CC BY
0
THE POSSIBILITY OF EVALUATION MRNA EXPRESSION PROFILING TO PREDICT PROGRESSION OF LOCAL STAGE COLORECTAL CANCER
Goncharov SV 153, Bozhenko VK, Zakharenko MV, Chaptykov AA, Kulinich TM, Solodkiy VA
Russian Scientific Center of Roentgenoradiology, Moscow, Russia
Progression assessment enables implementation of the colorectal cancer (CRC) tertiary prevention measures aimed at early detection and timely treatment of metastatic cancer. The study was aimed to develop a model of CRC progression using pathomorphological and molecular genetic characteristics of tumors. Relative expression of mRNAs of 63 genes from various functional groups was determined in the tumor specimens of 223 patients with stage T1_4N0_2M0 CRC. The median follow-up period was 42 months. Binary logistic regression models were constructed, in which likelihood of progression within 36 months after the CRC diagnosis was a target variable. Explanatory variables were as follows: tumor grade, angiolymphatic invasion, ratio of the number of metastatic lymph nodes to the total number of lymph nodes in the surgical specimen, patient's age and tumor localization, as well as expression levels of genes CCNB1, Ki67, GRB7, IGF1, Il2, Il6, Il8, GATA3. Prediction accuracy of the model using clinical and morphological characteristics was 56.6%. Inclusion of CCNB1, Ki67, GRB7, IGF1, Il2, Il6, Il8, GATA3 expression profiles in the model increased accuracy to 80.6%. Thus, prediction of CRC progression for treatment personalization requires additional parameters beyond information acquired within the framework of conventional morphological TNM classification. The use of molecular markers as predictors significantly increases the CRC progression prediction accuracy. Further research is needed for validation and quality improvement of prognostic models.
Keywords: colorectal cancer, predictive markers, prognostic models of colorectal cancer progression, CRC
Funding: the study was supported through the grant by RSF 22-15-00448.
Author contribution: Goncharov SV Bozhenko VK, Solodkiy VA — study concept and design; Bozhenko VK, Goncharov SV, Kulinich TM, Zakharenko MV Solodkiy VA — data acquisition and processing; Goncharov SV, Chaptykov AA, Bozhenko VK, Kulinich TM — manuscript writing; Goncharov SV Chaptykov AA, Bozhenko VK, Kulinich TM, Zakharenko MV, Solodkiy VA — editing.
Compliance with ethical standards: the study was approved by the Ethics Committee of the Russian Scientific Center of Roentgenoradiology (protocol № 4 dated 26 April 2018) and conducted in accordance with the World Medical Association Declaration of Helsinki. All subjects submitted the informed consent to study participation.
[X] Correspondence should be addressed: Sergey V. Goncharov Profsoyuznaya, 86, 117997, Moscow, Russia; 9015@mail.ru
Received: 13.09.2023 Accepted: 01.12.2023 Published online: 23.12.2023
DOI: 10.24075/brsmu.2023.050
ВОЗМОЖНОСТИ ИСПОЛЬЗОВАНИЯ ПРОФИЛИРОВАНИЯ ЭКСПРЕССИИ МРНК ДЛЯ ПРОГНОЗА ПРОГРЕССИРОВАНИЯ ЛОКАЛЬНЫХ СТАДИЙ КОЛОРЕКТАЛЬНОГО РАКА
С. В. Гончаров В. К. Боженко, М. В. Захаренко, А. А. Чаптыков, Т. М. Кулинич, В. А. Солодкий Российский научный центр рентгенорадиологии, Москва, Россия
Оценка прогрессирования позволяет реализовать меры третичной профилактики колоректального рака (КРР), направленные на раннее выявление и своевременное лечение метастатической болезни. Целью исследования было разработать модель прогрессирования КРР с использованием патоморфологических и молекулярно-генетических характеристик опухоли. У 223 пациентов с КРР в стадиях T1-4N0-2M0 в образцах из опухоли были определены относительные уровни экспрессии мРНК 63 генов из разных функциональных групп. Медиана наблюдения 42 месяца. Сформированы логистические модели бинарного выбора, в которых целевой переменной служила вероятность прогрессирования в течение 36 месяцев от момента установки диагноза КРР. Объясняющими переменными были дифференцировка опухоли, ангиолимфатическая инвазия, соотношение числа метастатических лимфоузлов к их общему числу в операционном препарате, возраст пациента и локализация опухоли, а также показатели экспрессии генов CCNB1, Ki67, GRB7, IGF1, Il2, Il6, Il8, GATA3. Точность прогноза в модели с использованием клинико-морфологических показателей составила 56,6%. Включение в модель профиля экспрессии генов CCNB1, Ki67, GRB7, IGF1, Il2, Il6, Il8, GATA3 повышает точность до 80,6%. Таким образом, прогнозирование прогрессирования КРР для персонализации лечения требует дополнительных показателей, выходящих за пределы информации, получаемой в рамках традиционной морфологической классификации TNM. Использование молекулярных маркеров в качестве предикторов значительно повышает точность прогнозирования прогрессирования КРР. Необходимы дальнейшие исследования для валидации и улучшения качества прогностических моделей.
