Identification and validation of immune-related lncRNA prognostic signature for breast cancer

A novel immune‑related lncRNA as a prognostic biomarker in HER2+ breast cancer

Human epidermal growth factor receptor 2 (HER2)+ breast cancer is characterized by high malignancy and poor prognosis. Long non-coding (lnc)RNAs are crucial in breast cancer progression and prognosis, especially in tumor-associated immune processes. The present study aimed to elucidate novel lncRNAs related to immune function that could serve as biomarkers for both diagnosis and prognosis of this cancer subtype. Using data from The Cancer Genome Atlas and The Immunology Database and Analysis Portal, correlation analysis was performed to identify differentially expressed lncRNAs and immune-related genes. Through receiver operating characteristic analysis, the diagnostic value of specific lncRNAs was identified and evaluated, with a focus on their capacity to distinguish between cancerous and non-cancerous states. The present research revealed 22 differentially expressed lncRNAs and 23 differentially expressed immune-related genes, with 19 immune-related lncRNAs. A total of 13 of these lncRNAs demonstrated diagnostic relevance. In particular, it was demonstrated that the expression of lncRNA CTC-537E7.2 was significantly correlated with patient survival, suggesting its potential as a prognostic marker. Additionally, the expression of lncRNA CTC-537E7.2 was significantly correlated with clinical parameters, such as hormone receptor status and patient demographics. Moreover, it exhibited associations with four distinct immune cell types and demonstrated involvement in the Janus kinase-signal transducer and activator of transcription pathway. Further assessment by in situ hybridization confirmed the increased expression of lncRNA CTC-537E7.2 in samples from HER2+ patients, reinforcing its significance. In summary, the present study uncovered a novel prognostic biomarker for HER2+ breast cancer, thereby laying the groundwork for investigating the underlying molecular mechanisms driving the development of this subtype of breast cancer.

Identification and validation of a novel risk model based on cuproptosis‑associated m6A for head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a prevalent cancer with a poor survival rate due to anatomical limitations of the head and a lack of reliable biomarkers. Cuproptosis represents a novel cellular regulated death pathway, and N6-methyladenosine (m6A) is the most common internal RNA modification in mRNA. They are intricately connected to tumor formation, progression, and prognosis. This study aimed to construct a risk model for HNSCC using a set of mRNAs associated with m6A regulators and cuproptosis genes (mcrmRNA).

METHODS

RNA-seq and clinical data of HNSCC patients from The Cancer Genome Atlas (TCGA) database were analyzed to develop a risk model through the least absolute shrinkage and selection operator (LASSO) analysis. Survival analysis and receiver operating characteristic (ROC) analysis were performed for the high- and low-risk groups. Additionally, the model was validated using the GSE41613 dataset from the Gene Expression Omnibus (GEO) database. GSEA and CIBERSORT were applied to investigate the immune microenvironment of HNSCC.

RESULTS

A risk model consisting of 32 mcrmRNA was developed using the LASSO analysis. The risk score of patients was confirmed to be an independent prognostic indicator by multivariate Cox analysis. The high-risk group exhibited a higher tumor mutation burden. Additionally, CIBERSORT analysis indicated varying levels of immune cell infiltration between the two groups. Significant disparities in drug sensitivity to common medications were also observed. Enrichment analysis further unveiled significant differences in metabolic pathways and RNA processing between the two groups.

CONCLUSIONS

Our risk model can predict outcomes for HNSCC patients and offers valuable insights for personalized therapeutic approaches.

Expression and Significance of LINC02418 in Breast Cancer

Purpose

The complicated pathogenesis and poor prognosis of breast cancer have become a major difficulty in medical research. This study aims to explore new lncRNA as prognostic markers for breast cancer and explore their roles and molecular mechanisms to lay a foundation for the treatment of cancer patients.

Patients and Methods

The expression of LINC02418 and miR-766-5p in breast cancer tissues and cells was first identified using polymerase chain reaction, and Pearson was used to examine the correlation between the two. The cancer cells activities under different transfection conditions were detected using the Transwell assay and CCK8 assay. The correlation between LINC02418 and patient prognosis was analyzed using multifactor Cox regression and Kaplan-Meier.

Results

It was shown that LINC02418 expression was upregulated in breast cancer tissues and cells. There are significant differences in lymph node metastasis and TNM stage between high and low LINC02418 expression groups. The higher the expression of LINC02418, the higher the mortality rate of breast cancer patients. miR-766-5p expression was downregulated and negatively correlated with LINC02418. There are binding sites between LINC02418 and miR-766-5p; Transfection with miR-766-5p inhibitor boosted LINC02418 luciferase activity, but transfection with miR-766-5p mimic decreased it. Knockdown of LINC02418 promoted miR-766-5p expression and inhibited cancer progression, which was alleviated to some extent by transfection with miR-766-5p inhibitors.

Conclusion

LINC02418 has the potential to serve as a poor prognostic marker for breast cancer and plays a pro-oncogenic role by targeting miR-766-5p.

Identification and Validation of eRNA as a Prognostic Indicator for Cervical Cancer

The survival of CESC patients is closely related to the expression of enhancer RNA (eRNA). In this work, we downloaded eRNA expression, clinical, and gene expression data from the TCeA and TCGA portals. A total of 7936 differentially expressed eRNAs were discovered by limma analysis, and the relationship between these eRNAs and survival was analyzed by univariate Cox hazard analysis, LASSO regression, and multivariate Cox hazard analysis to obtain an 8-eRNA model. Risk score heat maps, KM curves, ROC analysis, robustness analysis, and nomograms further indicate that this 8-eRNA model is a novel indicator with high prognostic performance independent of clinicopathological classification. The model divided patients into high-risk and low-risk groups, compared pathway diversity between the two groups through GSEA analysis, and provided potential therapeutic agents for high-risk patients.

Identification and prognostic evaluation of differentially expressed long noncoding RNAs associated with immune infiltration in osteosarcoma

Osteosarcoma is a malignant bone cancer that originates from the bone with the strongest invasiveness. Tumor formation strongly correlates with immune cell infiltration into the tumor immune microenvironment (TIME). Therefore, we aimed to identify TIME-related biomarkers as potential prognostic markers of osteosarcoma. The mRNA and long noncoding RNA (lncRNA) transcriptome data of 88 patients with osteosarcoma and the expression profile of GSE99671 were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. Immune infiltration scores and types were evaluated using ESTIMATE and CIBERSORT. A linear model was established to identify the differentially expressed genes (DEGs) and lncRNAs (DElncRNAs). Functional enrichment analysis of DEGs was conducted by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis, and gene set variation analysis. DElncRNAs were analyzed using a weighted gene co-expression network. Least absolute shrinkage and selection operator regression was applied to screen for prognostic markers. Patient survival was predicted by the risk score and analyzed by receiver operating characteristic curve. Clinical features affecting patient survival were assessed. Immune infiltration positively correlated with osteosarcoma patient survival. Different immune cell infiltrates in patients with osteosarcma may serve as prognostic indicators and targets for immunotherapy. In total, 1125 DEGs, 80 DElncRNAs, and 11 pairs of co-expressed lncRNA-mRNAs were identified. DEGs in the three modules were associated with immune infiltration into the TIME. Four DElncRNAs, namely AC015819.1, AC015911.3, AL365361.1, and USP30-AS1, showed good prognostic ability for osteosarcoma and were positively correlated with the immune score. Tumor metastasis and risk scores alone were good prognostic indicators, and a combination of the two variables can better predict the prognosis of osteosarcoma. We identified four lncRNAs, AC015819.1, AC015911.3, AL365361.1, and USP30-AS1, as potential biomarkers for osteosarcoma prognosis.

Upregulation of vesicle-associated membrane protein 7 in breast cancer tissues

BACKGROUND

Vesicle-associated membrane protein 7 (VAMP7) plays oncogenic roles in cancers. However, its clinical significance in breast cancer (BC) tissues remains unknown.

OBJECTIVE

To elucidate the clinical implications of VAMP7, as well as its involvement in the tumor microenvironment and molecular pathways of breast cancer.

METHODS

BC (n=100) and non-cancerous breast tissues (n= 100) were collected for an immunohistochemical experiment (1:200). The protein expression level of VAMP7 was determined by using a semi-quantitative scoring method. High-throughput RNA-sequencing data of BC tissues were analyzed to confirm the mRNA expression trend of VAMP7. Additionally, the largest BC prognosis cohort data were collected to mine the potential impact VAMP7 has on BC progression. The association between VAMP7 and the microenvironment of BC was evaluated by using a CIBERSORT algorithm. Moreover, we explored the co-expressed molecular mechanisms of VAMP7 in BC by calculating Pearson correlation coefficients and overexpressed genes. Finally, the biological mechanism underlying the relationship between VAMP7 and the key pathways was also explored using gene set enrichment analysis (GSEA). Potential therapeutic strategies were predicted targeting VAMP7.

RESULTS

VAMP7 protein was significantly over-expressed in BC tissue than that in controls (p< 0.001). Compared with 459 normal breast tissues and 113 non-cancerous breast tissues, the expression level of VAMP7 mRNA was significantly increased in 1111 BC tissues. CD4+T cells, macrophages, and naïve B cells had a higher infiltration rate in BC tissues with high VAMP7 expression, while regulatory T cells and CD8+T cells had a lower infiltration rate. Over-expressed VAMP7 was associated with macrophages activation and transition from M1 to M2 polarization. Upregulated VAMP7 could predicted poorer OS, DMFS, PPS, and RFS outcomes. Upregulated VAMP7 co-expressed genes were significantly enriched in the cell cycle checkpoints. GSEA confirmed that over-expressed VAMP7 are markedly associated with functional enrichment in cell cycle related categories, including mitotic spindle, G2M checkpoint, and E2F targets. KU-55933 was predicted as a putative therapeutic drug for BC targeting VAMP7.

CONCLUSIONS

VAMP7 was upregulated in BC tissue and correlated with poor prognosis of BC patients. VAMP7 may promote BC progression by targeting the cell cycle pathway.

