JPWO2007132883A1 - Differentiation of liver cancer, determination of disease stage or prognosis prediction method and kit - Google Patents

Abstract

By measuring the expression of a specific gene in any one of blood, liver cancer cells or liver cancer tissue, it becomes possible to distinguish liver cancer, determine disease stage or predict prognosis. A method for differentiation of liver cancer, determination of disease stage or prediction of prognosis, comprising measuring expression of a specific gene in any of blood, liver cancer cells or liver cancer tissue, wherein the specific gene is SP of liver cancer cells The method as described above, wherein the expression is different between the fraction and the Non-SP fraction. Kits for differentiation of liver cancer, determination of disease stage or prediction of prognosis, methods for detecting liver cancer stem cells, methods for distinguishing liver cancer stem cells from liver cancer non-stem cells, liver cancer stem cells and methods for preparing the same are also provided.

Description

  The present invention relates to a method and kit for predicting liver cancer, determining disease stage, or prognosis.

  In general, cancer cells exhibit functional heterogeneity in various types of cancer (Non-Patent Document 1), but cancer has been considered to be a single cell in its origin (Non-Patent Documents 2 and 3). Colony formation assay and spleen colony assay in soft agar medium showed that cells having colony forming ability are a small population, and most cancer cells have no colony forming ability (Non-patent Documents 4 and 5). ). In the in vivo transplantation assay, cancer occurred even when a small subset of cancer cells were injected (Non-patent Documents 6 and 7). These results indicate that only a small population of cells have the ability to elicit tumors and the majority of cells lack the activity to elicit tumors. By observing these experiments, two general theories were derived (Non-Patent Documents 8 and 9). Probabilistic models show that by experiencing a stochastic event, a cancer is composed of a relatively homogeneous population of cells and a few cells that gain extreme growth potential and form new tumors. ing. Another hypothesis (ie, hierarchical model) assumes that a subpopulation of cancer stem cells forms a hierarchical structure that includes a variety of differentiated progeny cells and induces new tumors with high frequency by proliferating extremely.

  Technological advances in stem cell biology such as cell separation using flow cytometry, cell culture, and transplantation into immunodeficient mice have facilitated identification of stem cells in tumors. A small subset of cells having the ability to induce human acute myeloid leukemia in non-obese diabetic / severe combined immunodeficiency (NOD / SCID) mice was identified from leukemia patients (Non-Patent Document 10). These cells were isolated as CD34 + CD38- cells and could induce leukemia even in serial transplantation. Furthermore, it has been reported that there is a common mechanism for regulating self-renewal of both hematopoietic stem cells and leukemia stem cells (Non-patent Document 11). Beyond the leukemia stem cell field, attempts have also been made to detect tumorigenic subpopulations in solid tumors using flow cytometry. Recently, CD44 + CD24− / low ESA (epithelial specific antigen) + breast cancer cells and CD133 + cells of medulloblastoma or glioblastoma have been identified as expected “cancer stem cells” (Non-patent Document 12). , 13). In these studies, a small subset of cells purified as cancer stem cells showed tumorigenic potential, but the majority of tumor cells did not show such potential in in vivo transplantation assays. Cancer stem cell phenotype and solid tumors in leukemia recycle most of their origin stem cells, so cancer stem cells probably regulate the balance between self-renewal and differentiation, and normal stem cells form tissues in development and regeneration They are reconstructing the tumor in the same way they do it. From these observations obtained from culture and in vivo transplantation assays, a hierarchical model may be proposed (Non-Patent Document 14).

  Liver cancer cells (HCC) are a common malignant tumor all over the world and still have a high mortality rate (Non-patent Document 15). It has been estimated that accumulation of genetic and epigenetic changes contributes to multistage liver cancer formation (Non-patent Documents 16-18). However, the overall mechanism underlying liver cancer formation has clearly not been demonstrated. Furthermore, the cellular origin of HCC remains unclear. Identifying what is expected to be cancer stem cells and elucidating the hierarchy in HCC cells may help understand liver carcinogenesis and explore new therapies.

  Side population (SP) cell sorting is first applied to the identification of hematopoietic stem cells, and is used to concentrate stem cell compartments in a wide variety of tissues and organs (Non-patent Documents 19-21). SP cells are detected by their ability to efflux Hoechst 33342 dye through the ATP binding cassette (ABC) membrane transporter. Recently, SP cells have also been used in attempts to isolate a stem cell-like fraction of cancer cells (Non-patent Document 22). This method seems reasonable and valuable. This is because various cancers including HCC highly express the ABC transporter and have been shown to contribute closely to multidrug resistance (Non-patent Document 23).

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  The present invention clarified whether SP cells purified from hepatoma cells have cancer stem cell-like properties, investigated the difference in gene expression profiles between SP cells and Non-SP cells, and utilized the information Thus, an object is to enable identification of liver cancer, determination of disease stage, or prediction of prognosis.

Recent advances in stem cell biology have made it possible to identify cancer stem cells in solid tumors as well as putative stem cells in normal solid tissues. In order to detect subpopulations functioning as cancer stem cells and elucidate their role in tumorigenesis, the present inventors performed side population (SP) cell analysis and sorting, and hepatocellular carcinoma (HCC) cell line Established. SP subpopulations in Huh7 and PLC / PRF / 5 cells were detected in 0.25% and 0.80% of the total cell number, respectively. SP cells showed higher proliferation ability and anti-apoptosis than non-SP cells. Immunocytochemistry revealed that the SP fraction contains a large number of cells that display lineage characteristics of both hepatocytes and bile duct cells. Xenotransplantation experiments in non-obese diabetic / severe combined immunodeficiency (NOD / SCID) mice showed that only 1 × 10 ³ SP cells were sufficient for tumor formation, whereas 1 × 10 ⁶ Non-SP It became clear that injection of cells did not cause tumors. Re-analysis of tumors induced by SP cells revealed that SP cells generated both SP cells and Non-SP cells, and the potential for inducing tumors was maintained only in SP cells in successive transplants. Microarray analysis revealed differences in gene expression profiles between SP cells and Non-SP cells, and several so-called “stemness genes” were up-regulated in SP cells in HCC cells. In conclusion, we have found that a small number of SP cells detected in HCC cells have a very high tumorigenic potential and are heterogeneous in the cancer stem cell system characterized by a clear hierarchical structure I advocate that it is composed.

  When gene expression profiles are compared between SP and Non-SP cells, genes that are up-regulated and down-regulated in SP cells can be used to differentiate liver cancer, determine disease stage, or predict prognosis. It can be used as a marker.

The gist of the present invention is as follows.
(1) A method for differentiation of liver cancer, determination of disease stage or prediction of prognosis, comprising measuring the expression of a specific gene in any of blood, liver cancer cells or liver cancer tissue, wherein the specific gene is liver cancer The aforementioned method, wherein the expression is different between the SP fraction of cells and the Non-SP fraction.
(2) The method according to (1), wherein the specific gene is at least one of the genes listed in any of Tables A, B, C, or D.
(3) When the expression of at least one of the genes listed in Tables A and / or C is elevated, liver cancer is highly malignant, the disease is progressing, or the patient has a poor prognosis, or The method according to (2), wherein a risk of recurrence is predicted to be high.