Ключевые слова: колоректальный рак, предиктивные маркеры, прогностические модели прогрессирования КРР, КРР Финансирование: работа поддержана грантом РНФ 22-15-00448.
Вклад авторов: С. В. Гончаров, В. К. Боженко, В. А. Солодкий — концепция и дизайн исследования; В. К. Боженко, С. В. Гончаров, Т. М. Кулинич, М. В. Захаренко, В. А. Солодкий — cбор и обработка материала; С. В. Гончаров, А. А. Чаптыков, В. К. Боженко, Т. М. Кулинич — написание текста; С. В. Гончаров, А. А. Чаптыков, В. К. Боженко, Т. М. Кулинич, М. В. Захаренко, В. А. Солодкий — редактирование.
Соблюдение этических стандартов: исследование одобрено этическим комитетом ФГБУ «Российского научного центра рентгенорадиологии» МЗ РФ (протокол № 4 от 26 апреля 2018 г.), проведено в соответствии с Хельсинкской декларацией Всемирной медицинской ассоциации. Все участники подписали добровольное информированное согласие на участие в исследовании.
[23 Для корреспонденции: Сергей Владимирович Гончаров
ул. Профсоюзная, д. 86, 117997, г. Москва, Россия; 9015@mail.ru
Статья получена: 13.09.2023 Статья принята к печати: 01.12.2023 Опубликована онлайн: 23.12.2023 DOI: 10.24075/vrgmu.2023.050
Colorectal cancer (CRC) occupies one of the leading places in the structure of cancer incidence in Russia and all over the world. Furthermore, prevalence of colon cancer in the Russian Federation increased from 116.7 to 161.0 cases per 100,000 population over the last decade, while prevalence of rectal cancer increased from 90.4 to 121.1 cases [1].
Today, cancer stage according to the TNM system and grade are the main parameters determining the CRC prognosis. However, even the groups of patients with CRC clinically homogeneous based on stage and grade are characterized by high heterogeneity of the disease course and uncertain prognosis. Such diversity is due to the fact that several tumor variants with different molecular pathogenesis that form the tumor biological heterogeneity go under the guise of the same morphological type of cancer.
Clinical prediction tools that are traditionally based on the statistical regression models represent one of the methods to combine all the prognostic information allowing one to avoid further stratification of the intermediate TNM system based on binary logistic regression [2]. With appropriate development and testing, these tools will be able to integrate and personalize information about the prognosis of certain patient and provide refined assessment of progression risk for clinical use.
To date, several test systems for determination of the disease prognosis and therapy efficacy based on assessing expression of genes in the tumor tissue have been created. Such test systems, as OncotypeDX, ColoPrint, ColDx, determine the likelihood of cancer progression based on the estimated expression of a number of genes in the tumor [3]. The efficiency of these commercially available systems is criticized in some papers. Thus, there is a report on the creation of a more effective system for prediction of CRC progression and I-II stage cancer response to treatment compared to OncotypeDX and ColoPrint [4]. The authors create a patient-specific treatment plan for early stage CRC, suggesting to include adjuvant chemotherapy in the treatment regimen, which is usually not done according to the current international and national guidelines.
There are also specific biological predictors for prediction of the possibility of CRC metastasis to the lymph nodes, such as heat shock protein 47 (HSP47) [5]. According to the authors, detection of this protein will also contribute to the treatment approach personalization in patients at high risk of metastasis to the lymph nodes.
Currently, various prognostic models based both on the use of advanced mathematical methods (neural network models, artificial intelligence models, construction of binary classification tree (BCT), peer reviews, etc.) and expansion of the set of explanatory variables (determination of point mutations, microsatellite instability, investigation of tumor microenvironment
and expression profiles) are constantly proposed. However, there is still a need for prognostic model improvement. These issues prompted us to assess the possibility of expression profiling of mRNAs from tumor specimens in order to evaluate the colorectal cancer prognosis.
