Long Non-coding RNA LINC01094 Promotes the Development of Clear Cell Renal Cell Carcinoma by Upregulating SLC2A3 via MicroRNA-184

LINC01094: A key long non-coding RNA in the regulation of cancer progression and therapeutic targets

LINC01094 is a long non-coding RNA that plays a crucial role in cancer progression by modulating key signaling pathways, such as PI3K/AKT, Wnt/β-catenin and TGF-β Signaling Pathway Feedback Loop. In this review we summarize the recent research on the functional mechanisms of LINC01094 in various cancers, including its impact on tumor growth, metastasis, and resistance to therapy. We also discuss the therapeutic potential of targeting LINC01094 and highlight the current strategies and challenges in this area. Perspectives on future development of LINC01094-based therapies are also provided.

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.

Transcription Analysis of the THBS2 Gene through Regulation by Potential Noncoding Diagnostic Biomarkers and Oncogenes of Gastric Cancer in the ECM-Receptor Interaction Signaling Pathway: Integrated System Biology and Experimental Investigation

Background

Gastric cancer (GC) is the second most frequent cause of cancer-related death worldwide and the fourth most common malignancy. Despite significant improvements in patient survival over the past few decades, the prognosis for patients with GC remains dismal because of the high recurrence rate. In this comprehensive system biology and experimental investigation, we aimed to find new novel diagnostic biomarkers of GC through a regulatory RNA interaction network.

Methods

Gene expression, coexpression, and survival analyses were performed using microarray and RNAseq datasets (analyzed by RStudio, GEPIA2, and ENCORI). RNA interaction analysis was performed using miRWalk and ENCORI online databases. Gene set enrichment analysis (GSEA) was performed to find related signaling pathways of up- and downregulated genes in the microarray dataset. Gene ontology and pathway enrichment analysis were performed by the enrichr database. Protein interaction analysis was performed by STRING online database. Validation of expression and coexpression analyses was performed using a qRT-PCR experiment.

Results

Based on bioinformatics analyses, THBS2 (FC: 7.14, FDR < 0.0001) has a significantly high expression in GC samples. lncRNAs BAIAP2-AS1, TSIX, and LINC01215 have RNA interaction with THBS2. BAIAP2-AS1 (FC: 1.44, FDR: 0.018), TSIX (FC: 1.34, FDR: 0.038), and LINC01215 (FC: 1.19, FDR: 0.046) have significant upregulation in GC samples. THBS2 has a significant role in the regulation of the ECM-receptor signaling pathway. miR-4677-5p has a significant RNA interaction with THBS2. The expression level of THBS2, BAIAP2-AS1, TSIX, and LINC01215 has a nonsignificant negative correlation with the survival rate of GC patients (HR: 0.28, logrank p: 0.28). qRT-PCR experiment validates mentioned bioinformatics expression analyses. BAIAP2-AS1 (AUC: 0.7136, p value: 0.0096), TSIX (AUC: 0.7456, p value: 0.0029), and LINC01215 (AUC: 0.7872, p value: 0.0005) could be acceptable diagnostic biomarkers of GC.

Conclusion

BAIAP2-AS1, lncRNA LINC01215, lncRNA TSIX, and miR-4677-5p might modulate the ECM-receptor signaling pathway via regulation of THBS2 expression level, as the high-expressed noncoding RNAs in GC. Furthermore, mentioned lncRNAs could be considered potential diagnostic biomarkers of GC.

Galectin-1-dependent ceRNA network in HRMECs revealed its association with retinal neovascularization

BACKGROUND

Retinal neovascularization (RNV) is a leading cause of blindness worldwide. Long non-coding RNA (lncRNA) and competing endogenous RNA (ceRNA) regulatory networks play vital roles in angiogenesis. The RNA-binding protein galectin-1 (Gal-1) participates in pathological RNV in oxygen-induced retinopathy mouse models. However, the molecular associations between Gal-1 and lncRNAs remain unclear. Herein, we aimed to explore the potential mechanism of action of Gal-1 as an RNA-binding protein.

