GC.

Construction and validation of a cuproptosis-related lncRNA prognosis signature in bladder carcinoma

BACKGROUND

Bladder cancer (BLCA) is a prevalent urological tumor with high morbidity and mortality. However, BLCA treatment remains challenging due to a lack of effective biomarkers. Long non-coding RNAs (lncRNAs), as active participants in tumor progression are involved in multiple biological regulatory mechanisms, and cuproptosis-related genes participate in the development of cancer. It is important to discover cuproptosis- related lncRNAs for BLCA diagnosis and treatment.

METHODS

A predictive signature was constructed based on least absolute shrinkage and selection operator regression (LASSO) and Cox regression analyses of the 9 cuproptosis-related lncRNAs. Samples were divided into high-risk group and low-risk group based on their median risk scores to explore their prognosis.

RESULTS

This signature is well predictive, as evidenced by the receiver operating characteristic curves (ROC curves) and K-M curves. Based on the nomogram, we were able to visually forecast the survival rates of patients with BLCA at 1-, 3-, and 5-year, and the calibration plots displayed that the actual results were well matched with the predicted 1-, 3-, and 5-year survival rates. Furthermore, BLCA patients in the high-risk group had a higher Tumor Immune Dysfunction and Exclusion (TIDE) score and lower TMB. Finally, we investigated the response of antitumor drugs for BLCA patients in different risk groups, and a statistically significant difference was observed in the sensitivity of those drugs between low- and the high-risk groups.

CONCLUSION

According to the 9 cuproptosis-related lncRNAs, we constructed a signature which can be served as a promising prognostic biomarker for BLCA patients.

A novel necroptosis-related long noncoding RNA model for predicting clinical features, immune characteristics, and therapeutic response in clear cell renal cell carcinoma

Background

Necroptosis is an immune-related cell death pathway involved in the regulation of the tumor microenvironment (TME). Here, we aimed to explore the role of necroptosis in clear cell renal cell carcinoma (ccRCC) and construct a necroptosis-related lncRNA (NRL) model to assess its potential association with clinical characteristics and immune status.

Methods

Gene expression profiles and clinical data for ccRCC patients were obtained from the Cancer Genome Atlas (TCGA). Pearson's correlation, univariate Cox, and least absolute shrinkage and selection operator analyses were used to develop an NRL model. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curve analyses were used to determine the prognostic value of the NRL model. The clinical information was used to assess the diagnostic value of the NRL model. The TME, immune function, immune cell infiltration, and immune checkpoints associated with the NRL model risk score were studied using the ESTIMATE, GSEA, ssGSEA, and CIBERSORT algorithms. The immunophenoscore (IPS) and half-maximal inhibitory concentration (IC50) were used to compare the efficacies of immunotherapy and chemotherapy based on the NRL model. Finally, in vitro assays were performed to confirm the biological roles of NRLs.

Results

A total of 18 necroptosis-related genes and 285 NRLs in ccRCC were identified. A four-NRL model was constructed and showed good performance in the diagnosis and prognosis of ccRCC patients. The ESTIMATE scores, tumor mutation burden, and tumor stemness indices were significantly correlated with NRL model risk score. Immune functions such as chemokine receptors and immune receptor activity showed differences between different risk groups. The infiltration of immunosuppressive cells such as Tregs was higher in high-risk patients than in low-risk patients. High-risk patients were more sensitive to immunotherapy and some chemotherapy drugs, such as sunitinib and temsirolimus. Finally, the expression of NRLs included in the model was verified, and knocking down these NRLs in tumor cells affected cell proliferation, migration, and invasion.

Conclusion

Necroptosis plays an important role in the progression of ccRCC. The NRL model we constructed can be used to predict the clinical characteristics and immune features of ccRCC patients.

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

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

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.

Identification of hub necroptosis-related lncRNAs for prognosis prediction of esophageal carcinoma