A novel disulfidptosis-related lncRNAs signature for predicting survival and immune response in hepatocellular carcinoma

The accumulation of intracellular disulfides induces a novel and unique form of metabolic-related cell death known as disulfidptosis. A previous study revealed the prognostic value of a risk model of disulfidptosis-related genes in hepatocellular carcinoma (HCC). However, to date, no studies have investigated the relationship between disulfidptosis-related long non-coding RNAs (DRLs) and HCC. In this study, we collected and analyzed RNA sequencing data from 370 HCC samples to explore the DRLs in the tumorigenesis and development of HCC. By employing Lasso Cox regression and multivariate Cox regression analyses, we identified five prognostic DRLs, which were used to construct a prognostic signature. The signature was subsequently validated using receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, Cox regression analyses, nomograms, and calibration curves. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed, revealing that the DRLs signature was associated with HCC and several cancer-related pathways. Furthermore, the DRLs signature showed correlations with the infiltration of M0 and M1 macrophages, immune-related functions, and multiple immune checkpoints, including PDCD1, LAG3, CTLA4, TIGIT, CD47, and others. Analysis using the tumor immune dysfunction and exclusion (TIDE) approach demonstrated that the DRLs signature could predict the response to immunotherapy. Finally, we screened potential chemotherapy drugs that could sensitize HCC. In conclusion, our novel DRLs signature provides valuable insights into predicting patient survival and immunotherapy responses.

Identification and characterization of long non-coding RNAs in mammary gland tissues of Chinese Holstein cows

LncRNAs (Long non-coding RNA) is an RNA molecule with a length of more than 200 bp. LncRNAs can directly act on mRNA, thus affecting the expression of downstream target genes and proteins, and widely participate in many important physiological and pathological regulation processes of the body. In this study, RNA-Seq was performed to detect lncRNAs from mammary gland tissues of three Chinese Holstein cows, including three cows at 7 d before calving and the same three cows at 30 d postpartum (early lactation stage). A total of 1,905 novel lncRNAs were detected, 57.3% of the predicted lncRNAs are ≥ 500 bp and 612 lncRNAs are intronic lncRNAs. The exon number of lncRNAs ranged from 2 to 10. A total of 96 lncRNAs were significantly differentially expressed between two stages, of which 47 were upregulated and 49 were downregulated. Pathway analysis found that target genes were mainly concentrated on the ECM-receptor interaction, Jak-STAT signaling pathway, PI3K-Akt signaling pathway, and TGF-beta signaling pathway. This study revealed the expression profile and characteristics of lncRNAs in the mammary gland tissues of Holstein cows at non-lactation and early lactation periods, and provided a basis for studying the functions of lncRNAs in Holstein cows during different lactation periods.

Involvement of FAM170B-AS1, hsa-miR-1202, and hsa-miR-146a-5p in breast cancer

BACKGROUND

FAM170B-AS1 is usually expressed low in all organs except for testicular tissues. No study was performed to explore its role in breast cancer (BC). Contradictory results were reported about hsa-miR-1202 and hsa-miR-146a-5p in BC.

OBJECTIVE

The present study aimed to explore the involvement of FAM170B-AS1 in BC using bioinformatics predictive tools, followed by a practical validation besides exploring the impact of hsa-miR-1202 and hsa-miR-146a-5p in BC.

METHODS

This study enrolled 96 female patients with BC, 30 patients with benign breast diseases (BBD), and 25 control subjects. The expressions of circulating FAM170B-AS1, hsa-miR-1202, and hsa-miR-146a-5p were quantified using qRT-PCR. These ncRNAs' associations, predictive, and diagnostic roles in BC were statistically tested. The underlying miRNA/mRNA targets of FAM170B-AS1 in BC were bioinformatically predicted followed by confirmation based on the GEPIA and TCGA databases.

RESULTS

The expression of FAM170B-AS1 was upregulated in sera of BC patients and hsa-miR-1202 was upregulated in sera of BBD and BC patients while that of hsa-miR-146a-5p was downregulated in BC. These FAM170B-AS1 was significantly associated with BC when compared to BBD. FAM170B-AS1 and hsa-miR-1202 were statistically associated with the BC's stage, grade, and LN metastasis. FAM170B-AS1 and hsa-miR-146a-5p gave the highest specificity and sensitivity for BC. KRAS and EGFR were predicted to be targeted by FAM170B-AS1 through interaction with hsa-miR-143-3p and hsa-miR-7-5p, respectively. Based on the TCGA database, cancer patients having mutations in FAM170B show good overall survival.

CONCLUSIONS

The present study reported that for the first time, FAM170B-AS1 may be a potential risk factor, predictive, and diagnostic marker for BC. In addition, FAM170B-AS1 might be involved in BC by interacting with hsa-miR-143-3p/KRAS and hsa-miR-7-5p/EGFR through enhancement or repression that may present a new therapeutic option for BC.

Cuproptosis-Associated lncRNA Gene Signature Establishes New Prognostic Profile and Predicts Immunotherapy Response in Endometrial Carcinoma

Uterine corpus endometrial carcinoma (UCEC), a prevalent kind of cancerous tumor in female reproductive system that has a dismal prognosis in women worldwide. Given the very limited studies of cuproptosis-related lncRNAs (CRLs) in UCEC. Our purpose was to construct a prognostic profile based on CRLs and explore its assess prognostic value in UCEC victims and its correlation with the immunological microenvironment.

METHODS

554 UCEC tumor samples and 23 normal samples' RNA-seq statistics and clinical details were compiled from data in the TCGA database. CRLs were obtained using Pearson correlation analysis. Using LASSO Cox regression, multivariate Cox regression, and univariate Cox regression analysis, six CRLs are confirmed to develop a risk prediction model at last.We identified two main molecular subtypes and observed that multilayer CRLs modifications were related to patient clinicopathological features, prognosis, and tumor microenvironment (TME) cell infiltration characteristics, and then we verified the prognostic hallmark of UCEC and examined its immunological landscape.Finally, using qRT-PCR, model hub genes' expression patterns were confirmed.

RESULTS

A unique CRL signature was established by the combination of six differently expressed CRLs that were highly linked with the prognosis of UCEC patients. According to their CRLs signatures, the patients were divided into two groups: the low-risk and the high-risk groups. Compared to individuals at high risk, patients at low risk had higher survival rates (p < 0.001). Additionally, Cox regression reveals that the profiles of lncRNAs linked to cuproptosis may independently predict prognosis in UCEC patients. The 1-, 3-, and 5-year risks' respective receiver operating characteristics (ROC) exhibited AUC values of 0.778, 0.810, and 0.854. Likewise, the signature could predict survival in different groups based on factors like stage, age, and grade, among others. Further investigation revealed differences between the different risk score groups in terms of drug sensitivity,immune cell infiltration,tumor mutation burden (TMB) score and microsatellite instability (MSI) score. Compared to the group of high risk, the low-risk group had greater rates of TMB and MSI. Results from qRT-PCR revealed that in UCEC vs normal tissues, AC026202.2, NRAV, AC079466.2, and AC090617.5 were upregulated,while LINC01545 and AL450384.1 were downregulated.

CONCLUSIONS

Our research clarified the relationship between CRLs signature and the immunological profile and prognosis of UCEC.This signature will establish the framework for future investigations into the endometrial cancer CRLs mechanism as well as the exploitation of new diagnostic tools and new therapeutic.

Machine learning and experimental screening of chromatin regulator signatures and potential drugs in hepatitis B related hepatocellular carcinoma

Many evidences have confirmed that chromatin regulator factors (CRs) are involved in the progression of cancer, but its potential mechanism of affecting hepatitis B related hepatocellular carcinoma still needs to be studied. Our study detected the CRs that affect hepatitis B related hepatocellular carcinoma (HBV-HCC) through machine learning analysis, conducted the analysis of immune cells, constructed the relevant risk model and immune function infiltration, and predicted the potential therapeutic drugs. We found that these CRs were significantly related to the immune cells of Macrophages, B cells, CD8+T cells, etc., and PBK, AURKA, TOP2A and AURKB were the potential risk CRs of HBV-HCC. The expression levels of these four CRs increased in HepG2.2.15 cells and the liver of HBV-HCC patients, consistent with the predicted risk model. Subsequently, ten potential drugs closely related to the risk CRs were finally obtained, experimental research on resveratrol has shown that it can inhibit the proliferation of HepG2.2.15 cells and potentially inhibit the occurrence and development of HBV-HCC. Our study provides novel insights into the function of CRs in HBV-HCC and certain ideas for more accurate targeted therapy.Communicated by Ramaswamy H. Sarma.

Tumor infiltrating lymphocytes (TILs) are a prognosis biomarker in Colombian patients with triple negative breast cancer

Triple negative breast cancer (TNBC) is highly immunogenic and high levels of tumor infiltrating lymphocytes (TILs) have been associated with a better prognosis and higher probability to achieve pathological complete response. Here, we explore the potential role of stromal TILs level and composition as a prognostic and predictive biomarker in TNBC. 195 Tumor biospecimens from patients diagnosed with TNBC were included. Stromal TILs (sTILs), positive CD4/CD8 cells were evaluated. Differences in clinic-pathological characteristics according to immune infiltration were assessed. The predictive and prognostic value of immune infiltration was analyzed by multivariate models. Higher immune infiltration was observed in patients with favorable clinical-pathological features. Survival analysis showed that longer overall survival times were observed in patients with a higher infiltration of sTILs (p = 0.00043), CD4 + (p = 0.0074) and CD8 + (p = 0.008). In the multivariate analysis, low levels of sTILs were found to be associated with a higher mortality hazard (HR: 1.59, 95% CI 1.01-2.48). CD4 and CD8 immune infiltration were associated with higher odds for pathological complete response (OR: 1.20, 95% CI 1.00-1.46, OR: 1.28, 1.02-1.65, respectively). Our results suggest that immune infiltration could be used as a prognostic marker for overall survival in TNBC patients.

Immune-related lncRNAs pairs prognostic score model for prediction of survival in acute myeloid leukemia patients

Acute myeloid leukemia (AML) is one of the most common malignant and aggressive hematologic tumors, and risk stratification is indispensable to ensure proper treatment. But immune-related long noncoding RNAs (ir-lncRNAs) pairs prognostic risk models used to stratify AML have yet to be reported. In this study, we established a prognostic risk model based on eight ir-lncRNAs pairs using LASSO-penalized Cox regression analysis and successfully validated the model in an independent cohort. According to risk scores, patients were divided into a high-risk group and a low-risk group. High-risk patients presented more tumor mutation frequency and higher expression of human leukocyte antigen (HLA)-related genes and immune checkpoint molecules. Gene Set Enrichment Analysis (GSEA) indicated that the transforming growth factors β (TGFβ) pathway was activated in the high-risk group; meanwhile, we found that TGFβ1 mRNA levels were significantly elevated in AML patients and correlated with poor prognosis, which is closely related to drug resistance. Consistently, in vitro studies found that exogenous TGFβ1 can protect AML cells from chemotherapy-induced apoptosis. Collectively, we developed an ir-lncRNA prognostic model that helps predict the prognosis of AML patients and provides valuable information about their response to immune checkpoint inhibitors, and we found that increased TGFβ1 levels resulting in chemoresistance may be one of the leading causes of treatment failure in high-risk AML patients.

