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Mitochondrial type 2 pathway and MOMP. Bax/Bak proteins are activated by the action of BH3 only proteins Bid/Bim. Activated Bax/Bak oligomerize to form pores on mitochondrial outer membrane (MOM) as a result of which apoptotic factors such as cyto-c are released, a process known as mitochondrial outer membrane permeabilization (MOMP), and lead the cell towards death. Anti-apoptotic proteins such as Bcl2, BclXL and Mcl1 bind with both BH3 only proteins as well as pro-apoptotic Bax/Bak to inhibit their function.
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One of the major challenges in managing the treatment of colorectal cancer (CRC) patients is to predict risk scores or level of risk for CRC patients. In past, several biomarkers, based on concentration of proteins involved in type-2/intrinsic/mitochondrial apoptotic pathway, have been identified for prognosis of colorectal cancer patients. Recentl...
Context in source publication
Context 1
... type 2 (intrinsic) pathway consists of a cascade of Bcl2 family proteins which are broadly classified into two categories: Anti-apoptotic proteins that include Bcl2, BclXL, Mcl1 and pro-apoptotic proteins that include Bax, Bak, Bid, Bim [9]. Each of these has a definite functional role in regulating the process of mitochondrial pore formation (Mitochondrial Outer Membrane Permeabilization) leading to activation of caspases and ultimately, the demise of the cell [8] (Fig 1). While anti-apoptotic proteins are https://doi.org/10.1371/journal.pone.0217527.g001 ...
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Cervical cancer is one of the most common gynecological malignancies worldwide. However, the pathogenesis of cervical cancer remains to be fully elucidated. Increasing evidence shows that microRNAs (miRNAs) may be involved in the pathogenesis of cervical cancer. The present study tested the hypothesis that the overexpression of miRNA (miR)‑411 may...
Citations
... It is related to positive stepwise regression. Lassolars algorithm used in this paper is a combination of Lars algorithm and lasso model, which can automatically select the optimal parameters l and have better performance than LASSO algorithm alone (13). Finally, we counted the frequency of each feature selected in the 5 folds, the features that get selected three or more times repeatedly were considered stable features, and different models were built using various combinations of those selected features. ...
Objective
To evaluate the diagnostic ability of magnetic resonance imaging (MRI) based radiomics and traditional characteristics to differentiate between Ovarian sex cord-stromal tumors (SCSTs) and epithelial ovarian cancers (EOCs).
Methods
We consecutively included a total of 148 patients with 173 tumors (81 SCSTs in 73 patients and 92 EOCs in 75 patients), who were randomly divided into development and testing cohorts at a ratio of 8:2. Radiomics features were extracted from each tumor, 5-fold cross-validation was conducted for the selection of stable features based on development cohort, and we built radiomics model based on these selected features. Univariate and multivariate analyses were used to identify the independent predictors in clinical features and conventional MR parameters for differentiating SCSTs and EOCs. And nomogram was used to visualized the ultimately predictive models. All models were constructed based on the logistic regression (LR) classifier. The performance of each model was evaluated by the receiver operating characteristic (ROC) curve. Calibration and decision curves analysis (DCA) were used to evaluate the performance of models.
Results
The final radiomics model was constructed by nine radiomics features, which exhibited superior predictive ability with AUCs of 0.915 (95%CI: 0.869-0.962) and 0.867 (95%CI: 0.732-1.000) in the development and testing cohorts, respectively. The mixed model which combining the radiomics signatures and traditional parameters achieved the best performance, with AUCs of 0.934 (95%CI: 0.892-0.976) and 0.875 (95%CI: 0.743-1.000) in the development and testing cohorts, respectively.
Conclusion
We believe that the radiomics approach could be a more objective and accurate way to distinguish between SCSTs and EOCs, and the mixed model developed in our study could provide a comprehensive, effective method for clinicians to develop an appropriate management strategy.
... To our knowledge, there is no report on the association of the combination of MCL1 and Bcl-xL expression. Although, single protein Bcl-xL and MCL1 was able to stratify CRC outcome, a combination of Bcl-2, Bcl-xL, MCL1, Bax, and Bak was a better predictor of clinical outcomes, with Bcl-xL and MCL1 being the key dominating proteins [53]. Pre-clinical data emphasizes that Bcl-xL and MCL1 function as resistant factors to small molecule inhibitors that respectively target these two anti-apoptotic proteins [15]. ...