RESULTS

A comprehensive network of Gal-1, ceRNAs, and neovascularization-related genes was constructed based on transcriptome chip data and bioinformatics analysis of human retinal microvascular endothelial cells (HRMECs). We also conducted functional enrichment and pathway enrichment analyses. Fourteen lncRNAs, twenty-nine miRNAs, and eleven differentially expressed angiogenic genes were included in the Gal-1/ceRNA network. Additionally, the expression of six lncRNAs and eleven differentially expressed angiogenic genes were validated by qPCR in HRMECs with or without siLGALS1. Several hub genes, such as NRIR, ZFPM2-AS1, LINC0121, apelin, claudin-5, and C-X-C motif chemokine ligand 10, were found to potentially interact with Gal-1 via the ceRNA axis. Furthermore, Gal-1 may be involved in regulating biological processes related to chemotaxis, chemokine-mediated signaling, the immune response, and the inflammatory response.

CONCLUSIONS

The Gal-1/ceRNA axis identified in this study may play a vital role in RNV. This study provides a foundation for the continued exploration of therapeutic targets and biomarkers associated with RNV.

Cuproptosis-Related Ferroptosis genes for Predicting Prognosis in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is a main subtype of kidney cancers. Cuproptosis and ferroptosis are correlated with immune infiltration and prognosis in tumors. However, the role of Cuproptosis-related Ferroptosis genes (CRFGs) in KIRC has rarely been fully understood. Therefore, we constructed a prognostic signature based on different expression of CRFGs in KIRC. All raw data of this study were extracted from public TCGA datasets. Cuproptosis and Ferroptosis genes were collected from the previous research. Finally, a total of 36 significantly different CRFGs were identified from TCGA-KIRC cohort. Six-gene signature (TRIB3, SLC2A3, PML, CD44, CDKN2A and MIOX) was identified by LASSO Cox regression based on the significantly different CRFGs. The CRFGs signature was correlated with worse overall survival and the AUC was 0.750. Functional enrichment indicated that CRFGs were mainly enriched in metabolism, drug resistance, tumor immunity pathways. Besides, the IC50 and immune checkpoint differentially expressed between different groups. The proposed 6-CRFGs signature is a promising biomarker to predict clinical outcomes and therapeutic responses for KIRC patient.

LncRNA LINC01094 Promotes Cells Proliferation and Metastasis through the PTEN/AKT Pathway by Targeting AZGP1 in Gastric Cancer

Long noncoding RNAs (lncRNAs) were recently reported to play an essential role in multiple cancer types. Herein, through next-generation sequencing, we screened metastasis-driving molecules by using tissues from early-stage gastric cancer (GC) patients with lymph node metastasis, and we identified a lncRNA LINC01094, which was associated with the metastasis of GC. According to the clinical data from the TCGA, GSE15459, and GSE62254 cohorts, the high expression of LINC01094 was associated with an unfavorable prognosis. Moreover, 106 clinical GC and paired normal samples were collected, and the qRT-PCR results showed that the high expression of LINC01094 was associated with high T and N stages and a poor prognosis. We found that LINC01094 promotes the proliferation and metastasis of GC in vitro and in vivo. AZGP1 was found as the protein-binding partner of LINC01094 by using RNA pulldown and RNA-binding protein immunoprecipitation (RIP) assays. LINC01094 antagonizes the function of AZGP1, downregulates the expression of PTEN, and further upregulates the AKT pathway. Collectively, our results suggested that LINC01094 might predict the prognosis of GC patients and become the therapy target for GC.