Necroptosis is a newly identified programmed cell death associated with the biological process of various cancers, including esophageal carcinoma (ESCA). Meanwhile, the dysregulation of long non-coding RNAs (lncRNAs) is greatly implicated in ESCA progression and necroptosis regulation. However, the lncRNAs involved in regulating necroptosis in ESCA are still unclear. In this study, we aim to explore the expression profile of necroptosis-related lncRNAs (NRLs), and evaluate their roles in ESCA prognosis and treatment. In the present study, 198 differentially expressed NRLs were identified between the ESCA and adjacent normal tissues through screening the data extracted from the Cancer Genome Atlas (TCGA) database. And, a prognostic panel consisting of 6 NRLs was constructed using the LASSO algorithm and multivariate Cox regression analysis. The ESCA patients with high risks had a markedly reduced survival time and higher mortality prevalence. Moreover, C-index of 6 NRLs-panel was superior to 48 published prognostic models based on lncRNAs or mRNAs for ESCA. There were significant differences between the high-risk and low-risk groups in tumor-related pathways, genetic mutations, and drug sensitivity responses. In vitro analysis revealed that inhibition of PVT1 impeded the proliferation, migration, and colony formation of ESCA cells, increased the expressions of p-RIP1 and p-MLKL and promoted necroptosis. By contrast, PVT1 overexpression resulted in a decrease in necroptotic cell death events, thus promoting tumor progression. Collectively, the established 6-NRLs panel was a promising biomarker for the prognostic prediction of ESCA. Moreover, our current findings provided potential targets for individualized therapy for ESCA patients.

LncRNAs and regulated cell death in tumor cells

Regulated Cell Death (RCD) is a mode of cell death that occurs through drug or genetic intervention. The regulation of RCDs is one of the significant reasons for the long survival time of tumor cells and poor prognosis of patients. Long non-coding RNAs (lncRNAs) which are involved in the regulation of tumor biological processes, including RCDs occurring on tumor cells, are closely related to tumor progression. In this review, we describe the mechanisms of eight different RCDs which contain apoptosis, necroptosis, pyroptosis, NETosis, entosis, ferroptosis, autosis and cuproptosis. Meanwhile, their respective roles in the tumor are aggregated. In addition, we outline the literature that is related to the regulatory relationships between lncRNAs and RCDs in tumor cells, which is expected to provide new ideas for tumor diagnosis and treatment.

Natural products targeting signaling pathways associated with regulated cell death in gastric cancer: Recent advances and perspectives

Gastric cancer (GC) is one of the most serious gastrointestinal malignancies with high morbidity and mortality. The complexity of GC process lies in the multi-phenotypic linkage regulation, in which regulatory cell death (RCD) is the core link, which largely dominates the fate of GC cells and becomes a key determinant of GC development and prognosis. In recent years, increasing evidence has been reported that natural products can prevent and inhibit the development of GC by regulating RCDs, showing great therapeutic potential. In order to further clarify its key regulatory characteristics, this review focused on specific expressions of RCDs, combined with a variety of signaling pathways and their crosstalk characteristics, sorted out the key targets and action rules of natural products targeting RCD. It is highlighted that a variety of core biological pathways and core targets are involved in the decision of GC cell fate, including the PI3K/Akt signaling pathway, MAPK-related signaling pathways, p53 signaling pathway, ER stress, Caspase-8, gasdermin D (GSDMD), and so on. Moreover, natural products target the crosstalk of different RCDs by modulating above signaling pathways. Taken together, these findings suggest that targeting various RCDs in GC with natural products is a promising strategy, providing a reference for further clarifying the molecular mechanism of natural products treating GC, which warrants further investigations in this area.

Identification of featured necroptosis-related genes and imbalanced immune infiltration in sepsis via machine learning

Background: The precise diagnostic and prognostic biological markers were needed in immunotherapy for sepsis. Considering the role of necroptosis and immune cell infiltration in sepsis, differentially expressed necroptosis-related genes (DE-NRGs) were identified, and the relationship between DE-NRGs and the immune microenvironment in sepsis was analyzed.

Methods: Machine learning algorithms were applied for screening hub genes related to necroptosis in the training cohort. CIBERSORT algorithms were employed for immune infiltration landscape analysis. Then, the diagnostic value of these hub genes was verified by the receiver operating characteristic (ROC) curve and nomogram. In addition, consensus clustering was applied to divide the septic patients into different subgroups, and quantitative real-time PCR was used to detect the mRNA levels of the hub genes between septic patients (SP) (n = 30) and healthy controls (HC) (n = 15). Finally, a multivariate prediction model based on heart rate, temperature, white blood count and 4 hub genes was established.

Results: A total of 47 DE-NRGs were identified between SP and HC and 4 hub genes (BACH2, GATA3, LEF1, and BCL2) relevant to necroptosis were screened out via multiple machine learning algorithms. The high diagnostic value of these hub genes was validated by the ROC curve and Nomogram model. Besides, the immune scores, correlation analysis and immune cell infiltrations suggested an immunosuppressive microenvironment in sepsis. Septic patients were divided into 2 clusters based on the expressions of hub genes using consensus clustering, and the immune microenvironment landscapes and immune function between the 2 clusters were significantly different. The mRNA levels of the 4 hub genes significantly decreased in SP as compared with HC. The area under the curve (AUC) was better in the multivariate prediction model than in other indicators.

