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

The combination of docetaxel, cisplatin, and S-1 (DCS) is a common chemotherapy regimen for patients with gastric cancer (GC). However, studies on long noncoding RNAs (lncRNAs) associated with the chemotherapeutic response to and prognosis after DCS remain lacking. The aim of the present study was to identify DCS mRNAs-lncRNAs associated with chemotherapy response and prognosis in GC patients. In the present study, we identified 548 lncRNAs associated with these 16 mRNAs in the TCGA and {"type":"entrez-geo","attrs":{"text":"GSE31811","term_id":"31811"}}GSE31811 datasets. Eleven lncRNAs were used to construct a prognostic signature by least absolute shrinkage and selection operator (LASSO) regression. A model including the 11 lncRNAs (LINC02532, {"type":"entrez-nucleotide","attrs":{"text":"AC007277.1","term_id":"4580503","term_text":"AC007277.1"}}AC007277.1, AC005324.4, {"type":"entrez-nucleotide","attrs":{"text":"AL512506.1","term_id":"12044702","term_text":"AL512506.1"}}AL512506.1, {"type":"entrez-nucleotide","attrs":{"text":"AC068790.7","term_id":"8571647","term_text":"AC068790.7"}}AC068790.7, {"type":"entrez-nucleotide","attrs":{"text":"AC022509.2","term_id":"6938434","term_text":"AC022509.2"}}AC022509.2, {"type":"entrez-nucleotide","attrs":{"text":"AC113139.1","term_id":"18875234","term_text":"AC113139.1"}}AC113139.1, LINC00106, {"type":"entrez-nucleotide","attrs":{"text":"AC005165.1","term_id":"3242748","term_text":"AC005165.1"}}AC005165.1, MIR100HG, and UBE2R2-AS1) associated with the prognosis of GC was constructed. The signature was validated in the TCGA database, model comparison, and qRT-PCR experiments. The results showed that the risk signature was a more effective prognostic factor for GC patients. Furthermore, the results showed that this model can well predicting chemotherapy drug response and immune infiltration of GC patients. In addition, our experimental results indicated that lower expression levels of LINC00106 and UBE2R2-AS1 predicted worse drug resistance in AGS/DDP cells. The experimental results agreed with the predictions. Furthermore, knockdown of LINC00106 or UBE2R2-AS1 can significantly enhanced the proliferation and migration of GC AGS cells in vitro. In conclusion, a novel DCS therapy-related lncRNA signature may become a new strategy to predict chemotherapy response and prognosis in GC patients. LINC00106 and UBE2R2-AS1 may exhibit a tumor suppressive function in GC.     

A prognostic eight-lncRNA expression signature in predicting recurrence of ER-positive breast cancer receiving endocrine therapy

Long noncoding RNAs (lncRNAs) have the main role in the tumorigenesis of breast cancer. In the present study, lncRNA expression profiling was collected to identify a lncRNA expression signature from the Gene Expression Omnibus database. An eight-lncRNA signature was established to predict the survival of patients with estrogen receptor (ER)-positive breast cancer receiving endocrine therapy. Patients were separated into a low-risk group and a high-risk group based on this signature. Patients in high-risk group have worse survival compared to those in low-risk group using Kaplan–Meier curve analysis with log-rank test. Receiver operating characteristic analysis suggested good diagnostic efficiency of the eight-lncRNA signature. When adjusting the clinical features, including age, grade, lymph node status, and tumor size, this signature was independently associated with the relapse-free survival. The prognostic value of the lncRNA prognostic model was then validated in validation sets. When validated in a cohort of patients treated with neoadjuvant chemotherapy and endocrine therapy, this signature demonstrated good performance as well. Besides, we have built a nomogram that integrated the conventional clinicopathological features and the eight-lncRNA-based signature. To sum up, our results indicated that the eight-lncRNA prognostic model was a reliable tool to group patients at high and low risk of disease relapse. This signature may have possible implication in prognostic evaluations of patients with ER-positive breast cancer receiving endocrine therapy.     