Alternative splicing of IRF3 plays an important role in the development of hepatocarcinoma

Alternative splicing is a process causing mRNA translation to produce different proteins, and it is crucial for the development of tumours. In this study, we constructed a prognostic model related to alternative splicing events in hepatocarcinoma using bioinformatics analysis, including the alternative splicing of CSAD, AFMID, ZDHHC16, and IRF3. The model is an independent prognostic factor and can accurately predict a patient's prognosis. IRF3 is a transcription factor related to the immune response. Its alternative splicing can affect the expression of various genes related to prognosis and plays an essential role in the tumour microenvironment. We also verified the expression of IRF3 exon skipping isoform in hepatocarcinoma at the mRNA level. In conclusion, we discovered that the alternative splicing of IRF3 is essential for the development of hepatocarcinoma. This study provides new insight into the development of treatments for hepatocarcinoma.

LINC02086 promotes cell viability and inhibits cell apoptosis in breast cancer by sponging miR-6757-5p and up-regulating EPHA2

BACKGROUND

Long non-coding RNAs (lncRNAs) are critical regulators in the initiation and progression of breast cancer. Our study aims to characterize the functions of LINC02086 which few published in breast cancer and decipher the downstream molecular mechanisms.

METHODS

LINC02086 expression is tested in RNA-seq data from GEPIA database, tumor tissue samples from hospital patients and breast cancer cell lines. LINC02086 was silenced or overexpressed by lenti-virus-mediated shRNAs, or pLVX-Puro plasmids. Luciferase reporter assay and RNA pull-down assay were applied to study interactions between LINC02086, miR-6757-5p and ephrin type-A receptor 2 (EPHA2). LINC02086-silencing MCF-7 cells were injected into mice to establish xenograft animal models.

RESULTS

Using RNA-seq data, tumor tissue samples and breast cancer cells, LINC02086 was consistently found to be up-regulated in breast cancer, and correlated with poorer prognosis. LINC02086 knockdown decreased cell viability, promoted cell apoptosis and suppressed tumor growth. LINC02086 interacted with miR-6757-5p that interacted with EPHA2.LINC02086 expression was negatively correlated with miR-6757-5p expression (r = -0.5698, P < 0.001) but was positively correlated with EPHA2 expression (r = 0.5061, P < 0.001). miR-6757-5p expression was negatively correlated with EPHA2 expression (r = -0.5919, P < 0.001). LINC02086 regulated EPHA2 via miR-6757-5p. miR-6757-5p/EPHA2 axis was a mediator of the effect of LINC02086 on cell viability and apoptosis.

CONCLUSION

LINC02086 increases cell viability and decreases apoptotic cells in breast cancer by sponging miR-6757-5p to upregulate EPHA2. This study presents LINC02086/miR-6757-5p/EPHA2 axis as promising therapeutic targets for breast cancer intervention.

MIRS: An AI scoring system for predicting the prognosis and therapy of breast cancer

Tumor-infiltrating immune cells (TIICs) and metastasis are crucial characteristics for tumorigenesis. However, the potential role of their combination in breast cancer (BRCA) remains elusive. Herein, on the basis of quantifying TIICs and tumor metastasis together, we established a precise prognostic scoring system named metastatic and immunogenomic risk score (MIRS) using a neural network model. MIRS showed better performance when compared with other published signatures. MIRS stratifies patients into a high risk subtype (MIRShigh) and a low risk subtype (MIRSlow). The MIRShigh patients exhibit significantly lower survival rate compared with MIRSlow patients (P < 0.0001 ), higher response to chemotherapy, but lower response to immunotherapy. Conversely, higher infiltration level of TIICs and significantly prolonged survival (P = 0.029 ) are observed in MIRSlow patients, indicating sensitive response in immunotherapy. This work presents a promising indicator to guide treatment options of the BRCA population and provides a predicted webtool that is almost universally applicable to BRCA patients.

Development and validation of an individualized gene expression-based signature to predict overall survival of patients with high-grade serous ovarian carcinoma

BACKGROUND

High-grade serious ovarian carcinoma (HGSOC) is a subtype of ovarian cancer with a different prognosis attributable to genetic heterogeneity. The prognosis of patients with advanced HGSOC requires prediction by genetic markers. This study systematically analyzed gene expression profile data to establish a genetic marker for predicting HGSOC prognosis.

METHODS

The RNA-seq data set and information on clinical follow-up of HGSOC were retrieved from Gene Expression Omnibus (GEO) database, and the data were standardized by DESeq2 as a training set. On the other hand, HGSOC RNA sequence data and information on clinical follow-up were retrieved from The Cancer Genome Atlas (TCGA) as a test set. Additionally, ovarian cancer microarray data set was obtained from GEO as the external validation set. Prognostic genes were screened from the training set, and characteristic selection was performed using the least absolute shrinkage and selection operator (LASSO) with 80% re-sampling for 5000 times. Genes with a frequency of more than 2000 were selected as robust biomarkers. Finally, a gene-related prognostic model was validated in both the test and GEO validation sets.

RESULTS

A total of 148 genes were found to be significantly correlated with HGSOC prognosis. The expression profile of these genes could stratify HGSOC prognosis and they were enriched to multiple tumor-related regulatory pathways such as tyrosine metabolism and AMPK signaling pathway. AKR1B10 and ANGPT4 were obtained after 5000-time re-sampling by LASSO regression. AKR1B10 was associated with the metastasis and progression of several tumors. In this study, Cox regression analysis was performed to create a 2-gene signature as an independent prognostic factor for HGSOC, which has the ability to stratify risk samples in all three data sets (p < 0.05). The Gene Set Enrichment Analysis (GSEA) discovered abnormally active REGULATION_OF_AUTOPHAGY and OLFACTORY_TRANSDUCTION pathways in the high-risk group samples.

CONCLUSION

This study resulted in the creation of a 2-gene molecular prognostic classifier that distinguished clinical features and was a promising novel prognostic tool for assessing the prognosis of HGSOC. RiskScore was a novel prognostic model which might be effective in guiding accurate prognosis of HGSOC.

Advances in immunotyping of colorectal cancer

Immunotherapy has transformed treatment for various types of malignancy. However, the benefit of immunotherapy is limited to a minority of patients with mismatch-repair-deficient (dMMR) and microsatellite instability-high (MSI-H) (dMMR-MSI-H) colorectal cancer (CRC). Understanding the complexity and heterogeneity of the tumor immune microenvironment (TIME) and identifying immune-related CRC subtypes will improve antitumor immunotherapy. Here, we review the current status of immunotherapy and typing schemes for CRC. Immune subtypes have been identified based on TIME and prognostic gene signatures that can both partially explain clinical responses to immune checkpoint inhibitors and the prognosis of patients with CRC. Identifying immune subtypes will improve understanding of complex CRC tumor heterogeneity and refine current immunotherapeutic strategies.

A prognostic mathematical model based on tumor microenvironment-related genes expression for breast cancer patients

Background

Tumor microenvironment (TME) status is closely related to breast cancer (BC) prognosis and systemic therapeutic effects. However, to date studies have not considered the interactions of immune and stromal cells at the gene expression level in BC as a whole. Herein, we constructed a predictive model, for adjuvant decision-making, by mining TME molecular expression information related to BC patient prognosis and drug treatment sensitivity.

Methods

Clinical information and gene expression profiles were extracted from The Cancer Genome Atlas (TCGA), with patients divided into high- and low-score groups according to immune/stromal scores. TME-related prognostic genes were identified using Kaplan-Meier analysis, functional enrichment analysis, and protein-protein interaction (PPI) networks, and validated in the Gene Expression Omnibus (GEO) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to construct and verify a prognostic model based on TME-related genes. In addition, the patients' response to chemotherapy and immunotherapy was assessed by survival outcome and immunohistochemistry (IPS). Immunohistochemistry (IHC) staining laid a solid foundation for exploring the value of novel therapeutic target genes.

Results

By dividing patients into low- and high-risk groups, a significant distinction in overall survival was found (p < 0.05). The risk model was independent of multiple clinicopathological parameters and accurately predicted prognosis in BC patients (p < 0.05). The nomogram-integrated risk score had high prediction accuracy and applicability, when compared with simple clinicopathological features. As predicted by the risk model, regardless of the chemotherapy regimen, the survival advantage of the low-risk group was evident in those patients receiving chemotherapy (p < 0.05). However, in patients receiving anthracycline (A) therapy, outcomes were not significantly different when compared with those receiving no-A therapy (p = 0.24), suggesting these patients may omit from A-containing adjuvant chemotherapy. Our risk model also effectively predicted tumor mutation burden (TMB) and immunotherapy efficacy in BC patients (p < 0.05).

Conclusion

The prognostic score model based on TME-related genes effectively predicted prognosis and chemotherapy effects in BC patients. The model provides a theoretical basis for novel driver-gene discover in BC and guides the decision-making for the adjuvant treatment of early breast cancer (eBC).

PD-1/PD-L1 Inhibitors Response in Triple-Negative Breast Cancer: Can Long Noncoding RNAs Be Associated?

As immune checkpoint inhibitors (ICI) emerge as a paradigm-shifting treatment option for patients with advanced or metastatic cancer, there is a growing demand for biomarkers that can distinguish which patients are likely to benefit. In the case of triple-negative breast cancer (TNBC), characterized by a lack of therapeutic targets, pembrolizumab approval for high-risk early-stage disease occurred regardless of PD-L1 status, which keeps the condition in a biomarker limbus. In this review, we highlight the participation of long non-coding RNAs (lncRNAs) in the regulation of the PD-1/PD-L1 pathway, as well as in the definition of prognostic immune-related signatures in many types of tumors, aiming to shed light on molecules that deserve further investigation for a potential role as biomarkers. We also conducted a bioinformatic analysis to investigate lncRNAs already investigated in PD-1/PDL-1 pathways in other cancer types, considering the TNBC molecular context. In this sense, from the generated data, we evidence here two lncRNAs, UCA1 and HCP5, which have not yet been identified in the context of the tumoral immune response in breast cancer. These candidates can be further explored to verify their use as biomarkers for ICI response. In this article, we present an updated review regarding the use of lncRNA as biomarkers of response to ICI, highlighting the versatility of using these molecules.