Conclusion: This study indicated that these necroptosis hub genes might have great potential in prognosis prediction and personalized immunotherapy for sepsis.

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.

Solute Carrier Family 7 Member 11 (SLC7A11) is a Potential Prognostic Biomarker in Uterine Corpus Endometrial Carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is a common type of gynecological cancers, second only to cervical cancer in incidence. Thus, it is necessary to develop effective therapies and identify biomarkers for its prognosis. Solute carrier family 7 member 11 (SLC7A11) is well known for its role in maintaining the intracellular glutathione level and preventing oxidative-stress-induced cell death. However, the association between SLC7A11 expression and prognosis as well as the correlation between tumor-infiltrating immune cells (TIICs) and immunotherapy of UCEC has rarely been reported. This study aims to evaluate the prognostic significance and immune cell infiltration level of SLC7A11 in UCEC.

Methods

Bioinformatics analysis tools and databases, including R software, National Center for Biotechnology Information (NCBI), The Cancer Genome Atlas (TCGA), GEPIA2, Sangerbox, Kaplan-Meier (K-M) Plotter, TISIDB, and TIMER2, were utilized to measure the expression level and clarify the clinical significance of SLC7A11 in UCEC.

Results

SLC7A11 expression was dramatically up-regulated in UCEC patients and associated with prognosis. DNA methylation levels in the SLC7A11-promoter region were significantly higher in normal participants than in patients with UCEC. We also showed that SLC7A11 overexpression was associated with TIICs, immune checkpoint blockers (ICBs), and immunotherapy response in UCEC. The half-maximal inhibitory concentration (IC50) results obtained with the cohort from TCGA showed that Z-VAD-FMK (Caspase inhibitor), S-Triphenylmethyl-L-cysteine (S-Trityl-L-cysteine), and TAE684 (ALK inhibitor) had higher IC50 values in low-expression patient (p < 0.05).

Conclusion

SLC7A11 overexpression is associated with favorable prognosis of patients with UCEC and is associated with TIICs and the responses to immunotherapy.

Necroptosis Related Genes Predict Prognosis and Therapeutic Potential in Gastric Cancer

The clinical significance of necroptosis in gastric cancer (GC) has yet to be fully elucidated. The purpose of our study was to identify a necroptosis-relevant gene and to establish a prediction model to estimate the prognosis and therapeutic potential in GC. Here, we explored the expression profile of 76 necroptosis-related genes in TCGA-STAD patients. A six-gene risk score prediction model was established via regression analysis of the least absolute shrinkage and selection operator (LASSO) and validated in a separate cohort. Patients were separated into low- or high-risk groups according to the median risk score. We then compared and analyzed the biological process characteristics of two risk groups. Additionally, cell-to-cell communications and metabolic activity were analyzed in a single-cell solution. The in vitro experiments were conducted to explore the biological functions and drug sensitivity of necroptosis-related genes in gastric cancer. Our results identified that compared with the low-risk group, the high-risk group was associated with a higher clinical stage or grade and a worse prognosis. In addition, the low-risk group had higher levels of immunity and immune cell infiltration. Necroptosis was triggered by the TNF pathway in myeloid cells and the glycolysis pathway was altered. Necroptosis-related genes modulated the cell function, including proliferation and migration in vitro. Furthermore, the potential drugs' sensitivity was higher in the low-risk subgroup. These findings could facilitate a better understanding and improve the treatment potential and prognosis of GC patients.

A telomerase regulation-related lncRNA signature predicts prognosis and immunotherapy response for gastric cancer

BACKGROUND

Telomeres are involved in the development and progression of gastric cancer (GC). However, the association of telomerase regulation-related lncRNAs with prognosis and immunotherapy responsiveness in gastric cancer is unclear.

METHODS

This study systematically evaluated the relationship between lncRNAs co-expressed with 67 telomerase regulatory genes and gastric cancer prognosis. The risk scores of the samples were calculated based on telomerase regulation-related lncRNAs with prognostic value, and the samples were classified into high-/low-risk groups. The prognostic value of risk groups was then evaluated, a GC prognostic prediction model based on risk groups and clinical characteristics was established, and the prediction accuracy of the model was clarified by receiving operating characteristic (ROC) curves and calibration curves. Finally, the value of risk grouping in GC immunotherapy sensitivity was predicted by comparing MSI status and tumor mutation load between the high- and low-risk groups.