Development and validation of a novel epigenetic signature for predicting prognosis in colon cancer

Epigenetic factors play a critical role in carcinogenesis by imparting a distinct feature to the chromatin architecture. The present study aimed to develop a novel epigenetic signature for evaluating the relapse-free survival of colon cancer patients. Public microarray datasets were acquired from the Gene Expression Omnibus databases: GSE39582, GSE17538, GSE33113, and GSE37892 set. Patients from GSE39582 set were randomized 1:1 into training and internal validation series. Patients were divided into high-risk and low-risk groups in training series based on a set of 11 epigenetic factors (p < .001). The good reproducibility for the prognostic value of the epigenetic signature was confirmed in the internal validation series (p < .001), external validation series (a combination of GSE17538 set, GSE33113 set, and GSE37892 set; p = .018), and entire series (p < .001). Furthermore, a nomogram, which integrated the epigenetic signature, pathological stage, and postoperative chemotherapy, was developed based on the GSE39582 set. The time-dependent receiver operating characteristic curve at 1 year demonstrated that the comprehensive signature presented superior prognostic value than the pathological stage. In conclusion, an epigenetic signature, which could be utilized to divide colon cancer patients into two groups with significantly different risk of relapse, was established. This biomarker would aid in identifying patients who require an intensive follow-up and aggressive therapeutic intervention.     

A prognostic eight-gene expression signature for patients with breast cancer receiving adjuvant chemotherapy

Breast cancer is a popularly diagnosed malignant tumor. Genomic profiling studies suggest that breast cancer is a disease with heterogeneity. Chemotherapy is one of the chief means to treat breast cancer, while its responses and clinical outcomes vary largely due to the conventional clinicopathological factors and inherent chemosensitivity of breast cancer. Using the least absolute shrinkage and selection operator (LASSO) Cox regression model, our study established a multi-mRNA-based signature model and constructed a relative nomogram in predicting distant-recurrence-free survival for patients receiving surgery and following chemotherapy. We constructed a signature of eight mRNAs (IPCEF1, SYNDIG1, TIGIT, SPESP1, C2CD4A, CLCA2, RLN2, and CCL19) with the LASSO model, which was employed to separate subjects into groups with high- and low-risk scores. Obvious differences of distant-recurrence-free survival were found between these two groups. This eight-mRNA-based signature was independently associated with the prognosis and had better prognostic value than classical clinicopathologic factors according to multivariate Cox regression results. Receiver operating characteristic results demonstrated excellent performance in diagnosing 3-year distant-recurrence by the eight-mRNA signature. A nomogram that combined both the eight-mRNA-based signature and clinicopathological risk factors was constructed. Comparing with an ideal model, the nomograms worked well both in the training and validation sets. Through the results that the eight-mRNA signature effectively classified patients into low- and high-risk of distant recurrence, we concluded that this eight-mRNA-based signature played a promising predictive role in prognosis and could be clinically applied in breast cancer patients receiving adjuvant chemotherapy.     

Adoptive transfer of natural killer cells in combination with chemotherapy improves outcomes of patients with locally advanced colon carcinoma

Background

Despite the availability of multiple treatment strategies, patients with advanced colon carcinoma (CC) have poor prognoses. The aim of this study was to evaluate the efficacy and safety of natural killer (NK) cell therapy in combination with chemotherapy in patients with locally advanced CC.

Construction of an immune-related LncRNA signature with prognostic significance for bladder cancer