METHODS
The mRNA expression profiles of 63 genes (Table 1) of potential contributors to various carcinogenesis pathways determined in 217 specimens of colorectal adenocarcinoma of different localization were included in the study. Adenocarcinoma specimens from the right half of the colon constituted 23% (50 specimens), while that from the left half of the colon constituted 39.6% (86 specimens). Rectal tumor specimens made up 37.4% (81 specimens). There were 97 males (44.7%), 120 females (45.3%). Specimens were collected during pathomorphological examination of surgical material. Inclusion criterion: morphologically verified colorectal adenocarcinoma, local stages T1-3, N0-2, M0. No specific anticancer treatment was performed before surgery. During the study the long-term outcomes were monitored in all patients for at least 36 months. The median follow-up period was 42 months. Exclusion criterion: multiple primary colorectal cancer, history of other type of cancer or cancer of other type at the time of inclusion in the study.
We had earlier reported the method of RNA extraction and real-time PCR settings [6]. As a result, relative expression of mRNAs of the studied panel of genes belonging to different functional groups was determined in each specimen of adenocarcinoma having different embryonic-anatomical localization.
Statistical data processing was performed using the Jamovi open source statistical software package (The Jamovi project; Australia). The logistic regression models constructed were evaluated based on R2 Durbin-Watson test for autocorrelation (DW). The model quality was considered acceptable at R2 > 0.3 and DW > 1.5.
We set the task to construct a binary logistic regression model for prediction of 3-year disease-free survival in patients with CRC using the mRNA expression profiles obtained considering the data of pathomorphological report. For that we formed a set of explanatory variables conditionally divided into two categories. The first category included the data of the surgical specimen pathomorphological examination: T and N, grade, lymphovascular and angiolymphatic invasion, ratio of the total number of resected lymph nodes to the number of metastatic ones. The second category of explanatory variables included mRNA expression profiles of 63 genes in the tumor specimens. To test predictive ability of the model developed, initial sample of patients was randomized into two subsamples:
Table 1. Genes included In the analysis
Biological effects In the cell Gene
Proliferation Ki-67, CCND1, CCNB1, PTEN, STK-15(AURKA), P16INK4A, P14ARF, HER2/neu(C-erbB2), TERT
Apoptosis BCL2, BAX, BAG1, NDRG1, BIRC5
Transcription С-MYC, MYBL2
Cell-cell interactions MMP2, MMP7, MMP8, MMP9, ММР11, CTSL2, PAPPA, TPA
Cell differentiation ESR1, PGR, CYP19A1, GRB7, CD45, CD56, CD68, CD69
Growth factors VEGFA121, VEGFA165, VEGFA189, SCUBE2, IGF1, IGF2, TGFp
Immune response regulation IL1e, IL2, IL6, IL7, IL8, IL10, IL12a, IL15, COX-2, TNFa, TLR2, TLR4, TLR7, IFNy, GNLY, HLA-G1, HLA-G5, LIF, LIFR, LGALS1, GATA3, IL2Ra, GREM1
Metabolism GSTM1
Table 2. Logit model using conventional criteria to predict the risk of CRC progression
Variables Coefficient Standard error z-Statistics Probability
Grade (G) -0.834489 0.368047 -2.267346 0.0234
Lymphovascular/angiolymphatic invasion -2.444482 0.582805 -4.194337 0
Resected to metastatic lymph node ratio -1.511334 0.583617 -2.589598 0.0096
Age 0.047712 0.017803 2.680001 0.0074
Tumor localization 0.724943 0.454279 1.595811 0.1105
constant 0.33256 1.114196 0.298475 0.7653
McFadden R2 0.292908
Note: Grade (G) Is "0" when grade Is G1 or G2 and "1" when grade Is G3; the variable Is "0" when histological assessment reveals no signs of anglolymphatlc and/or lymphovascular invasion and "1" when there is at least one such sign; the ratio of the number of metastatic lymph nodes to the total number of resected lymph nodes in the surgical specimen is 0-1; Age is the patient's age in whole years; Localization is tumor localization, which takes the value of "0" in cases of right-sided tumors (cecum, ascending colon, transverse colon) and "1" in cases of left-sided tumors (descending colon, sigmoid, rectum); 0.332 is a constant coefficient (constant) used in the model.
index (90% of observations) used to construct the model and control (10% of observations) used to assess the likelihood of prediction made using the model constructed. The percentages of the above subsamples suit the general practice adopted in modern scientific literature. Calculation was performed in the EViews v. 7.0 software package (IHS Global Inc.; USA). Assessment and comparison of the concurrent probit and logit models based on the McFadden's coefficient of determination and Akaike and Schwartz information criteria showed that logit specification was the most successful.
