Identification of an autophagy-related gene signature for predicting prognosis and immune activity in pancreatic adenocarcinoma

Nilotinib in combination with sunitinib renders MCL-1 for degradation and activates autophagy that overcomes sunitinib resistance in renal cell carcinoma

PURPOSE

Sunitinib is a recommended drug for metastatic renal cell carcinoma (RCC). However, the therapeutic potential of sunitinib is impaired by toxicity and resistance. Therefore, we seek to explore a combinatorial strategy to improve sunitinib efficacy of low-toxicity dose for better clinical application.

METHODS

We screen synergistic reagents of sunitinib from a compound library containing 1374 FDA-approved drugs by in vitro cell viability evaluation. The synergistically antiproliferative and proapoptotic effects were demonstrated on in vitro and in vivo models. The molecular mechanism was investigated by phosphoproteomics, co-immunoprecipitation, immunofluorescence and western-blot assays, etc. RESULTS: From the four-step screening, nilotinib stood out as a potential synergistic killer combined with sunitinib. Subsequent functional evaluation demonstrated that nilotinib and sunitinib synergistically inhibit RCC cell proliferation and promote apoptosis in vitro and in vivo. Mechanistically, nilotinib activates E3-ligase HUWE1 and in combination with sunitinib renders MCL-1 for degradation via proteasome pathway, resulting in the release of Beclin-1 from MCL-1/Beclin-1 complex. Subsequently, Beclin-1 induces complete autophagy flux to promote antitumor effect.

CONCLUSION

Our findings revealed that a novel mechanism that nilotinib in combination with sunitinib overcomes sunitinib resistance in RCC. Therefore, this novel rational combination regimen provides a promising therapeutic avenue for metastatic RCC and rationale for evaluating this combination clinically.

Apolipoprotein L1 is a tumor suppressor in clear cell renal cell carcinoma metastasis

The 5-year survival rate of kidney cancer drops dramatically from 93% to 15% when it is metastatic. Metastasis constitutes for 30% of kidney cancer cases, in which clear cell renal cell carcinoma (ccRCC) is the most prominent subtype. By sequencing mRNA of ccRCC patient samples, we found that apolipoprotein L1 (APOL1) was highly expressed in tumors compared to their adjacent normal tissues. This gene has been previously identified in a large body of kidney disease research and was reported as a potential prognosis marker in many types of cancers. However, the molecular function of APOL1 in ccRCC, especially in metastasis, remained unknown. In this study, we modulated the expression of APOL1 in various renal cancer cell lines and analyzed their proliferative, migratory, and invasive properties. Strikingly, APOL1 overexpression suppressed ccRCC metastasis both in vitro and in vivo. We then explored the mechanism by which APOL1 alleviated ccRCC malignant progression by investigating its downstream pathways. APOL1 overexpression diminished the activity of focal adhesive molecules, Akt signaling pathways, and EMT processes. Furthermore, in the upstream, we discovered that miR-30a-3p could inhibit APOL1 expression. In conclusion, our study revealed that APOL1 play a role as a tumor suppressor in ccRCC and inhibit metastasis, which may provide novel potential therapeutic approaches for ccRCC patients.

Novel prognostic gene signature for pancreatic ductal adenocarcinoma based on hypoxia

BACKGROUND

Currently, there is lack of marker to accurately assess the prognosis of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC). This study aims to establish a hypoxia-related risk scoring model that can effectively predict the prognosis and chemotherapy outcomes of PDAC patients.

METHODS

Using unsupervised consensus clustering algorithms, we comprehensively analyzed The Cancer Genome Atlas (TCGA) data to identify two distinct hypoxia clusters and used the weighted gene co-expression network analysis (WGCNA) to examine gene sets significantly associated with these hypoxia clusters. Then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were used to construct a signature and its efficacy was evaluated using the International Cancer Genome Consortium (ICGC) PDAC cohort. Further, the correlation between the risk scores obtained from the signature and carious clinical, pathological, immunophenotype, and immunoinfiltration factors as well as the differences in immunotherapy potential and response to common chemotherapy drugs between high-risk and low-risk groups were evaluated.

RESULTS

From a total of 8 significantly related modules and 4423 genes, 5 hypoxia-related signature genes were identified to construct a risk model. Further analysis revealed that the overall survival rate (OS) of patients in the low-risk group was significantly higher than the high-risk group. Univariate and multivariate Cox regression analysis showed that the risk scoring signature was an independent factor for prognosis prediction. Analysis of immunocyte infiltration and immunophenotype showed that the immune score and the anticancer immune response in the high-risk were significantly lower than that in the low-risk group.