RESULTS

We identified 13 lncRNAs with prognostic value co-expressed with telomerase regulatory genes and observed that the prognosis of the low-risk group was significantly better than that of the high-risk group. Meanwhile, a GC overall survival (OS) prediction model based on risk grouping and clinical characteristics was developed, and ROC curves and calibration curves confirmed the good predictive ability of the model. In addition, the low-risk group exhibited a higher tumor mutation load and MSI-H, suggesting a possible benefit of immunotherapy.

CONCLUSION

We found that telomerase regulation-related lncRNAs have prognostic value in GC patients and contribute to the exploration of more effective immunotherapeutic strategies.

Antisense lncRNA CHROMR is linked to glioma patient survival

Background: Natural non-coding antisense transcripts (ncNATs) are long non-coding RNAs (lncRNA) transcribed from the opposite strand of a separate protein coding or non-coding gene. As such, ncNATs can increase overlapping mRNA (and the coded protein) levels by stabilizing mRNA, absorbing inhibitory miRNAs and protecting the mRNA from degradation, or conversely decrease mRNA (or protein) levels by directing the mRNA towards degradation or inhibiting protein translation. Recently, growing numbers of ncNATs were shown to be dysregulated in cancerous cells, however, actual impact of ncNATs on cancer progression remains largely unknown. We therefore investigated gene expression levels of natural antisense lncRNA CHROMR (Cholesterol Induced Regulator of Metabolism RNA) and its sense protein coding gene PRKRA (Protein Activator of Interferon Induced Protein Kinase EIF2AK2) in gliomas. Next, we checked CHROMR effect on the survival of glioma patients. Methods: We performed RNA-seq on post-surgical tumor samples from 26 glioma patients, and normal brain tissue. Gene expression in TPM values were extracted for CHROMR and PRKRA genes. These data were validated using the TCGA and GTEx gene expression databases. Results: The gene expression level of ncNAT lncRNA CHROMR in glioma tissue was significantly higher compared to healthy brain tissue, while the expression of its sense counterpart protein coding PRKRA mRNA did not differ between glioma and healthy samples. Survival analysis showed lower survival rates in patients with low mRNA PRKRA/lncRNA CHROMR gene expression ratio compared to high ratio showing a link between lncRNA CHROMR and glioma patient survival prognosis. Conclusion: Here we show that elevated levels of lncRNA CHROMR (i.e., low ratio of mRNA PRKRA/lncRNA CHROMR) is associated with poor prognosis for glioma patients.

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.

Construction of a Necroptosis-Related lncRNA Signature for Predicting Prognosis and Immune Response in Kidney Renal Clear Cell Carcinoma

Necroptosis is a new type of programmed cell death and involves the occurrence and development of various cancers. Moreover, the aberrantly expressed lncRNA can also affect tumorigenesis, migration, and invasion. However, there are few types of research on the necroptosis-related lncRNA (NRL), especially in kidney renal clear cell carcinoma (KIRC). In this study, we analyzed the sequencing data obtained from the TGCA-KIRC dataset, then applied the LASSO and COX analysis to identify 6 NRLs (AC124854.1, AL117336.1, DLGAP1-AS2, EPB41L4A-DT, HOXA-AS2, and LINC02100) to construct a risk model. Patients suffering from KIRC were divided into high- and low-risk groups according to the risk score, and the patients in the low-risk group had a longer OS. This signature can be used as an indicator to predict the prognosis of KIRC independent of other clinicopathological features. In addition, the gene set enrichment analysis showed that some tumor and immune-associated pathways were more enriched in a high-risk group. We also found significant differences between the high and low-risk groups in the infiltrating immune cells, immune functions, and expression of immune checkpoint molecules. Finally, we use the "pRRophetic" package to complete the drug sensitivity prediction, and the risk score could reflect patients' response to 8 small molecule compounds. In general, NRLs divided KIRC into two subtypes with different risk scores. Furthermore, this signature based on the 6 NRLs could provide a promising method to predict the prognosis and immune response of KIRC patients. To some extent, our findings helped give a reference for further research between NRLs and KIRC and find more effective therapeutic drugs for KIRC.

