Transcriptional signatures for coupled predictions of stage II and III colorectal cancer metastasis and fluorouracil-based adjuvant chemotherapy benefit

Identification of an Individualized Prognostic Biomarker for Serous Ovarian Cancer: A Qualitative Model

Serous ovarian cancer is the most common type of ovarian epithelial cancer and usually has a poor prognosis. The objective of this study was to construct an individualized prognostic model for predicting overall survival in serous ovarian cancer. Based on the relative expression orderings (Ea > Eb/Ea ≤ Eb) of gene pairs closely associated with serous ovarian prognosis, we tried constructing a potential individualized qualitative biomarker by the greedy algorithm and evaluated the performance in independent validation datasets. We constructed a prognostic biomarker consisting of 20 gene pairs (SOV-P20). The overall survival between high- and low-risk groups stratified by SOV-P20 was statistically significantly different in the training and independent validation datasets from other platforms (p < 0.05, Wilcoxon test). The average area under the curve (AUC) values of the training and three validation datasets were 0.756, 0.590, 0.630, and 0.680, respectively. The distribution of most immune cells between high- and low-risk groups was quite different (p < 0.001, Wilcoxon test). The low-risk patients tended to show significantly better tumor response to chemotherapy than the high-risk patients (p < 0.05, Fisher’s exact test). SOV-P20 achieved the highest mean index of concordance (C-index) (0.624) compared with the other seven existing prognostic signatures (ranging from 0.511 to 0.619). SOV-P20 is a promising prognostic biomarker for serous ovarian cancer, which will be applicable for clinical predictive risk assessment.

Identification of Prognostic Gene Signatures by Developing a scRNA-Seq-Based Integration Approach to Predict Recurrence and Chemotherapy Benefit in Stage II–III Colorectal Cancer

Prospective identification of robust biomarkers related to prognosis and adjuvant chemotherapy has become a necessary and critical step to predict the benefits of adjuvant therapy for patients with stage II–III colorectal cancer (CRC) before clinical treatment. We proposed a single-cell-based prognostic biomarker recognition approach to identify and construct CRC up- and down-regulated prognostic signatures (CUPsig and CDPsig) by integrating scRNA-seq and bulk datasets. We found that most genes in CUPsig and CDPsig were known disease genes, and they had good prognostic abilities in CRC validation datasets. Multivariate analysis confirmed that they were two independent prognostic factors of disease-free survival (DFS). Significantly, CUPsig and CDPsig could effectively predict adjuvant chemotherapy benefits in drug-treated validation datasets. Additionally, they also performed well in patients with CMS4 subtype. Subsequent analysis of drug sensitivity showed that expressions of these two signatures were significantly associated with the sensitivities of CRC cell lines to multiple drugs. In summary, we proposed a novel prognostic biomarker identification approach, which could be used to identify novel prognostic markers for stage II–III CRC patients who will undergo adjuvant chemotherapy and facilitate their further personalized treatments.

Restoring HOXD10 Exhibits Therapeutic Potential for Ameliorating Malignant Progression and 5-Fluorouracil Resistance in Colorectal Cancer

Emerging evidence suggests that hypermethylation of HOXD10 plays an important role in human cancers. However, the biological and clinical impacts of HOXD10 overmethylation and its downstream targets in colorectal cancer remain unknown. We evaluated the methylation level of HOXD10 in paired cancer and normal tissues (n = 42) by using pyrosequencing, followed by validation of the methylation status of HOXD10 from The Cancer Genome Atlas (TCGA) datasets with 302 cancer tissues and 38 normal tissues. The biological function of HOXD10 was characterized in cell lines. We further evaluated the effects of HOXD10 and its targets on chemoresistance in our established resistant cell lines and clinical cohort (n = 66). HOXD10 was found frequently methylated in colorectal cancer, and its hypermethylation correlates with its low expression level, advanced disease, and lymph node metastasis. Functionally, HOXD10 acts as a tumor suppressor gene, in which HOXD10-expressing cells showed suppressed cell proliferation, colony formation ability, and migration and invasion capacity. Mechanistically, DNMT1, DNMT3B, and MeCP2 were recruited in the HOXD10 promoter, and demethylation by 5-Aza-2′-deoxycytidine (5-Aza-CdR) treatment or MeCP2 knockdown can sufficiently induce HOXD10 expression. HOXD10 regulates the expressions of miR-7 and IGFBP3 in a promoter-dependent manner. Restoration of the expression of HOXD10 in 5-fluorouracil (5-FU)-resistant cells significantly upregulates the expressions of miR-7 and IGFBP3 and enhances chemosensitivity to 5-FU. In conclusion, we provide novel evidence that HOXD10 is frequently methylated, silenced, and contributes to the development of colorectal cancers. Restoration of HOXD10 activates the expressions of miR-7 and IGFBP3 and results in an inhibited phenotype biologically, suggesting its potential therapeutic relevance in colorectal cancer (CRC).