(4) When the expression of at least one of the genes listed in Tables B and / or D is decreased, the malignancy of liver cancer is high, the disease is progressing, or the patient has a poor prognosis, or The method according to (2), wherein a risk of recurrence is predicted to be high.
(5) Specific genes are CHM, MSH6, NCOA7, CYP2E1, TM4SF1, F2RL1, TRPM3, AKR1C1, FOXH1, RAB40C, MGC27085, PPP1R3E, UST, CANP, PRSS3, UACA, FOSL2, PSPC1, EXOSC6, LOC388558, RTOS558 FAM20A, FGF18, HOXA13, FOLR1, TMF1, PCGF5, GSTA1, XRCC5, MAP3K7IP2, RTN4, BCDO2, CDH22, MYCNOS, FMNL2, PHEX, WDR56, HERC6, SBNO1, FDFT1, ZFR and PF6 and PCOTH, ZCCHC6, INHBC, ATP8B3, TAL1, FGF1, ADCY9, CAPS2, ANXA2, FLJ34077, AD7C-NTP, GLRB, MGAT2, SASH1, HIF1A, LMTK3, EYA3, IPP, CAPN2, GAFA1, HELLS, NF1, NUSAP1 and SESN3 The method according to (1), which is at least one gene to be selected.

(6) CHM, MSH6, NCOA7, CYP2E1, TM4SF1, F2RL1, TRPM3, AKR1C1, FOXH1, RAB40C, MGC27085, PPP1R3E, UST, CANP, PRSS3, UACA, FOSL2, PSPC1, EXOSC6, LOC388558, XTN20, RTTN, FAM Expression of at least one gene selected from the group consisting of FOLR1, TMF1, PCGF5, GSTA1, XRCC5, MAP3K7IP2, RTN4, BCDO2, CDH22, MYCNOS, FMNL2, PHEX, WDR56, HERC6, SBNO1, FDFT1, ZFR and PF6 The method according to (5), wherein when it is elevated, it is predicted that the malignancy of liver cancer is high, the disease is progressing, the prognosis of the patient is poor, or the risk of recurrence is high.
(7) PCOTH, ZCCHC6, HRMT1L1, INHBC, ATP8B3, TAL1, FGF1, ADCY9, CAPS2, ANXA2, FLJ34077, AD7C-NTP, GLRB, MGAT2, SASH1, HIF1A, LMTK3, EYA3, IPP, CAPN2, NF A1, HELLS, , If the expression of at least one gene selected from the group consisting of NUSAP1 and SESN3 is reduced, the risk of liver cancer is high, the disease is advanced, the patient has a poor prognosis, or recurrence (5) The method of description which estimates that it is high.

(8) The method according to (1), wherein the specific gene is XRCC5.
(9) The method according to (8), wherein when the expression of XRCC5 is elevated, the cancer malignancy is high, the disease is progressing, the patient has a poor prognosis, or the risk of recurrence is high .
(10) The method according to any one of (1) to (9), wherein expression of a specific gene is measured at a protein level.
(11) The method according to any one of (1) to (9), wherein the expression of a specific gene is measured at the RNA level.
(12) For differentiation of liver cancer, determination of disease stage, or prediction of prognosis, including a reagent capable of measuring the expression of a gene whose expression is different between the SP fraction and non-SP fraction of liver cancer cells kit.

(13) A method for detecting liver cancer stem cells, comprising measuring the expression of a specific gene in any one of blood, liver cancer cells or liver cancer tissue, wherein the specific gene comprises an SP fraction of a liver cancer cell and a Non-SP The above-mentioned method, wherein the expression is different between the fractions.
(14) A method for distinguishing between hepatoma stem cells and hepatoma non-stem cells, comprising measuring the expression of a particular gene in any of blood, liver cancer cells or liver cancer tissue, wherein the particular gene is an SP image of hepatoma cells. The method, wherein the expression is different between the fraction and the Non-SP fraction.
(15) Liver cancer cells retaining tumorigenicity when secondary transplantation is performed.
(16) The method for preparing a hepatoma cell according to (15), comprising separating a cell population having a weak fluorescence signal by a side population cell separation method.
(17) A method for screening an anticancer agent using the liver cancer cell according to (15).

According to the present invention, diagnostic markers for differentiation of liver cancer, determination of disease stage, or prediction of prognosis are provided.
The present invention also provides a method and kit for predicting liver cancer, determining a disease stage, or predicting prognosis.
The present invention also provides a method for detecting liver cancer stem cells, a method for distinguishing liver cancer stem cells from liver cancer non-stem cells, a liver cancer stem cell, and a method for preparing the same.
This specification includes the contents described in the specification and / or drawings of Japanese Patent Application No. 2006-138072 which is the basis of the priority of the present application.

SP cell analysis. HepG2 and Huh6 cells do not contain the SP fraction, whereas 0.25% and 0.80% SP subpopulations were detected in Huh7 and PLC / PRF / 5 cells, respectively. SP cells disappeared by Hoechst and verapamil treatment. Cell viability (A, B) and apoptotic sensitivity (C, D) at 24, 48 and 72 hours after separation in Huh7 and PLC / PRF / 5 cells. SP cells purified from Huh7 and PLC / PRF / 5 cells showed higher survival than the corresponding Non-SP cells. The number of TUNEL positive cells in SP cells was consistently higher than the number of TUNEL positive cells in Non-SP cells in both cells. * Indicates statistical significance. Immunohistochemical analysis in Huh7 and PLC / PRF / 5 SP cells. (AD) AFP and CK19 expression was examined in Huh7 SP cells (A), Huh7 Non-SP cells (B), PLC / PRF / 5 SP cells (C) and PLC / PRF / 5 Non-SP cells (D) . (E, F) Cells positive for either AFP or CK19 were found preferentially in Non-SP cells, but many cells positive for both AFP and CK19 were Huh7 SP cells (E) And observed in PLC / PRF / 5 SP cells (F). Scale bar = (a, b) 20 μm; (c, d) = 50 μm Tumorigenicity in SP cells. (a) Representative subcutaneous tumor (arrow) by injection of 1 × 10 ³ Huh7 SP cells. Hematoxylin and eosin staining of tumors derived from Huh7 SP cells (b) and PLC / PRF / 5 SP cells (c). Non-SP cell transplantation failed to elicit tumors in 16 weeks. Scale bar = 50 μm Reanalysis of SP-derived tumors. SP subpopulations in tumor cells derived from Huh7 and PLC / PRF / 5 SP were detected at 0.34% and 0.90%, respectively. The SP analysis pattern was similar to that of previously separated Huh7 and PLC / PRF / 5 cells. Gene expression profile based on microarray. (A) Of the genes up-regulated in Huh7 and PLC / PRF / 5 SP cells (ratio> 2.0), 440 and 789 genes were characterized, respectively. Those genes were categorized into 11 groups. (B) Overlapping of stem cell genes in gene expression profiles in HCC SP cells with stem cell enriched genes reported by Ramalho-Santos et al. And Ivanova et al. (C) The fold change of SP / Non-SP calculated by real-time RT-PCR is relatively consistent with that of microarray data. ESC is embryonic stem cell, NSC is neural stem cell, HSC is hematopoietic stem cell.