Colorectal cancer (CRC) is one of the most common and deadliest forms of cancer. Myeloid Cell Leukemia 1 (MCL1), a pro-survival member of the Bcl-2 protein family is associated with chemo-resistance in CRC. The ability of MCL1 to inhibit apoptosis by binding to the BH3 domains of pro-apoptotic Bcl-2 family members is a well-studied means by which this protein confers resistance to multiple anti-cancer therapies. We found that specific DNA damaging chemotherapies promote nuclear MCL1 translocation in CRC models. In p53 null CRC, this process is associated with resistance to chemotherapeutic agents, the mechanism of which is distinct from the classical mitochondrial protection. We previously reported that MCL1 has a noncanonical chemoresistance capability, which requires a novel loop domain that is distinct from the BH3-binding domain associated with anti-apoptotic function. Herein we disclose that upon treatment with specific DNA-damaging chemotherapy, this loop domain binds directly to alpha-enolase which in turn binds to calmodulin; we further show these protein−protein interactions are critical in MCL1’s nuclear import and chemoresistance. We additionally observed that in chemotherapy-treated p53 −/− CRC models, MCL1 nuclear translocation confers sensitivity to Bcl-xL inhibitors, which has significant translational relevance given the co-expression of these proteins in CRC patient samples. Together these findings indicate that chemotherapy-induced MCL1 translocation represents a novel resistance mechanism in CRC, while also exposing an inherent and targetable Bcl-xL co-dependency in these cancers. The combination of chemotherapy and Bcl-xL inhibitors may thus represent a rational means of treating p53 −/− CRC via exploitation of this unique MCL1-based chemoresistance mechanism.
... CancerSPP (Bhalla, Kaur, Dhall, & Raghava, 2019) Skin cancer progression prediction from genomic profiles http://webs.iiitd.edu.in/raghava/cancerspp/ CRCRpred (Lathwal, Arora, & Raghava, 2019) Prediction of risk scores of colorectal cancer patients from protein concentration https://webs.iiitd.edu.in/raghava/crcrpred OSblca A web server to investigate prognostic biomarkers of bladder cancer patients http://bioinfo.henu.edu.cn/BLCA/BLCAList.jsp ...
Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.
... Regressors such as Linear, Ridge, Lasso, Lasso-Lars, Elastic-Net, Random-forest (RF) and K-nearest neighbours (KNN) were used. Five-fold cross-validation was used for training and validation studies, as done in previous studies [36][37][38][39][40]. All five test datasets were combined as 'predicted OS' and stratification was performed using it. ...
... Where g i represent genes and α i represent regression coefficients obtained from Cox univariate regression analysis as done in [38,[41][42][43][44][45]. Risk groups were stratified based on best PI cut-off estimated using cutp from 'survMisc' package in R. HR, p-values, C index were then evaluated using this cut-off. ...
Aberrant expressions of apoptotic genes have been associated with papillary thyroid carcinoma (PTC) in the past, however, their prognostic role and utility as biomarkers remains poorly understood. In this study, we analysed 505 PTC patients by employing Cox-PH regression techniques, prognostic index models and machine learning methods to elucidate the relationship between overall survival (OS) of PTC patients and 165 apoptosis related genes. It was observed that nine genes ( ANXA1 , TGFBR3 , CLU , PSEN1 , TNFRSF12A , GPX4 , TIMP3 , LEF1 , BNIP3L ) showed significant association with OS of PTC patients. Five out of nine genes were found to be positively correlated with OS of the patients, while the remaining four genes were negatively correlated. These genes were used for developing risk prediction models, which can be utilized to classify patients with a higher risk of death from the patients which have a good prognosis. Our voting-based model achieved highest performance (HR = 41.59, p = 3.36x10 ⁻⁴ , C = 0.84, logrank-p = 3.8x10 ⁻⁸ ). The performance of voting-based model improved significantly when we used the age of patients with prognostic biomarker genes and achieved HR = 57.04 with p = 10 ⁻⁴ (C = 0.88, logrank-p = 1.44x10 ⁻⁹ ). We also developed classification models that can classify high risk patients (survival ≤ 6 years) and low risk patients (survival > 6 years). Our best model achieved AUROC of 0.92. Further, the expression pattern of the prognostic genes was verified at mRNA level, which showed their differential expression between normal and PTC samples. Also, the immunostaining results from HPA validated these findings. Since these genes can also be used as potential therapeutic targets in PTC, we also identified potential drug molecules which could modulate their expression profile. The study briefly revealed the key prognostic biomarker genes in the apoptotic pathway whose altered expression is associated with PTC progression and aggressiveness. In addition to this, risk assessment models proposed here can help in efficient management of PTC patients.