RESULTS
During the first phase, a logit model for prediction of CRC progression within 36 months after the diagnosis was constructed using conventional clinical and morphological criteria for CRC progression risk only as explanatory variables (Table 2).
R2 was 0.29, and Durbin-Watson statistic was 1.51. These characteristics of binary logistic regression model suggest that the analysis of conventional morphological risk factors of progression, such as adenocarcinoma grade, tumor localization, total number of resected lymph nodes and number of metastatic ones predicts the CRC progression probability with the minimally satisfactory accuracy. Table 3 provides a classification matrix of this logit model.
Fig. 1 shows accuracy of the model developed with inclusion of conventional prognostic factors.
As we have pointed out before, the overall prediction accuracy of this model (56.62%) was not high, while prediction accuracy of 37% in patients with no progression was considered to be unsatisfactory. We used mRNA expression profiles of a panel of 63 genes from tumor specimens as supplementary explanatory variables during the next phase of the study.
The research resulted in construction of the second logit model, in which mRNA expression profiles from tumor specimens were added to morphological characteristics as explanatory variables. A total of 12 characteristics (variables) turned out to be significant in the mathematical model (Table 4).
R2 coefficient was 0.4 in this model, while Durbin-Watson statistic was 1.64. A significant increase in the accuracy of the model constructed was achieved by including the expression levels of genes CCNB1, Ki67, GRB7, IGF1, IL2, IL6, IL8, GATA3 from tumor specimens in the regression equation. Classification matrix is provided in Table 5.
The overall classification accuracy was 80.6% (Fig. 2). We would like to emphasize that prediction accuracy in patients with no progression increased from 37 to 70.5% relative to the first model.
This model was used to calculate a personalized prognosis for each patient in our sample. Fig. 3 presents graphic representation of personal risk distribution based on progression detection. The median risk indicator was 57.1% [38.2; 70.7] in the group with no progression detected and 79.2% [68.3; 96.4] in cases of progression detection. The differences in risk indicators turned out to be significant (Kruskal-Wallis test: p < 0.05).
The risk factors of colorectal cancer progression assessed by pathomorphologists during the routine examination enable construction of the prognostic model that is minimally satisfactory in terms of accuracy. The increase in the prognostic model accuracy can be achieved through analysis of information beyond the bounds of pathomorphological stage. Assessment of mRNA expression profiles of genes CCNB1, Ki67, GRB7, IGF1, IL2, IL6, IL8, GATA3 in tumor specimens makes it possible to increase accuracy from 56.62 to 80.6%. The changes in expression of other genes of the panel also seem to be important, however, inclusion of those in the model does not result in higher accuracy due to multicollinearity, which can testify additionally that changes in the large intestinal mucosa associated with colorectal cancer are systemic.
We have noted that classical pathomorphological signs of high risk of CRC progression, such as lymphovascular and angiolymphatic invasion, grade, type of lymph node involvement, have negative regression coefficients, while GATA3 tumor suppressor has a positive coefficient. This pattern seems to be consistent: increased activity of the GATA3 cancer suppressor is typical for the less aggressive CRC course [7],
Table 3. Classification matrix of the logit model based on conventional clinical and morphological risk factors of CRC progression
Actual data (number of patients) Accurate prediction (number of patients) Prediction accuracy (%)
No progression 139 123 88.4
Progression 78 29 37
Overall accuracy 56.62
120 —
Progression No progression General prognosis
| Prognosis H Actual data
Prediction accuracy when using conventional clinical and morphological risk factors of CRC progression
Fig. 1.
while the presence of metastatic lymph nodes in the specimen, low grade, and angiolymphatic invasion indicate high risk of progression.
DISCUSSION
The urgent task of improving the outcomes of local stage colorectal cancer tertiary prevention is inextricably linked with objective stratification of cancer prognosis aimed at treatment personalization and, which is especially important, assessing the effectiveness of the existing and prospective treatment regimens. Despite the fact that to date pathomorphological CRC stage is the basis for the disease progression prognosis, it is the study of information about pathological progression without any reference to CRC stage that can become the key to
Table 4. Logit model using mRNA expression profiles
overcoming the challenge of progression risk assessment. This information can be represented primarily by molecular genetic data obtained by analysis of tissues of the affected organ. We used 12 indicators obtained during pathomorphological examination and molecular genetic testing of the tumor to develop a prognostic logit model of progression. These include both generally accepted risk factors, such as tumor grade, angiolymphatic and perineural invasion, as well as nature of changes in the lymph nodes resected during surgery, and mRNA expression of eight genes: CCNB1, Ki67, GRB7, IGF1, IL2, IL6, IL8, GATA3.