An immune infiltration-related long non-coding RNAs signature predicts prognosis for hepatocellular carcinoma

Background: With a high incidence and dismal survival rate, hepatocellular carcinoma (HCC) tops the list of the world's most frequent malignant tumors. Immunotherapy is a new approach to cancer treatment, and its effect on prolonging overall survival (OS) varies from patient to patient. For a more effective prognosis and treatment of HCC, we are committed to identifying immune infiltration-related long non-coding RNAs (IIRLs) with prognostic value in hepatocellular carcinoma. Methods: In our study, we calculated immune scores of 369 hepatocellular carcinoma samples from the Cancer Genome Atlas (TCGA) database by using an estimation algorithm, and obtained long non-coding RNAs (lncRNAs) associated with immune infiltration by using Weighted Gene Co-expression Network analysis (WGCNA). For training cohort, univariate Cox, least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analysis were used to determine prognostic IIRLs, we established a prognostic IIRLs signature. By testing cohort and entire cohort, we confirmed that the signature is practical. The prognosis of people with different clinicopathological stages and risk scores were predicted by the nomogram we constructed. In addition, Immune cell infiltration analysis and prediction of therapeutic drugs were performed. Results: 93 IIRLs were obtained by WGCNA. Furthermore, the prognostic value of these IIRLs were evaluated by using univariate Cox, Lasso and multivariate Cox analysis. Four IIRLs were used to create a signature with a prognosis. Time-related receiver operating characteristic (ROC) curve revealed that this model had an acceptable prognostic value for HCC patients. By using univariate and multivariate Cox regression analysis, this risk score has been shown to be an independent prognostic factor for HCC. The nomogram we made showed good predictions. Except for that, the treatment with immune checkpoint inhibitors (ICI) was likely to be more effective for low-risk patients. Conclusion: Based on four IIRLs, a prognostic signature was created in this research showed good accuracy in predicting OS. This study also provided valuable references for Immunotherapy of hepatocellular carcinoma.

Development and validation of a ferroptosis-related lncRNAs signature to predict prognosis and microenvironment for melanoma

Ferroptosis plays an important role in cancer. However, studies about ferroptosis-related lncRNAs (FRLs) in skin cutaneous melanoma (SKCM) are scarce. Moreover, the relationship between prognostic FRLs and tumor microenvironment (TME) in melanoma remains unclear. This study investigates the potential prognostic value of FRLs and their association with TME in SKCM. The RNA-sequencing data of SKCM were downloaded from The Cancer Genome Atlas (TCGA) database. Melanoma patients were randomly divided into training and testing groups in a 1:1 ratio. A signature composed of 19 FRLs was developed by the least absolute shrinkage and selection operator (LASSO) regression analysis to divide patients into a low-risk group with a better prognosis and a high-risk group with a poor prognosis. Multivariate Cox regression analysis suggested that the risk score was an independent prognostic factor. The Area Under Curve (AUC) value of the risk score reached 0.768 in the training group and 0.770 in the testing group. Subsequent analysis proved that immune-related signaling pathways were significantly enriched in the low-risk group. The tumor immune cell infiltration analysis demonstrated that melanoma in the high-risk group tended to be immunologically "cold". We identified a novel FRLs signature which could accurately predict the prognosis of patients with melanoma.

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

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

Computational construction of TME-related lncRNAs signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma

Background

The tumor microenvironment (TME) is closely related to clear cell renal cell carcinoma (ccRCC) prognosis, and immunotherapy response. In current study, comprehensive bio‐informative analysis was adopted to construct a TME‐related lncRNA signature for immune checkpoint inhibitors (ICIs) and targeted drug responses in ccRCC patients.

Methods

The TME mRNAs were screened following the immune and stromal scores with the data from GSE15641, GSE29609, GSE36895, GSE46699, GSE53757, and The Cancer Genome Atlas (TCGA)‐kidney renal clear cell carcinoma (KIRC). And the TME‐related lncRNAs were recognized using correlation analysis. The TME‐related lncRNAs prognostic model was constructed using the training dataset. Kaplan–Meier analysis, principal‐component analysis, and time‐dependent receiver operating characteristic were used to evaluate the risk model. The immune cell infiltration in TME was evaluated using the single‐sample gene set enrichment analysis (ssGSEA), ESTIMATE, and microenvironment cell populations counter algorithm. The immunophenoscore (IPS) was used to assess the response to immunotherapy with the constructed model.