Two novel qualitative transcriptional signatures robustly applicable to non-research-oriented colorectal cancer samples with low-quality RNA

Currently, due to the low quality of RNA caused by degradation or low abundance, the accuracy of gene expression measurements by transcriptome sequencing (RNA‐seq) is very challenging for non‐research‐oriented clinical samples, majority of which are preserved in hospitals or tissue banks worldwide with complete pathological information and follow‐up data. Molecular signatures consisting of several genes are rarely applied to such samples. To utilize these resources effectively, 45 stage II non‐research‐oriented samples which were formalin‐fixed paraffin‐embedded (FFPE) colorectal carcinoma samples (CRC) using RNA‐seq have been analysed. Our results showed that although gene expression measurements were significantly affected, most cancer features, based on the relative expression orderings (REOs) of gene pairs, were well preserved. We then developed two REO‐based signatures, which consisted of 136 gene pairs for early diagnosis of CRC, and 4500 gene pairs for predicting post‐surgery relapse risk of stage II and III CRC. The performance of our signatures, which included hundreds or thousands of gene pairs, was more robust for non‐research‐oriented clinical samples, compared to that of two published concise REO‐based signatures. In conclusion, REO‐based signatures with relatively more gene pairs could be robustly applied to non‐research‐oriented CRC samples.

CCMAlnc Promotes the Malignance of Colorectal Cancer by Modulating the Interaction Between miR-5001-5p and Its Target mRNA

Objective

Colorectal cancer (CRC) is highly malignant and cancer metastasis remains the predominant cause of CRC death. The potential molecular mechanism of long non-coding RNA (lncRNAs) in CRC malignance is still poorly elucidated.

Methods

CCMAlnc expression was analyzed by using the Sequence ReadArchive (SRA) database. Target gene expression was examined by real-time PCR and Western blotting. The biological function of CCMAlnc and miR-5001-5p was detected by cell invasion, CCK8 proliferation, and colony formation assays in loss of function and gain of function experiments in vitro. A luciferase assay was performed to validate the target site of miR-5001-5p on the 3′-UTR of HES6 mRNA.

Results

CCMAlnc was identified as a novel functional lncRNA in CRC. Elevated CCMAlnc was detected in CRC cells as well as in clinical CRC tissue samples, and the expression of this lncRNA positively correlated with the poor prognosis of CRC patients. Functional validation assays revealed that downregulation of CCMAlnc impaired CRC cell proliferation and invasion in vitro, but upregulation of CCMAlnc reversed this effect. Moreover, CCMAlnc was validated to act as a competing endogenous RNA (ceRNA) that stabilizes the expression of HES6 by downregulating miR-5001-5p.

Conclusion

CCMAlnc/miR-5001-5p/HES6 signaling is strongly activated to promote CRC malignance. CCMAlnc is defined as a potential candidate biomarker for metastasis prediction in CRC patients and as a potential therapeutic target for CRC treatment.

A Qualitative Transcriptional Signature for Predicting CpG Island Methylator Phenotype Status of the Right-Sided Colon Cancer

A part of colorectal cancer which is characterized by simultaneous numerous hypermethylation CpG islands sites is defined as CpG island methylator phenotype (CIMP) status. Stage II and III CIMP−positive (CIMP+) right-sided colon cancer (RCC) patients have a better prognosis than CIMP−negative (CIMP−) RCC treated with surgery alone. However, there is no gold standard available in defining CIMP status. In this work, we selected the gene pairs whose relative expression orderings (REOs) were associated with the CIMP status, to develop a qualitative transcriptional signature to individually predict CIMP status for stage II and III RCC. Based on the REOs of gene pairs, a signature composed of 19 gene pairs was developed to predict the CIMP status of RCC through a feature selection process. A sample is predicted as CIMP+ when the gene expression orderings of at least 12 gene pairs vote for CIMP+; otherwise the CIMP−. The difference of prognosis between the predicted CIMP+ and CIMP− groups was more significantly different than the original CIMP status groups. There were more differential methylation and expression characteristics between the two predicted groups. The hierarchical clustering analysis showed that the signature could perform better for predicting CIMP status of RCC than current methods. In conclusion, the qualitative transcriptional signature for classifying CIMP status at the individualized level can predict outcome and guide therapy for RCC patients.