... Apoptosis is also one of the widely studied processes in the context of development of prognostic biomarkers and therapeutics which target its key components [12]. [14][15][16]. Apoptosis related molecules have also been identified to play prognostic roles in other cancers such as identification of survival related proteins AGR2, ENO1, GDI2, GRP78, GRP94, PPIA, PRDX1, PTEN, and gene KIF15 by means of immunohistochemical staining in gastric cancer [17,18]; high protein and mRNA levels of BIK as markers of tumour recurrence in breast cancer [19]; gene expression of MCL1 as prognostic biomarker in Nonsmall cell lung cancer [20]; high expression of TOP2A was shown to be linked with poor survival in bladder urothelial carcinoma [21]; a seven gene signature was established to distinguish patients with isocitratewildtype glioblastoma [22] and increased FER1L4 expression was found to promote apoptosis and suppress epithelial-mesenchymal transition in osteosarcoma [23]. In thyroid cancer, alterations in apoptotic molecules such as p53, BCL2, BCL-XL, BAX, p73, Fas/FasL, PPARG, TGFb, and NFKb have also been associated with carcinogenesis [24]. ...
Numerous cancer-specific prognostic models have been developed in the past, wherein one model is applicable for only one type of cancer. In this study, an attempt has been made to identify universal or multi-cancer prognostic biomarkers and develop models for predicting survival risk across different types of cancer patients. In order to accomplish this, we gauged the prognostic role of mRNA expression of 165 apoptosis-related genes across 33 cancers in the context of patient survival. Firstly, we identified specific prognostic biomarker genes for 30 cancers. The cancer-specific prognostic models achieved a minimum Hazard Ratio, HRSKCM = 1.99 and maximum HRTHCA = 41.59. Secondly, a comprehensive analysis was performed to identify universal biomarkers across many cancers. Our best prognostic model consisted of 11 genes (TOP2A, ISG20, CD44, LEF1, CASP2, PSEN1, PTK2, SATB1, SLC20A1, EREG, and CD2) and stratified risk groups across 27 cancers (HROV = 1.53-HRUVM = 11.74). The model was validated on eight independent cancer cohorts and exhibited a comparable performance. Further, we clustered cancer-types on the basis of shared survival related apoptosis genes. This approach proved helpful in development of cross-cancer prognostic models. To show its efficacy, a prognostic model consisting of 15 genes was thereby developed for LGG-KIRC pair (HRKIRC = 3.27, HRLGG = 4.23). Additionally, we predicted potential therapeutic candidates for LGG-KIRC high risk patients.
... Among these studies, Aerts et al. [13] found four feature signatures representing four main groups of features: original, tumor intensity, first order, and wavelet, and used these four features in turn to evaluate the effectiveness of predicting survival in NSCLC patients in the training set. Most of the authors work towards finding sets of radiomics features closely related to survival prediction, called sets of radiomics signatures, and used them for survival prediction for cancer patients [5,6,9,10,12,[14][15][16]. Similarly, some authors used the expression of a group of signature genes to predict survival in cancer diseases; some constructed risk scores to represent sets of signature genes to optimize survival prediction in cancer patients [17][18][19][20][21]. It is also necessary to use a representative scale score similar applied to radiomics field. ...