Karrow N, Mao Y. Identification and Classification of Long Non-Coding RNAs in the Mammary Gland of the Holstein Cow

The mammary glands, responsible for milk secretion, are regulated at a local level by various hormones, growth factors, non-coding RNAs, and other elements. Recent research has discovered the presence of lncRNAs in these glands, with suggestions that they may be essential for the maintenance and function of mammary glands. Besides directly controlling the gene and protein expression, lncRNAs are believed to play a significant part in numerous physiological and pathological processes. This study focused on examining the mammary gland tissues of Chinese Holstein cows, to identify and categorize long non-coding RNAs (lncRNAs). The research intended to distinguish lncRNAs in the mammary tissues of Holstein cows and contrast them between lactation and non-lactation periods. In this study, mammary gland tissues were sampled from three Holstein cows in early lactation (n = 3, 30 days postpartum) and non-lactation (n = 3, 315 days postpartum) on a large dairy farm in Jiangsu province. Mammary tissue samples were collected during early lactation and again during non-lactation. In total, we detected 1905 lncRNAs, with 57.3% being 500 bp and 612 intronic lncRNAs. The exon count for lncRNAs varied from 2 to 10. It was observed that 96 lncRNA expressions markedly differed between the two stages, with 83 genes being upregulated and 53 downregulated. Enrichment analysis results revealed that Gene Ontology (GO) analysis was primarily abundant in cellular processes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that target genes were predominantly abundant in metabolic pathways, fatty acid biosynthesis, the immune system, and glycosphingolipid biosynthesis. This study analyzed the expression profile and characteristics of lncRNAs in the mammary gland tissues of Holstein cows during both lactation and non-lactation stages, forming a foundation for further investigation into the functional roles of lncRNAs in Holstein cows throughout lactation.

lncRNA and circRNA expression profiles in the hippocampus of Aβ25‑35‑induced AD mice treated with Tripterygium glycoside

Tripterygium glycosides (TG) have been reported to ameliorate Alzheimer's disease (AD), although the mechanism involved remains to be determined. In the present study, the lncRNA and circRNA expression profiles of an AD mouse model treated with TG were assessed using microarrays. lncRNAs, mRNAs, and circRNAs in the hippocampi of 3 AD+normal saline (NS) mice and 3 AD+TG mice were detected using microarrays. The most differentially expressed lncRNAs, mRNAs, and circRNAs were screened between the AD+NS and AD+TG groups. The differentially expressed lncRNAs and circRNAs were analyzed using GO enrichment and KEGG analyses. Co-expression analysis of lncRNAs, circRNAs, and mRNAs was performed by calculating the correlation coefficients. Protein-protein interaction (PPI) network analysis was performed on mRNAs using STRING. The lncRNA-target-transcription factor (TF) network was analyzed using the Network software. In total, 661 lncRNAs, 64 circRNAs, and 503 mRNAs were found to be differentially expressed in AD mice treated with TG. Pou4f1, Egr2, Mag, and Nr4a1 were the hub genes in the PPI network. The KEGG results showed that the mRNAs that were co-expressed with lncRNAs were enriched in the TNF, PI3K-Akt, and Wnt signaling pathways. LncRNA-target-TF network analysis indicated that TFs, including Cebpa, Zic2, and Rxra, were the most likely to regulate the detected lncRNAs. The circRNA-miRNA interaction network indicated that 275 miRNAs may bind to the 64 circRNAs. In conclusion, these findings provide a novel perspective on AD pathogenesis, and the detected lncRNAs, mRNAs, and circRNAs may serve as novel therapeutic targets for the management of AD.

Study of LncRNA BANCR Expression in Tumor Tissues and Adjacent Normal Tissues in Gastric Cancer Patients

Background

Long non-coding RNAs (lncRNAs) have emerged as crucial regulators in various biological processes, including cancer development and progression. This study aimed to investigate the expression differences of the BRAF-activated non-coding RNA (BANCR) gene in GC tissues compared to adjacent normal tissues. The potential diagnostic significance of BANCR in GC was explored, with the aim of improving diagnostic and therapeutic approaches for this global health burden.

Materials and Methods

Tissue samples from 100 gastric cancer (GC) patients were collected, and BANCR expression was analyzed using quantitative real-time PCR. Correlations between BANCR expression and clinicopathological features were assessed, and its biomarker potential was evaluated.

Results

In individuals diagnosed with GC, the expression of BANCR was notably elevated in tumor tissues compared to adjacent normal tissues (P < 0.0001). However, the analysis of gene expression data did not demonstrate any statistically significant correlation between elevated BANCR expression and clinicopathological features. According to the ROC analysis, BANCR demonstrated an AUC of 0.6733 (P < 0.0001), with a sensitivity of 73% and a specificity of 45%. However, further evaluation is required to determine its potential as a biomarker (CI 95% = 0.5992 to 0.7473).

Conclusions

The observed upregulation of BANCR in GC tissues implies its potential involvement as an oncogenic lncRNA in GC patients. Furthermore, BANCR may serve as a promising biomarker for identification and treatment of GC.

Necrotic related-lncRNAs: Prediction of prognosis and differentiation between cold and hot tumors in head and neck squamous cell carcinoma

Treatment of head and neck squamous cell carcinoma (HNSCC) is a substantial clinical challenge due to the high local recurrence rate and chemotherapeutic resistance. This project seeks to identify new potential biomarkers of prognosis prediction and precision medicine to improve this condition. A synthetic data matrix for RNA transcriptome datasets and relevant clinical information on HNSCC and normal tissues was downloaded from the Genotypic Tissue Expression Project and The Cancer Genome Atlas (TCGA). The necrosis-associated long-chain noncoding RNAs (lncRNAs) were identified by Pearson correlation analysis. Then 8-necrotic-lncRNA models in the training, testing and entire sets were established through univariate Cox (uni-Cox) regression and Lasso-Cox regression. Finally, the prognostic ability of the 8-necrotic-lncRNA model was evaluated via survival analysis, nomogram, Cox regression, clinicopathological correlation analysis, and receiver operating characteristic (ROC) curve. Gene enrichment analysis, principal component analysis, immune analysis and prediction of risk group semi-maximum inhibitory concentration (IC50) were also conducted. Correlations between characteristic risk score and immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and anti-cancer drug sensitivity were analyzed. Eight necrosis-associated lncRNAs (AC099850.3, AC243829.2, AL139095.4, SAP30L-AS1, C5orf66-AS1, LIN02084, LIN00996, MIR4435-2HG) were developed to improve the prognosis prediction of HNSCC patients. The risk score distribution, survival status, survival time, and relevant expression standards of these lncRNAs were compared between low- and high-risk groups in the training, testing and entire sets. Kaplan-Meier analysis showed the low-risk patients had significantly better prognosis. The ROC curves revealed the model had an acceptable predictive value in the TCGA training and testing sets. Cox regression and stratified survival analysis indicated that the 8 necrosis-associated lncRNAs were risk factors independent of various clinical parameters. We recombined the patients into 2 clusters through Consensus ClusterPlus R package according to the expressions of necrotic lncRNAs. Significant differences were found in immune cell infiltration, immune checkpoint molecules, and IC50 between clusters, suggesting these characteristics can be used to evaluate the clinical efficacy of chemotherapy and immunotherapy. This risk model may serve as a prognostic signature and provide clues for individualized immunotherapy for HNSCC patients.

The current advances of lncRNAs in breast cancer immunobiology research

Breast cancer is the most frequently diagnosed malignancy and the leading cause of cancer-related death in women worldwide. Breast cancer development and progression are mainly associated with tumor-intrinsic alterations in diverse genes and signaling pathways and with tumor-extrinsic dysregulations linked to the tumor immune microenvironment. Significantly, abnormal expression of lncRNAs affects the tumor immune microenvironment characteristics and modulates the behavior of different cancer types, including breast cancer. In this review, we provide the current advances about the role of lncRNAs as tumor-intrinsic and tumor-extrinsic modulators of the antitumoral immune response and the immune microenvironment in breast cancer, as well as lncRNAs which are potential biomarkers of tumor immune microenvironment and clinicopathological characteristics in patients, suggesting that lncRNAs are potential targets for immunotherapy in breast cancer.

Identification of a cuproptosis-related lncRNA prognostic signature in lung adenocarcinoma

PURPOSE

Cuproptosis-related long non-coding RNA (lncRNA) diseases are associated with the occurrence and development of tumors. This study aimed to investigate whether cuproptosis-related lncRNA can predict the prognosis of patients with lung adenocarcinoma (LUAD).

METHODS

Cuproptosis-related lncRNA prognosis (CLPS) model was successfully constructed through cox regression and lasso regression analyses. Then, the prognostic value of CLPS model was tested through the survival analysis, the ROC curve and the nomogram. Finally, the correlation of CLPS model with tumor immunity and tumor mutation burden was analyzed, and the potential susceptibility of drugs for LUAD were predicted.

RESULTS

CLPS model for LUAD (AC090948.1, CRIM1-DT, AC026356.2, AC004832.5, AL161431.1) was successfully constructed, which has an independent prognostic value. Furthermore, the risk score of CLPS model was correlated with tumor immune characteristics and immune escape, which can predict the sensitivity of drugs including Cisplatin, Etoposide, Gemcitabine, and Erlotinib.

CONCLUSIONS

In conclusion, it was found that CLPS model was associated with tumor immunity and tumor mutation load, which also predicted four potentially sensitive drugs for LUAD patients at different risks.