A novel necroptosis-related lncRNA based signature predicts prognosis and response to treatment in cervical cancer

Background: Necroptosis has been demonstrated to play a crucial role in the prognosis prediction and assessment of treatment outcome in cancers, including cervical cancer. The purpose of this study was to explore the potential prognostic value of necroptosis-related lncRNAs and their relationship with immune microenvironment and response to treatment in cervical cancer. Methods: Data from The Cancer Genome Atlas (TCGA) were collected to obtain synthetic data matrices. Necroptosis-related lncRNAs were identified by Pearson Correlation analysis. Univariate Cox and multivariate Cox regression analysis and Lasso regression were used to construct a necroptosis-related LncRNAs signature. Kaplan-Meier analysis, univariate and multivariate Cox regression analyses, receiver operating characteristic (ROC) curve, nomogram, and calibration curves analysis were performed to validate this signature. Gene set enrichment analyses (GSEA), immunoassays, and the half-maximal inhibitory concentration (IC50) were also analyzed. Results: Initially, 119 necroptosis-related lncRNAs were identified based on necroptosis-related genes and differentially expressed lncRNAs between normal and cervical cancer samples. Then, a prognostic risk signature consisting of five necroptosis-related lncRNAs (DDN-AS1, DLEU1, RGS5, RUSC1-AS1, TMPO-AS1) was established by Cox regression analysis, and LASSO regression techniques. Based on this signature, patients with cervical cancer were classified into a low- or high-risk group. Cox regression confirmed this signature as an independent prognostic predictor with an AUC value of 0.789 for predicting 1-year OS. A nomogram including signature, age, and TNM stage grade was then established, and showed an AUC of 0.82 for predicting 1-year OS. Moreover, GSEA analysis showed that immune-related pathways were enriched in the low-risk group; immunoassays showed that most immune cells, ESTIMAT scores and immune scores were negatively correlated with risk score and that the expression of immune checkpoint-proteins (CD27, CD48, CD200, and TNFRSF14) were higher in the low-risk group. In addition, patients in the low-risk group were more sensitive to Rucaparib, Navitoclax and Crizotinib than those in the high-risk group. Conclusion: We established a novel necroptosis-related lncRNA based signature to predict prognosis, tumor microenvironment and response to treatment in cervical cancer. Our study provides clues to tailor prognosis prediction and individualized immunization/targeted therapy strategies.

Comprehensive analysis of cuproptosis-related lncRNAs to predict prognosis and immune infiltration characteristics in colorectal cancer

Background: Cuproptosis is a novel form of cell death discovered in recent. A great quantity of researches has confirmed the close relationships and crucial roles between long non-coding RNAs (lncRNAs) with the progression of colorectal cancer (CRC). However, the relationship between cuproptosis and lncRNAs remains unclear in CRC.

Methods: 1,111 co-expressed lncRNAs with 16 cuproptosis regulators were retrieved from CRC samples of The Cancer Genome Atlas (TCGA) database. Through univariate Cox and least absolute shrinkage and selection operator regression analysis, a prognosis model was constructed with 15 lncRNAs. The Kaplan-Meier, receiver operating characteristic curve, C-index and principal component analysis identified the prognostic power. Furthermore, a cuproptosis-related cluster was generated based on the 15 lncRNAs by unsupervised methods. The correlations between the cuproptosis-related signatures with immune cell infiltration and anti-tumor therapy were explored by multiple algorithms.

Results: A risk score and nomogram with great prediction ability were constructed for CRC prognosis evaluation. The immune activate pathways, immune infiltration cells, immune functions, immune score and immune activation genes were remarkably enriched in the high risk group. The cuproptosis-related cluster was generated, of which the cluster 2 showed longer overall survival. The immune cell infiltration analysis indicated the similar results of cluster 2 with the high risk group, implying a significant marker for “hot tumor.” The cluster 2 also presented high expression of immune checkpoint molecules, MSI-H status and higher susceptibility to multiple immunotherapy drugs.

Conclusion: We appraised a novel cuproptosis-related prognosis model and molecular signature associated with prognosis, immune infiltration and immunotherapy. The identification of cuproptosis-related lncRNAs improved our understanding of immune infiltration and provided a significant marker for prognosis and immunotherapy in CRC.

Qualification of Necroptosis-Related lncRNA to Forecast the Treatment Outcome, Immune Response, and Therapeutic Effect of Kidney Renal Clear Cell Carcinoma

Background

Kidney renal clear cell carcinoma (KIRC) is considered as a highly immune infiltrative tumor. Necroptosis is an inflammatory programmed cell death associated with a wide range of diseases. Long noncoding RNAs (lncRNAs) play important roles in gene regulation and immune function. lncRNA associated with necroptosis could systematically explore the prognostic value, regulate tumor microenvironment (TME), etc.