Hereinafter, embodiments of the present invention will be described in more detail.
The present invention provides a method for differentiating liver cancer, determining a disease stage, or predicting prognosis, including measuring the expression of a specific gene in any of blood, liver cancer cells, or liver cancer tissue. Specific genes are expressed differently in the SP fraction and Non-SP fraction of hepatoma cells. Examples of “different expression” include the presence or absence of gene expression and the difference in expression level. The specific gene is preferably at least one of the genes listed in any of Tables A, B, C, or D, and may be one or more. For example, CHM, MSH6, NCOA7, CYP2E1, TM4SF1, F2RL1, TRPM3, AKR1C1, FOXH1, RAB40C, MGC27085, PPP1R3E, UST, CANP, PRSS3, UACA, FOSL2, PSPC1, EXOSC6, LOC388558, RTTN, FAM20, RTTN, FAM20 FOLR1, TMF1, PCGF5, GSTA1, XRCC5, MAP3K7IP2, RTN4, BCDO2, CDH22, MYCNOS, FMNL2, PHEX, WDR56, HERC6, SBNO1, FDFT1, ZFR, PF6, PCOTH, ZCCHC6, HRMT1L1, INHBC, ATP8B3 Examples include ADCY9, CAPS2, ANXA2, FLJ34077, AD7C-NTP, GLRB, MGAT2, SASH1, HIF1A, LMTK3, EYA3, IPP, CAPN2, GAFA1, HELLS, NF1, NUSAP1, SESN3, and XRCC5.

Table A summarizes genes up-regulated in Huh7 SP cells (confirmed in the examples below). Table B summarizes the genes down-regulated in Huh7 SP cells (confirmed in the examples below). Table C summarizes genes upregulated in Alexander cells (same cell lines as PLC / PRF / 5 SP cells) (confirmed in Examples described below). Table D summarizes the genes down-regulated in Alexander cells (same cell lines as PLC / PRF / 5 SP cells) (confirmed in the examples below). In the table, expression level in the first column, gene name in the second column (abbreviation: NCBI symbol name), GeneBank registration number in the third column, gene name in the fourth to seventh columns, and gene name in the eighth column Indicates the cloning method and the 9th column contains the Unigene registration number.
PCOTH, ZCCHC6, HRMT1L1, INHBC, ATP8B3, TAL1, FGF1, ADCY9, CAPS2, ANXA2, FLJ34077, AD7C-NTP, GLRB, MGAT2, SASH1, HIF1A, LMTK3, EYA3, IPP, CAPN2, GAFA1, HELLS, NF1, SAP1 SESN3 is a gene that was down-regulated in both Huh7 SP cells and Alexander cells (the same cell line as PLC / PRF / 5 SP cells) (confirmed in the examples described later).

CHM, MSH6, NCOA7, CYP2E1, TM4SF1, F2RL1, TRPM3, AKR1C1, FOXH1, RAB40C, MGC27085, PPP1R3E, UST, CANP, PRSS3, UACA, FOSL2, PSPC1, EXOSC6, LOC388558, RTTN, FAM20A, FGF TMF1, PCGF5, GSTA1, XRCC5, MAP3K7IP2, RTN4, BCDO2, CDH22, MYCNOS, FMNL2, PHEX, WDR56, HERC6, SBNO1, FDFT1, ZFR and PF6 are Huh7 SP cells and Alexander cells (with PLC / PRF / 5 SP cells) Genes up-regulated in both (same cell line) (confirmed in Examples below).
XRCC5 is a stem cell gene that is up-regulated in both Huh7 SP cells and Alexander cells (the same cell line as PLC / PRF / 5 SP cells) (confirmed in the examples described later).
For example, liver cancer can be identified, disease stage can be determined, or prognosis can be predicted as follows.

1. Risk of increased malignancy of liver cancer, advanced disease, poor patient prognosis, or recurrence when expression of at least one of the genes listed in Tables A and / or C is elevated Is expected to be high.
2. Risk of high grade of liver cancer, advanced disease, poor patient prognosis, or recurrence when expression of at least one of the genes listed in Tables B and / or D is reduced Is expected to be high.
3. CHM, MSH6, NCOA7, CYP2E1, TM4SF1, F2RL1, TRPM3, AKR1C1, FOXH1, RAB40C, MGC27085, PPP1R3E, UST, CANP, PRSS3, UACA, FOSL2, PSPC1, EXOSC6, LOC388558, RTTN, FAM20A, FGF Increased expression of at least one gene selected from the group consisting of TMF1, PCGF5, GSTA1, XRCC5, MAP3K7IP2, RTN4, BCDO2, CDH22, MYCNOS, FMNL2, PHEX, WDR56, HERC6, SBNO1, FDFT1, ZFR and PF6 Predictive that liver cancer is highly malignant, disease is progressing, patient prognosis is poor, or recurrence risk is high.

4). PCOTH, ZCCHC6, HRMT1L1, INHBC, ATP8B3, TAL1, FGF1, ADCY9, CAPS2, ANXA2, FLJ34077, AD7C-NTP, GLRB, MGAT2, SASH1, HIF1A, LMTK3, EYA3, IPP, CAPN2, GAFA1, HELLS, NF1, SAP1 When the expression of at least one gene selected from the group consisting of SESN3 is decreased, the malignancy of liver cancer is high, the disease is progressing, the patient has a poor prognosis, or the risk of recurrence is high Predict.
5. If the expression of XRCC5 is elevated, predict that liver cancer is highly malignant, the disease is progressing, the patient has a poor prognosis, or the risk of recurrence is high.

In the differentiation, determination or prediction method of the present invention, when it is determined that the expression of a specific gene is increased or decreased, normal hepatocytes or liver tissue may be compared as a control. Alternatively, expression of a specific gene may be compared between liver cancer with poor prognosis (with recurrence / invasion / metastasis) and liver cancer with good prognosis (without recurrence / invasion / metastasis). In that case, the control is a liver cancer with a good prognosis.
In the method of the present invention, the expression of a specific gene in any one of blood, liver cancer cells or liver cancer tissue may be measured at the protein level or at the RNA level. For example, it can be measured by Northern blotting, PCR, Western blotting, immunohistochemical analysis and the like. Alternatively, measurement may be performed using a cDNA microarray, ELISA method or the like.

In order to measure the expression of a specific gene at the protein level, an antibody that specifically recognizes the protein to be measured may be used. The antibody may be either a monoclonal antibody or a polyclonal antibody. These antibodies can be produced by known methods, and some are commercially available. When measured by Western blotting, the antibody is secondarily detected using ¹²⁵ I-labeled protein A, peroxidase-conjugated IgG, or the like. When measurement is performed by immunohistochemical analysis, the antibody may be labeled with a fluorescent dye, ferritin, enzyme, or the like.

  In order to measure the expression of a specific gene at the RNA level, a nucleic acid probe that can specifically hybridize with the mRNA of the gene to be measured may be used (when measured by Northern blotting). Alternatively, a pair of nucleic acid primers comprising a nucleic acid primer that can specifically hybridize with mRNA of a gene to be measured and a nucleic acid primer that can specifically hybridize with cDNA synthesized using the mRNA as a template may be used. Good (when measuring by PCR method). The nucleic acid probe and the nucleic acid primer can be designed based on the sequence information of the gene to be measured. The nucleic acid probe is usually about 15 to 1500 bases. The nucleic acid probe may be labeled with a radioactive element, a fluorescent dye, an enzyme, or the like. The nucleic acid primer is usually about 15 to 30 bases.