Results

In the current study, 364 TME‐related lncRNAs were selected based on the integrated bioinformatical analysis. Six TME‐related lncRNAs (LINC00460, LINC01094, AC008870.2, AC068792.1, and AC007637.1) were identified as the prognostic signature in the training dataset and subsequently verified in the testing and entire datasets. Patients in the high‐risk group exhibited poor overall survival and disease‐free survival than those in the low‐risk group. The 1‐, 3‐, and 5‐year areas under the curves of the prognostic signature in the entire dataset were 0.704, 0.683, and 0.750, respectively. The risk score independently predicted ccRCC survival based on univariate and multivariate Cox regression. GSEA analysis suggested that the high‐risk group was concentrated on immune‐related pathways. The high‐risk group were characterized by high immune cell infiltration, high TMB and somatic mutation counters, high IPS‐PD‐1 + CTLA4 scores, and immune checkpoints expression upregulation, reflecting the higher ICIs response. The half inhibitory concentrations of sunitinib, temsirolimus, and rapamycin were low in the high‐risk group.

Conclusion

The TME‐related lncRNAs signature constructed could reliably predict the prognosis and immunotherapy response and targeted ccRCC patients' therapy.

MicroRNAs in the Regulation of Solute Carrier Proteins Behind Xenobiotic and Nutrient Transport in Cells

Altered metabolism, such as aerobic glycolysis or the Warburg effect, has been recognized as characteristics of tumor cells for almost a century. Since then, there is accumulating evidence to demonstrate the metabolic reprogramming of tumor cells, addiction to excessive uptake and metabolism of key nutrients, to support rapid proliferation and invasion under tumor microenvironment. The solute carrier (SLC) superfamily transporters are responsible for influx or efflux of a wide variety of xenobiotic and metabolites that are needed for the cells to function, as well as some medications. To meet the increased demand for nutrients and energy, SLC transporters are frequently dysregulated in cancer cells. The SLCs responsible for the transport of key nutrients for cancer metabolism and energetics, such as glucose and amino acids, are of particular interest for their roles in tumor progression and metastasis. Meanwhile, rewired metabolism is accompanied by the dysregulation of microRNAs (miRNAs or miRs) that are small, noncoding RNAs governing posttranscriptional gene regulation. Studies have shown that many miRNAs directly regulate the expression of specific SLC transporters in normal or diseased cells. Changes of SLC transporter expression and function can subsequently alter the uptake of nutrients or therapeutics. Given the important role for miRNAs in regulating disease progression, there is growing interest in developing miRNA-based therapies, beyond serving as potential diagnostic or prognostic biomarkers. In this article, we discuss how miRNAs regulate the expression of SLC transporters and highlight potential influence on the supply of essential nutrients for cell metabolism and drug exposure toward desired efficacy.

Clinicopathological and Prognostic Value of Necroptosis-Associated lncRNA Model in Patients with Kidney Renal Clear Cell Carcinoma