Bladder cancer (BLCA) is one of the most common urological cancer with increasing cases and deaths every year. In the present study, we aim to construct an immune-related prognostic lncRNA signature (IRPLS) in bladder cancer (BLCA) patients and explore its immunogenomic implications in pan-cancers. First, the immune-related differentially expressed lncRNAs (IRDELs) were identified by ‘limma’ R package and the score of IRPLS in every patient were evaluated by Cox regression. The dysregulation of IRDELs expression between cancer and para-cancer normal tissues was validated through RT-qPCR. Then, we further explore the biological functions of a novel lncRNA from IRPLS, RP11-89 in BLCA using CCK8 assay, Transwell assay and Apoptosis analysis, which indicated that RP11-89 was able to promote cell proliferation and invasive capacity while inhibits cell apoptosis in BLCA. In addition, we performed bioinformatic methods and RIP to investigate and validate the RP11-89/miR-27a-3p/PPARγ pathway in order to explore the mechanism. Next, CIBERSORT and ESTIMATE algorithm were used to evaluate abundance of tumour-infiltrating immune cells and scores of tumour environment elements in BLCA with different level of IRPLS risk scores. Finally, multiple bioinformatic methods were performed to show us the immune landscape of these four lncRNAs for pan-cancers. In conclusion, this study first constructed an immune-related prognostic lncRNA signature, which consists of RP11-89, PSORS1C3, LINC02672 and MIR100HG and might shed lights on novel targets for individualized immunotherapy for BLCA patients.     

A robust immune-related lncRNA signature for the prognosis of human colorectal cancer

Background: Colorectal cancer (CRC) is one of the most prevalent malignant cancers worldwide. Immune-related long non-coding RNAs (IRlncRNAs) are proved to be essential in the development and progression of carcinoma. The purpose of the present study was to develop and validate a prognostic IRlncRNA signature for CRC patients.Methods: Gene expression profiles of CRC samples were downloaded from The Cancer Genome Atlas (TCGA) database. Immune-related genes were obtained from the ImmPort database and were used to identify IRlncRNA by correlation analysis. Through LASSO Cox regression analyses, a prognostic signature was constructed. Functional enrichment analysis was performed by gene set enrichment analysis (GSEA). TIMER2.0 web server and tumor immune dysfunction and exclusion (TIDE) algorithm were employed to analyze the association between our model and tumor-infiltrating immune cells and immunotherapy response. The expression levels of IRlncRNAs in cell lines were detected by quantitative real-time PCR (qPCR).Results: A 9-IRlncRNA signature was developed by a LASSO Cox proportional regression model. Based on the signature, CRC patients were divided into high- and low-risk groups with different prognoses. GSEA results indicated that patients in high-risk group were associated with cancer-related pathways. In addition, patients in low-risk group were found to have more infiltration of anti-tumor immune cells and might show a favorable response to immunotherapy. Finally, the result of qPCR revealed that most IRlncRNAs were differently expressed between normal and tumor cell lines.Conclusion: The constructed 9-IRlncRNA signature has potential to predict the prognosis of CRC patients and may be helpful to guide personalized immunotherapy.     

Identification of an m6A-related lncRNA prognostic signature in colorectal cancer

Data sets of colorectal cancer (CRC) were obtained from The Cancer Genome Atlas (TCGA), three N6-methyladenosine (m6A) subtypes were identified using 21 m6A-related long noncoding RNAs (lncRNAs) and differential m6A subtypes of different CRC tumors were determined in this study to evaluate the m6A expression and the prognosis of patients with CRC. Subsequently, eight key lncRNAs were identified based on co-expression with 21 m6A-related genes in CRC tumors using the single-factor Cox and least absolute shrinkage and selection operator. Finally, an m6A-related lncRNA risk score model of CRC tumor was established using multifactor Cox regression based on the eight important lncRNAs and found to have a better performance in evaluating the prognosis of patients in the TCGA-CRC data set. TCGA-CRC tumor samples were divided based on the risk scores: high and low. Then, the clinical characteristics, tumor mutation load, and tumor immune cell infiltration difference between the high- and low-risk-score groups were explored, and the predictive ability of the risk score was assessed for immunotherapeutic benefits. We found that the risk score model can determine the overall survival, be a relatively independent prognostic indicator, and better evaluate the immunotherapeutic benefits for patients with CRC. This study provides data support for accurate immunotherapy in CRC.     