  In the present invention, the presence or absence of specific gene expression in blood, liver cancer cells, or liver cancer tissue may be detected, or the expression level may be measured. The presence or absence of gene expression can be confirmed by the presence or absence of the appearance of spots or bands at a predetermined position. The gene expression level can be measured by the staining intensity of spots and bands. Alternatively, a protein that is a gene expression product and / or mRNA that is a gene transcription product may be quantified by a known method. In order to detect multiple gene expression and multiple protein expression at the same time, DNA array (probe is fixed to substrate) (NATURE REVIEWS, DRUG DISCOVERY, VOLUME 1, DECEMBER 2002, 951-960), protein chip (antibody substrate) It is preferable to use a detection method such as (NATURE REVIEWS, DRUG DISCOVERY, VOLUME 1, SEPTEMBER 2002, 683-695), Luminex (NATURE REVIEWS, DRUG DISCOVERY, VOLUME 1, JUNE 2002, 447-456).

The blood, liver cancer cells or liver cancer tissue is preferably derived from the subject to be determined, and the biological species of the subject is human, pig, monkey, chimpanzee, dog, cow, rabbit, rat, mouse, etc. Can be mentioned.
In order to differentiate liver cancer, determine disease stage or predict prognosis by the method of the present invention, a liver cancer biopsy sample, or cultured liver cancer cells, cultured liver cancer tissue, blood and the like obtained therefrom can be used. . An example of a liver cancer biopsy sample is a tumor tissue removed by surgery.
The present invention also provides a kit for differentiation of liver cancer, determination of disease stage, or prediction of prognosis. The kit of the present invention contains a reagent capable of measuring the expression of genes whose expression is different between the SP fraction and non-SP fraction of liver cancer cells.

  As one example, the kit of the present invention contains, as a reagent, an antibody capable of specifically recognizing a protein that is an expression product of a gene whose expression is different between the SP fraction and non-SP fraction of hepatoma cells. The gene whose expression is different between the SP fraction and the non-SP fraction of hepatoma cells is preferably at least one of the genes listed in Table A, B, C or D. However, multiple types may be used. For example, CHM, MSH6, NCOA7, CYP2E1, TM4SF1, F2RL1, TRPM3, AKR1C1, FOXH1, RAB40C, MGC27085, PPP1R3E, UST, CANP, PRSS3, UACA, FOSL2, PSPC1, EXOSC6, LOC388558, RTTN, FAM20, RTTN, FAM20 FOLR1, TMF1, PCGF5, GSTA1, XRCC5, MAP3K7IP2, RTN4, BCDO2, CDH22, MYCNOS, FMNL2, PHEX, WDR56, HERC6, SBNO1, FDFT1, ZFR, PF6, PCOTH, ZCCHC6, HRMT1L1, INHBC, ATP8B3 Examples include ADCY9, CAPS2, ANXA2, FLJ34077, AD7C-NTP, GLRB, MGAT2, SASH1, HIF1A, LMTK3, EYA3, IPP, CAPN2, GAFA1, HELLS, NF1, NUSAP1, SESN3, and XRCC5. The kit further includes an instrument for collecting blood, liver cancer cells or tissues, and immunochemically detects proteins that are expression products of genes that differ in expression between the SP and non-SP fractions of liver cancer cells. A set of reagents, instruction manuals, etc. may be included. In addition to the method of using the kit, the instruction manual may also include identification of liver cancer, determination of disease stage, prediction criteria for prognosis, and the like.

  As another example, the kit of the present invention contains, as a reagent, a nucleic acid probe that can specifically hybridize with mRNA, which is a transcription product of a gene whose expression is different between the SP fraction and non-SP fraction of liver cancer cells. The gene whose expression is different between the SP fraction and the non-SP fraction of hepatoma cells is preferably at least one of the genes listed in Table A, B, C or D. However, multiple types may be used. For example, CHM, MSH6, NCOA7, CYP2E1, TM4SF1, F2RL1, TRPM3, AKR1C1, FOXH1, RAB40C, MGC27085, PPP1R3E, UST, CANP, PRSS3, UACA, FOSL2, PSPC1, EXOSC6, LOC388558, RTTN, FAM20, RTTN, FAM20 FOLR1, TMF1, PCGF5, GSTA1, XRCC5, MAP3K7IP2, RTN4, BCDO2, CDH22, MYCNOS, FMNL2, PHEX, WDR56, HERC6, SBNO1, FDFT1, ZFR, PF6, PCOTH, ZCCHC6, HRMT1L1, INHBC, ATP8B3 Examples include ADCY9, CAPS2, ANXA2, FLJ34077, AD7C-NTP, GLRB, MGAT2, SASH1, HIF1A, LMTK3, EYA3, IPP, CAPN2, GAFA1, HELLS, NF1, NUSAP1, SESN3, and XRCC5. The kit further includes an instrument for collecting blood, liver cancer cells or tissues, reagents for extracting RNA from the collected blood, liver cancer cells or tissues, reagents for analyzing RNA by Northern blotting, Instruction manuals may be included. In addition to the method of using the kit, the instruction manual may also include identification of liver cancer, determination of disease stage, prediction criteria for prognosis, and the like.

  As yet another example, the kit of the present invention comprises a nucleic acid primer capable of specifically hybridizing with an mRNA that is a transcription product of a gene whose expression is different between the SP fraction and non-SP fraction of hepatoma cells, and the mRNA. A pair of nucleic acid primers comprising a nucleic acid primer that can specifically hybridize with cDNA synthesized as a template is included as a reagent. The gene whose expression is different between the SP fraction and the non-SP fraction of hepatoma cells is preferably at least one of the genes listed in Table A, B, C or D. However, multiple types may be used. For example, CHM, MSH6, NCOA7, CYP2E1, TM4SF1, F2RL1, TRPM3, AKR1C1, FOXH1, RAB40C, MGC27085, PPP1R3E, UST, CANP, PRSS3, UACA, FOSL2, PSPC1, EXOSC6, LOC388558, RTTN, FAM20, RTTN, FAM20 FOLR1, TMF1, PCGF5, GSTA1, XRCC5, MAP3K7IP2, RTN4, BCDO2, CDH22, MYCNOS, FMNL2, PHEX, WDR56, HERC6, SBNO1, FDFT1, ZFR, PF6, PCOTH, ZCCHC6, HRMT1L1, INHBC, ATP8B3 Examples include ADCY9, CAPS2, ANXA2, FLJ34077, AD7C-NTP, GLRB, MGAT2, SASH1, HIF1A, LMTK3, EYA3, IPP, CAPN2, GAFA1, HELLS, NF1, NUSAP1, SESN3, and XRCC5. The kit further includes an instrument for collecting blood, liver cancer cells or tissues, reagents for extracting RNA from the collected blood, liver cancer cells or tissues, and reagents for analyzing RNA by RT-PCR. , Instruction manual etc. should be included. In addition to the method of using the kit, the instruction manual may also include identification of liver cancer, determination of disease stage, prediction criteria for prognosis, and the like.

Furthermore, the present invention provides a method for detecting liver cancer stem cells, comprising measuring the expression of a specific gene in any one of blood, liver cancer cells or liver cancer tissues. Specific genes are expressed differently in the SP fraction and Non-SP fraction of hepatoma cells. The expression differences between the SP fraction and the non-SP fraction of hepatoma cells are as described above.
Furthermore, the present invention provides a method for distinguishing liver cancer stem cells from liver cancer non-stem cells, comprising measuring the expression of a specific gene in any of blood, liver cancer cells, or liver cancer tissues. Specific genes are expressed differently in the SP fraction and Non-SP fraction of hepatoma cells. The expression differences between the SP fraction and the non-SP fraction of hepatoma cells are as described above.