Downregulation of the long noncoding RNA DSCR9 (Down syndrome critical region 9) delays breast cancer progression by modulating microRNA-504-5p-dependent G protein-coupled receptor 65

Possible roles of long noncoding RNAs (lncRNAs) in cancer stem cells (CSCs) have often been reported. Here, we focused on the regulatory function of the lncRNA Down syndrome critical region 9 (DSCR9) in breast cancer stem cells (BCSCs). Through bioinformatics analysis, DSCR9, microRNA-504-5p (miR-504-5p), and G protein-coupled receptor 65 (GPR65) were identified as targets implicated in breast cancer development. Then, clinical tissue samples, breast cancer cells, and isolated BCSCs were used to determine the expression of DSCR9, miR-504-5p, and GPR65. The results confirmed the overexpression of DSCR9 and GPR65 but low expression of miR-504-5p in breast cancer tissues and cells as well as in BCSCs. Following mechanistic investigation, it was found that DSCR9 targeted miR-504-5p, and that silencing DSCR9 inhibited the proliferation of BCSCs by elevating the expression of miR-504-5p. Additionally, miR-504-5p targeted GPR65 and inhibited its expression. Moreover, GPR65 activated the MEK/ERK signaling pathway to regulate BCSC proliferation. Finally, animal study verified that depletion of DSCR9 inhibited the proliferation of BCSCs in vivo and that BCSC proliferation was restored by overexpression of GPR65. Altogether, our findings revealed that DSCR9 elevated GPR65 expression by targeting miR-504-5p to exacerbate breast cancer, highlighting a new treatment modality for breast cancer.

Establishment of a N1-methyladenosine-related risk signature for breast carcinoma by bioinformatics analysis and experimental validation

OBJECTIVES

Breast carcinoma (BRCA) has resulted in a huge health burden globally. N1-methyladenosine (m¹A) RNA methylation has been proven to play key roles in tumorigenesis. Nevertheless, the function of m¹A RNA methylation-related genes in BRCA is indistinct.

METHODS

The RNA sequencing (RNA-seq), copy-number variation (CNV), single-nucleotide variant (SNV), and clinical data of BRCA were acquired via The Cancer Genome Atlas (TCGA) database. In addition, the GSE20685 dataset, the external validation set, was acquired from the Gene Expression Omnibus (GEO) database. 10 m¹A RNA methylation regulators were obtained from the previous literature, and further analyzed through differential expression analysis by rank-sum test, mutation by SNV data, and mutual correlation by Pearson Correlation Analysis. Furthermore, the differentially expressed m¹A-related genes were selected through overlapping m¹A-related module genes obtained by weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs) in BRCA and DEGs between high- and low- m¹A score subgroups. The m¹A-related model genes in the risk signature were derived by univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. In addition, a nomogram was built through univariate and multivariate Cox analyses. After that, the immune infiltration between the high- and low-risk groups was investigated through ESTIMATE and CIBERSORT. Finally, the expression trends of model genes in clinical BRCA samples were further confirmed by quantitative real-time PCR (RT‒qPCR).

RESULTS

Eighty-five differentially expressed m¹A-related genes were obtained. Among them, six genes were selected as prognostic biomarkers to build the risk model. The validation results of the risk model showed that its prediction was reliable. In addition, Cox independent prognosis analysis revealed that age, risk score, and stage were independent prognostic factors for BRCA. Moreover, 13 types of immune cells were different between the high- and low-risk groups and the immune checkpoint molecules TIGIT, IDO1, LAG3, ICOS, PDCD1LG2, PDCD1, CD27, and CD274 were significantly different between the two risk groups. Ultimately, RT-qPCR results confirmed that the model genes MEOX1, COL17A1, FREM1, TNN, and SLIT3 were significantly up-regulated in BRCA tissues versus normal tissues.

CONCLUSIONS

An m¹A RNA methylation regulator-related prognostic model was constructed, and a nomogram based on the prognostic model was constructed to provide a theoretical reference for individual counseling and clinical preventive intervention in BRCA.

Bioinformatics identification and validation of aging‑related molecular subtype and prognostic signature in breast cancer

Patients with metastatic breast cancer have a poor clinical outcome, accounting for more than 90 percent of breast cancer-related deaths. Aging could regulate many biological processes in malignancies by regulating cell senescence. The role of aging has not been fully clarified. Consensus cluster analysis was performed to differentiate The Cancer Genome Atlas (TCGA) breast cancer cases. Least absolute shrinkage and selection operator (LASSO) cox regression analysis was performed to construct an aging-related prognostic signature. A total of 118 differentially expressed aging-related genes (ARGs) was obtained in breast cancer. Consensus clustering analysis identified 3 categories of TCGA-breast cancer with significant difference in prognosis and immune infiltration. We also constructed an aging-related prognostic signature for breast cancer, which had a good performance in predicting the 1-year, 3-year and 5-year OS and disease specific survival (DSS) of breast cancer patients. Further single gene analysis revealed that the expression of PIK3R1 was significantly different in different pT and pN stages of breast cancer. Moreover, low expression of PIK3R1 showed resistance to many drugs based on the data of Genomics of Drug Sensitivity in Cancer (GDSC) and Genomics of Therapeutics Response Portal (CTRP). PIK3R1 played a vital role in many well-known cancer-related pathways. The current study identified 3 clusters of TCGA-breast cancer cases with significant differences in prognosis and immune infiltration. We also constructed an aging-related prognostic signature for breast cancer. However, further in vivo and in vitro studies should be conducted to verify these results.

Immune-Related Long Non-Coding RNA Signatures for Tongue Squamous Cell Carcinoma

BACKGROUND

Tongue squamous cell carcinoma (TSCC) represents one of the major subsets of head and neck cancer, which is characterized by unfavorable prognosis, frequent lymph node metastasis, and high mortality rate. The molecular events regulating tongue tumorigenesis remain elusive. In this study, we aimed to identify and evaluate immune-related long non-coding RNAs (lncRNAs) as prognostic biomarkers in TSCC.

METHODS

The lncRNA expression data for TSCC were obtained from The Cancer Genome Atlas (TCGA) and the immune-related genes were downloaded from the Immunology Database and Analysis Portal (ImmPort). Pearson correlation analysis was performed to identify immune-related lncRNAs. The TCGA TSCC patient cohort was randomly divided into training and testing cohorts. In the training cohort, univariate and multivariate Cox regression analyses were used to determining key immune-related lncRNAs, which were then validated through Cox regression analysis, principal component analysis (PCA), and receiver operating characteristic (ROC) analysis in the testing cohort.

RESULTS

Six immune-related signature lncRNAs (MIR4713HG, AC104088.1, LINC00534, NAALADL2-AS2, AC083967.1, FNDC1-IT1) were found to have prognostic value in TSCC. Multivariate and univariate cox regression analyses showed that the risk score based on our six-lncRNA model, when compared to other clinicopathological factors (age, gender, stage, N, T), was an important indicator of survival rate. In addition, Kaplan-Meier survival analysis demonstrated significantly higher overall survival in the low-risk patient group than the high-risk patient group within both training and testing cohorts. The ROC analysis indicated that the AUCs for 5-year overall survival were 0.790, 0.691, and 0.721, respectively, for training, testing, and entire cohorts. Finally, PCA analysis demonstrated that the high-risk and low-risk patient groups presented significant deviation regarding their immune status.

CONCLUSIONS

A prognostic model based on six immune-related signature lncRNAs was established. This six-lncRNA prognostic model has clinical significance and may be helpful in the development of personalized immunotherapy strategies.

The Clinical Utility of lncRNAs and Their Application as Molecular Biomarkers in Breast Cancer

Given their tumor-specific and stage-specific gene expression, long non-coding RNAs (lncRNAs) have demonstrated to be potential molecular biomarkers for diagnosis, prognosis, and treatment response. Particularly, the lncRNAs DSCAM-AS1 and GATA3-AS1 serve as examples of this because of their high subtype-specific expression profile in luminal B-like breast cancer. This makes them candidates to use as molecular biomarkers in clinical practice. However, lncRNA studies in breast cancer are limited in sample size and are restricted to the determination of their biological function, which represents an obstacle for its inclusion as molecular biomarkers of clinical utility. Nevertheless, due to their expression specificity among diseases, such as cancer, and their stability in body fluids, lncRNAs are promising molecular biomarkers that could improve the reliability, sensitivity, and specificity of molecular techniques used in clinical diagnosis. The development of lncRNA-based diagnostics and lncRNA-based therapeutics will be useful in routine medical practice to improve patient clinical management and quality of life.

Identification of a novel defined inflammation-related long noncoding RNA signature contributes to predicting prognosis and distinction between the cold and hot tumors in bladder cancer

Purpose

Bladder cancer (BLCA) is one of the most frequently diagnosed urological malignancies and is the 4th most common cancer in men worldwide. Molecular targets expressed in bladder cancer (BLCA) are usually used for developing targeted drug treatments. However, poor prognosis and poor immunotherapy efficacy remain major challenges for BLCA. Numerous studies have shown that long non-coding RNAs (LncRNAs) play an important role in the development of cancer. However, the role of lncRNAs related to inflammation in BLCA and their prognostic value remain unclear. Therefore, this study is aimed to explore new potential biomarkers that can predict cancer prognosis.

Methods

We downloaded BLCA-related RNA sequencing data from The Cancer Genome Atlas (TCGA) and searched for inflammation-related prognostic long non-coding RNAs (lncRNAs) by univariate Cox (uniCox) regression and co-expression analysis. We used the least absolute shrinkage and selection operator (LASSO) analysis to construct an inflammation-related lncRNA prognosis risk model. Samples were divided into high-risk score (HRS) group and low-risk score (LRS) group based on the median value of risk scores. The independent variable factors were identified by univariate Cox (uni-Cox) and multivariate Cox (multi-Cox) regression analyses, and receiver operating characteristic (ROC) curves were used to compare the role of different factors in predicting outcomes. Nomogram and Calibration Plot were generated by the R package rms to analyze whether the prediction results are correct and show good consistency. Correlation coefficients were calculated by Pearson analysis. The Kaplan-Meier method was used to assess the prognostic value. The expression of 7 lncRNAs related with inflammation was also confirmed by qRT-PCR in BLCA cell lines. Kyoto Encyclopedia of Gene and Genome (KEGG) pathways that were significantly enriched (P &lt; 0.05) in each risk group were identified by the GSEA software. The R package pRRophetic was used to predict the IC50 of common chemotherapeutic agents. TIMER, XCELL, QUANTISEQ, MCPCOUNTER, EPIC and CIBERSORT were applied to quantify the relative proportions of infiltrating immune cells. We also used package ggpubr to evaluate TME scores and immune checkpoint activation in LRS and HRS populations. R package GSEABase was used to analyze the activity of immune cells or immune function. Different clusters of principal component analysis (PCA), t-distribution random neighborhood embedding (t-SNE), and Kaplan-Meier survival were analyzed using R package Rtsne’s. The R package ConsensesClusterPlus was used to class the inflammation-related lncRNAs.