Background. Necroptosis, a recently identified type of programmed necrotic cell death, is closely related to the tumorigenesis and development of cancer. However, it remains unclear whether necroptosis-associated long noncoding RNAs (lncRNAs) can be used to predict the prognosis of kidney renal clear cell carcinoma (KIRC). This work was designed to probe the possible prognostic worth of necroptosis-associated lncRNAs along with their impact on the tumor microenvironment (TME) in KIRC. Methods. The Cancer Genome Atlas (TCGA) database was used to extract KIRC gene expression and clinicopathological data. Pearson correlation analysis was used to evaluate necroptosis-associated lncRNAs against 159 known necroptosis-associated genes. To define molecular subtypes, researchers used univariate Cox regression analysis and consensus clustering, as well as clinical significance, TME, and tumor immune cells in each molecular subtype. We develop the necroptosis-associated lncRNA prognostic model using univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Patients were divided into high- and low-risk groups according to prognostic model. Moreover, comprehensive analyses, including prognostic value, gene set enrichment analysis (GSEA), immune infiltration, and immune checkpoint gene expression, were performed between the two risk groups. Finally, anticancer drug sensitivity analyses were employed for assessing associations for necroptosis-associated lncRNA expression profile and anticancer drug chemosensitivity. Results. Through univariate analysis, sixty-nine necroptosis-associated lncRNAs were found to have a significant relationship with KIRC prognosis. Two molecular clusters were identified, and significant differences were found with respect to clinicopathological features and prognosis. The segregation of patients into two risk groups was done by the constructed necroptosis-associated lncRNA model. The survival prognosis, clinical features, degree of immune cell infiltration, and expression of immune checkpoint genes of high-risk and low-risk groups were all shown to vary. Conclusions. Our study identified a model of necroptosis-associated lncRNA signature and revealed its prognostic role in KIRC. It is expected to provide a reference for the screening of KIRC prognostic markers and the evaluation of immune response.

An immune-related lncRNA pairs signature to identify the prognosis and predict the immune landscape of laryngeal squamous cell carcinoma

Background

Laryngeal squamous cell carcinoma (LSCC) is the most common squamous cell carcinoma. Though significant effort has been focused on molecular pathogenesis, development, and recurrence of LSCC, little is known about its relationship with the immune-related long non-coding RNA (lncRNA) pairs.

Methods

After obtaining the transcriptome profiling data sets and the corresponding clinical characteristics of LSCC patients and normal samples from The Cancer Genome Atlas (TCGA) database, a series of bioinformatic analysis was conducted to select the differently expressed immune-related lncRNAs and build a signature of immune-related lncRNA pairs. Then, the effectiveness of the signature was validated.

Results

A total of 111 LSCC patients and 12 normal samples’ transcriptome profiling data sets were retrieved from TCGA. 301 differently expressed immune-related lncRNAs were identified and 35,225 lncRNA pairs were established. After univariate Cox analysis, LASSO regression and multivariate Cox analysis, 7 lncRNA pairs were eventually selected to construct a signature. The riskscore was computed using the following formula: Riskscore = 0.95 × (AL133330.1|AC132872.3) + (-1.23)  × (LINC01094|LINC02154) + 0.65 × (LINC02575|AC122685.1) + (-1.15)  × (MIR9-3HG|LINC01748) + 1.45 × (AC092687.3|SNHG12) + (-0.87)  × (AC090204.1|AL158166.1) + 0.64 × (LINC01063|Z82243.1). Patients were classified into the high-risk group (> 1.366) and the low-risk group (< 1.366) according to the cutoff value (1.366), which is based on the 5-year riskscore ROC curve. The survival analysis showed that the low-risk group had a better prognosis (P < 0.001). The riskscore was better than other clinical characteristics in prognostic prediction and the area under the curves (AUCs) for the 1-, 3-, and 5-year survivals were 0.796, 0.946, and 0.895, respectively. Combining age, gender, grade, stage, and riskscore, a nomograph was developed to predict survival probability in LSCC patients. Then, the riskscore was confirmed to be related with the content of tumor-infiltration immune cells and the model could serve as a potential predictor for chemosensitivity.

Conclusion

We successfully established a more stable signature of 7 immune-related lncRNA pairs, which has demonstrated a better prognostic ability for LSCC patients and may assist clinicians to precisely prescribe chemo drugs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09524-1.