  The present invention provides hepatoma cells that retain tumorigenicity when secondary transplantation is performed. This cell is considered a liver cancer stem cell. This cell can be prepared by separating a cell population having a weak fluorescence signal by a side population cell separation method. Specifically, hepatoma stem cells can be prepared as described in Examples below. Briefly, after incubating liver cancer-derived cells with Hoechst 33342, single suspension cells are obtained by techniques such as filtration. Hoechst 33342 remaining in the cells is excited with a UV laser at 350 nm, and fluorescence emission is measured with a 405 / BP30 (Hoechst blue) and 570 / BP20 (Hoechst red) light filter. Hoechst 33342 is a DNA-binding dye with high membrane permeability, and emits short-wavelength blue and long-wavelength red fluorescence by UV excitation. Thus, hepatoma stem cells are detected as a subpopulation of cells with weak fluorescence signals. This subpopulation can be sorted by sorting using a flow cytometer. The cells of this subpopulation are transplanted into an immunodeficient animal, and the formed tumor is collected and minced, and then the cells that have been separated by digestion with collagenase are cultured. When a cultured cell is collected, and a cell population having a weak fluorescent signal is separated again by a side population cell separation method and transplanted into an immunodeficient animal, a tumor can be formed. If a drug targeting such cells can be developed, it can be effectively used as a novel anticancer agent.

The present invention also provides a method of screening for an anticancer agent using hepatoma cells retaining tumorigenicity when secondary transplantation is performed.
For example, the liver cancer stem cells of the present invention are cultured in the presence or absence of the test substance, and the growth rate or survival rate of the cells cultured in the presence of the test substance and the cells cultured in the absence of the test substance are compared. Good. If the growth rate or survival rate of cells cultured in the presence of the test substance is reduced compared to cells cultured in the absence of the test substance, the substance is considered to be effective as an anticancer agent. The culture of liver cancer stem cells may be performed under any conditions suitable for screening. The cell viability and proliferation rate can be examined, for example, by measuring the number of living cells by a technique such as the MTT method (Carmichael et al., Cancer Res. 47: 936-942, 1987).

Alternatively, the anticancer activity of the test substance may be examined using an immunodeficient animal that has formed a tumor by transplanting the liver cancer stem cells of the present invention. Anticancer activity can be evaluated, for example, by comparing tumor weight, animal survival rate, number of days of survival, etc., between an animal administered with the test substance and a control (animal not administered with the test substance). . If a decrease in tumor weight, an increase in the survival rate of animals, or an increase in the number of days of survival are observed in animals administered a test substance compared to controls, the substance is considered to be effective as an anticancer agent.
The test substance may be either a natural product or a synthetic product. Specifically, it is used for extracts derived from plants and microorganisms and purified products thereof, low molecular synthetic compounds, antibodies, peptides, aptamers, siRNA, gene therapy. Examples include nucleic acids used, modified products and derivatives thereof.

EXAMPLES Hereinafter, although this invention is demonstrated in detail based on an Example, this invention is not limited to these Examples.
Example 1
Materials and methods

Cell culture human liver cancer cell lines HepG2, Huh6, Huh7 and PLC / PRF / 5 were obtained from Human Science Research Resource Bank (HSRRB, Osaka, Japan). These cells are cultured in Dulbecco's Modified Eagle Medium (DMEM) (Invitrogen Life Technologies, Carlsbad, CA) containing 10% FCS and 1% penicillin / streptomycin (Invitrogen) in an incubator containing 5% CO ₂ at 37 ° C. Incubated with.

Side population analysis using flow cytometry. Using trypsin-EDTA (Invitrogen), cells were detached from the dish and suspended at 1x10 ⁶ cells / ml in Hank's balanced salt solution supplemented with 3% FCS and 10 mM Hepes. . The cells were then incubated at 37 ° C. for 90 minutes alone or with 20 μg / ml Hoechst 33342 (Sigma Chemical, St Louis, Mo.) in the presence of 50 μM verapamil (Sigma). After incubation, 1 μg / ml propidium iodide (BD Pharmingen, San Diego, Calif.) Was added and then filtered through a 40 μm cell strainer (BD Falcon) to obtain single suspension cells. Cell analysis and purification were performed using MoFlo (DakoCytomation, Fort Collins, CO) holding a triple laser. Hoechst 33342 was excited with a UV laser at 350 nm, and fluorescence emission was measured with 405 / BP30 (Hoechst blue) and 570 / BP20 (Hoechst red) light filters. Propidium iodide labeling was measured through a 630 / BP30 filter to discriminate dead cells.

Proliferation assay and apoptosis detection For each well, SP cells and Non-SP cells (5 × 10 ³ ) were separated into 96-well microplates and 3 (4,5-dimethylthiazol-2-yl) -5- (3-carboxymethoxyphenyl) ) -2- (4-sulfophenyl) -2H-tetrazolium inner salt (MTS) assay was performed, and (24) cell viability was examined for each of three specimens as previously reported. According to the manufacturer's instructions (Takara Bio Inc., Tokyo, Japan), apoptotic cells were quantified in each of the three specimens by TUNEL assay. Cells were labeled with fluorescein isothiocyanate (FITC) using terminal nucleotide transferase and the DNA free ends were stained with anti-FITC horseradish peroxidase and diaminobenzidine. The ratio of TUNEL positive cells was calculated by counting 1 × 10 ³ cells randomly. MTS and TUNEL assays were performed 24, 48 and 72 hours after cell sorting.

Immunocytochemistry assay Sorted SP cells and Non-SP cells were cultured for 12 hours and washed in PBS. The cells were fixed with methanol at −20 ° C. for 20 minutes and washed with PBS containing 0.1% Tween 20 (Wako). After blocking with 10% goat normal serum for 1 hour, together with the primary antibodies rabbit anti-α-1-fetoprotein (AFP) (DakoCytomation) and mouse anti-cytokeratin 19 (CK19) (WakoCytomation) at 4 ° C in a humid chamber In the evening, the fixed cells were incubated. These cells were washed in PBS containing 0.1% Tween 20, blocked again for 30 minutes, Alexa 555-conjugated goat anti-rabbit IgG (Molecular Probes, Eugene, OR) and Alexa 488-conjugated goat anti-mouse IgG (Molecular Probes) for 1 hour at room temperature. After washing in PBS, a mounting medium containing DAPI (Vector Laboratories, Burlingame, Calif.) Was placed on the cells, covered with a cover glass, and observed under an Olympus IX70 microscope.

Non-obese diabetes / severe combined immunodeficiency xenograft NOD / SCID mice were purchased from Sankyo Laboratory Co. Ltd. (Tsukuba, Japan). 100-1x10 ⁶ different numbers of SP and Non-SP cells suspended in 200 μl DMEM and Matrigel (BD) (1: 1) and male NOD / SCID under anesthesia with a cocktail of ketamine and xylazine Mice (6-10 weeks old) were transplanted. SP cells and Non-SP cells were injected into the subcutaneous space of the right and left backs, respectively. Tumor formation was monitored for 16 weeks by weekly observation and palpation. Subcutaneous tumors were fixed in formalin and embedded in paraffin. Sections were stained with hematoxylin-eosin for histological analysis. Next, SP-derived tumors were removed and minced in sterile PBS on ice. A small section of the obtained tumor was placed in DMEM containing 5 mg / ml type II collagenase (Sigma) and digested at 37 ° C. for 3 hours. After washing and filtering, the cells were cultured for 7 days to remove non-epithelial cells such as hematopoietic cells and fibroblasts. The collected cells were analyzed by flow cytometry and injected again into NOD / SCID mice as described above. These experiments were conducted according to institutional guidelines for laboratory animal use.