Results

In this study, a model containing 7 inflammation-related lncRNAs was constructed. The calibration plot of the model was consistent with the prognosis prediction outcomes. The 1-, 3-, and 5-year ROC curve (AUC) were 0.699, 0.689, and 0.699, respectively. High-risk patients were enriched in lncRNAs related with tumor invasion and immunity, and had higher levels of immune cell infiltration and immune checkpoint activation. Hot tumors and cold tumors were effectively distinguished by clusters 2 and 3 and cluster 1, respectively, which indicated that hot tumors are more susceptible to immunotherapy.

Conclusion

Our study showed that inflammation-related LncRNAs are closely related with BLCA, and inflammation-related lncRNA can accurately predict patient prognosis and effectively differentiate between hot and cold tumors, thus improving individualized immunotherapy for BLCA patients. Therefore, this study provides an effective predictive model and a new therapeutic target for the prognosis and clinical treatment of BLCA, thus facilitating the development of individualized tumor therapy.

Development and validation of cuproptosis-related lncRNA signatures for prognosis prediction in colorectal cancer

BACKGROUND

Cuproptosis, a novel form of programmed cell death, plays an essential role in various cancers. However, studies of the function of cuproptosis lncRNAs (CRLs) in colorectal cancer (CRC) remain limited. Thus, this study aims to identify the cuprotosis-related lncRNAs (CRLs) in CRC and to construct the potential prognostic CRLs signature model in CRC.

METHODS

First, we downloaded RNA-Seq data and clinical information of CRC patients from TCGA database and obtained the prognostic CRLs based on typical expression analysis of cuproptosis-related genes (CRGs) and univariate Cox regression. Then, we constructed a prognostic model using the Least Absolute Shrinkage and Selection Operator algorithm combined with multiple Cox regression methods (Lasso-Cox). Next, we generated Kaplan-Meier survival and receiver operating characteristic curves to estimate the performance of the prognostic model. In addition, we also analysed the relationships between risk signatures and immune infiltration, mutation, and drug sensitivity. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT -PCR) to verify the prognostic model.

RESULT

Lasso-Cox analysis revealed that four CRLs, SNHG16, LENG8-AS1, LINC0225, and RPARP-AS1, were related to CRC prognosis. Receiver operating characteristic (ROC) and Kaplan-Meier analysis curves indicated that this model performs well in prognostic predictions of CRC patients. The DCA results also showed that the model included four gene signatures was better than the traditional model. In addition, GO and KEGG analyses revealed that DE-CRLs are enriched in critical signalling pathway, such as chemical carcinogenesis-DNA adducts and basal cell carcinoma. Immune infiltration analysis revealed significant differences in immune infiltration cells between the high-risk and low-risk groups. Furthermore, significant differences in somatic mutations were noted between the high-risk and low-risk groups. Finally, we also validated the expression of four CRLs in FHCs cell lines and CRC cell lines using qRT-PCR.

CONCLUSION

The signature composed of SNHG16, LENG8-AS1, LINC0225, and RPARP-AS1, which has better performance in predicting colorectal cancer prognosis and are promising biomarkers for prognosis prediction of CRC.

Construction of a prognostic model for HCC based on ferroptosis-related lncRNAs expression and its potential to predict the response and irAEs of immunotherapy

Background: Ferroptosis is an iron-dependent programmed cell death process, and studies have confirmed that it plays an important regulatory role in the occurrence and development of various malignancies including hepatocellular carcinoma (HCC). In addition, the role of abnormally expressed long non-coding RNAs (lncRNAs) in regulating and driving the occurrence and development of HCC has attracted more and more attention. However, there is still a lack of research on the role of ferroptosis-related lncRNAs in the prognosis prediction of HCC patients.

Method: In this study, we used the Pearson test method to analyze the association between differentially expressed lncRNAs and ferroptosis-related genes in HCC and normal tissues obtained from The Cancer Genome Atlas (TCGA), and found 68 aberrantly expressed and prognosis-related ferroptosis-related lncRNAs. Based on this, we established an HCC prognostic model composed of 12 ferroptosis-related lncRNAs. In addition, HCC patients were divided into a high-risk group and a low-risk group according to the risk score of this 12 ferroptosis-related lncRNAs prognostic model. Gene enrichment analysis indicated that ferroptosis-related lncRNA-based expression signatures may regulate HCC immune microenvironment signaling pathways through ferroptosis, chemical carcinogenesis-reactive oxygen species, and NK cell-mediated cytotoxicity pathways. In addition, immune cell correlation analysis showed that there were significant differences in immune infiltrating cell subtypes, such as Th cells, macrophages, monocytes, and Treg cells between the two groups. In addition, the expression of multiple immune checkpoint molecules was found to be significantly increased in the high-risk group (eg, PD1, CTLA-4, CD86, etc.).

Results: Our research provides a new method for predicting prognosis using a ferroptosis-related lncRNA expression signature prognostic model in hepatocellular carcinoma. And it provides new tools for predicting patient response and adverse effects of immunotherapy.

Conclusion: In conclusion, ferroptosis-related lncRNA expression signatures can be used to construct a prognostic prediction model to predict the overall survival of HCC patients, and can be used as an independent influencing factor for prognosis. Further analysis showed that ferroptosis-related lncRNAs may affect the efficacy of immunotherapy in patients with HCC by altering the tumor microenvironment, so this model may serve as a new indicator of the response and irAEs of HCC to immunotherapy.

Characterization of protein-based risk signature to predict prognosis and evaluate the tumor immune environment in breast cancer

BACKGROUND

Proteomics profiles have enabled a systematic insight into the prognosis of cancer. This study aimed to establish a valuable protein-based risk signature to assess the prognosis and immune status in patients with breast cancer (BC).

METHODS

Protein expression profile, RNA expression data, and clinical information were acquired from The Cancer Genome Atlas (TCGA). The whole cohort was randomly split into two cohorts, one for establishing the risk signature and the other for testing. Univariate Cox analysis and Least absolute shrinkage and selection operator (LASSO) Cox regression were utilized to construct the protein-based risk signature. All cohorts were divided into high- and low-risk groups, which were applied to investigate the clinical relevance, tumor microenvironment, and therapeutic response.

RESULTS

The prognostic proteomics signature was established based on prognostic proteins, thus categorizing patients into low-risk and high-risk groups with different prognoses. A predictive nomogram was also developed to predict 1, 3, and 5-year survival possibility for BC patients, and the calibration curves confirmed the predictive significance of this signature. Afterward, the low-risk group displayed higher immune activities, immune checkpoint expression, and immunotherapeutic response. Moreover, GSEA analysis indicated that immune-associated pathways were rich in the low-risk group. Additionally, this prognostic signature demonstrated potential predict significance for chemotherapeutic agents.

CONCLUSION

This study established an effective prognostic proteomics signature with reliable predictive performance for survival, immune activity, and drug sensitivity. It might provide a novel perspective into the protein function in BC, and guide the individual treatment strategies for BC patients.

Aberrantly expressed long noncoding RNAs as potential prognostic biomarkers in newly diagnosed multiple myeloma: A systemic review and meta-analysis

BACKGROUND

Numerous studies have manifested long noncoding RNAs (lncRNAs) as biomarkers to determine the prognosis of multiple myeloma (MM) patients. Nevertheless, the prognostic role of lncRNAs in MM is still ambiguous. Herein, we performed a meta-analysis to evaluate the predictive value of aberrantly expressed lncRNAs in MM.

METHODS

A systemic literature search was performed in PubMed, EMBASE, Cochrane, and Web of Science databases until October 9, 2021, and the protocol was registered in the PROSPERO database (CRD42021284364). Our study extracted the hazard ratios (HRs) and 95% confidence intervals (CIs) of overall survival (OS), progression-free survival (PFS), or event-free survival (EFS). Begg's and Egger's tests were employed to correct publication bias.

RESULT

Twenty-six individual studies containing 3501 MM patients were enrolled in this study. The results showed that aberrant expression of lncRNAs was associated with poor OS and PFS of MM patients. The pooled HRs for univariate OS and PFS were 1.48 (95% CI = 1.17-1.88, p < 0.001) and 1.30 (95% CI = 1.18-1.43, p < 0.001), respectively, whereas the pooled HRs for multivariate OS and PFS were 1.50 (95% CI = 1.16-1.95, p < 0.001) and 1.59 (95% CI = 1.22-2.07, p < 0.001), respectively. Subgroup analysis suggested that MALAT1, TCF7, NEAT1, and PVT1 upregulation were associated with poor OS (p < 0.05), PVT1, and TCF7 upregulation were implicated with worse PFS (p < 0.05), while only TCF7 overexpression was correlated with reduced EFS (p < 0.05). Moreover, the contour-enhanced funnel plot demonstrated the reliability of our current conclusion, which was not affected by publication bias.

CONCLUSION

Aberrantly expressed particular lncRNAs are critical prognostic indicators in long-term survival as well as promising biomarkers in progression-free status. However, different cutoff values and dissimilar methods to assess lncRNA expression among studies may lead to heterogeneity.

Comprehensive analysis of immune-related lncRNAs and their clinical relevance in gastric adenocarcinoma

Increasing evidence has demonstrated that lncRNA plays a significant role in the immunity regulation of gastric adenocarcinoma. However, the immune-related lncRNAs and the prognostic value in immunotherapies remain largely unexplored. We collected immune-related lncRNA and the associated pathways of gastric cancer from the ImmLnc database. The cox regression model is used to analyze the prognostic value of these lncRNAs. Gastric cancer is further divided into different subtypes based on these lncRNAs. Tumor microenvironment analysis, functional enrichment analysis, and genomic alteration analysis are performed for different subtypes. Furthermore, chemotherapeutic and immunotherapeutic sensitivity are also analyzed among different subtypes. Nine lncRNAs are identified as significant regulators of the immune pathway of gastric cancer. Gastric cancer can be classified into 5 subtypes based on these lncRNAs. Tumor microenvironment analysis shows that cluster C3 has the highest immune score and C5 has the lowest score. Functional analysis shows that these subtypes are enriched with distinct biological processes. Genomic analysis shows that LAMA2 mutation is a protective factor in C3 but a risk factor in C5. Furthermore, these subtypes are found to respond distinctly to the same chemotherapeutic and immunotherapeutic drugs. In this study, we analyzed the immune-related lncRNA and identified the crucial role in the regulation of immune properties, biological processes, and immunotherapeutic sensitivity. These findings can improve our understanding of the epigenetic immunoregulation of lncRNA and advance the research of immunotherapy.