The role of these genes in carcinogenesis was repeatedly discussed in the literature [8-12]. These genes belong to functional groups of regulators of cell cycle (CCNB1) and proliferation (Ki-67 GRB7), growth factors (IGF-1) and cytokines
Variables Coefficient Standard error z-Statistics Probability
Lymphovascular/angiolymphatic invasion -2.631111 0.657195 -4.003548 0.0001
Resected to metastatic lymph node ratio ("lymph nodes") -2.101045 0.67982 -3.09059 0.002
Age 0.05032 0.019404 2.593334 0.0095
Grade (G) -1.028091 0.415533 -2.474153 0.0134
CCNB1 0.535019 0.207419 2.579411 0.0099
Ki67 -0.511469 0.276974 -1.846634 0.0648
GRB7 -0.456156 0.185794 -2.455168 0.0141
IGF1 -0.196378 0.079666 -2.465027 0.0137
IL2 -0.245648 0.100843 -2.435952 0.0149
IL6 0.202048 0.092241 2.190427 0.0285
IL8 -0.234825 0.094272 -2.490925 0.0127
GATA3 0.333421 0.174847 1.906929 0.0565
Constant 11.04682 3.481664 3.172857 0.0015
McFadden R2 0.403
Note: Grade (G) Is "0" when grade is G1 or G2 and "1" when grade is G3; the variable Is "0" when histological assessment reveals no signs of angiolymphatic and/or lymphovascular invasion and "1" when there is at least one such sign; the ratio of the number of metastatic lymph nodes to the total number of resected lymph nodes in the surgical specimen is 0-1; Age is the patient's age in whole years; CCNB1, Ki67, GRB7, IGF1, Il2, Il6, Il8, GATA3 are expression levels of appropriate genes in the tumor; 11.05 is a constant coefficient used in the model.
Table 5. Classification matrix of the logit model using mRNA expression profiles of eight genes
Actual data (number of patients) Accurate prediction (number of patients) Prediction accuracy (%)
No progression 139 120 86.3
Progression 78 55 70.5
Overall accuracy 80.6
100 -
80 -
60 -
40 -
20 -0
Progression
No progression
Prognosis
General prognosis
Actual data
Fig. 2. Prediction accuracy of the logit model using mRNA expression profiles of eight genes
(IL2, IL6, IL8) involved in the colorectal cancer invasion and metastasis [12].
When comparing our findings with the earlier reported data, including the reports of large-scale studies, we noticed that it was difficult to clearly interpret the characteristics of tumor grade, angiolymphatic and lymphovascular invasion due to the lack of common classification system and assessment standards [13-15]. This enables considerable variation of these characteristics across different clinics [16, 17]. Thus, for example, it is believed that PNI detection rate is usually underestimated, and the detection rate values vary between 9 and 42% [18]. The role of the resected to metastatic lymph node ratio in the specimen in CRC was first explored in 2005 [19]. This indicator was defined as a negative independent prognostic factor in stage III disease associated with overall and disease-free survival of patients with CRC. The indicator has a stronger influence on the prognosis of rectal cancer, than that of colon cancer. Its prognostic value increases when assessing more than 12 lymph nodes. Critical values of this indicator vary between 0.125 and 0.3 in different studies. There is still no consensus about the minimum number of lymph nodes harvested for appropriate estimation of this parameter.
It should be noted that, thanks to the efforts of medical associations, the clinical genomic databases have become available In the recent years. The analysis of such datasets
100 -
T3
о о
75
50 -
25 -
allows one to better understand the CRC genomic landscape and assess treatment efficacy and safety in the subgroups of patients with different genomic profiles. It is noted in the literature that the differences between the databases on demographic, clinical characteristics, treatment regimens and overall survival should be considered when developing research and interpreting the results acquired from the clinical genomic databases [20].
Anyway, our findings confirm the trend: analysis of additional information, primarily molecular genetic data, beyond the bounds of pathomorphological stage in individuals with colorectal cancer significantly increases accuracy of predicting the likelihood of progression. Furthermore, the search for new predictors and, just as important, extensive validation of prognostic systems should be continued.