RNA extraction and oligonucleotide microarray analysis Total RNA was extracted separately from SP cells and Non-SP cells using Isogen reagent (Nippon Gene, Toyama, Japan) according to the manufacturer's instructions. RNA quality and concentration were measured by NanoDrop (NanoDrop Technologies, Wilmington, DE) and Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA). Microarray analysis was performed according to a standard protocol (Affymetrix GeneChip Manual). Briefly, first and second cDNAs were generated from 1 μg of total RNA using oligo (dT) ₂₄ -T7 primer and SuperScript kit (Invitrogen). Subsequently, biotin-labeled cRNA was synthesized using Bioarray, High Yield RNA Transcript Labeling Kit (Enzo Diagonostics, Farmingdale, NY). After purification and fragmentation, 15 μg of cRNA was hybridized to Human Genome U133A Plus 2.0 Arrays (Affymetrix, Santa Clara, Calif.). This array presents over 47,000 gene transcripts containing 38,500 characterized genes. After amplification and detection of the hybridization signal through streptavidin-conjugated phycoerythrin fluorescence, the array image was scanned using GeneChip Scanner 3000 (Affymetrix). The obtained data was analyzed with GeneChip Operating Software. The detected P value was calculated using perfect match and mismatch probe pairs, and each probe was assigned as absent (A), marginal (M) or present (P) call. Probes scored as A call in both SP cells and corresponding Non-SP cells were excluded from the following analysis. After standardization of the experiment, data mining including fold change measurement and categorization was performed using GeneSpring system (Agilent Technologies).

After pretreatment with quantitative real-time reverse transcriptase-polymerase chain reaction amplification grade DNase I (Invitrogen), reverse transcription (RT) was performed using first-strand superscript (Invitrogen). Polymerase chain reaction (PCR) was performed with TaqMan technology using ABI PRISM 7000 Sequence Detection System (Applied Biosystem, Foster City, CA, USA). ITGB1, FMN2, ABCF2, USP9X, ZFX, TXNL, WTAP, ELAVL4, AF5Q31, ADARB1, FZD7, ABCB1, STATIP1, GCLM, XRCC5 and β-actin (assay ID is Hs00236976_m1, Hs00298949_m1, Hs00255026_m1, Hs00245 Hs00169455_m1, Hs00191727_m1, Hs00222634_m1, Hs00232683_m1, Hs00210562_m1, Hs00275833_s1, Hs00184500_m1, Hs00217448_m1, Hs00157694_m1, Hs00221707_m1 and Hs99999903_m1 Thermal cycling was performed using 45 samples at 95 ° C. for 15 minutes and 60 ° C. for 1 minute, respectively. β-actin was used for standardization. Relative quantification was performed using a comparative cycle threshold (C _T ) as described by the manufacturer.

Statistical analysis data are expressed as mean ± SEM. Statistical differences between the two groups were analyzed using the Mann-Whitney U test. If the P value was less than 0.05, it was considered significant.

result
Detection of Side Population in Liver Cancer Cells Flow cytometric analysis by Hoechst 33342 staining showed that Huh7 and PLC / PRF / 5 cells contain 0.25% and 0.80% SP subpopulation, respectively. These SP cells were actually reduced in the presence of Hoechst 33342 and the calcium channel blocker verapamil. On the other hand, SP cells were not detected in Huh6 and HepG2 cells. SP cells and Non-SP cells in Huh7 and PLC / PRF / 5 cells were sorted separately and used for further experiments (FIG. 1). Morphologically, there was no significant difference between SP cells and Non-SP cells.

In order to examine the difference in proliferation activity and in vitro proliferation activity of apoptosis-resistant SP cells and Non-SP cells, the number of viable cells was measured using MTS assay. In Huh cells (FIG. 2A) and PLC / PRF / 5 cells (FIG. 2B), the cell variability of SP cells was consistently higher than that of Non-SP cells. SP cells in 72-hour cultures of PLC / PRF / 5 cells showed significantly (p <0.05) higher cell viability than non-SP cells. Differences in apoptosis sensitivity between SP cells and Non-SP cells were examined by TUNEL assay. In Huh7 and PLC / PRF / 5 cells, the percentage of TUNEL positive cells in SP cells was lower than in Non-SP cells (Huh7 cells (FIG. 2C), PLC / PRF / 5 cells (FIG. 2D)). Statistically significant difference between TUNEL positive in Huh 7 SP cells in 24-hour and 72-hour cultures and TUNEL positive in PLC / PRF / 5 SP cells in 72-hour cultures compared to that of Non-SP cells (p <0.05).

Analysis of lineage marker expression Dual immunocytochemical analysis was performed to compare the differentiation potential of SP cells and Non-SP cells (FIGS. 3A-D). Huh7 and PLC / PRF / 5 cells contained a large number of cells marked with both the hepatocyte-specific marker α-fetoprotein (AFP) and the cholangiocyte-specific marker cytokeratin 19 (CK19). In Huh7 and PLC / PRF / 5 cells, the percentage of cells positive for both AFP and CK19 was 68.2% and 55.0%, respectively. On the other hand, the majority of Non-SP Huh7 and PLC / PRF / 5 cells were labeled with either AFP or CK19. In Huh7 and PLC / PRF / 5 Non-SP cells, the percentages of cells that expressed only one marker were 74.4% and 79.6%, respectively. In these cell lines, a small number of cells that were not marked by either AFP or CK19 were present in both SP and Non-SP cells of these cell lines as well.

To examine whether tumor-induced tumorigenic activity caused by Side Population transplantation is different between SP cells and Non-SP cells, we examined the number of SP cells in Huh7 and PLC / PRF / 5 cells and Non-SP cells were injected into NOD / SCID mice (FIG. 4a). Subcutaneous tumor formation required injection of at least 1 × 10 ⁶ unsorted Huh7 and PLC / PRF / 5 cells. In Huh7 and PLC / PRF / 5 SP cells, tumors were induced in 8 out of 9 mice and 8 out of 8 mice, respectively, by injection of a small amount of cells of about 1 × 10 ³ (Table 1). However, injection of 1 × 10 ⁶ Non-SP cells in Huh7 and PLC / PRF / 5 cells consistently did not form tumors in all mice. These SP subpopulations are at least 1,000 times enriched for tumorigenic cells. Histological analysis of tumors originating from Huh7 and PLC / PRF / 5 SP cells showed similar characteristics to tumors formed by injection of unsorted cells (FIGS. 4b, 4c).

To elucidate whether Side Population-derived tumors are re-analyzed and consecutive transplanted SP cells self-replicate and generate non-tumorigenic Non-SP cells by asymmetric cell division in vivo. Flow cytometric analysis of SP-derived subcutaneous tumors was performed. SP analysis showed that Huh7 and PLC / PRF / 5 cells contained 0.34% and 0.90% SP subpopulations, respectively (FIG. 5). These analysis patterns were similar to those of previously isolated HCC cells. Huh7 and PLC / PRF / 5 cells generated both SP and Non-SP cells after 4 weeks of culture (data not shown). Subsequently, secondary transplantation of SP cells and Non-SP cells into NOD / SCID mice was performed (Table 2). No new tumors were formed when non-SP cells were injected, but as few as 1x10 ³ SP cells in both cell lines formed tumors in 4 out of 5 NOD / SCID mice.