Upregulated long intergenic non-protein coding RNA 1094 (LINC01094) is linked to poor prognosis and alteration of cell function in colorectal cancer

Colorectal cancer (CRC) showed high cancer-related mortality in recent years partly due to the absence of an effective prognostic predictor. This research intended to evaluate the prognostic value and potential role of long intergenic non-protein coding RNA 1094 (LINC01094) in CRC. In this work, we evaluated the LINC01094 level in 122 CRC patients’ tissues and in human CRC cell lines. We explored the ability of LINC01094 in overall survival and progression-free survival estimate. The effect of LINC01094 dysregulation on the CRC cells was investigated. LINC01094 is highly expressed in CRC tissues and cells than normal ones. This high expression was correlated with absent vascular invasion, positive lymph node metastasis, and advanced TNM stage. With the result of Kaplan-Meier analysis and multivariate Cox’s proportional hazard analysis, LINC01094 was an effective biomarker for CRC overall survival. Downregulation of LINC01094 impeded the malignant biological behavior (proliferation, invasion, and migration) of CRC cells, while overexpression of LINC01094 boosted that maybe by sponging miR-1266-5p. LINC01094 might function as an oncogene in CRC and allowed the discovery of a new biomarker for prognosis and therapy of CRC.

Ferroptosis‑related long non‑coding RNAs and the roles of LASTR in stomach adenocarcinoma

Ferroptosis is a form of programmed cell death that participates in diverse physiological processes. Increasing evidence suggests that long noncoding RNAs (lncRNAs) regulate ferroptosis in tumors, including stomach adenocarcinoma (STAD). In the present study, RNA-sequencing data from The Cancer Genome Atlas database and ferroptosis-related markers from the FerrDb data resource were analyzed to select differentially expressed lncRNAs. Univariate and multivariate Cox regression analyses were performed on these differentially expressed lncRNAs to screen 12 lncRNAs linked with overall survival (OS) and 13 associated with progression-free survival (PFS). Subsequently, two signatures for predicting OS and PFS were established based on these lncRNAs. Kaplan-Meier analyses indicated that the high-risk group of patients with STAD had relatively poor prognosis. The areas under the receiver operating characteristic curves of the two signatures indicated their excellent efficacy in predicting STAD prognosis. In addition, the effect of the lncRNA LASTR on proliferation and migration in gastric cancer was confirmed and the relationship between LASTR and ferroptosis was initially explored through experiments. These results provide potential novel targets for tumor treatment and promote personalized medicine.

Stage-Specific Non-Coding RNA Expression Patterns during In Vitro Human B Cell Differentiation into Antibody Secreting Plasma Cells

The differentiation of B cells into antibody secreting plasma cells (PCs) is governed by a strict regulatory network that results in expression of specific transcriptomes along the activation continuum. In vitro models yielding significant numbers of PCs phenotypically identical to the in vivo state enable investigation of pathways, metabolomes, and non-coding (ncRNAs) not previously identified. The objective of our study was to characterize ncRNA expression during human B cell activation and differentiation. To achieve this, we used an in vitro system and performed RNA-seq on resting and activated B cells and PCs. Characterization of coding gene transcripts, including immunoglobulin (Ig), validated our system and also demonstrated that memory B cells preferentially differentiated into PCs. Importantly, we identified more than 980 ncRNA transcripts that are differentially expressed across the stages of activation and differentiation, some of which are known to target transcription, proliferation, cytoskeletal, autophagy and proteasome pathways. Interestingly, ncRNAs located within Ig loci may be targeting both Ig and non-Ig-related transcripts. ncRNAs associated with B cell malignancies were also identified. Taken together, this system provides a platform to study the role of specific ncRNAs in B cell differentiation and altered expression of those ncRNAs involved in B cell malignancies.

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

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

The Effect of SLC2A3 Expression on Cisplatin Resistance of Colorectal Cancer Cells

BACKGROUND

To study the molecular mechanism of cisplatin chemotherapy resistance in colorectal cancer cells and to explore the effect of miRNA in regulating the expression of glucose transporter 3 (SLC2A3) and the proliferation and migration of colon cancer cells.

METHODS

All samples were obtained from the People's Hospital of Wuhai, Wuhai, China between June 2019 and June 2020. Real-time quantitative PCR (qRT-PCR) was carried out to check the expression of miR-103a in these cell lines. Western blotting and Luciferase reporter gene detection confirmed the regulation of the miR-103a/SLC2A3 axis. Western blotting detected the activation of SLC2A3, caspased-9 and -3.