Method

The patients' data was collected from TCGA datasets. We used the univariate Cox regression (UCR) to select prediction lncRNAs that are related to necroptosis. Meanwhile, risk models were constructed using LASSO Cox regression (LCR). Kaplan–Meier (KM) analysis, accompanied with receiver operating characteristic (ROC) curves, was performed to assess the independent risk factors of different clinical characteristics. The evaluated factors are age, gender, disease staging, grade, and their related risk score. Databases such as Gene Ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Gene set enrichment analysis (GSEA) were used to search the probable biological characteristics that could influence the risk groups, containing signaling pathway and immue-related pathways. The single-sample gene set enrichment analysis (ssGSEA) was chosen to perform gene set variation analysis (GSVA), and the GSEABase package was selected to detect the immune and inflammatory infiltration profiles. The TIDE and IC₅₀ evaluation were used to estimate the effectiveness of clinical treatment on KIRC.

Results

Based on the above analysis, we have got a conclusion that patients who show high risk had higher immune infiltration, immune checkpoint expression, and poorer prognosis. We identified 19 novel prognostic necroptosis-related lncRNAs, which could offer opinions for a deeper study of KIRC.

Conclusion

The risk model we constructed makes it possible to predict the prognosis of KIRC patients and offers directions for further research on the prognostication and treatment strategies for KIRC.

Identification of m6A-related long non-coding RNAs for predicting prognosis and immune characterizations in gastric cancer

Background: N6-methyladenosine (m⁶A) mRNA modification triggers malignant behavior in tumor cells, which promotes malignant progression and migration of gastric cancer (GC). Nevertheless, studies on the prognostic value of m⁶A-related long non-coding RNA (MRlncRNA) in GC remain quite restricted. The study aimed to develop a reasonable predictive model to explore the prognostic potential of MRlncRNAs in predicting the prognosis of GC patients and monitoring the efficacy of immunotherapy.

Methods: Transcriptomic and clinical data for GC were derived from TCGA. Next, univariate Cox, LASSO and multivariate Cox regression analyses were next used to identify prognostic MRlncRNAs, calculate risk scores and build risk assessment models. The predictive power of the risk models was then validated by Kaplan-Meier analysis, ROC curves, DCA, C-index, and nomogram. We attempted to effectively differentiate between groups in terms of immune cell infiltration status, ICI-related genes, immunotherapy responses, and common anti-tumor drug sensitivity.

Results: A risk model based on 11 MRlncRNAs was developed with an AUC of 0.850, and the sensitivity and specificity of this model in predicting survival probability is satisfactory. The Kaplan-Meier analysis revealed that the low-risk group in the model had a significantly higher survival rate, and the model was highly associated with survival status, clinical features, and clinical stage. Furthermore, the model was verified to be an independent prognostic risk factor, and the low-risk group in the model had a remarkable positive correlation with a variety of immune cell infiltrates. The expression levels of ICI-related genes differed significantly between the different groups. Lastly, immunotherapy responses and common anti-tumor drug sensitivity also differed significantly between different groups.

Conclusion: The risk model on the basis of 11-MRlncRNAs can serve as independent predictors of GC prognosis and may be useful in developing personalized treatment strategies for patients.

Long Noncoding RNAs and Circular RNAs Regulate AKT and Its Effectors to Control Cell Functions of Cancer Cells

AKT serine-threonine kinase (AKT) and its effectors are essential for maintaining cell proliferation, apoptosis, autophagy, endoplasmic reticulum (ER) stress, mitochondrial morphogenesis (fission/fusion), ferroptosis, necroptosis, DNA damage response (damage and repair), senescence, and migration of cancer cells. Several lncRNAs and circRNAs also regulate the expression of these functions by numerous pathways. However, the impact on cell functions by lncRNAs and circRNAs regulating AKT and its effectors is poorly understood. This review provides comprehensive information about the relationship of lncRNAs and circRNAs with AKT on the cell functions of cancer cells. the roles of several lncRNAs and circRNAs acting on AKT effectors, such as FOXO, mTORC1/2, S6K1/2, 4EBP1, SREBP, and HIF are explored. To further validate the relationship between AKT, AKT effectors, lncRNAs, and circRNAs, more predicted AKT- and AKT effector-targeting lncRNAs and circRNAs were retrieved from the LncTarD and circBase databases. Consistently, using an in-depth literature survey, these AKT- and AKT effector-targeting database lncRNAs and circRNAs were related to cell functions. Therefore, some lncRNAs and circRNAs can regulate several cell functions through modulating AKT and AKT effectors. This review provides insights into a comprehensive network of AKT and AKT effectors connecting to lncRNAs and circRNAs in the regulation of cancer cell functions.

Identification and validation of a novel necroptosis-related prognostic signature in cervical squamous cell carcinoma and endocervical adenocarcinoma

Background

The purpose of this study was to investigate the prognostic signature of necroptosis-related lncRNAs (NRLs) and explore their association with immune-related functions and sensitivity of the therapeutic drug in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC).