Gene expression profile based on microarray analysis Microarray analysis was performed to clarify the difference in gene expression profiles between SP cells and Non-SP cells. In total, 26,764 probes in Huh7 cells and 25,755 probes in PLC / PRF / 5 cells were analyzed according to the call algorithm described above. Compared to corresponding Non-SP cells, the number of genes up-regulated (ratio> 2.0) in Huh7 and PLC / PRF / 5 SP cells was 1,015 and 1,542, respectively. Using available websites, these genes were functionally categorized as follows: Gene Ontology (http://www.geneontology.org) and GeneCards (http://thr.cit.nih.gov/cards/index.shtml). The percentage of genes (characterized) that showed upregulation in Huh7 and PLC / PRF / 5 SP cells was 43.3% (440 / 1,015) and 51.2% (789 / 1,542), respectively. These genes were categorized into the following 11 groups according to their functions. Transcriptional regulation, Nucleic acid binding, Signaling, Transport, Metabolism, Protein synthesis, Ubiquitination / Proteolysis, Acceptance These are body activity (Receptor activity), apoptosis / cell cycle, cytoskeleton / membrane (Cytoskeleton / Membrane), and others (FIG. 6A). About one third of these genes were categorized into “transcriptional regulation” and “nucleic acid binding” groups. 62 up-regulated genes were found commonly in both cell lines (Table 3). On the other hand, 940 and 941 genes showed down regulation in Huh7 and PLC / PRF / 5 SP cells, respectively (ratio <0.5), and 51 genes were duplicated in both cell lines.

  Next, profiling of genes up-regulated in SP cells was compared with previous microarray-based profiling of so-called “stem cell genes”. “Stem cell genes” are commonly expressed in stem cells (embryonic stem cells (ESC), neural stem cells (NSC) and hematopoietic stem cells (HSC)) (25, 26). These profiling data were converted to human homologues using available websites and compared to our current data (Figure 6B, Table 4). As a result, Ramalho-Santos et al. Reported 25 genes including ITGB1, USP9X and ZFX in Huh7 SP cells, TXNL, WTAP, ABCB1, STATIP1 and GCLM in PLC / PRF / 5 SP cells (25). Consistent with the expression profiling of the gene. Furthermore, XRCC5 was shown to be upregulated in both Huh7 SP cells and PLC / PRF / 5 SP cells. Four genes including FMN2 and ABCF2 in Huh7 SP cells and AF5Q31, ADARB1, ELAVL4 and FZD7 in PLC / PRF / 5 SP cells were included in the stem cell gene profiling (26) shown by Ivanova et al.

Confirmation of microarray data using quantitative real-time reverse transcription-polymerase chain reaction Quantitative real-time expression of mRNA of 6 genes including ITGB1, FMN2, ABCF2, XRCC5, USP9X and ZFX in Huh7 SP cells and Non-SP cells Analyzed by RT-PCR. Ten genes in PLC / PRF / 5 SP cells and Non-SP cells were also examined, namely TXNL, WTAP, ELAVL4, XRCC5, AF5Q31, ADARB1, FZD7, ABCB1, STATIP1 and GCLM. Real-time RT-PCR showed that these 16 genes had higher mRNA expression levels in SP cells than in Non-SP cells, which agreed very well with the microarray data (FIG. 6C).

Discussion The ability to proliferate, self-renew and differentiate is the most characteristic property of stem cells. In this study, we evaluated whether SP subpopulations purified from HCC cells had stem cell-like properties in culture and in xenograft assays. SP subpopulations accounting for less than 1% of total cells could be detected in Huh7 and PLC / PRF / 5 cells. However, no such subpopulation was found in HepG2 and Huh6 cells. HepG2 and Huh6 cells showed little change in the flow cytometric analysis pattern when stained with increasing doses of Hoechst dye. It may not always be effective to detect stem cell-like subpopulations based on high efflux capacity. This is because it has been demonstrated that such subpopulations were detected only in less than 30% of the cancer cell lines examined (27).

  Initially, the inventors have shown that SP cells in Huh7 and PLC / PRF / 5 cells have a higher proliferative capacity than the corresponding Non-SP cells in culture systems. This ability was accompanied by resistance to apoptosis. Normal stem / progenitor cells of various tissue origins have been reported to express high levels of anti-apoptotic proteins such as Bcl-2 (28, 29). In summary, the anti-apoptotic effect is essentially a mechanism provided in both normal and cancer stem cells due to exhaustion concerns.

Next, the present inventors examined the tumorigenic potential and self-renewal ability of SP cells in the NOD / SCID mouse xenograft assay. Injection of 1 × 10 ³ SP cells purified from Huh7 and PLC / PRF / 5 cells allowed tumors to develop. However, Non-SP cells, which constitute the majority of cancer cells, failed to form tumors despite increasing the number of cells injected. Additional markers of the SP phenotype may promote the enrichment of cancer stem cells such as CD44 + CD24− / low ESA + cells (12) in breast cancer. These results indicate that only the SP subpopulation has the ability to induce tumors and that functional and phenotypic heterogeneity exists in the HCC cell line. These analyzes of SP cells in the 4 week culture showed the presence of both SP and Non-SP cells. Furthermore, SP-derived tumors also generate SP and Non-SP cells, indicating the ability of both SP cells to self-renew and differentiate. The proportion of SP and Non-SP cells in SP-derived tumors was similar to that of previously isolated cells in both Huh7 and PLC / PRF / 5 cells. The ratio of SP and Non-SP cells appears to be closely regulated and maintained in vitro and in vivo. In addition, secondary transplantation of SP cells results in the formation of tumors, and the tumorigenicity seems to be well retained in sequential transplants.

  It is well known that Huh7 and PLC / PRF / 5 cells both produce AFP and are CK19 positive cell lines (30, 31). Transformed cells are usually thought to inherit the differentiation potential from their origin. For example, MYC inactivation in a conditional transgenic model leads to several liver tumor cells with stem cell properties and differentiates into normal hepatocyte and bile duct lineages (32). Analysis of surgical specimens showed bile marker positive HCC, indicating that at least some of the HCC may originate from a subpopulation with bipartite stem / progenitor properties ( 33). Immunocytochemical analysis revealed an imbalance in marker expression between SP and Non-SP cells. While cells expressing both AFP and CK19 were highly enriched in these SP cells, most Non-SP cells were positive for either AFP or CK19. Considering the ability of the egg cell or hepatoblastoma (34), which is considered to be a hypothetical liver stem / progenitor, the SP subpopulation exists in the hierarchy upstream of the HCC cells and is It retains the potential to differentiate into cells and bile duct cells.

  Exhaustive genome screening using oligonucleotide arrays revealed that gene expression profiling is different between SP and Non-SP cells in Huh7 and PLC / PRF / 5 cells. Duplicates of 1,000 genes in these SP cells showed variations in gene expression, which indicates that HCC cells are clearly heterogeneous from a genetic point of view. In both Huh7 and PLC / PRF / 5 SP cells, it has been previously demonstrated that only HOXA13 and CYP2E1 genes are included in HCC out of 62 genes that are up-regulated (35, 36). It should be emphasized that SP cells show a completely different mRNA expression pattern compared to most HCC cell lines (30, 36) and surgical samples (37, 38).