Identification of a necroptosis-related gene signature as a novel prognostic biomarker of cholangiocarcinoma

Background

Cholangiocarcinoma (CHOL) is the most prevalent type of malignancy and the second most common form of primary liver cancer, resulting in high rates of morbidity and mortality. Necroptosis is a type of regulated cell death that appears to be involved in the regulation of several aspects of cancer biology, including tumorigenesis, metastasis, and cancer immunity. This study aimed to construct a necroptosis-related gene (NRG) signature to investigate the prognosis of CHOL patients using an integrated bioinformatics analysis.

Methods

CHOL patient data were acquired from the Gene Expression Omnibus (GEO) (GSE89748, GSE107943) and The Cancer Genome Atlas (TCGA) databases, with NRGs data from the necroptosis pathway in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Univariate and multivariate regression analyses were performed to establish the NRG signatures. Kaplan-Meier (KM) curves were used to evaluate the prognosis of patients with CHOL. Functional enrichment analysis was performed to identify key NRG-associated biological signaling pathways. We also applied integrative multi-omics analysis to the high- and low-risk score groups. Spearman's rank correlation was used to clarify the relationship between the NRG signature and immune infiltration.

Results

65 differentially expressed (DE) NRGs were screened, five of which were selected to establish the prognostic signature of NRG_S based on multivariate Cox regression analysis. We observed that low-risk patients survived significantly longer than high-risk patients. We found that patients with high-risk scores experienced higher immune cell infiltration, drug resistance, and more somatic mutations than patients with low-risk scores. We further found that sensitivities to GW843682X, mitomycin C, rapamycin, and S-trityl-L-cysteine were significantly higher in the low-risk group than in the high-risk group. Finally, we validated the expression of five NRGs in CHOL tissues using the TCGA database, HPA database and our clinical data.

Conclusion

These findings demonstrate that the five-NRG prognostic signature for CHOL patients is reasonably accurate and valid, and it may prove to be of considerable value for the treatment and prognosis of CHOL patients in the future.

The interplay of long noncoding RNAs and hepatitis B virus

Hepatitis B Virus (HBV) infections remain a major global health burden with an estimated 296 million people living with a chronic infection and 884,000 HBV-related deaths annually. Notably, patients with a chronic hepatitis B (CHB) infection are at a 30-fold greater risk of developing hepatocellular carcinoma (HCC), the most common type of primary liver cancer, which is the 3rd deadliest cancer worldwide. Several groups have assessed HBV-related aberrant expression of host-cell long noncoding RNAs (lncRNAs) and how altered expression of specific lncRNAs affects HBV replication and progression to associated disease states. Given the challenges in establishing effective HBV models and analyzing transcriptomic data, this review focuses on lncRNA expression data primarily collected from clinical patient samples and primary human hepatocytes, with the subsequent mechanism of action analysis in cell lines or other model systems. Ultimately, understanding HBV-induced lncRNA-expression dysregulation could lead to new treatments and biomarkers for HBV infection and its associated diseases.

Development and Evaluation of a Novel Cuproptosis-Related lncRNA Signature for Gastric Cancer Prognosis

Background

According to a growing body of research, long noncoding RNAs (lncRNAs) participate in the progress of gastric cancer (GC). Cuproptosis is a distinct kind of programmed cell death, separating it from several other forms of programmed cell death that may be caused by genetic programming. Consequently, it is crucial to examine cuproptosis-related lncRNAs (CRLs) prognostic importance for the prognosis and treatment response in GC.

Method

The Cancer Genome Atlas (TCGA) database was used to retrieve RNA-seq data, pertinent clinical information, and somatic mutation data. A list of cuproptosis-related genes (CRGs) was obtained from prior work. We can distinguish prognostic CRLs using coexpression and univariate Cox analysis. Then, using CRLs, we developed a risk prediction model using multivariate Cox regression analysis and the least absolute shrinkage selection operator (LASSO) technique. To evaluate the diagnostic accuracy of this model, a Kaplan-Meier (K-M) survival analysis and a receiver operating characteristic (ROC) analysis were used. Moreover, the relationships between the risk model and immunological function, somatic mutation, and drug sensitivity were also investigated.

Results

Using the multivariate Cox analysis technique, we developed a signature based on cuproptosis-related four lncRNAs. We then classified patients into high-risk and low-risk groups based on the likelihood of unfavorable outcomes. The model was subjected to further testing, including K-M survival analysis, ROC analysis, and multivariate Cox regression analysis, all of which proved the model's exceptional robustness and predictive capacity. In addition, a nomogram that has a strong capacity for prediction ability was built. This nomogram included age, gender, clinical grade, pathologic stage, T stage, and risk score. Furthermore, we discovered substantial disparities in immune function and the number of mutations carried by tumors between the high-risk and low-risk groups. Moreover, this research also found that the IC50 values for 27 chemotherapeutic drugs varied widely across patients within high- and low-risk groups.

Conclusion

The proposed 4-CRLs signature is a promising biomarker to predict clinical outcomes in GC.

Nidogen-2 (NID2) is a Key Factor in Collagen Causing Poor Response to Immunotherapy in Melanoma

Background

The incidence of cutaneous melanoma continues to rise rapidly and has an extremely poor prognosis. Immunotherapy strategies are the most effective approach for patients who have developed metastases, but not all cases have been successful due to the complex and variable mechanisms of melanoma response to immune checkpoint inhibition.

Methods

We synthesized collagen-coding gene expression data (second-generation and single-cell sequencing) from public Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Bioinformatics analysis was performed using R software and several database resources such as Metascape database, Gene Set Cancer Analysis (GSCA) database, and Cytoscape software, etc., to investigate the biological mechanisms that may be related with collagens. Immunofluorescence and immunohistochemical staining were used to validate the expression and localization of Nidogen-2 (NID2).

Results

Melanoma patients can be divided into two collagen clusters. Patients with high collagen levels (C1) had a shorter survival than those with low collagen levels (C2) and were less likely to benefit from immunotherapy. We demonstrated that NID2 is a potential key factor in the collagen phenotype, is involved in fibroblast activation in melanoma, and forms a barrier to limit the proximity of CD8+ T cells to tumor cells.

Conclusion

We clarified the adverse effects of collagen on melanoma patients and identified NID2 as a potential therapeutic target.

Establishment and validation of a redox-related long non-coding RNAs prognostic signature in head and neck squamous cell carcinoma

Reduction and oxidation (redox) reactions occur in living organisms as part of normal cellular metabolism. Here, we established a novel redox-related long non-coding RNAs (rrlncRNAs) signature to predict the prognosis and therapeutic response in Head and neck squamous cell carcinoma (HNSCC). The expression profile and clinical information were obtained from the TCGA project. In total, 10 differently expressed rrlncRNAs associated with prognosis were identified and involved in a prognostic risk score signature by the least absolute shrinkage and selection operator penalized Cox analysis. The area under the receiver operating characteristic curves of the survival rates predicted by the rrlncRNAs signature over one, two, and three years were found to be 0.651, 0.670, and 0.679. Following the completion of the Kaplan-Meier survival study, we discovered that the lower-risk cohort exhibited a much longer overall survival period in contrast with the higher-risk cohort. Univariate and multivariable Cox regression analyses demonstrated that the risk score independently served as a significant predictive factor. GO annotation and KEGG pathway analyses illustrated that the rrlncRNAs signature was strongly associated with immune-related functions as well as signaling pathways. The tumor-infiltrating immune cells, tumor microenvironment, immune-related functions, HLA gene family expression, immune checkpoint genes expression, and somatic variants differed substantially between the low- and high-risk cohorts. Moreover, patients in low-risk group were predicted to present a favorable immunotherapy responsiveness, while in contrast, the high-risk group patients might have a stronger sensitivity to "docetaxel". According to our findings, the rrlncRNAs signature showed an excellent prognosis predictive value and might indicate therapeutic response to immunotherapy in HNSCC.

A 5-Pathway Signature Predicts Prognosis Based on Immune-Derived lncRNAs in Patients with Breast Cancer

Background

Currently, predictive models were not developed based on the signaling pathway signatures of immune-related lncRNAs in breast cancer (BRCA) patients.

Methods

We selected unsupervised hierarchical clustering algorithm to classify patients with BRCA based on the significant immune-derived lncRNAs from the TCGA dataset. And different methods including ESTIMATE, ImmuneCellAI, and CIBERSORT were performed to evaluate the immune infiltration of tumor microenvironment. Using Lasso regression algorithm, we filtered the significant signaling pathways enriched by GSEA, GSVA, or PPI analysis to develop a prognostic model. And a nomogram integrated with clinical factors and significant pathways was constructed to predict the precise probability of overall survival (OS) of BRCA patients in the TCGA dataset (n = 1,098) and another two testing sets (n = 415).

Results

BRCA patients were stratified into the PC (n = 571) and GC (n = 527) subgroup with significantly different prognosis with 550 immune-related lncRNAs in the TCGA dataset. Integrated analysis revealed different immune response, oncogenic signaling, and metabolic reprograming pathways between these two subgroups. And a 5-pathway signature could predict the prognosis of BRCA patients between these two subgroups independently in the TCGA dataset, which was confirmed in another two cohorts from the GEO dataset. In the TCGA dataset, 5-year OS rate was 78% (95% CI: 73-84) vs. 82% (95% CI: 77-87) for the PC and GC group (HR = 1.63 (95% CI: 1.17-2.28), p = 0.004). The predictive power was similar in another two testing sets (HR > 1.20, p  <  0.01). Finally, a nomogram is developed for clinical application, which integrated this signature and age to accurately predict the survival probability in BRCA patients.

Conclusion

This 5-pathway signature correlated with immune-derived lncRNAs was able to precisely predict the prognosis for patients with BRCA and provided a rich source characterizing immune-related lncRNAs and further informed strategies to target BRCA vulnerabilities.