This study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (including 336, 84, and 157 patients for training, testing, and validation set, respectively). A total of 851 radiomics features for each patient from CT images were extracted for further analyses. The most important features (strongly linked with overall survival) were chosen by pairwise correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and univariate Cox proportional hazard regression. Multivariate Cox proportional hazard model survival analysis was used to create risk scores for each patient, and Kaplan–Meier was used to separate patients into two groups: high-risk and low-risk, respectively. ROC curve assessed the prediction ability of the risk score model for overall survival compared to clinical parameters. The risk score, which developed from ten radiomics signatures model, was found to be independent of age, gender, and stage for predicting overall survival in NSCLC patients (HR, 2.99; 95% CI, 2.27–3.93; p < 0.001) and overall survival prediction ability was 0.696 (95% CI, 0.635–0.758), 0.705 (95% CI, 0.649–0.762), 0.657 (95% CI, 0.589–0.726) (AUC) for 1, 3, and 5 years, respectively, in the training set. The risk score is more likely to have a better accuracy in predicting survival at 1, 3, and 5 years than clinical parameters, such as age 0.57 (95% CI, 0.499–0.64), 0.552 (95% CI, 0.489–0.616), 0.621 (95% CI, 0.544–0.689) (AUC); gender 0.554, 0.546, 0.566 (AUC); stage 0.527, 0.501, 0.459 (AUC), respectively, in 1, 3 and 5 years in the training set. In the training set, the Kaplan–Meier curve revealed that NSCLC patients in the high-risk group had a lower overall survival time than the low-risk group (p < 0.001). We also had similar results that were statistically significant in the testing and validation set. In conclusion, risk scores developed from ten radiomics signatures models have great potential to predict overall survival in NSCLC patients compared to the clinical parameters. This model was able to stratify NSCLC patients into high-risk and low-risk groups regarding the overall survival prediction.
... For developing the machine learning-based model we have used the standard protocols as used in different previous studies [22]. We divided our dataset in an 80:20 proportion as the training and testing sets, respectively, similar to several studies in the past. ...
Interleukin-2 (IL-2) based immunotherapy has been already approved to treat certain type of cancers as it plays vital role in immune system. Thus it is important to discover new peptides or epitopes that can induce IL-2 with high efficiency. We analyzed experimentally validated IL-2 inducing and non-inducing peptides and observed differ in average amino acid composition, motifs, length, and positional preference of amino acid residues at the N- and C-terminus. In this study, 2528 IL-2 inducing and 2104 non-IL-2 inducing peptides have been used for traning, testing, traing and validation of our models. A large number of machine learning techniques and around 10,000 peptide features have been used for developing prediction models. The Random Forest-based model using hybrid features achieved a maximum accuracy of 73.25%, with AUC of 0.73 on the training set; accuracy of 72.89% with AUC of 0.72 on validation dataset. A web-server IL2pred has been developed for predicting IL-2 inducing peptides, scanning IL-inducing regions in a protein and designing IL-2 specific epitopes by ranking peptide analogs ( https://webs.iiitd.edu.in/raghava/il2pred/ ).
... Thus, analysis of all splicing isoforms with the pancreatic cancer progression needs to be considered to find out the reliable prognostic markers. Nowadays, machine learning techniques have been widely used for the identification of prognostic markers and the development of prediction classifiers (Chang & Chen, 2019;Lathwal, Arora, & Raghava, 2019). Therefore, in the current study, we have illuminated the role of AS events in the prognosis of pancreatic adenocarcinoma (PAAD). ...
In literature, several mRNA, miRNA, lncRNA based biomarkers are identified by genomic analysis to stratify the patients into high and low risk groups of pancreatic adenocarcinoma (PAAD). The identified biomarkers are of limited use in terms of sensitivity and prediction ability. Thus, we aimed to identify the prognostic alternative splicing events and their related mutations in the PAAD. PAAD splicing data of 174 samples (17874 AS events in 6209 genes) and corresponding clinical information was obtained from the SpliceSeq and The Cancer Genome Atlas (TCGA), respectively. Prognostic-index based modeling was used to obtain the best predictive models for the seven AS types. However, model based on multiple spliced events genes (APP; LATS1; MRPL4; LAS1L; STARD10; PHF21A; NMRAL1) outperformed the single event models with a remarkable HR of 9.13 (p-value = 6.42e-10) as well as other existing models. Results from g:Profiler suggest that transcription factors ZF5, ER81, E2F-1/2/3, ER81, Erg, and PEA3 are most related to the prognostic spliced genes. We also identified 565 mutations across 160 spliced genes that have a strong association with the prognostic AS events. The analysis revealed that around 560 of these mutations were not reported before in context to splice event/region. Overall, we conclude that altered AS events may serve as strong indicators for overall survival in pancreatic cancer patients, and novel linkage of the known mutations to the survival-related AS events may provide a new dimension to the advancement of diagnostic and therapeutic interventions in these patients.