CONCLUSION

The constructed hypoxia-associated prognostic signature demonstrated could be used as a potential risk classifier for PDAC.

Gene signature developed for predicting early relapse and survival in early-stage pancreatic cancer

BACKGROUND

The aim of this study was to construct a predictive signature integrating tumour-mutation- and copy-number-variation-associated features using machine learning to precisely predict early relapse and survival in patients with resected stage I-II pancreatic ductal adenocarcinoma.

METHODS

Patients with microscopically confirmed stage I-II pancreatic ductal adenocarcinoma undergoing R0 resection at the Chinese PLA General Hospital between March 2015 and December 2016 were enrolled. Whole exosome sequencing was performed, and genes with different mutation or copy number variation statuses between patients with and without relapse within 1 year were identified using bioinformatics analysis. A support vector machine was used to evaluate the importance of the differential gene features and to develop a signature. Signature validation was performed in an independent cohort. The associations of the support vector machine signature and single gene features with disease-free survival and overall survival were assessed. Biological functions of integrated genes were further analysed.

RESULTS

Overall, 30 and 40 patients were included in the training and validation cohorts, respectively. Some 11 genes with differential patterns were first identified; using a support vector machine, four features (mutations of DNAH9, TP53, and TUBGCP6, and copy number variation of TMEM132E) were further selected and integrated to construct a predictive signature (the support vector machine classifier). In the training cohort, the 1-year disease-free survival rates were 88 per cent (95 per cent c.i. 73 to 100) and 7 per cent (95 per cent c.i. 1 to 47) in the low-support vector machine subgroup and the high-support vector machine subgroup respectively (P < 0.001). Multivariable analyses showed that high support vector machine was significantly and independently associated with both worse overall survival (HR 29.20 (95 per cent c.i. 4.48 to 190.21); P < 0.001) and disease-free survival (HR 72.04 (95 per cent c.i. 6.74 to 769.96); P < 0.001). The area under the curve of the support vector machine signature for 1-year disease-free survival (0.900) was significantly larger than the area under the curve values of the mutations of DNAH9 (0.733; P = 0.039), TP53 (0.767; P = 0.024), and TUBGCP6 (0.733; P = 0.023), the copy number variation of TMEM132E (0.700; P = 0.014), TNM stage (0.567; P = 0.002), and differentiation grade (0.633; P = 0.005), suggesting higher predictive accuracy for prognosis. The value of the signature was further validated in the validation cohort. The four genes included in the support vector machine signature (DNAH9, TUBGCP6, and TMEM132E were novel in pancreatic ductal adenocarcinoma) were significantly associated with the tumour immune microenvironment, G protein-coupled receptor binding and signalling, cell-cell adhesion, etc.

CONCLUSION

The newly constructed support vector machine signature precisely and powerfully predicted relapse and survival in patients with stage I-II pancreatic ductal adenocarcinoma after R0 resection.

Cuproptosis-related gene signature correlates with the tumor immune features and predicts the prognosis of early-stage lung adenocarcinoma patients

Background: Although a majority of early-stage lung adenocarcinoma (es-LUAD) patients have a favorable prognosis, there are still some cases with a risk of recurrence and metastasis. Cuproptosis is a new form of death that differs from other programmed cell death. However, no study has been reported for setting a prognostic model of es-LUAD using cuproptosis pattern-related genes.

Methods: Using multiple R packages, the data from the GEO database was processed, and es-LUAD patients was classified into two patterns based on cuproptosis-related genes. Key differentially expressed genes (DEGs) in the two patterns were screened to construct a prognostic signature to assess differences in biological processes and immunotherapy responses in es-LUAD. Tumor microenvironment (TME) in es-LUAD was analyzed using algorithms such as TIMER and ssGSEA. Then, a more accurate nomogram was constructed by combining risk scores with clinical factors.

Results: Functional enrichment analysis revealed that DEGs in two patterns were correlated with organelle fission, nuclear division, chromosome segregation, and cycle-related pathways. Univariate Cox regression and Lasso-Cox regression analyses identified six prognostic genes: ASPM, CCNB2, CDC45, CHEK1, NCAPG, and SPAG5. Based on the constructed model, we found that the high-risk group patients had higher expression of immune checkpoints (CTLA4, LAG3, PD-L1, TIGIT and TIM3), and a lower abundance of immune cells. Lastly, the nomogram was highly accurate in predicting the 1-, 3-, and 5-year survival status of patients with es-LUAD based on risk scores and clinical factors.

