Identification of LncRNA Prognostic Signature Associated With Genomic Instability in Pancreatic Adenocarcinoma

A disulfidptosis-related lncRNA signature for predicting prognosis and evaluating the tumor immune microenvironment of lung adenocarcinoma

As a novel form of regulated cell death (RCD), disulfidptosis offering a significant opportunity in better understanding of tumor pathogenesis and therapeutic strategies. Long non-coding RNAs (lncRNAs) regulate the biology functions of tumor cells by engaging with a range of targets. However, the prognostic value of disulfidptosis-related lncRNAs (DRlncRNAs) in lung adenocarcinoma (LUAD) remains unclear. Therefore, our study aimed at establishing a prognostic model for LUAD patients based on DRlncRNAs. RNA-seq data and clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Subsequently, a prognostic model based on DRlncRNAs was constructed using LASSO and COX regression analysis. Patients were stratified into high- and low-risk groups based on their risk scores. Differences between the high-risk and low-risk groups were investigated in terms of overall survival (OS), functional enrichment, tumor immune microenvironment (TIME), somatic mutations, and drug sensitivity. Finally, the role of lncRNA GSEC in LUAD was validated through in vitro experiments. Using the prognostic model consists of 5 DRlncRNAs (AL365181.2, GSEC, AC093673.1, AC012615.1, AL606834.1), the low-risk group exhibited a markedly superior survival in comparison to the high-risk group. The significant differences were observed among patients from different risk groups in OS, immune cell infiltration, immune checkpoint expression, immunotherapy response, and mutation landscape. Experimental results from cellular studies demonstrate the knockdown of lncRNA GSEC leading to a significant reduction in the proliferation and migration abilities of LUAD cells. Our prognostic model, constructed using 5 DRlncRNAs, exhibited the capacity to independently predict the survival of LUAD patients, providing the potentially significant assistance in prognosis prediction, and treatment effects optimization. Moreover, our study established a foundation for further research on disulfidptosis in LUAD and proposed new perspectives for the treatment of LUAD.

Integrating bioinformatics and experimental validation to unveil disulfidptosis-related lncRNAs as prognostic biomarker and therapeutic target in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) stands as a prevalent malignancy globally, characterized by significant morbidity and mortality. Despite continuous advancements in the treatment of HCC, the prognosis of patients with this cancer remains unsatisfactory. This study aims at constructing a disulfidoptosis‑related long noncoding RNA (lncRNA) signature to probe the prognosis and personalized treatment of patients with HCC.

METHODS

The data of patients with HCC were extracted from The Cancer Genome Atlas (TCGA) databases. Univariate, multivariate, and least absolute selection operator Cox regression analyses were performed to build a disulfidptosis-related lncRNAs (DRLs) signature. Kaplan-Meier plots were used to evaluate the prognosis of the patients with HCC. Functional enrichment analysis was used to identify key DRLs-associated signaling pathways. Spearman's rank correlation was used to elucidate the association between the DRLs signature and immune microenvironment. The function of TMCC1-AS1 in HCC was validated in two HCC cell lines (HEP3B and HEPG2).

RESULTS

We identified 11 prognostic DRLs from the TCGA dataset, three of which were selected to construct the prognostic signature of DRLs. We found that the survival time of low-risk patients was considerably longer than that of high-risk patients. We further observed that the composition and the function of immune cell subpopulations were significantly different between high- and low-risk groups. Additionally, we identified that sorafenib, 5-Fluorouracil, and doxorubicin displayed better responses in the low-score group than those in the high-score group, based on IC50 values. Finally, we confirmed that inhibition of TMCC1-AS1 impeded the proliferation, migration, and invasion of hepatocellular carcinoma cells.

CONCLUSIONS

The DRL signatures have been shown to be a reliable prognostic and treatment response indicator in HCC patients. TMCC1-AS1 showed potential as a novel prognostic biomarker and therapeutic target for HCC.