... Nowadays, machine learning techniques have been widely used for the identi cation of prognostic markers and the development of prediction classi ers (Chang & Chen, 2019;Lathwal, Arora, & Raghava, 2019). Therefore, in the current study, we have illuminated the role of AS events in the prognosis of pancreatic adenocarcinoma (PAAD). ...
In literature, several mRNA, miRNA, lncRNA based biomarkers are identified by genomic analysis to stratify the patients into high and low risk groups of pancreatic adenocarcinoma (PAAD). The identified biomarkers are of limited use in terms of sensitivity and prediction ability. Thus, we aimed to identify the prognostic alternative splicing events and their related mutations in the PAAD. PAAD splicing data of 174 samples (17874 AS events in 6209 genes) and corresponding clinical information was obtained from the SpliceSeq and The Cancer Genome Atlas (TCGA), respectively. Prognostic-index based modeling was used to obtain the best predictive models for the seven AS types. However, model based on multiple spliced events genes ( APP ; LATS1 ; MRPL4 ; LAS1L ; STARD10 ; PHF21A ; NMRAL1 ) outperformed the single event models with a remarkable HR of 9.13 (p-value = 6.42e-10) as well as other existing models. Results from g:Profiler suggest that transcription factors ZF5, ER81, E2F-1/2/3, ER81, Erg , and PEA3 are most related to the prognostic spliced genes. We also identified 565 mutations across 160 spliced genes that have a strong association with the prognostic AS events. The analysis revealed that around 560 of these mutations were not reported before in context to splice event/region. Overall, we conclude that altered AS events may serve as strong indicators for overall survival in pancreatic cancer patients, and novel linkage of the known mutations to the survival-related AS events may provide a new dimension to the advancement of diagnostic and therapeutic interventions in these patients.
... The integration of data to mine the biologically meaningful insight is the major challenge faced by the researchers and clinicians. In this regard, a growing number of researchers worldwide continuously integrated the various omics dataset to get a better insight into the disease etiology [57][58][59][60][61][62][63][64][65][66]. This data integration approach can uncover the hidden dynamic properties of cancer cells that otherwise would be impossible with the static or single omics data analysis. ...
... Various machine learning techniques have been employed for the development of a computational pipeline that can classify samples into primary and metastatic categories independently. In addition to genebased markers, researchers also developed computational tools for the risk stratification of colorectal patients based on their protein expression profiles [61]. The weight factor for each protein was taken into consideration for the development of the prediction model to classify the patients into the various risk groups. ...
Cancer is one of the most prevailing, deadly and challenging diseases worldwide. The advancement in technology led to the generation of different types of omics data at each genome level that may potentially improve the current status of cancer patients. These data have tremendous applications in managing cancer effectively with improved outcome in patients. This review summarizes the various computational resources and tools housing several types of omics data related to cancer. Major categorization of resources includes—cancer-associated multiomics data repositories, visualization/analysis tools for omics data, machine learning-based diagnostic, prognostic, and predictive biomarker tools, and data analysis algorithms employing the multiomics data. The review primarily focuses on providing comprehensive information on the open-source multiomics tools and data repositories, owing to their broader applicability, economic-benefit and usability. Sections including the comparative analysis, tools applicability and possible future directions have also been discussed in detail. We hope that this information will significantly benefit the researchers and clinicians, especially those with no sound background in bioinformatics and who lack sufficient data analysis skills to interpret something from the plethora of cancer-specific data generated nowadays.