  Next, we compared data showing a subset of genes up-regulated in SP cells with the so-called “stem cell gene” profiling (25, 26). These genes are believed to be very important in maintaining functional and phenotypic properties in stem cells. In this analysis, 6 genes up-regulated in Huh7 SP cells and 10 genes up-regulated in PLC / PRF / 5 SP cells overlap with the “stem cell genes” demonstrated in previous microarray studies. Was. ITGB1 (CD29) up-regulated in Huh7 SP cells is recognized as one of the stem cell markers in the liver. Monoclonal antibodies against ITGB1 are often used to isolate hepatic stem cells using flow cytometry (39). FZD7, which is up-regulated in PLC / PRF / 5 SP cells, is one of the receptors of the Wnt signaling pathway, which is closely related to the regulation of stem cell self-renewal. Furthermore, WNT5A, WNT6, CTNNB1 and JUN are important components of the Wnt pathway, which are also up-regulated in PLC / PRF / 5 SP cells. The dysregulated Wnt pathway sometimes allows extensive proliferation of tumor cells (40), and the role of FZD7 oncogenesis has already been reported in HCC (41). Taken together, these results may indicate that these genes are deeply involved in the maintenance of both normal and cancer stem cells in the liver. In the future, it would be better to investigate whether the expression of these genes is seriously involved in determining cancer stem cell-like properties, including tumorigenesis in SP cells.

  The SP phenotype has been reported to be determined by ABCG2 / BCRP1 since a marked decrease in SP cells was observed in the bone marrow of ABCG2 / BCRP1 null mice (42, 43). However, it is debatable whether other ABC transporters such as ABCB1 (MDR1) are similarly related to the phenotype (44). According to our microarray analysis, ABCG1 and ABCF2 in Huh7 SP cells, ABCB2, ABCC7, ABCA5 and ABCB1 in PLC / PRF / 5 SP cells are upregulated, but in both SP cells ABCG2 / BCRP1 was shown not to be up-regulated. ABCA3 was also demonstrated to be highly expressed in non-SP cells in neuroblastoma SP cells (45). In summary, another mechanism that excretes Hoechst dye may be operating between normal and transformed cells.

  In conclusion, we were able to define a tumorigenic subpopulation using SP cell sorting in Huh7 and PLC / PRF / 5 cells. These data show heterogeneity characterized by a clear hierarchy starting from cancer stem cells of the HCC cell line. Most recent cancer research and treatment modalities seem to consider cancer as a mass of relatively homogeneous cells, but recognize tumor heterogeneity and search for treatment methods that target cancer stem cells There is a need to. It has been demonstrated that the SP subpopulation showed low sensitivity to anticancer drugs in culture (45, 46). Further research to identify and characterize cells with stem cell-like properties in primary HCC samples will contribute to the establishment of new therapeutic strategies.

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All publications, patents and patent applications cited herein are incorporated herein by reference in their entirety.

We found genes whose expression is different between SP and Non-SP fractions of liver cancer cells. By using these genes as markers, it is possible to differentiate liver cancer, determine disease stage, or predict prognosis.
Moreover, the hepatoma stem cell obtained by this invention can be utilized for development of the anticancer agent which targets this cell.

Claims (17)

A method for differentiation of liver cancer, determination of disease stage or prediction of prognosis, comprising measuring expression of a specific gene in any of blood, liver cancer cells or liver cancer tissue, wherein the specific gene is SP of liver cancer cells The method as described above, wherein the expression is different between the fraction and the Non-SP fraction. The method according to claim 1, wherein the specific gene is at least one of the genes listed in any of Tables A, B, C or D. Risk of increased malignancy of liver cancer, advanced disease, poor patient prognosis, or recurrence when expression of at least one of the genes listed in Tables A and / or C is elevated 3. The method of claim 2, wherein the method predicts a high value. Risk of high grade of liver cancer, advanced disease, poor patient prognosis, or recurrence when expression of at least one of the genes listed in Tables B and / or D is reduced 3. The method of claim 2, wherein the method predicts a high value. Specific genes are CHM, MSH6, NCOA7, CYP2E1, TM4SF1, F2RL1, TRPM3, AKR1C1, FOXH1, RAB40C, MGC27085, PPP1R3E, UST, CANP, PRSS3, UACA, FOSL2, PSPC1, EXOSC6, LOC388558, ATF , HOXA13, FOLR1, TMF1, PCGF5, GSTA1, XRCC5, MAP3K7IP2, RTN4, BCDO2, CDH22, MYCNOS, FMNL2, PHEX, WDR56, HERC6, SBNO1, FDFT1, ZFR and PF6 and PCOTH, ZCCHC6, HRMBC1 ATP8B3, TAL1, FGF1, ADCY9, CAPS2, ANXA2, FLJ34077, AD7C-NTP, GLRB, MGAT2, SASH1, HIF1A, LMTK3, EYA3, IPP, CAPN2, GAFA1, HELLS, NF1, NUSAP1, and SESN3 The method of claim 1, wherein the method is at least one gene. CHM, MSH6, NCOA7, CYP2E1, TM4SF1, F2RL1, TRPM3, AKR1C1, FOXH1, RAB40C, MGC27085, PPP1R3E, UST, CANP, PRSS3, UACA, FOSL2, PSPC1, EXOSC6, LOC388558, RTTN, FAM20A, FGF Increased expression of at least one gene selected from the group consisting of TMF1, PCGF5, GSTA1, XRCC5, MAP3K7IP2, RTN4, BCDO2, CDH22, MYCNOS, FMNL2, PHEX, WDR56, HERC6, SBNO1, FDFT1, ZFR and PF6 The method according to claim 5, wherein liver cancer is highly malignant, the disease is progressing, the patient has a poor prognosis, or the risk of recurrence is high. PCOTH, ZCCHC6, HRMT1L1, INHBC, ATP8B3, TAL1, FGF1, ADCY9, CAPS2, ANXA2, FLJ34077, AD7C-NTP, GLRB, MGAT2, SASH1, HIF1A, LMTK3, EYA3, IPP, CAPN2, GAFA1, HELLS, NF1, SAP1 When the expression of at least one gene selected from the group consisting of SESN3 is decreased, the malignancy of liver cancer is high, the disease is progressing, the patient has a poor prognosis, or the risk of recurrence is high The method of claim 5, wherein the prediction is performed. The method according to claim 1, wherein the specific gene is XRCC5. 9. The method according to claim 8, wherein when the expression of XRCC5 is increased, it is predicted that the malignancy of liver cancer is high, the disease is progressing, the patient has a poor prognosis, or the risk of recurrence is high. The method according to any one of claims 1 to 9, wherein the expression of a specific gene is measured at a protein level. The method according to any one of claims 1 to 9, wherein the expression of a specific gene is measured at the RNA level. A kit for differentiation of liver cancer, determination of disease stage, or prediction of prognosis, comprising a reagent capable of measuring the expression of genes whose expression is different between the SP fraction and non-SP fraction of liver cancer cells. A method for detecting a liver cancer stem cell, comprising measuring the expression of a specific gene in any one of blood, liver cancer cells or liver cancer tissue, wherein the specific gene comprises an SP fraction and a non-SP fraction of liver cancer cells. Wherein said expression is different. A method for distinguishing between hepatoma stem cells and hepatoma non-stem cells, comprising measuring the expression of a particular gene in either blood, liver cancer cells or liver cancer tissue, wherein the particular gene comprises an SP fraction of liver cancer cells and Non -The method as described above, wherein the expression is different between the SP fraction. Liver cancer cells that retain the ability to form tumors after secondary transplantation. The method for preparing liver cancer cells according to claim 15, comprising separating a cell population having a weak fluorescence signal by a side population cell separation method. A method for screening an anticancer agent using the liver cancer cell according to claim 15.