An endoplasmic reticulum stress-related signature could robustly predict prognosis and closely associate with response to immunotherapy in pancreatic ductal adenocarcinoma

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors. Endoplasmic reticulum stress (ERS) plays an essential role in PDAC progression. Here, we aim to identify the ERS-related genes in PDAC and build reliable risk models for diagnosis, prognosis and immunotherapy response of PDAC patients as well as investigate the potential mechanism.

METHODS

We obtained PDAC cohorts with transcriptional profiles and clinical data from the ArrayExpress, The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Univariate Cox regression, LASSO regression and multivariate Cox regression analyses were used to construct an ERS-related prognostic signature. The CIBERSORT and ssGSEA algorithms were applied to explore the correlation between the prognostic signature and immune cell infiltration and immune-related pathways. The GDSC database and TIDE algorithm were used to predict responses to chemotherapy and immunotherapy, identifying potential drugs for treating patients with PDAC.

RESULTS

We established and validated an ERS-related prognostic signature comprising eight genes (HMOX1, TGFB1, JSRP1, GAPDH, CAV1, CHRNE, CD74 and ERN2). Patients with higher risk scores displayed worse outcomes than those with lower risk scores. PDAC patients in low-risk groups might benefit from immunotherapy. Dasatinib and lapatinib might have potential therapeutic implications in high-risk PDAC patients.

CONCLUSION

We established and validated an ERS-related prognostic signature comprising eight genes to predict the overall survival outcome of PDAC patients, which closely correlating with the response to immunotherapy and sensitivity to anti-tumor drugs, as well as could be beneficial for formulating clinical strategies and administering individualized treatments.

Pan-cancer analysis of Krüppel-like factor 3 and its carcinogenesis in pancreatic cancer

Background

Krüppel-like factor 3 (KLF3) is a key transcriptional repressor, which is involved in various biological functions such as lipogenesis, erythropoiesis, and B cell development, and has become one of the current research hotspots. However, the role of KLF3 in the pan-cancer and tumor microenvironment remains unclear.

Methods

TCGA and GTEx databases were used to evaluate the expression difference of KLF3 in pan-cancer and normal tissues. The cBioPortal database and the GSCALite platform analyzed the genetic variation and methylation modification of KLF3. The prognostic role of KLF3 in pan-cancer was identified using Cox regression and Kaplan-Meier analysis. Correlation analysis was used to explore the relationship between KLF3 expression and tumor mutation burden, microsatellite instability, and immune-related genes. The relationship between KLF3 expression and tumor immune microenvironment was calculated by ESTIMATE, EPIC, and MCPCOUNTER algorithms. TISCH and CancerSEA databases analyzed the expression distribution and function of KLF3 in the tumor microenvironment. TIDE, GDSC, and CTRP databases evaluated KLF3-predicted immunotherapy response and sensitivity to small molecule drugs. Finally, we analyzed the role of KLF3 in pancreatic cancer by in vivo and in vitro experiments.

Results

KLF3 was abnormally expressed in a variety of tumors, which could effectively predict the prognosis of patients, and it was most obvious in pancreatic cancer. Further experiments verified that silencing KLF3 expression inhibited pancreatic cancer progression. Functional analysis and gene set enrichment analysis found that KLF3 was involved in various immune-related pathways and tumor progression-related pathways. In addition, based on single-cell sequencing analysis, it was found that KLF3 was mainly expressed in CD4Tconv, CD8T, monocytes/macrophages, endothelial cells, and malignant cells in most of the tumor microenvironment. Finally, we assessed the value of KLF3 in predicting response to immunotherapy and predicted a series of sensitive drugs targeting KLF3.

Conclusion

The role of KLF3 in the tumor microenvironment of various types of tumors cannot be underestimated, and it has significant potential as a biomarker for predicting the response to immunotherapy. In particular, it plays an important role in the progression of pancreatic cancer.

Establishment and validation of a ubiquitination-related gene signature associated with prognosis in pancreatic duct adenocarcinoma

Background

Patients with pancreatic duct adenocarcinoma (PDAC) have varied prognoses that depend on numerous variables. However, additional research is required to uncover the latent impact of ubiquitination-related genes (URGs) on determining PDAC patients' prognoses.