CONCLUSIONS
We have found that the risk factors of CRC progression identified during standard pathomorphological examination ensure prediction accuracy of 56.62% when using a binary prognostic logit model in our sample of patients. Moreover, classification errors occur primarily because of patients showing no progression throughout the 36-month follow-up period. Inclusion of mRNA expression levels of genes CCNB1, Ki67, GRB7, IGF1, Il2, Il6, Il8, GATA3 from tumor
097]
I?
. [Ц9]
0 1 Fig. 3. Individual prognosis distribution in the model using clinical and morphological risk factors of progression
specimens in the model as explanatory variables increases prediction accuracy to 80.6%. This suggests that expansion of the search for outcome predictors beyond the bounds of
the TNM pathomorphological stage is a promising way to increase accuracy in order to implement effective CRC tertiary prevention measures.
References
1. Kaprin AD, Starinskij VV Shahzadova AO. Sostojanie onkologicheskoj pomoshhi naseleniju Rossii v 2021 godu. Rossijskij Centr informacionnyh tehnologij i jepidemiologicheskih issledovanij v oblasti onkologii. Moskva, 2022. Russian.
2. Steyerberg EW, Moons KGM, van der Windt DA, et al. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS medicine. 2013; 10: e1001381.
3. Brenner H, Chen C. The colorectal cancer epidemic: challenges and opportunities for primary, secondary and tertiary prevention. Br J Cancer. 2018; 119 (7): 785-92. DOI: 10.1038/s41416-018-0264-x. Epub 2018 Oct 4. PMID: 30287914; PMCID: PMC6189126.
4. Lin XT, Wu QN, Qin S, Fan DJ, Lv MY, Chen X, et al. Identification of an Autophagy-Related Gene Signature for the Prediction of Prognosis in Early-Stage Colorectal Cancer. Front Genet. 2021; 12: 755789. DOI: 10.3389/fgene.2021.755789. PMID: 34899841; PMCID: PMC8657766.
5. Mori K, Toiyama Y, Okugawa Y, Ichikawa T, Nagano Y, Oki S, et. al. Preoperative heat shock protein 47 levels identify colorectal cancer patients with lymph node metastasis and poor prognosis. Oncol Lett. 2020; 20 (6): 333. DOI: 10.3892/ol.2020.12196. Epub 2020 Oct 7. PMID: 33123244; PMCID: PMC7583735.
6. Bozhenko VK, Stanojevic US, Trotsenko ID, et al. Comparison of matrix proteinase mRNA expression in morphologically normal, neoplastic, and metastatic colon tissue and colon biopsies from healthy donors. Biomed Khim. 2018; 64 (1): 46-52. DOI: 10.18097/PBMC20186401046. Russian.
7. French CL, Ye F, Revetta F, et al. Linking patient outcome to high throughput protein expression data identifies novel regulators of colorectal adenocarcinoma aggressiveness. F1000Res. 2015; 4: 99. DOI: 10.12688/f1000research.6388.1.
8. Fang Y, Yu H, Liang X, Xu J, Cai X. Chk1-induced CCNB1 overexpression promotes cell proliferation and tumor growth in human colorectal cancer. Cancer Biol Ther. 2014; 15 (9): 1268-79. DOI: 10.4161/cbt.29691. Epub 2014 Jun 27. PMID: 24971465; PMCID: PMC4128869.
9. Hu B, Song X, Ding W, Wang H, Cai H, Huang Z. Expression and Clinical Significance of Ki67 and SOX2 in Colorectal Cancer. J Healthc Eng. 2023; 2023: 3783631. DOI: 10.1155/2023/3783631. PMID: 37457497; PMCID: PMC10348847.
10. Vermehren-Schmaedick A, Mhawech-Fauceglia P, Park BS, Pejovic T, Luoh SW. The prognostic significance of GRB7 protein expression and localization in human breast and ovarian cancers. Oncotarget. 2020; 11 (24): 2273-89. DOI: 10.18632/
oncotarget.27593. PMID: 32595827; PMCID: PMC7299530.
11. Kasprzak A. Insulin-Like Growth Factor 1 (IGF-1) Signaling in Glucose Metabolism in Colorectal Cancer. Int J Mol Sci. 2021; 22 (12): 6434. DOI: 10.3390/ijms22126434. PMID: 34208601; PMCID: PMC8234711.
12. Li J, Huang L, Zhao H, Yan Y, Lu J. The Role of Interleukins in Colorectal Cancer. Int J Biol Sci. 2020; 16 (13): 2323-39. DOI: 10.7150/ijbs.46651. PMID: 32760201; PMCID: PMC7378639.