Circulating lncRNA UCA1 and lncRNA PGM5-AS1 act as potential diagnostic biomarkers for early-stage colorectal cancer

Background: Colorectal cancer (CRC) is one of the most common and significant malignant diseases worldwide. In the present study, we evaluated two long non-coding RNAs (lncRNAs) in CRC patients as diagnostic markers for early-stage CRC.Methods: Using Gene Expression Omnibus (GEO) datasets {"type":"entrez-geo","attrs":{"text":"GSE102340","term_id":"102340"}}GSE102340, {"type":"entrez-geo","attrs":{"text":"GSE126092","term_id":"126092"}}GSE126092, {"type":"entrez-geo","attrs":{"text":"GSE109454","term_id":"109454"}}GSE109454 and {"type":"entrez-geo","attrs":{"text":"GSE115856","term_id":"115856"}}GSE115856, 14 differentially expressed lncRNAs were identified between cancer and adjacent tissues, among which, the two most differentially expressed were confirmed using quantitative real-time polymerase chain reaction (qRT-PCR) in 200 healthy controls and 188 CRC patients. A receiver operating characteristic (ROC) analysis was employed to evaluate the diagnostic accuracy for CRC.Results: From four GEO datasets, three up-regulated and eleven down-regulated lncRNAs were identified in CRC tissues, among which, lncRNA urothelial carcinoma-associated 1 (UCA1) and lncRNA phosphoglucomutase 5-antisense RNA 1 (PGM5-AS1) were the most significantly up- and down-regulated lncRNAs in CRC patient plasma, respectively. The area under the ROC curve was calculated to be 0.766, 0.754 and 0.798 for UCA1, PGM5-AS1 and the combination of these two lncRNAs, respectively. Moreover, the diagnostic potential of these two lncRNAs was even higher for the early stages of CRC. The combination of UCA1 and PGM5-AS1 enhanced the AUC to 0.832, and when the lncRNAs were used with carcinoembryonic antigen (CEA), the AUC was further improved to 0.874.Conclusion: In the present study, we identified two lncRNAs, UCA1 and PGM5-AS1, in CRC patients’ plasma, which have the potential to be used as diagnostic biomarkers of CRC.     

The optimal dose of CAF regimen in adjuvant chemotherapy for breast cancer patients at stage IIB

This paper deals with the idea of balancing drug effects on tumor and normal cell populations based on a variety of criteria, which is evaluated by the oncologist for breast cancer patients at stage IIB. In this paper, the optimal controller represents the optimal drug dosage of CAF (Cyclophosphamide, Adriamycin and Fluorouracil) regimen in adjuvant chemotherapy after surgery for these patients. We determined the doses of CAF regimen by minimizing a cost function with some constraints. The cost function includes the cancer cell and the normal cell growth dynamics with prescribed weighting coefficients for each patient. The physician determines these weighting coefficients based on some individual parameters. The optimal treatment schedules are computed based on a trade-off between the cancer cell reduction and the normal cell preserving. Numerical simulations are given to illustrate the accuracy of the optimal controller.     

Identification of cancer hallmark‐associated gene and lncRNA cooperative regulation pairs and dictate lncRNA roles in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is the most common malignant tumour in the oral and maxillofacial region. Numerous cancers share ten common traits (“hallmarks”) that govern the transformation of normal cells into cancer cells. Long non‐coding RNAs (lncRNAs) are important factors that contribute to tumorigenesis. However, very little is known about the cooperative relationships between lncRNAs and cancer hallmark‐associated genes in OSCC. Through integrative analysis of cancer hallmarks, somatic mutations, copy number variants (CNVs) and expression, some OSCC‐specific cancer hallmark‐associated genes and lncRNAs are identified. A computational framework to identify gene and lncRNA cooperative regulation pairs (GLCRPs) associated with different cancer hallmarks is developed based on the co‐expression and co‐occurrence of mutations. The distinct and common features of ten cancer hallmarks based on GLCRPs are characterized in OSCC. Cancer hallmark insensitivity to antigrowth signals and self‐sufficiency in growth signals are shared by most GLCRPs in OSCC. Some key GLCRPs participate in many cancer hallmarks in OSCC. Cancer hallmark‐associated GLCRP networks have complex patterns and specific functions in OSCC. Specially, some key GLCRPs are associated with the prognosis of OSCC patients. In summary, we generate a comprehensive landscape of cancer hallmark‐associated GLCRPs that can act as a starting point for future functional explorations, the identification of biomarkers and lncRNA‐based targeted therapy in OSCC.     