LncRNA RP11-551L14.4 suppresses breast cancer development by inhibiting the expression of miR-4472

Background

Previous studies have been reported that long non-coding RNA (lncRNA) can regulate the expression of genes which are involved in many important cellular processes The potential role of lncRNA RP11-551L14.4 in the development of breast cancer and the possible regulatory mechanisms was investigated.

Methods

Quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to analyze RP11-551L14.4 expression in 36 paired breast cancer tissues and adjacent tissues. The expression of RP11-551L14.4 in multiple breast cancer cell lines was detected by qRT-PCR. Meanwhile, overexpression of RP11-551L14.4 models was established using lentivirus in BT474 and T47D breast cancer cells. Cell counting kit-8 (CCK-8), cell colony formation and cell cycle assays were performed to detect the effects of RP11-551L14.4 on the biological function of breast cancer cells. Besides, bioinformatics techniques, dual luciferase reporter gene assay and rescue experiments were used to investigate the potential mechanisms.

Results

RP11-551L14.4 expression was negatively associated with the advanced tumor stage. Breast cancer patients with low RP11-551L14.4 expression manifested a poorer prognosis. The results of qRT-PCR showed that RP11-551L14.4 expression in breast cancer tissues was significantly lower than in adjacent tissues. Meanwhile, overexpression of RP11-551L14.4 significantly decreased the cell proliferation and cell cycle. Bioinformatics technology showed that RP11-551L14.4 could complementarily bind to miR-4472. qRT-PCR results indicated that the expression levels of miR-4472 and RP11-551L14.4 in breast cancer were negatively correlated. Luciferase reporter gene assay showed that miR-4472 remarkably decreased the relative luciferase activity of the wild-type RP11-551L14.4 vector. miR-4472 is a direct target gene of RP11-551L14.4. miR-4472 levels were reduced, and repulsive guidance molecule A (RGMA) mRNA or protein levels were increased after overexpression of RP11-551L14.4 in the breast cancer cells. miR-4472 reversed the effects caused by RP11-551L14.4 in breast cancer cells.

Conclusion

RP11-551L14.4 expression was remarkably decreased in breast cancer tissues and cells. RP11-551L14.4 may inhibit the malignant progression of breast cancer by regulating miR-4472 expression.

Tumor mutation burden-related long non-coding RNAs is predictor for prognosis and immune response in pancreatic cancer

BACKGROUND

Pancreatic cancer is one of the most common malignant tumors with extremely poor prognosis. It is urgent to identify promising prognostic biomarkers for pancreatic cancer.

METHODS

A total of 266 patients with pancreatic adenocarcinoma (PAAD) in the Cancer Genome Atlas (TCGA)-PAAD cohort and the PACA-AU cohort were enrolled in this study. Firstly, prognostic tumor mutation burden (TMB)-related long non-coding RNAs (lncRNAs) were identified by DESeq2 and univariate analysis in the TCGA-PAAD cohort. And then, the TCGA-PAAD cohort was randomized into the training set and the testing set. Least absolute shrinkage and selection operator (LASSO) was used to construct the model in the training set. The testing set, the TCGA-PAAD cohort and the PACA-AU cohort was used as validation. The model was evaluated by multiple methods. Finally, functional analysis and immune status analysis were applied to explore the potential mechanism of our model.

RESULTS

A prognostic model based on fourteen TMB-related lncRNAs was established in PAAD. Patients with High risk score was associated with worse prognosis compared to those with low risk score in all four datasets. Besides, the model had great performance in the prediction of 5-year overall survival in four datasets. Multivariate analysis also indicated that the risk score based on our model was independent prognostic factor in PAAD. Additionally, our model had the best predictive efficiency in PAAD compared to typical features and other three published models. And then, our findings also showed that high risk score was also associated with high TMB, microsatellite instability (MSI) and homologous recombination deficiency (HRD) score. Finally, we indicated that high risk score was related to low immune score and less infiltration of immune cells in PAAD.

CONCLUSION

we established a 14 TMB-related lncRNAs prognostic model in PAAD and the model had excellent performance in the prediction of prognosis in PAAD. Our findings provided new strategy for risk stratification and new clues for precision treatment in PAAD.

Machine learning-based tumor-infiltrating immune cell-associated lncRNAs for predicting prognosis and immunotherapy response in patients with glioblastoma

Long noncoding ribonucleic acids (RNAs; lncRNAs) have been associated with cancer immunity regulation. However, the roles of immune cell-specific lncRNAs in glioblastoma (GBM) remain largely unknown. In this study, a novel computational framework was constructed to screen the tumor-infiltrating immune cell-associated lncRNAs (TIIClnc) for developing TIIClnc signature by integratively analyzing the transcriptome data of purified immune cells, GBM cell lines and bulk GBM tissues using six machine learning algorithms. As a result, TIIClnc signature could distinguish survival outcomes of GBM patients across four independent datasets, including the Xiangya in-house dataset, and more importantly, showed superior performance than 95 previously established signatures in gliomas. TIIClnc signature was revealed to be an indicator of the infiltration level of immune cells and predicted the response outcomes of immunotherapy. The positive correlation between TIIClnc signature and CD8, PD-1 and PD-L1 was verified in the Xiangya in-house dataset. As a newly demonstrated predictive biomarker, the TIIClnc signature enabled a more precise selection of the GBM population who would benefit from immunotherapy and should be validated and applied in the near future.

Identification of prognostic immune-related lncRNAs in pancreatic cancer

Long noncoding RNAs (lncRNAs) play a critical role in the immune regulation and tumor microenvironment of pancreatic cancer (PaCa). To construct a novel immune-related prognostic risk model for PaCa and evaluate the prognostic prediction of lncRNAs, essential immune-related lncRNAs (IRlncRNAs) were identified by Pearson correlation analysis of differentially expressed immune-related genes (IRGs) and IRlncRNAs in PaCa from The Cancer Genome Atlas (TCGA) and GTEx databases. Least absolute shrinkage and selection operator (LASSO) regression was also applied to construct a prognostic risk model of IRlncRNAs, and gene set enrichment analysis (GSEA) was further applied for functional annotation for these IRlncRNAs. A total of 148 IRlncRNAs were identified in PaCa to construct a prognostic risk model. Among them, lncRNA LINC02325, FNDC1-AS1, and ZEB2-AS1 were significantly upregulated in 69 pairs of PaCa tissues by qRT-PCR. ROC analyses showed that LINC02325 (AUC = 0.80), FNDC1-AS1 (AUC = 0.76), and ZEB2-AS1 (AUC = 0.75) had a good predictive effect on 5-year survival prognosis. We demonstrated that high expression levels of ZEB2-AS1 and LINC02325 were not only positively associated with tumor size and CA199, but elevated levels of ZEB2-AS1 and FNDC1-AS1 were also positively correlated with tumor stage. GSEA further revealed that immune-related pathways were mainly enriched in the high-risk groups. Several immune-related algorithms demonstrated that four IRlncRNAs were related to immune infiltration, immune checkpoints, and immune-related functions. Thus, the prognostic risk model based on IRlncRNAs in Paca indicates that the four IRlncRNA signatures may serve as predictors of survival and potential predictive biomarkers of the pancreatic tumor immune response.

Construction of prognostic signature of breast cancer based on N7-Methylguanosine-Related LncRNAs and prediction of immune response

Background: Long non-coding RNA (LncRNA) is a prognostic factor for malignancies, and N7-Methylguanosine (m7G) is crucial in the occurrence and progression of tumors. However, it has not been documented how well m7G-related LncRNAs predict the development of breast cancer (BC). This study aims to develop a predictive signature based on long non-coding RNAs (LncRNAs) associated with m7G to predict the prognosis of breast cancer patients.

Methods: The Cancer Genome Atlas (TCGA) database provided us with the RNA-seq data and matching clinical information of individuals with breast cancer. To identify the signature of N7-Methylguanosine-Related LncRNAs and create a prognostic model, we employed co-expression network analysis, least absolute shrinkage selection operator (LASSO) regression analysis, univariate Cox regression analysis, and multivariate Cox regression analysis. The signature was assessed using the Kaplan-Meier analysis and Receiver Operating Characteristic (ROC) curve. A nomogram and principal component analysis (PCA) were employed to confirm the predictive signature’s usefulness. Then, we examined the drug sensitivity between the two risk groups and utilized single-sample gene set enrichment analysis (ssGSEA) to investigate the association between predictive factors and the tumor immune microenvironment in high-risk and low-risk groups.

Results: Nine m7G-related LncRNAs (LINC01871, AP003469.4, Z68871.1, AC245297.3, EGOT, TFAP2A-AS1, AL136531.1, SEMA3B-AS1, AL606834.2) that are independently associated with the overall survival time (OS) of BC patients make up the signature we developed. For predicting 1-, 3-, and 5-year survival rates, the areas under the ROC curve (AUC) were 0.715, 0.724, and 0.726, respectively. The Kaplan-Meier analysis revealed that the prognosis of BC patients in the high-risk group was worse than that of those in the low-risk group. When compared to clinicopathological variables, multiple regression analysis demonstrated that risk score was a significant independent predictive factor for BC patients. The results of the ssGSEA study revealed a substantial correlation between the predictive traits and the BC patients’ immunological status, low-risk BC patients had more active immune systems, and they responded better to PD1/L1 immunotherapy.

Conclusion: The prognostic signature, which is based on m7G-related LncRNAs, can be utilized to inform patients’ customized treatment plans by independently predicting their prognosis and how well they would respond to immunotherapy.

Identification of a prognostic risk-scoring model and risk signatures based on glycosylation-associated cluster in breast cancer

Breast cancer is a heterogeneous disease whose subtypes represent different histological origins, prognoses, and therapeutic sensitivity. But there remains a strong need for more specific biomarkers and broader alternatives for personalized treatment. Our study classified breast cancer samples from The Cancer Genome Atlas (TCGA) into three groups based on glycosylation-associated genes and then identified differentially expressed genes under different glycosylation patterns to construct a prognostic model. The final prognostic model containing 23 key molecules achieved exciting performance both in the TCGA training set and testing set GSE42568 and GSE58812. The risk score also showed a significant difference in predicting overall clinical survival and immune infiltration analysis. This work helped us to understand the heterogeneity of breast cancer from another perspective and indicated that the identification of risk scores based on glycosylation patterns has potential clinical implications and immune-related value for breast cancer.