Conclusion: The cuproptosis pattern-related signature can serve as a potential marker for clinical decision-making. It has huge potential in the future to guide the frequency of follow-up and adjuvant therapy for es-LUAD patients.

Cuproptosis-related gene index: A predictor for pancreatic cancer prognosis, immunotherapy efficacy, and chemosensitivity

Aim

The term "Cuproptosis" was coined to describe a novel type of cell death triggered by intracellular copper buildup that is fundamentally distinct from other recognized types such as autophagy, ferroptosis, and pyroptosis in recent days. As the underlying mechanism was newly identified, its potential connection to pancreatic adenocarcinoma (PAAD) is still an open issue.

Methods

A set of machine learning algorithms was used to develop a Cuproptosis-related gene index (CRGI). Its immunological characteristics were studied by exploring its implications on the expression of the immunological checkpoints, prospective immunotherapy responses, etc. Moreover, the sensitivity to chemotherapeutic drugs was predicted. Unsupervised consensus clustering was performed to more precisely identify different CRGI-based molecular subtypes and investigate the immunotherapy and chemotherapy efficacy. The expression of DLAT, LIPT1 and LIAS were also investigated, through real-time quantitative polymerase chain reaction (RT-qPCR), western blot, and immunofluorescence staining (IFS).

Results

A novel CRGI was identified and validated. Additionally, correlation analysis revealed major changes in tumor immunology across the high- and low-CRGI groups. Through an in-depth study of each medication, it was determined that the predictive chemotherapeutic efficacy of 32 regularly used anticancer drugs differed between high- and low-CRGI groups. The results of the molecular subtyping provided more support for such theories. Expressional assays performed at transcriptomic and proteomic levels suggested that the aforementioned Cuproptosis-related genes might serve as reliable diagnostic biomarkers in PAAD.

Significance

This is, to the best of our knowledge, the first study to examine prognostic prediction in PAAD from the standpoint of Cuproptosis. These findings may benefit future immunotherapy and chemotherapeutic therapies.

Autophagy-Related Gene Signature Highlights Metabolic and Immunogenic Status of Malignant Cells in Non-Small Cell Lung Cancer Adenocarcinoma

Simple Summary

The role of autophagy in lung cancers is still controversial, mainly because the visualization of autophagy levels in patients remains challenging. One interesting approach consists of studying autophagy at the transcriptomic level. In this line, many transcriptomics analyses performed on autophagy genes focused on the discovery of new biomarkers to predict the efficiency of antitumor therapies. However, the majority of these studies were based on global transcriptomic analysis of the whole tumor microenvironment, and few investigations have been performed on malignant cells themselves. The goal of this study was not to determine another new predictive signature based on autophagy-related genes. Instead, we investigated the expression of autophagy genes to understand the involvement of this process in lung cancer homeostasis. Specifically, we discovered a new autophagy signature that correlates with the metabolic and immunogenic status of malignant cells, supporting the relationship between autophagy and tumor growth in lung cancer patients.

Abstract

Autophagy is a self-degradative mechanism involved in many biological processes, including cell death, survival, proliferation or migration. In tumors, autophagy plays an important role in tumorigenesis as well as cancer progression and resistance to therapies. Usually, a high level of autophagy in malignant cells has been associated with tumor progression and poor prognostic for patients. However, the investigation of autophagy levels in patients remains difficult, especially because quantification of autophagy proteins is challenging in the tumor microenvironment. In this study, we analyzed the expression of autophagy genes in non-small cell lung (NSCLC) cancer patients using public datasets and revealed an autophagy gene signature for proliferative and immune-checkpoint-expressed malignant cells in lung adenocarcinoma (LUAD). Analysis of autophagy-related gene expression profiles in tumor and adjacent tissues revealed differential signatures, namely signature A (23 genes) and signature B (12 genes). Signature B correlated with a bad prognosis and poor overall and disease-specific survival. Univariate and multivariate analyses revealed that this signature was an independent factor for prognosis. Moreover, patients with high expression of signature B exhibited more genes related to proliferation and fewer genes related to immune cells or immune response. The analysis of datasets from sorted fresh tumor cells or single cells revealed that signature B is predominantly represented in malignant cells, with poor expression in pan-immune population or in fibroblast or endothelial cells. Interestingly, autophagy was increased in malignant cells exhibiting high levels of signature B, which correlated with an elevated expression of genes involved in cell proliferation and immune checkpoint signaling. Taken together, our analysis reveals a novel autophagy-based signature to define the metabolic and immunogenic status of malignant cells in LUAD.