Methods

UCSC Xena provided lncRNA sequencing and clinical data about CESC, and a necroptosis gene list was obtained from the KEGG database. NRLs were selected by structuring a co-expression network of lncRNAs and necroptosis-related genes. To further screen lncRNAs, we used the univariate Cox regression method, Lasso regression, and multivariate Cox regression. Afterward, an NRL signature was established. We used the xCell algorithm and single-sample gene set enrichment analysis (ssGSEA) to clarify the pertinence between immune infiltration and NRL expressions in CESC patients and explored the relationship between the target lncRNAs and immune-related genes. By leveraging the GDSC database, the therapy-sensitive response of the prognostic signature was forecasted and an experimental validation was performed. We performed GSEA with the aim of recognizing the potential pathway related to the individual prognostic signature.

Results

The two prognostic NRLs (AC009095.1 and AC005332.4) showed significant diversity and constituted the NRL signature. On the grounds of our signature, risk score was an independent element which was bound up with patient outcome (HR = 4.97 CI: 1.87–13.2, P = 0.001). The CESC patients were classified by the median risk score. Immune infiltration analysis revealed significant increases in CD4 + Tcm, eosinophils, epithelial cells, fibroblasts, NKT, plasma cells, platelets, and smooth muscle in the high-risk group (P&lt; 0.05). Target lncRNAs also showed some correlation with NRGs. The estimated IC50 values of bicalutamide, CHIR.99021, and imatinib were lower in the high-risk group. Through the subsequent experimental validation, both AC009095.1 and AC005332.4 were significantly more highly expressed in SiHa than in Hela. AC009095.1 was expressed more highly in SiHa than in HUCEC, but the expression of AC005332.4 was reversed.

Conclusions

This study elucidated that NRLs, as a novel signature, were indispensable factors which can significantly influence the prognosis of patients with CESC and could provide novel clinical evidence to serve as a potential molecular biomarker for future therapeutic targets.

A novel cuproptosis-related lncRNA nomogram to improve the prognosis prediction of gastric cancer

Background

Cuproptosis is a copper-triggered modality of mitochondrial cell death and cuproptosis process may play important roles in gastric cancer development. However, little is known about cuproptosis-related lncRNAs in gastric adenocarcinoma (STAD). This study is aimed to investigate the potential prognostic signatures of cuproptosis-related lncRNAs in STAD.

Methods

The Cancer Genome Atlas (TCGA) database were used to obtain gene expression profiles, clinicopathological, and OS information for STAD. Cuproptosis-related genes were collected based on previous studies and cuproptosis-related lncRNAs were screened out by co-expression analysis. The nomogram constructed by Cox regression analysis with the minimum absolute contraction and selection operator (lasso) algorithm. In addition, the potential response of ICB therapy and immune evasion incidence were estimated with Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Immune checkpoint expressions associated with risk scores were also analyzed. The correlation of immune checkpoint CD209 and HAVCR2 expressions associated with risk scores were experimentally testified by RT-qPCR, Western Blot, and IHC.

Results

Patients were classified into high-risk and low-risk groups based on the risk score calculated in this model. The Kaplan–Meier survival curve analysis revealed that the high-risk group was associated with poor prognosis. Multivariate Cox regression analysis suggested that this lncRNA prediction model was an independent risk factor affecting the OS rate. Furthermore, ROC curve indicates that the nomogram was superior to traditional clinicopathological features in predicting STAD prognosis. Finally, functional enrichment analysis and immune checkpoint investigation revealed that the nomogram is notably associated with cholesterol metabolism and immune functions, RT-qPCR and Western Blotting demonstrated the co-expression relationship of LINC01150 with CD209 and HAVCR2.

Conclusion

A novel cuproptosis-related lncRNAs signature impacts on the prognosis and immunological features of GC.

Cell death affecting the progression of gastric cancer

Gastric cancer is a gastrointestinal tumor with high morbidity and mortality rates. Several factors influence its progression, cell death being an important element. In this review, we summarized the effects of necrosis, apoptosis, necroptosis, pyroptosis, ferroptosis, and eight less common cell death modalities on gastric cancer cells and the tumor microenvironment, detailed the molecular mechanisms of various cell death and their major regulatory pathways in gastric cancer, explored the prevalence and complexity of cell death in gastric cancer progression and highlighted the potentials of cell death-related therapies in gastric cancer.

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.