13. Ueno H, Hase K, Hashiguchi Y, Shimazaki H, Tanaka M, Miyake O, et al. Site-specific tumor grading system in colorectal cancer: Multicenter pathologic review of the value of quantifying poorly differentiated clusters. Am J Surg Pathol. 2014; 38: 197-204. Available from: https://doi.org/10.1097/pas.0000000000000113/
14. Yuan H, Dong Q, Zheng BA, Hu X, Xu J-B, Tu S. Lymphovascular invasion is a high risk factor for stage I/II colorectal cancer: A systematic review and meta-analysis. Oncotarget. 2017; 8: 46565-79. Available from: https://doi.org/10.18632/ oncotarget.15425.
15. Al-Sukhni E, Attwood K, Gabriel EM, LeVea CM, Kanehira K, Nurkin SJ. Lymphovascular and perineural invasion are associated with poor prognostic features and outcomes in colorectal cancer: A retrospective cohort study. Int J Surg. 2017; 37: 42-49. Available from: https://doi.org/10.1016/j.ijsu.2016.08.528.
16. Harris EI, Lewin DN, Wang HL, Lauwers GY, Srivastava A, Shyr Y, et al. Lymphovascular invasion in colorectal cancer: An interobserver variability study. Am J Surg Pathol. 2008; 32: 1816.
17. Liebig C, Ayala G, Wilks JA, Berger DH, Albo D. Perineural invasion in cancer: A review of the literature. Cancer: Inter-discip. Int J Am Cancer Soc. 2009; 115: 3379-91.
18. (CAP) CoAP. Protocol for the Examination of Specimens from Patients with Primary Carcinoma of the Colon and Rectum; 2020. Available online: https://documents.cap.org/protocols/ cp-gilower-colonrectum-resection-20-4100.pdf (accessed on 15 August 2021).
19. Berger AC, Sigurdson ER, LeVoyer T, Hanlon A, Mayer RJ, Macdonald JS, et.al. Colon cancer survival is associated with decreasing ratio of metastatic to examined lymph nodes. J Clin Oncol Off J Am Soc Clin Oncol. 2005; 23; 8706-12. Available from: https://doi.org/10.1200/jco.2005.02.8852.
20. Wang CY, Shao C, McDonald AC, Amonkar MM, Zhou W, Bortnichak EA, et al. Evaluation and Comparison of Real-World Databases for Conducting Research in Patients With Colorectal Cancer. JCO Clin Cancer Inform. 2023; 7: e2200184. DOI: 10.1200/CCI.22.00184. PMID: 37437227.
Литература
1. Каприн А. Д., Старинский В. В., Шахзадова А.О. Состояние онкологической помощи населению России в 2021 году. Российский Центр информационных технологий и эпидемиологических исследований в области онкологии. Москва, 2022.
2. Steyerberg EW, Moons KGM, van der Windt DA, et al. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS medicine. 2013; 10: e1001381.
3. Brenner H, Chen C. The colorectal cancer epidemic: challenges and opportunities for primary, secondary and tertiary prevention. Br J Cancer. 2018; 119 (7): 785-92. DOI: 10.1038/s41416-018-0264-x. Epub 2018 Oct 4. PMID: 30287914; PMCID: PMC6189126.
4. Lin XT, Wu QN, Qin S, Fan DJ, Lv MY, Chen X, et al. Identification of an Autophagy-Related Gene Signature for the Prediction
of Prognosis in Early-Stage Colorectal Cancer. Front Genet. 2021; 12: 755789. DOI: 10.3389/fgene.2021.755789. PMID: 34899841; PMCID: PMC8657766.
5. Mori K, Toiyama Y, Okugawa Y, Ichikawa T, Nagano Y, Oki S, et. al. Preoperative heat shock protein 47 levels identify colorectal cancer patients with lymph node metastasis and poor prognosis. Oncol Lett. 2020; 20 (6): 333. DOI: 10.3892/ol.2020.12196. Epub 2020 Oct 7. PMID: 33123244; PMCID: PMC7583735.
6. Боженко В. К., Станоевич У. С., Троценко И. Д. и др. Cравнение экспрессии мРНК матриксных металлопротеиназ в морфологически нормальной, неопластической и метастатической тканях толстого кишечника и в биоптатах здоровых доноров. Биомедицинская химия. 2018; 64 (1): 4652. DOI: 10.18097/PBMC20186401046.