Qualitative Ras pathway signature for cetuximab therapy reveals resistant mechanism in colorectal cancer

Cetuximab therapy, which heavily relies on the activation of Ras pathway, has been used in KRAS, NRAS, BRAF, and PIK3CA wild-type colorectal cancer (CRC) (Ras-normal). However, the response rate only reached 60%, due to false-negative mutation detection and mutation-like transcriptome features in wild-type patients. Herein, by integrating RNA-seq, microarray, and mutation data, we developed a Ras pathway signature by characterizing KRAS/NRAS/BRAF/PIK3CA mutations to identify the hidden nonresponders from the Ras-normal patients by mutation detection. Using public and in-house data of CRC patients treated with cetuximab, discovery of the signature could identify cetuximab-resistant samples from the Ras-normal samples. Cetuximab resistance-related genes, such as PTEN, were significantly and frequently mutated in the identified Ras-activated samples, whereas two cetuximab sensitivity-related genes, APC and TP53, showed comutation and significantly higher mutation frequencies in the remaining Ras-normal samples. Furthermore, all the NF1- and BCL2L1-mutated samples were identified as Ras-activated from the Ras-normal samples by the Ras pathway signature with significantly under-regulated expression. Genes co-expressed with the two genes were both involved in Ras signaling pathway, the out-of-control of which could be attributed by the genes' loss-of-function mutations. To improve the treatment of cetuximab in CRC, NF1 and BCL2L1 could be used as complementary detection technique to those applied in clinical. In conclusion, the proposed Ras pathway signature could identify the hidden CRC patients resistant to cetuximab therapy and help to reveal resistance mechanisms.

Qualitative transcriptional signature for predicting pathological response of colorectal cancer to FOLFOX therapy

FOLFOX (5‐fluorouracil, leucovorin and oxaliplatin) is one of the main chemotherapy regimens for colorectal cancer (CRC), but only half of CRC patients respond to this regimen. Using gene expression profiles of 96 metastatic CRC patients treated with FOLFOX, we first selected gene pairs whose within‐sample relative expression orderings (REO) were significantly associated with the response to FOLFOX using the exact binomial test. Then, from these gene pairs, we applied an optimization procedure to obtain a subset that achieved the largest F‐score in predicting pathological response of CRC to FOLFOX. The REO‐based qualitative transcriptional signature, consisting of five gene pairs, was developed in the training dataset consisting of 96 samples with an F‐score of 0.90. In an independent test dataset consisting of 25 samples with the response information, an F‐score of 0.82 was obtained. In three other independent survival datasets, the predicted responders showed significantly better progression‐free survival than the predicted non‐responders. In addition, the signature showed a better predictive performance than two published FOLFOX signatures across different datasets and is more suitable for CRC patients treated with FOLFOX than 5‐fluorouracil‐based signatures. In conclusion, the REO‐based qualitative transcriptional signature can accurately identify metastatic CRC patients who may benefit from the FOLFOX regimen.

A qualitative transcriptional signature for the histological reclassification of lung squamous cell carcinomas and adenocarcinomas

Background

Targeted therapy for non-small cell lung cancer is histology dependent. However, histological classification by routine pathological assessment with hematoxylin-eosin staining and immunostaining for poorly differentiated tumors, particularly those from small biopsies, is still challenging. Additionally, the effectiveness of immunomarkers is limited by technical inconsistencies of immunostaining and lack of standardization for staining interpretation.

Results

Using gene expression profiles of pathologically-determined lung adenocarcinomas and squamous cell carcinomas, denoted as pADC and pSCC respectively, we developed a qualitative transcriptional signature, based on the within-sample relative gene expression orderings (REOs) of gene pairs, to distinguish ADC from SCC. The signature consists of two genes, KRT5 and AGR2, which has the stable REO pattern of KRT5 > AGR2 in pSCC and KRT5 < AGR2 in pADC. In the two test datasets with relative unambiguous NSCLC types, the apparent accuracy of the signature were 94.44 and 98.41%, respectively. In the other integrated dataset for frozen tissues, the signature reclassified 4.22% of the 805 pADC patients as SCC and 12% of the 125 pSCC patients as ADC. Similar results were observed in the clinical challenging cases, including FFPE specimens, mixed tumors, small biopsy specimens and poorly differentiated specimens. The survival analyses showed that the pADC patients reclassified as SCC had significantly shorter overall survival than the signature-confirmed pADC patients (log-rank p = 0.0123, HR = 1.89), consisting with the knowledge that SCC patients suffer poor prognoses than ADC patients. The proliferative activity, subtype-specific marker genes and consensus clustering analyses also supported the correctness of our signature.