Monitoring HOTTIP levels on extracellular vesicles for predicting recurrence in surgical non-small cell lung cancer patients

In resected non-small cell lung cancer (NSCLC), postsurgical recurrence is the major factor affecting long-term survival. The identification of biomarkers in extracellular vesicles (EV) obtained from serial blood samples after surgery could enhance early detection of relapse and improve NSCLC outcome. Since EV cargo contains long non-coding RNAs (lncRNAs), we aimed to analyze whether the oncogenic lncRNA HOTTIP, which higher expression in tumor tissue was related to worse outcome in NSCLC, could be detected in EV from NSCLC patients and serve as recurrence biomarker. After purification of EVs by ultracentrifugation in 52 serial samples from 18 NSCLC patients, RNA was isolated and HOTTIP was quantified by Real time PCR. We observed that patients that relapsed after surgery displayed increased postsurgical EV HOTTIP levels in comparison with presurgical levels. In the relapsed patients with several samples available between surgery and relapse, we observed an increment in the EV HOTTIP levels when approaching to relapse, which indicated its potential utility for monitoring disease recurrence. When we focused in EV HOTTIP levels in the first post-surgical sample, we observed that the detection of an increment of the expression levels in comparison to presurgical sample, predicted recurrence with high sensitivity (85.7%) and specificity (90.9%) and that patients had shorter time to relapse and shorter overall survival. In conclusion, our pilot study showed that EV HOTTIP is a potential biomarker for monitoring disease recurrence after surgery in NSCLC.     

Effects of perioperative blood transfusion on the prognosis in hereditary and sporadic colon cancer

Abstract

We investigated the effects of perioperative blood transfusion in the prognosis of hereditary and sporadic colon cancer. There are 1075 colon cancer patients, including 936 sporadic colon cancer and 139 with hereditary colon cancer undergoing surgery at our hospital. All patients underwent 10 years of follow-up. In the sporadic group, mortality, local recurrence rate and distant metastases rate of transfused patients were significantly higher than non-transfused patients. The 10-year survival rates were significantly lower in patients receiving blood transfusions compared to non-transfused patients. In the hereditary group, mortality was higher in transfused patients compared to non-transfused patients.     

Development and validation of a five-lncRNA signature with prognostic value in colon cancer

Dysregulation of long noncoding RNAs (lncRNAs) has been found in a large number of human cancers, including colon cancer. Therefore, the implementation of potential lncRNAs biomarkers with prognostic prediction value are very much essential. GSE39582 data set was downloaded from database of Gene Expression Omnibus. Re-annotation analysis of lncRNA expression profiles was performed by NetAffx annotation files. Univariate and multivariate Cox proportional analyses helped select prognostic lncRNAs. Algorithm of random survival forest-variable hunting (RSF-VH) together with stepwise multivariate Cox proportional analysis were performed to establish lncRNA signature. The log-rank test was carried out to analyze and compare the Kaplan-Meier survival curves of patients’ overall survival (OS). Receiver operating characteristic (ROC) analysis was used for comparing the survival prediction regarding its specificity and sensitivity based on lncRNA risk score, followed by calculating the values of area under the curve (AUC). The single-sample GSEA (ssGSEA) analysis was used to describe biological functions associated with this signature. Finally, to determine the robustness of this model, we used the validation sets including GSE17536 and The Cancer Genome Atlas data set. After re-annotation analysis of lncRNAs, a total of 14 lncRNA probes were obtained by univariate and multivariate Cox proportional analysis. Then, the RSF-VH algorithm and stepwise multivariate Cox analysis helped to build a five-lncRNA prognostic signature for colon cancer. The patients in group with high risk showed an obviously shorter survival time compared with patients in group with low risk with AUC of 0.75. In addition, the five-lncRNA signature can be used to independently predict the survival of patients with colon cancer. The ssGSEA analysis revealed that pathways such as extracellular matrix-receptor interaction was activated with an increase in risk score. These findings determined the strong power of prognostic prediction value of this five-lncRNA signature for colon cancer.