7. French CL, Ye F, Revetta F, et al. Linking patient outcome to high
throughput protein expression data identifies novel regulators of colorectal adenocarcinoma aggressiveness. F1000Res. 2015; 4: 99. DOI: 10.12688/f1000research.6388.1.
8. Fang Y, Yu H, Liang X, Xu J, Cai X. Chk1-induced CCNB1 overexpression promotes cell proliferation and tumor growth in human colorectal cancer. Cancer Biol Ther. 2014; 15 (9): 1268-79. DOI: 10.4161/cbt.29691. Epub 2014 Jun 27. PMID: 24971465; PMCID: PMC4128869.
9. Hu B, Song X, Ding W, Wang H, Cai H, Huang Z. Expression and Clinical Significance of Ki67 and SOX2 in Colorectal Cancer. J Healthc Eng. 2023; 2023: 3783631. DOI: 10.1155/2023/3783631. PMID: 37457497; PMCID: PMC10348847.
10. Vermehren-Schmaedick A, Mhawech-Fauceglia P, Park BS, Pejovic T, Luoh SW. The prognostic significance of GRB7 protein expression and localization in human breast and ovarian cancers. Oncotarget. 2020; 11 (24): 2273-89. DOI: 10.18632/ oncotarget.27593. PMID: 32595827; PMCID: PMC7299530.
11. Kasprzak A. Insulin-Like Growth Factor 1 (IGF-1) Signaling in Glucose Metabolism in Colorectal Cancer. Int J Mol Sci. 2021; 22 (12): 6434. DOI: 10.3390/ijms22126434. PMID: 34208601; PMCID: PMC8234711.
12. Li J, Huang L, Zhao H, Yan Y, Lu J. The Role of Interleukins in Colorectal Cancer. Int J Biol Sci. 2020; 16 (13): 2323-39. DOI: 10.7150/ijbs.46651. PMID: 32760201; PMCID: PMC7378639.
13. Ueno H, Hase K, Hashiguchi Y, Shimazaki H, Tanaka M, Miyake O, et al. Site-specific tumor grading system in colorectal cancer: Multicenter pathologic review of the value of quantifying poorly differentiated clusters. Am J Surg Pathol. 2014; 38: 197-204. Available from: https://doi.org/10.1097/pas.0000000000000113/
14. Yuan H, Dong Q, Zheng BA, Hu X, Xu J-B, Tu S. Lymphovascular
invasion is a high risk factor for stage I/II colorectal cancer: A systematic review and meta-analysis. Oncotarget. 2017; 8: 46565-79. Available from: https://doi.org/10.18632/ oncotarget.15425.
15. Al-Sukhni E, Attwood K, Gabriel EM, LeVea CM, Kanehira K, Nurkin SJ. Lymphovascular and perineural invasion are associated with poor prognostic features and outcomes in colorectal cancer: A retrospective cohort study. Int J Surg. 2017; 37: 42-49. Available from: https://doi.org/10.1016/Ujsu.2016.08.528.
16. Harris EI, Lewin DN, Wang HL, Lauwers GY, Srivastava A, Shyr Y, et al. Lymphovascular invasion in colorectal cancer: An interobserver variability study. Am J Surg Pathol. 2008; 32: 1816.
17. Liebig C, Ayala G, Wilks JA, Berger DH, Albo D. Perineural invasion in cancer: A review of the literature. Cancer: Inter-discip. Int J Am Cancer Soc. 2009; 115: 3379-91.
18. (CAP) CoAP. Protocol for the Examination of Specimens from Patients with Primary Carcinoma of the Colon and Rectum; 2020. Available online: https://documents.cap.org/protocols/ cp-gilower-colonrectum-resection-20-4100.pdf (accessed on 15 August 2021).
19. Berger AC, Sigurdson ER, LeVoyer T, Hanlon A, Mayer RJ, Macdonald JS, et.al. Colon cancer survival is associated with decreasing ratio of metastatic to examined lymph nodes. J Clin Oncol Off J Am Soc Clin Oncol. 2005; 23; 8706-12. Available from: https://doi.org/10.1200/jco.2005.02.8852.
20. Wang CY, Shao C, McDonald AC, Amonkar MM, Zhou W, Bortnichak EA, et al. Evaluation and Comparison of Real-World Databases for Conducting Research in Patients With Colorectal Cancer. JCO Clin Cancer Inform. 2023; 7: e2200184. DOI: 10.1200/CCI.22.00184. PMID: 37437227.