Conclusions

The non-subjective qualitative REOs signature could effectively distinguish ADC from SCC, which would be an auxiliary test for the pathological assessment of the ambiguous cases.

A qualitative transcriptional signature for predicting microsatellite instability status of right-sided Colon Cancer

Background

Microsatellite instability (MSI) accounts for about 15% of colorectal cancer and is associated with prognosis. Today, MSI is usually detected by polymerase chain reaction amplification of specific microsatellite markers. However, the instability is identified by comparing the length of microsatellite repeats in tumor and normal samples. In this work, we developed a qualitative transcriptional signature to individually predict MSI status for right-sided colon cancer (RCC) based on tumor samples.

Results

Using RCC samples, based on the relative expression orderings (REOs) of gene pairs, we extracted a signature consisting of 10 gene pairs (10-GPS) to predict MSI status for RCC through a feature selection process. A sample is predicted as MSI when the gene expression orderings of at least 7 gene pairs vote for MSI; otherwise the microsatellite stability (MSS). The classification performance reached the largest F-score in the training dataset. This signature was verified in four independent datasets of RCCs with the F-scores of 1, 0.9630, 0.9412 and 0.8798, respectively. Additionally, the hierarchical clustering analyses and molecular features also supported the correctness of the reclassifications of the MSI status by 10-GPS.

Conclusions

The qualitative transcriptional signature can be used to classify MSI status of RCC samples at the individualized level.

A qualitative transcriptional signature for determining the grade of colorectal adenocarcinoma

Histological grading (HG) is an important prognostic factor of colorectal adenocarcinoma (CRAC): the high-grade CRAC patients have poorer prognosis after tumor resection. Especially, the high-grade stage II CRAC patients are recommended to receive adjuvant chemotherapy. Due to the subjective nature of HG assessment, it is difficult to achieve consistency among pathologists, which brings patients uncertain grading outcomes and inappropriate treatments. We developed a qualitative transcriptional signature based on the within-sample relative expression orderings (REOs) of gene pairs to discriminate high-grade and low-grade CRAC. Using the stage II-III CRAC samples, we detected gene pairs with stable REOs in the high-grade samples and reversal stable REOs in the low-grade samples, and retained the gene pairs whose specific REO patterns were significantly associated with the disease-free survival of patients by univariate Cox regression model. Then, we used a forward-backward searching procedure to extract gene pairs with the highest concordance index as the final grading signature. Finally, 9 gene pairs (9-GPS) were developed to divide CRAC patients into high-grade and low-grade groups. With the signature, there were more differential expression characteristics between reclassified high-grade and low-grade groups. Significant difference of prognosis between the classified two group patients could be seen in four independent datasets. Additionally, genomic analyses showed that the classified high-grade groups were characterized by hypermutation while classified low-grade groups were characterized by frequent copy number alternations. In conclusion, the 9-GPS can provide an objective and robust grading assessment for CRAC patients, which could assist clinical treatment decision.

A Qualitative Transcriptional Signature for Predicting Recurrence Risk of Stage I-III Bladder Cancer Patients After Surgical Resection

Background: Previously reported transcriptional signatures for predicting the prognosis of stage I-III bladder cancer (BLCA) patients after surgical resection are commonly based on risk scores summarized from quantitative measurements of gene expression levels, which are highly sensitive to the measurement variation and sample quality and thus hardly applicable under clinical settings. It is necessary to develop a signature which can robustly predict recurrence risk of BLCA patients after surgical resection.

Methods: The signature is developed based on the within-sample relative expression orderings (REOs) of genes, which are qualitative transcriptional characteristics of the samples.

Results: A signature consisting of 12 gene pairs (12-GPS) was identified in training data with 158 samples. In the first validation dataset with 114 samples, the low-risk group of 54 patients had a significantly better overall survival than the high-risk group of 60 patients (HR = 3.59, 95% CI: 1.34~9.62, p = 6.61 × 10⁻⁰³). The signature was also validated in the second validation dataset with 57 samples (HR = 2.75 × 10⁰⁸, 95% CI: 0~Inf, p = 0.05). Comparison analysis showed that the transcriptional differences between the low- and high-risk groups were highly reproducible and significantly concordant with DNA methylation differences between the two groups.

Conclusions: The 12-GPS signature can robustly predict the recurrence risk of stage I-III BLCA patients after surgical resection. It can also aid the identification of reproducible transcriptional and epigenomic features characterizing BLCA metastasis.