Methods

The URGs clusters were discovered via consensus clustering, and the prognostic differentially expressed genes (DEGs) across clusters were utilized to develop a signature using a least absolute shrinkage and selection operator (LASSO) regression analysis of data from TCGA-PAAD. Verification analyses were conducted across TCGA-PAAD, GSE57495 and ICGC-PACA-AU to show the robustness of the signature. RT-qPCR was used to verify the expression of risk genes. Lastly, we formulated a nomogram to improve the clinical efficacy of our predictive tool.

Results

The URGs signature, comprised of three genes, was developed and was shown to be highly correlated with the prognoses of PAAD patients. The nomogram was established by combining the URGs signature with clinicopathological characteristics. We discovered that the URGs signature was remarkably superior than other individual predictors (age, grade, T stage, et al). Also, the immune microenvironment analysis indicated that ESTIMATEscore, ImmuneScores, and StromalScores were elevated in the low-risk group. The immune cells that infiltrated the tissues were different between the two groups, as did the expression of immune-related genes.

Conclusion

The URGs signature could act as the biomarker of prognosis and selecting appropriate therapeutic drugs for PDAC patients.

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.

Construction and comprehensive analysis of a novel prognostic signature associated with pyroptosis molecular subtypes in patients with pancreatic adenocarcinoma

Background

Treatment of cancer with pyroptosis is an emerging strategy. Molecular subtypes based on pyroptosis-related genes(PRGs) seem to be considered more conducive to individualized therapy. It is meaningful to construct a pyroptosis molecular subtypes-related prognostic signature (PMSRPS) to predict the overall survival (OS) of patients with pancreatic adenocarcinoma(PAAD) and guide treatment.

Methods

Based on the transcriptome data of 23 PRGs, consensus clustering was applied to divide the TCGA and GSE102238 combined cohort into three PRGclusters. Prognosis-related differentially expressed genes(DEGs) among PRGclusters were subjected to LASSO Cox regression analysis to determine a PMSRPS. External cohort and in vitro experiments were conducted to verify this PMSRPS. The CIBERSORT algorithm, the ESTIMATE algorithm and the Immunophenoscore (IPS) were used to analyze the infiltrating abundance of immune cells, the tumor microenvironment (TME), and the response to immunotherapy, respectively. Wilcoxon analysis was used to compare tumor mutational burden (TMB) and RNA stemness scores (RNAss) between groups. RT-qPCR and in vitro functional experiments were used for evaluating the expression and function of SFTA2.

Results

Based on three PRGclusters, 828 DEGs were obtained and a PMSRPS was subsequently constructed. In internal and external validation, patients in the high-risk group had significantly lower OS than those in the low-risk group and PMSRPS was confirmed to be an independent prognostic risk factor for patients with PAAD with good predictive performance. Immune cell infiltration abundance and TME scores indicate patients in the high-risk group have typical immunosuppressive microenvironment characteristics. Analysis of IPS suggests patients in the high-risk group responded better to novel immune checkpoint inhibitors (ICIs) than PD1/CTLA4. The high-risk group had higher TMB and RNAss. In addition, 10 potential small-molecule compounds were screened out. Finally, we found that the mRNA expression of SFTA2 gene with the highest risk coefficient in PMSRPS was significantly higher in PAAD than in paracancerous tissues, and knockdown of it significantly delayed the progression of PAAD.

Conclusions

PMSRPS can well predict the prognosis, TME and immunotherapy response of patients with PAAD, identify potential drugs, and provide treatment guidance based on individual needs.

Construction of a cancer-associated fibroblasts-related long non-coding RNA signature to predict prognosis and immune landscape in pancreatic adenocarcinoma

Background: Cancer-associated fibroblasts (CAFs) are an essential cell population in the pancreatic cancer tumor microenvironment and are extensively involved in drug resistance and immune evasion mechanisms. Long non-coding RNAs (lncRNAs) are involved in pancreatic cancer evolution and regulate the biological behavior mediated by CAFs. However, there is a lack of understanding of the prognostic signatures of CAFs-associated lncRNAs in pancreatic cancer patients.