A DCS-related lncRNA signature predicts the prognosis and chemotherapeutic response of patients with gastric cancer

The combination of docetaxel, cisplatin, and S-1 (DCS) is a common chemotherapy regimen for patients with gastric cancer (GC). However, studies on long noncoding RNAs (lncRNAs) associated with the chemotherapeutic response to and prognosis after DCS remain lacking. The aim of the present study was to identify DCS mRNAs-lncRNAs associated with chemotherapy response and prognosis in GC patients. In the present study, we identified 548 lncRNAs associated with these 16 mRNAs in the TCGA and GSE31811 datasets. Eleven lncRNAs were used to construct a prognostic signature by least absolute shrinkage and selection operator (LASSO) regression. A model including the 11 lncRNAs (LINC02532, AC007277.1, AC005324.4, AL512506.1, AC068790.7, AC022509.2, AC113139.1, LINC00106, AC005165.1, MIR100HG, and UBE2R2-AS1) associated with the prognosis of GC was constructed. The signature was validated in the TCGA database, model comparison, and qRT-PCR experiments. The results showed that the risk signature was a more effective prognostic factor for GC patients. Furthermore, the results showed that this model can well predicting chemotherapy drug response and immune infiltration of GC patients. In addition, our experimental results indicated that lower expression levels of LINC00106 and UBE2R2-AS1 predicted worse drug resistance in AGS/DDP cells. The experimental results agreed with the predictions. Furthermore, knockdown of LINC00106 or UBE2R2-AS1 can significantly enhanced the proliferation and migration of GC AGS cells in vitro. In conclusion, a novel DCS therapy-related lncRNA signature may become a new strategy to predict chemotherapy response and prognosis in GC patients. LINC00106 and UBE2R2-AS1 may exhibit a tumor suppressive function in GC.

A novel signature based on necroptosis-related long non-coding RNAs for predicting prognosis of patients with glioma

Necroptosis is closely related to the occurrence and development of tumors, including glioma. A growing number of studies indicate that targeting necroptosis could be an effective treatment strategy against cancer. Long non-coding RNA (lncRNA) is also believed to play a pivotal role in tumor epigenetics. Therefore, it is necessary to identify the functions of necroptosis-related lncRNAs in glioma. In this study, the transcriptome and clinical characteristic data of glioma patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases were collected, and the differentially expressed necroptosis-related lncRNAs in TCGA that have an impact on overall survival (OS) were screened out to construct risk score (RS) formula, which was verified in CGGA. A nomogram was constructed to predict the prognosis of glioma patients based on clinical characteristics and RS. In addition, Gene Set Enrichment Analysis (GSEA) was used to analyze the main enrichment functions of these necroptosis-related lncRNAs and the immune microenvironment. A total of nine necroptosis-related lncRNAs have been identified to construct the RS formula, and the Kaplan–Meier (K-M) survival analysis showed significantly poorer outcomes in the high RS group in both TCGA and CGGA databases. Moreover, the receiver operating characteristic (ROC) curve shows that our prediction RS model has good predictability. Regarding the analysis of the immune microenvironment, significant differences were observed in immune function and immune checkpoint between the high RS group and the low RS group. In conclusion, we constructed a necroptosis-related lncRNA RS model that can effectively predict the prognosis of glioma patients and provided the theoretical basis and the potential therapeutic targets for immunotherapy against gliomas.

High Expression of EZH2 Mediated by ncRNAs Correlates with Poor Prognosis and Tumor Immune Infiltration of Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is the predominant form of liver cancer and is accompanied by a complex regulatory network. Increasing evidence suggests that an abnormal gene expression of EZH2 is associated with HCC progression. However, the molecular mechanism by which non-coding RNAs (ncRNAs) regulate EZH2 remains elusive. Methods: The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) data were used to perform differential expression analysis and prognostic analysis. We used the Encyclopedia of RNA Interactomes (ENCORI) database to predict candidate miRNAs and lncRNAs that may bind to EZH2. Subsequently, the comprehensive analysis (including expression analysis, correlation analysis, and survival analysis) identified ncRNAs that contribute to EZH2 overexpression. Results: EZH2 was found to be upregulated in the majority of tumor types and associated with a poor prognosis. Hsa-miR-101-3p was identified as a target miRNA of EZH2. Additionally, SNHG6 and MALAT1 were identified as upstream lncRNAs of hsa-miR-101-3p. Meanwhile, correlation analysis revealed that EZH2 expression was significantly associated with the infiltration of several immune cell types in HCC. Conclusion: SNHG6 or MALAT1/hsa-miR-101-3p/EZH2 axis were identified as potential regulatory pathways in the progression of HCC.