RESULTS

The expression of SLC2A3 protein in colon cancer cell lines was significantly higher than that of normal colon cancer cells, while the expression of SLC2A3 miRNA showed no significant difference (P<0.05). Then, through clone formation analysis, SLC2A3 was closely related to the proliferation of human colon cancer cells. Functional recovery experiments showed that increasing the expression of miR-103a could reverse the abnormal proliferation caused by overexpression of SLC2A3.

CONCLUSION

Overall, miR-103a can inhibit the proliferation of human colon cancer cells by targeting SLC2A3, and this result will provide a potential target for the treatment of colon cancer.

Identification of an Immune-Related LncRNA Signature in Gastric Cancer to Predict Survival and Response to Immune Checkpoint Inhibitors

Immune microenvironment in gastric cancer is closely associated with patient’s prognosis. Long non-coding RNAs (lncRNAs) are emerging as key regulators of immune responses. In this study, we aimed to construct a prognostic model based on immune-related lncRNAs (IRLs) to predict the overall survival and response to immune checkpoint inhibitors (ICIs) of gastric cancer (GC) patients. The IRL signature was constructed through a bioinformatics method, and its predictive capability was validated. A stratification analysis indicates that the IRL signature can distinguish different risk patients. A nomogram based on the IRL and other clinical variables efficiently predicted the overall survival of GC patients. The landscape of tumor microenvironment and mutation status partially explain this signature’s predictive capability. We found the level of cancer-associated fibroblasts, endothelial cells, M2 macrophages, and stroma cells was high in the high-risk group, while the number of CD8⁺ T cells and T follicular helper cells was high in the low-risk group. Immunophenoscore (IPS) is validated for ICI response, and the IRL signature low-risk group received higher IPS, representing a more immunogenic phenotype that was more inclined to respond to ICIs. In addition, we found RNF144A-AS1 was highly expressed in GC patients and promoted the proliferation, migration, and invasive capacity of GC cells. We concluded that the IRL signature represents a novel useful model for evaluating GC survival outcomes and could be implemented to optimize the selection of patients to receive ICI treatment.

LINC01094 promotes the invasion of ovarian cancer cells and regulates the Wnt/β-catenin signaling pathway by targeting miR-532-3p

Long non-coding RNAs (lncRNAs) participate in the development of ovarian cancer (OC). The present study aimed to explore the roles of long intergenic non-protein coding RNA 1094 (LINC01094) in OC. LINC01094 and microRNA (miR)-532-3p expression in OC tissues and cells were measured using reverse transcription-quantitative PCR. Cell migration and invasion were detected using wound healing assays and Transwell assays, respectively. The binding of LINC01094 or β-catenin to miR-126-5p was detected using a Dual-luciferase reporter assay, and protein expression was confirmed using western blot analysis. The expression level of LINC01094 in patients with OC was higher in OC tissues compared with in adjacent tissues, and LINC01094 was upregulated in OC cell lines. In addition, LINC01094 overexpression promoted the viability, migration, invasion and cell cycle progression of OC cells, and inhibited OC cell apoptosis. Moreover, LINC01094 negatively regulated miR-532-3p in OC cells and tissues. miR-532-3p overexpression decreased the viability, migration, invasion and cell cycle progression of OC cells alongside downregulation of Wnt/β-catenin signaling pathway protein expression, as well as increasing OC cell apoptosis. Inhibition of LINC01094 with small interfering (si)-LINC01094 and overexpression of LINC01094 respectively reversed the effect of miR-532-3p inhibitor and mimics on OC cells. miR-532-3p could directly target β-catenin, and miR-532-3p inhibitor increased β-catenin expression, while si-LINC01094 attenuated this effect. In addition, LINC01094 overexpression promoted tumor growth in vivo by regulating miR-532-3p. Taken together, LINC01094 promoted the growth, migration, invasion and Wnt/β-catenin signaling pathway expression of OC cells by modulating miR-532-3p.