Methods: Transcriptomic and clinical data for pancreatic adenocarcinoma (PAAD) and the corresponding mutation data were obtained from The Cancer Genome Atlas database. lncRNAs associated with CAFs were obtained using co-expression analysis. lncRNAs were screened by Cox regression analysis using least absolute shrinkage and selection operator (LASSO) algorithm for constructing predictive signature. According to the prognostic model, PAAD patients were divided into high-risk and low-risk groups. Kaplan-Meier analysis was used for survival validation of the model in the training and validation groups. Clinicopathological parameter correlation analysis, univariate and multivariate Cox regression, time-dependent receiver operating characteristic (ROC) curves, and nomogram were performed to evaluate the model. The gene set variation analysis (GSVA) and gene ontology (GO) analyses were used to explore differences in the biological behavior of the risk groups. Furthermore, single-sample gene set enrichment analysis (ssGSEA), tumor mutation burden (TMB), ESTIMATE algorithm, and a series of immune correlation analyses were performed to investigate the relationship between predictive signature and the tumor immune microenvironment and screen for potential responders to immune checkpoint inhibitors. Finally, drug sensitivity analyses were used to explore potentially effective drugs in high- and low-risk groups.

Results: The signature was constructed with seven CAFs-related lncRNAs (AP005233.2, AC090114.2, DCST1-AS1, AC092171.5, AC002401.4, AC025048.4, and CASC8) that independently predicted the prognosis of PAAD patients. Additionally, the high-risk group of the model had higher TMB levels than the low-risk group. Immune correlation analysis showed that most immune cells, including CD8⁺ T cells, were negatively correlated with the model risk scores. ssGSEA and ESTIMATE analyses further indicated that the low-risk group had a higher status of immune cell infiltration. Meanwhile, the mRNA of most immune checkpoint genes, including PD1 and CTLA4, were highly expressed in the low-risk group, suggesting that this population may be “hot immune tumors” and have a higher sensitivity to immune checkpoint inhibitors (ICIs). Finally, the predicted half-maximal inhibitory concentrations of some chemical and targeted drugs differ between high- and low-risk groups, providing a basis for treatment selection.

Conclusion: Our findings provide promising insights into lncRNAs associated with CAFs in PAAD and provide a personalized tool for predicting patient prognosis and immune microenvironmental landscape.

FAT10 promotes chemotherapeutic resistance in pancreatic cancer by inducing epithelial-mesenchymal transition via stabilization of FOXM1 expression

Pancreatic cancer (PC) is one of the deadliest malignant tumors, and its resistance to gemcitabine chemotherapy is the primary reason for poor prognosis in patients. Ubiquitin-like protein FAT10 has recently been reported to promote tumor chemotherapy resistance. In this study, the expression of FAT10 in PC was significantly higher than that in adjacent noncancerous tissues. Increased expression of FAT10 in PC was related to a late TNM stage and decreased overall survival. Functional experiments revealed that downregulating the expression of FAT10 inhibits the proliferation and epithelial-mesenchymal transition (EMT) of PC cells, promotes the apoptosis of PC cells, and enhances sensitivity to gemcitabine chemotherapy. In addition, upregulation of FAT10 increased the expression of FOXM1 protein. The effect of downregulating FAT10 was reversed by FOXM1 overexpression, and FOXM1 knockdown inhibited EMT driven by FAT10 overexpression. Mechanistically, FAT10 stabilized the expression of FOXM1 by competing with ubiquitin to bind FOXM1 and inhibiting the ubiquitination-mediated degradation of FOXM1. In conclusion, the FAT10-FOXM1 axis is a pivotal driver of PC proliferation and gemcitabine resistance, and the results provide novel insights into chemotherapy resistance in PC.